Abstract

Mucopolysaccharidoses (MPS) are a specific group of genetic diseases in which due to the accumulation of glycosaminoglycans (GAGs) in different organs and tissues, causes multisystemic changes that compromise the functionality and occupational performance of individuals. Occupational performance, understood as the participation and execution of activities of daily living, may be favoured using Assistive Technology (AT). Since there are no studies reporting the influence of AT on the occupational performance of children and adolescents with MPS, the objective of this study was to evaluate the occupational performance in self-care activities, based on the use of low-cost AT in children and adolescents with Mucopolysaccharidosis. Six individuals with MPS types I, IV-A and VI, aged 9 to 16 years participated. The instruments used for data collection were the Pediatric Disability Assessment Inventory (PEDI) – self-care area only, and the Canadian Occupational Performance Measure (COPM). The results showed that the tasks that presented the greatest disabilities in the performance are the areas of dressing, personal hygiene and bath. Thus, TA resources were made for five activities related to dressing and one for personal hygiene. After the use of AT, there was a positive and significant change in occupational performance and satisfaction of these individuals. Thus, the use of AT can significantly improve the occupational performance of this population.

Keywords

Adolescent, Assistive Technology, Child, Mucopolysaccharidosis, Occupational Performance, Self-Care Activities

Introduction

Mucopolysaccharidoses (MPS) are rare diseases, characterized by genetically determined metabolic errors, which are part of the Lysosomal Deposit Disease group. In these diseases there is accumulation of substrates that are normally degraded in lysosomes, and in MPS, deficiencies of specific enzymes lead to the accumulation of glycosaminoglycans (GAGs), resulting in a series of signs and symptoms, which together bring systemic impairment [1–3]. There is no cure for this group of diseases, and the current treatment is aimed at delaying its progress. Even with treatments, progression is nonetheless long-term, and changes in body structures and functions (joint stiffness, decreased range of motion, joint laxity, claw hand) result in limited functionality in the areas of occupational performance, especially in self-care tasks – related to dressing, personal hygiene and food [4].

Occupational performance is understood as the ability to perform routines and perform roles and tasks, involving the areas of self-care, productivity and leisure, being influenced by the factors of the individual, their skills and the context in which they are inserted [5]. Thus, for individuals with some form of physical limitation, occupational therapists may use Assistive Technology (AT) as an effort to enable improved independence and occupational performance, to the extent that limitations can be overcome through adaptations and use of ATs.

Assistive Technology allows a person with a limitation to perform activities and tasks more independently, and can be characterized as technology of high complexity (high cost – with electronic components) or low complexity (low cost), the latter being designed from everyday easily accessible materials that can often be made from materials available at home, in the office, at school or in the hospital. This type of AT is something that can be done right away to meet the needs of those who need it, with the resources at hand [7–9]. However, there are no studies linking the use of AT and MPS. Thus, this study aims to evaluate occupational performance in self-care activities, based on the use of low-cost assistive technology in children and adolescents with Mucopolysaccharidoses.

Methods

This is a prospective and descriptive longitudinal quantitative research, conducted at the outpatient infusion and enzyme replacement therapy center of a reference Hospital for the treatment of rare diseases, located in Rio de Janeiro – Brazil. Participated in the study: Six children and adolescents of both sexes between 9 years and 6 months and 16 years and 4 months of age, with type I, IV-A and VI MPS, with biochemical diagnosis of MPS that are treated with enzyme replacement in the institution’s medical genetics department. Were excluded from this research: Individuals with type III MPS because of neurological impairment; children and adolescents who had severe cognitive and / or motor impairment that prevented them from responding to assessments; and children and adolescents who reached the maximum PEDI score. For data collection, the Pediatric Disability Assessment Inventory – PEDI was used, only Part I – Child Abilities, which reports on the child’s functional abilities to perform daily activities and tasks and on the self-care scale [10] and, then, the Canadian Occupational Performance Measure – COPM was applied.

The PEDI was applied through a structured interview with children and adolescents, lasting on average 30 to 40 minutes, where it was identified if individuals can perform certain activities. The COPM was administered in around 10–15 minutes, with participants identifying issues related to their occupational performance related to the activities contained in PEDI. They chose the activities that were meaningful to them, quantifying the degree of satisfaction and importance they attributed to each of the activities. At the end of the application of the instruments, it was made a survey from the chosen activities (the activity that obtained the highest importance score in the COPM) and the possible assistive technology resources to be incorporated in the intervention process of the activity that gained the most quantification, by the participants, including from creating and building a low-cost TA resource to providing guidance to follow during activities performance. With the AT done, its use was trained with the participants and the responsible person accompanying them by the main researcher and after the participant’s minimum 2 weeks of AT use, the COPM was reapplied to assess if there were changes in occupational performance with the aid of the AT. This reapplication was made by a blinded evaluator who had no prior knowledge of previous results.

The COPM was created as an outcome measure, therefore, the total scores of the initial moment and the moment of re-evaluation were used with the objective of comparing the occurrence or not of changes in occupational performance and satisfaction, so it could be proved the effectiveness of an approach or intervention – in this case, the use of Assistive Technology. These changes were calculated by subtracting the evaluation values from the re-evaluation values, both for performance and satisfaction. The participants’ scores were not compared with each other, as COPM is an individual measure. With the completion of research data collection, the assistive technology resource made and/or adapted for each participant remained the same for continuous use. This study is part of a project approved by the Research Ethics Committee of the research site, under the number 1.827.932, valid until 31/10/2021, complying with the ethical principles in accordance with resolution 466/2012, and all participants were informed about the study, objectives, benefits and risks.

Results

From PEDI results we observed impacts on occupational performance, which consequently affects the ability to perform self-care tasks, especially in dressing, personal hygiene and bathing activities, as can be seen in Table 1. The changes in self-care activities observed from PEDI, participants chose the activities that were most significant through COPM, adding a value about it, to quantify its importance in performing it on a daily basis or wanting to execute it. Table 2 shows the chosen activities, the degree of importance and the AT made. It is noted that the activities varied, related to dressing or personal hygiene.

Table 1. Affected items grouped by tasks performed in PEDI self-care.

Table 2. Description of activities, importance given by participants – COPM and AT made

After making and training the ATs, Table 3 presents the changes in occupational performance and satisfaction in performing the selected tasks. The improvement of these two parameters was observed throughout the sample. However, it was observed that it was not possible to infer changes in two cases (participant 4 and participant 6), because they did not use the AT after training: participant 4 started training at home, but didn’t feel willing to keep using the AT, preferring that his mother did the activity for him; and participant 6, did not use, because he did not wear clothes that have button or zipper at home, only using to go out and preferring that his mother performed the activity.

Table 3. Importance / Performance / Satisfaction Relationship – Before and after the application of AT and observed changes

Discussion

Children and adolescents with MPS, the limitation of mobility caused by the accumulation of glycosaminoglycans in tissues and joints, causes a loss in the ability to perform occupational activities, especially related to activities of daily living (ADLs), especially those requiring fine movements (eg: buttoning) or of large amplitudes (brush hair) [11–15]. It is widely discussed in the literature that progressive musculoskeletal impairment, found regardless of the type of MPS, impacts occupational performance. Studies show that joint stiffness, common in MPS, and even MPS IV-A-specific ligament laxity and muscle weakness, as well as carpal tunnel syndrome and Dupuytren’s contractures, all contribute to important limitation in self-care activities such as eating, dressing and personal hygiene [14, 16,17,18]. From the knowledge of body structure and function deficiencies related to self-care activities, it is possible to establish intervention priorities and select better strategies to be used, in order to enhance occupational performance. Among the intervention strategies, AT is a possibility of occupational therapist resource for the promotion of functionality [10].

Although the entire sample showed impairment in the area of dressing, the choices of tasks for making the AT were diverse and did not show a pattern by MPS type. This is because each individual sees itself in a way, and different activities may be a priority for one but not to the other. The activities that a person chooses to engage in are full of meaning and purpose and are related to their roles and how they relate to the world/environment [19], and therefore each individual attaches meaning and importance to each task of your day to day, like doing one activity is more important than performing another. With the application of COPM, besides allowing the choices of self-care activities that are significant for individuals, it was possible to measure the importance of the activity and quantify its performance and satisfaction. This is because according to the COPM theory was developed occupational performance is viewed as a subjective individual experience [20].

As much as it is not possible to make inferences between participants and their scores, it is possible to say that in the initial assessment of occupational performance, the average among participants was 2.5 points and in the revaluation, an improvement of the results was observed with an average of 7,25 points (minimum value of 4 and maximum of 10) There was also some improvement in the performance rate of activities, in the initial rating the group average was 4 (minimum 2 and maximum of 5) and in the revaluation the average value was 8.75 points (minimum 7 and maximum of 10). According to Carswell (2004), the variation found from 2 or more points in the COPM can be considered a clinically significant intervention [21]. That said, there was an improvement in the occupational performance of individuals with MPS, based on assistive technology, thus seeking to increase the independence of these individuals.

With these changes presented in a significant way,  it is possible to suggest that the higher the performance in performing self-care activities, the better the satisfaction in performing it, as seen in the work of Mildner et al., 2017, where the use of AT was described as significant in another health condition [22]. According to Persson et al. (2014) changes in occupational performance are associated with changes in psychosocial functioning and psychological well-being of individuals [23]. Regarding the non-use or abandonment of AT devices by users (occurred with two participants), Costa and collaborators (2015) conducted a literature review on the reasons that led individuals to abandon their resources. The most quoted factors were: problems with the user’s physical state; lack of information and training from both professionals and users; pain; functional limitations; preference for another resource or use of remaining capacities [24]. Among the factors mentioned, only the “preference of using remaining capacities” was found in this paper. In addition to this factor it was also quoted “lack of user motivation” and “lack of device functionality”.

Regarding AT, social acceptance is an important variable that permeates the decision of the user or his family to use the resource, because even if a certain resource improves the quality of life and occupational performance, but represents a negative social connotation and stigmatizing, the user tends to abandon it. If there is no support or encouragement from family members or if the device is viewed as a validation of being sick/being different (by the individual or family members) the chances of abandonment may be high [24–26].

Conclusion

AT has become an importante occupation therapeutic resource for children and adolescentes with problems in performing activities of daily living, such as MPS, increasing their autonomy and personal satisfaction. Thus, we highlight the importance of investing in future research in AT field focusing on occupational performance, especially self-care of individuals with MPS to then guide the intervention and occupational therapeutic care.

References

1. Guarany NR, Schwartz IVD, Guarany FC, Giugliani R (2012) Functional capacity evaluation of patients with Mucopolysaccharidosis. J Pediatr Rehabil Med  1: 37–49.
2. Nussbaum RL, Mcinnes RR, Willard HF (2008) Thompson e Thompson Genética médica, 7º edição. Saunders, Elsevier.
3. Schwart, IVD, Boy R (2011) As doenças lisossômicas e tratamento das mucopolissacaridoses. Rev do Hosp Univ Ped Ernest 2.
4. Silva MCA, Horovitz DDG, Ribeiro CTM (2015) Desempenho ocupacional de crianças e adolescentes com mucopolissacaridose de uma instituição de saúde do município do Rio de Janeiro [dissertação de mestrado]. Rio de Janeiro.
5. Magalhães LC, Magalhães LV, Cardoso, AA (2009) Medida Canadense de Desempenho Ocupacional – COPM. Belo Horizonte: Editora UFMG.
6. Barata-Assad DA, Elui VMC (2010) Limitações no desempenho ocupacional de indivíduos portadores de hemofilia em centro regional de hemoterapia de Ribeiro Preto, Brasil. Rev. Ter. Ocup. São Paulo 3: 198–206.
7. Anson D. (2004) Tecnologia assistiva. In: Pedretti LW, Early MB. Terapia Ocupacional: Capacidades práticas para as disfunções físicas. Quinta edição. São Paulo: Roca P. 276–296
8. Rodrigues AC (2008) Reabilitação: Tecnologia Assistiva. In: Rodrigues, AC. Reabilitação. Práticas inclusivas e estratégias para a ação.  São Paulo: Livraria e Editora Andreoli p. 39–41.
9. Sfredo Y, Silva RCR. (2013) Terapia Ocupacional e o uso de tecnologia assistiva como recurso terapêutico na artrogripose. Cad Ter Ocup. UFSCar 3: 479 – 491.
10. Mancini, MC (2005) Inventário de Avaliação Pediátrica de Incapacidade (PEDI): Manual da versão brasileira adaptada. Belo Horizonte; UFMG.
11. Rocha JSM, Bonorandi AD, Oliveira LS, Silva MNS, Silva, VF (2012) Avaliação do desempenho motor em crianças com mucopolissacaridose II. Cad Ter Ocup UFSCar 2012 20(3): 403–12.
12. Amaral IABS, Filho RLO; Neto JAR, Reis MCS. Avaliação da capacidade funcional de adolescentes portadores de Mucopolissacaridose do tipo II. Cad Bras Ter Ocup, São Carlos. 2017; 25(2): 297–303.
13. Schwart, IVD; Boy, R. (2011) Às doenças lisossômicas e tratamento das mucopolissacaridoses. Rev do Hosp Univ Ped Ernest 2.
14. Santos AC, Azevedo ACMM, Fagondes S, Burin MG, Giugliani R, Schwartz IVD (2008) Mucopolysaccharidosis type VI (Maroteaux-Lamy syndrome): assessment of joint mobility and grip and pinch strength. Jorn de Ped 2: 130–5.
15. Pinto LLC, Schwartz IVD, Puga ACS, Vieira TA, Munoz MVR, Giugliani R, et al. (2006) Prospective study of 11 Brazilian patients with mucopolysaccharidosis II. Jornal de Ped 4: 273–8
16. Vieira TA, Giugliani R, Schwartz I (2007) História natural das mucopolissacaridoses: Uma investigação da trajetória dos pacientes desde o nascimento até o diagnóstico. [dissertação de mestrado] [online]. Universidade Federal do Rio Grande do Sul, Porto Alegre.
17. Viapina M, Burin MG, Wilke M, Schwartz IVD (2011) Síndrome de Morquio – Mucopolissacaridose IV-A. Serviço de Genética Médica – Hospital de Clínicas de Porto Alegre.
18. Azevedo ACMM, Giugliani R (2004) Estudo clínico e bioquímico de 28 pacientes com MPS tipo VI. [Dissertação de mestrado]. Universidade Federal do Rio Grande do Sul; Porto Alegre.
19. Pelosi, MB (2009) Tecnologias em comunicação alternativa sob o enfoque da terapia ocupacional. In: Deliberato D.; Gonçalves MJ; Macedo EC (Org.). Comunicação alternativa: teoria, prática, tecnologias e pesquisa. São Paulo: Memnon Edições Científicasp 163–173.
20. Andolfato C, Mariotti MC (2009) Avaliação do paciente em hemodiálise por meio da medida canadense de desempenho ocupacional. Rev Ter Ocup Univ São Paulo 1: 1–7.
21. Carswell A, Mccoll MA, Baptiste S, Law M, Polatajko HL, Pollock N (2004) The Canadian occupational performance measure: a research and clinical literature review. Can Jour Occup Ther 4: 210–222.
22. Mildner AR, Ponte AS, Pommerehn J, Estivalet KM, Duarte BSL, Delboni MCC. Desempenho ocupacional de pessoas hemiplégicas pós-avc a partir do uso de tecnologias assistivas.
23. Persson E (2014) Occupational performance and factors associated with outcomes in patients participating in a musculoskeletal pain rehabilitation programme. J Rehabil Med. Uppsala 46: 546–552.
24. Costa CR, Ferreira FMRM, Bortolus MV, Carvalho MGR (2015) Dispositivos de tecnologia assistiva: fatores relacionados ao abandono. Cad. Ter. Ocup. UFSCar, São Carlos 3: 611–624.
25. Kruger, JM; Ferreira, AR (2013) Aplicação da Tecnologia Assistiva para o desenvolvimento de uma classe ajustável para cadeirantes. Iberoamerican Journal of Industrial Engineering. Florianópolis 9: 43–69.
26. Zelia ZLC, Bittencourt DC, Cheraid RC, Montilha, Elisabete RF (2016) Expectativas quanto ao uso de tecnologia assistiva. Journal of Research in Special Educational Needs 1: 492–496

The first documented White Coat Ceremony was held 10 years after I entered medical school. Dr. Arnold P. Gold held his first White Coat Ceremony four years after that [1]. White Coat Ceremonies have spread throughout US medical schools and even internationally [2,3], largely through the support of the foundation established by Dr. Gold, his family and his colleagues [3]. I confess that when I first heard of these events sometime in the mid to late 1990s, the idea of presenting a white coat to entering medical students in a ceremony so that they would understand that they are beginning their entry into a profession, reminded me of an old Monty Python sketch. A middle-aged man with spectacles (not unlike me) goes into an employment office and asks if there are any job openings for a lion tamer. When asked about his qualifications, he pulls out a pith helmet and says, “I’ve got the hat”.

After I began attending White Coat Ceremonies in 2011, I realized my flippant initial reaction was unjust. I have come to appreciate White Coat Ceremonies as an opportunity for helping new students understand and embrace the values of the medical profession, with the white coat as a symbol of those values. Of course, the holistic admissions practices of most medical schools, at least in the US, aim to ensure that matriculated students possess many of the underlying humanistic qualities desired in physicians; and certainly, the students should understand that ultimately what makes one a physician is not the white coat but the person who is inside it.

However, even the apparently innocuous activity of the White Coat Ceremony has generated controversy. There was always some debate about the timing of the ceremony in the process of education: some schools would hold their ceremony at matriculation, while others might schedule it at the point in the curriculum where students shift from their preclinical studies to working in the clinics and wards. As earlier clinical exposure becomes more common, it is likely that White Coat Ceremonies held in the end of the second year of medical school will shift earlier in the educational process. More significant controversies revolve around the purpose and symbolism of the White Coat Ceremony itself.

For Dr. Arnold Gold, it seems clear that there was no intrinsic conflict between “humanism” and medical “professionalism” and the White Coat Ceremony represented both [3]. This perspective was certainly that held by physicians of his generation [4], and certainly is an aspirational goal even now. Even early in their history, White Coat Ceremonies were recognized as a tool for inculcating and teaching professionalism [5]. More recent commentators have argued that humanism, defined by values that are egalitarian and universal, has become distinct from professionalism, which may be parochial and culturally determined, and to at least some degree, self-interested [6]. It has also been suggested that the White Coat Ceremony is a defensive action by the medical profession, symbolizing a claim of entitlement in a world where physician leadership of healthcare is challenged [7]. Perhaps reflecting these perceived conflicts is a model in which a “profession-entry” ceremony is held early in the first year followed by a later “humanistic” ceremony including individual statements of values, a high level of student engagement, and artistic performances [2]. Most White Coat Ceremonies include recitation of some sort of commitment or oath: the meaning and appropriateness of such recitations has also been debated [8,9].

The widely discussed issue of physician burnout engages the issues reflected in debates about the appropriateness and meaning of White Coat Ceremonies. Challenges to the autonomy of the medical profession are not only of a financial or administrative nature, but also reflect challenges to the humanistic expectations of patient centeredness and empathy. For that reason, it has been suggested that the term “burnout” should be replaced by the term “moral injury” [10].

When I discuss these issues with students, either individually or in small group learning settings, I emphasize that medicine is one of the professions as traditionally defined. More specifically, it is one of the three characterized as “learned professions”. Medicine is also a vocation, or if one prefers, a “calling”. The word “vocation” derives from the same Latin root as “vocal”. It refers to something to which one is called or summoned, and accepting the call implies a commitment with attendant obligations. For medicine, the commitment is to the service of the patient. For each of us, the obligation is for that service always to reflect our best, with a further obligation that through lifelong learning we will strive to ensure that the gap between our best and the ever-shifting target of “THE best” is always small as circumstances permit. The White Coat Ceremony and the acceptance by a student of her or his first white coat symbolize recognition that they are beginning the path to that commitment and to the obligations that follow from it.

In thinking about these issues, I am reminded of things other than Monty Python. When I was in college, the US Navy ran a series of recruiting commercials with the tagline “It’s not a job, it’s an adventure”. Medicine is not just a job: it is a profession, a calling, a commitment. However, a lot of us believe it is also an adventure [11].

Adapted from remarks made at the James H. Quillen College of Medicine Class of 2022 White Coat Ceremony – July 20, 2018.  Dr. Means is a former dean of the College

The White Coat Ceremony was supported in part by the Arnold P. Gold Foundation.

References

1. Gold A, Gold S (2006) Humanism in medicine from the perspective of the Arnold Gold Foundation: challenges to maintaining the care in health care. Journal of child neurology 21: 546–549.
2. Tamai R, Koyawala N, Dietrick B, Pain D, Shochet R (2019) Cloaking as a community: re-imagining the White Coat Ceremony with a medical school learning community. J Med Educ Curric Dev 6: 2382120519830375.
3. Kavan MG (2009) The White Coat Ceremony: a tribute to the humanism of Arnold P. Gold. Journal of child neurology 24: 1051–1052.
4. Lepore MJ (1982) Death of the Clinician: Requiem Or Reveille? Springfield, IL USA: Charles C. Thomas; 1982.
5. Swick HM, Szenas P, Danoff D, Whitcomb ME (1999) Teaching professionalism in undergraduate medical education. Jama 282: 830–832.
6. Goldberg JL (2008) Humanism or professionalism? The White Coat Ceremony and medical education. Academic Medicine: Journal of the Association of American Medical Colleges 83: 715–722.
7. Russell PC (2002) The White Coat Ceremony: turning trust into entitlement. Teaching and learning in medicine 14: 56–59.
8. Huber SJ (2003) The White Coat Ceremony: a contemporary medical ritual. Journal of medical ethics. 29: 364–366.
9. Veatch RM (2002) White coat ceremonies: a second opinion. Journal of medical ethics. 28: 5–9.
10. Heston TF (2019) Pahang JA. Moral Injury or Burnout? South Med J 112: 483.
11. Robinson GC (1957) Adventures in Medical Education. A Personal Narrative of the Great Advance of American Medicine. Cambridge: Commonwealth Fund 1957.

Abstract

Introduction: For decades, intraoperative anemia has been treated with red blood cell transfusions since it was believed that oxygen supply would increase by increasing hemoglobin levels. There is evidence that blood transfusion is associated with adverse events and should be avoid as much as possible. For this purpose, it is essential to know the compensatory physiological mechanisms during anemia. Venous oxygen saturation is a clinical tool that integrates the relationship between oxygen intake and consumption, which is easy to obtain once a central venous catheter, is available.

Material and methods: A longitudinal, prospective, observational study was conducted which included patients schedule for elective or emergency procedures that, due to their clinical conditions, had a central venous catheter. A sample of venous blood was taken from the central venous catheter and sent to the laboratory for gasometry. The results were correlated with the clinical status and vital signs of the patient. The following variables were evaluated: vital signs, hemoglobin, and hematocrit and oxygen saturation before and after transfusion.

Results: 34 patients were evaluated with an average age of 52 years. 58.8% was transfused. Despite the transfusion and variations of hemoglobin, the SaO2 in the pulse oximeter remained without changes pre and post transfusion. In gasometry, a difference between hemoglobin and initial hematocrit and pre-transfusion was observed, this due to bleeding that occurred. No differences in SaO2 values were observed for pre-transfusion vs post-transfusion pulse oximetry.

Conclusions: We found no evidence to support the linear correlation of ScvO2 with the hemoglobin levels, there is great variability of ScvO2 at different hemoglobin level, we suggest the use of venous central saturation as a physiological marker for transfusions, avoiding with this practice making decisions only with the hemoglobin levels.

Keywords

Transfusion, Oxygenation, Saturation, Blood, Hemoglobin, Catheter

Introduction

For decades, intraoperative anemia has been treated with red blood cell transfusions based on the concept that the oxygen supply to the tissues is increased by increasing hemoglobin levels. Likewise, arbitrary transfusion rules such as the “10/30 rule” have been used indicating that the transfusion of erythrocyte concentrates is required when the hemoglobin concentration is less than 10g / dl or the hematocrit decreases by 30% [1]. There is evidence that blood transfusion is associated with adverse events, so it should be avoided as much as possible [1–3]. For this purpose, it is essential to know the compensatory physiological mechanisms during anemia. The main function of red blood cells is the transport of oxygen from the pulmonary capillaries to the peripherals. The oxygen delivery (DO2) is defined as the product of cardiac output (CO) and arterial oxygen concentration (CaO2). DO2 = CO × CaO2 where DO2 is expressed in mL/min, CO in dL/min, and CaO2 in mL/dL.

The arterial oxygen concentration can be defined with the following formula: CaO2 = (SaO2 × 1.34 × Hb) + (0.0031 × PaO2)

Where SaO2 is the arterial saturation (in %), 1.34 is the amount of oxygen carried on the hemoglobin (in mL/g), Hb represents the hemoglobin level (in g/dL), 0.0031 is the solubility coefficient of oxygen in human plasma at 37°C (in mL/dL*mmHg) and PaO2 the arterial tension measure in mmHg. From this equation, we can infer that to maintain the tissue oxygen supply the organism must adjust some variables such as: Hb, CO, oxygen consumption (VO2) and SaO2. The ratio of oxygen consumption (VO2) to oxygen delivery (DO2) is defined as “Oxygen extraction ratio” (O2ER) in normal circumstances the normal range is from 20–30% because the DO2 (800- 1200 mL/min) exceeds VO2 (200–300 mL/min) three to five times. In this way the hemoglobin concentration and the oxygen delivery (DO2) can decrease significantly without affecting the oxygen consumption, which makes it independent of DO2 [4,5].

However, below a critical threshold of hemoglobin concentration (HbCRIT) and critical oxygen delivery (DO2 CRIT), a level of VO2 / DO2 dependence is reached. This means that below this threshold any decrease in DO2 or Hb also results in a decrease in VO2 and therefore in tissue hypoxia. Venous oxygen saturation is a clinical tool that integrates the relationship between the intake and oxygen consumption in the body, in absence of a mixed venous saturation sample (SvO2) obtained through a pulmonary arterial catheter, central venous oxygen saturation (SvcO2) is being used as an accurate substitute. Central venous catheters are simpler to insert, safer and cheaper than pulmonary artery catheters [6].

By means of a central venous catheter, it is possible to take blood samples for the measurement of ScvO2, whose value ranges around 73% – 82% [6, 7]. Since, as stated, the Hb level does not guarantee an adequate tissue perfusion. Accordingly, the physiological transfusion markers should replace the arbitrary markers currently used based only on hemoglobin levels [8, 9]. In this way, we could avoid the unnecessary use of transfusions with the consequent savings in blood banks, reserving it only to those patients who really require it, avoid adverse transfusion reactions such as acute lung injury, infection transmission, among others. Transfusion guidelines should consider the individual ability of each patient to tolerate and compensate the acute decrement of hemoglobin concentration in the account that there is not a universal threshold to indicate a transfusion [10]. The markers should instead consider signs of tissue dysoxia, which may occur at different hemoglobin concentrations depending on the comorbidities of each patient. These signs may be based on signs and symptoms of inadequate oxygenation, however, before a decision is made regarding transfusion, it must be ensured that there is an adequate supply of volume with crystalloids and / or colloids and that the anesthetic management at the time is optimal. The objective of the present study was to demonstrate that physiological markers, specifically central venous saturation, are parameters that are useful to determine the use of blood transfusion

Materials and Methods

Study design and ethical aspects

A longitudinal, prospective, observational study was conducted which included patients from 18 to 60 years, of indistinct gender, who entered the operating room for any surgical procedure of any specialty in in a third level hospital. The procedures included needed to carry a risk of bleeding greater than 15–20% of the circulating blood volume and the patients that, due to its clinical conditions, required a central venous catheter. Exclusion criteria included patients who refuse to participate in the study, with active bleeding from the gastrointestinal tract, with anemia or blood dysrcrasias and hemodynamically unstable. The elimination criteria included patients in whom the history of blood dyscrasias is unknown or confirmed, patients who have some other pathological condition that alters the results and interpretation of the study, patients with active bleeding and who require an urgent blood transfusion. This protocol was submitted for evaluation to the ethics committee. Because the study was carried out only in patients who already have a central venous catheter in place, patient authorization was not required by informed consent. In the same way the patient was not intervened, since this is an observational study, only the data was collected and analyzed.

Study variables

For each patient who met the criteria a sample of venous blood was taken from the central venous catheter. The sample was then taken to the hospital’s gasometry laboratory where the study was conducted. Once the result of the sample was obtained, it was captured in a database including as variables the results of arterial gasometries, vital signs and laboratory tests that the patient will have, such as blood count and blood chemistry.

Statistical analysis

Epidemiological data such as age and sex will be obtained. The data will be analyzed with measures of central tendency such as mean, median and dispersion measures such as the standard deviation. In the bivariate analysis, it is planned to use the Shapiro-Wilk test to observe the dispersion of the data and classify it as parametric or non-parametric. Based on the results obtained, non-parametric statistical tests such as square chi for two groups and Wilcoxon were performed, given that related groups are compared. If the results obtained are parametric, tests such as Student’s T will be performed for related groups. The SPSS version 24 program was used to perform the statistical tests described above.

Results

34 patients were evaluated with an age average of 76 years (± 16 years). The average weight of the patients was 80 kg and the average size was 164 cm. 64.7% of the patients were male and 35.3% female (Table 1). 58.8% of the patients evaluated had the need to be transfused. An average of 777 cm3 of blood was transfused; the most common amount of transfused packets was two globular packages. Vital signs were evaluated with a range of 83–84 beats per minute, respiratory frequency varied from 21 breaths per minute prior the surgical procedure and an average pulse oximeter saturation of 98%. Systolic blood pressure fluctuated from 127–109 mmHg at the end of the procedure and the initial diastolic pressure was 74-mmHg compare to the 66 mmHg at the end of the procedure. (Figure 1–4). An initial mean Hb was obtained in venous gases of 8.9, pre-transfusion of 7.8 and post transfusion of 10. A statistically significant difference was observed in pre and post transfusion hemoglobin, as well as in pre and post transfusion hematocrit unlike SaO2, where there was no difference between pre-transfusion and post transfusion. In arterial gases, a statistically significant difference was found in the initial hematocrit levels and the hemoglobin levels pre transfusion, no differences were observed in SaO2 values or in any of the pre-transfusion vs. post-transfusion data (Table 2). Finally, a comparative analysis between the markets as cut- off points in the literature reviewed was made. A chi-square cross-tabulation table analysis was performed for qualitative variables, where no statistically significant differences were found in patients with indication of transfusion at the beginning of the procedure; pre-intervention and post-intervention (Table 3).

Table 1. Demographic characteristics of the patients.

Table 2. Venous and arterial gases, pre transfusion and post transfusion.

Table 3. Patients with indication of transfusion at the beginning of the procedure, pre intervention and post intervention.

* The variables evaluated are constant, so there is no statistically significant difference.

Figure 1. Average Hear Rate.

Figure 2. Average Breathing Rate.

Figure 3. Average Systolic Pressure.

Figure 4. Average Diastolic Pressure.

Figure 5. Average Arterial Oxygen Saturation.

Discussion

Based on the considerations above, the present investigation was carried out, with the aim of demonstrating, within our institution and in the operating room environment, the need to take other considerations in addition to a laboratory value when indicating a transfusion. The transfusion of erythrocyte concentrates is a very common practice within the operating room, it is very important for those who are responsible for carrying out this work to have deep knowledge about the physiology and biochemistry involved in the oxygenation process. This in order to achieve the main objective of hemotransfusion, without neglecting the other two variables that the blood influences, which are the rheological and volume effect [11, 12].

Unfortunately, routinely monitored variables such as blood pressure, heart rate, urine output, arterial gases and filling pressures do not necessarily reflect tissue perfusion. The mixed venous saturation (SvO2) and central venous oxygen saturation (SvcO2) are better indicators of oxygen delivery (DO2) and perfusion [13, 14]. The hemoglobin value has been considered as the determinant to indicate blood transfusion for many years. Although there are guides from different associations and different countries that provide us with great support when deciding if it is necessary to administer blood components to our patients, we propose that we also seek the support of physiological variables and markers when making this important decision. Taking into consideration that nowadays, at an international level, the transfusion of blood components cannot yet be performed without a residual risk [15,16].

The appropriate use of blood components should be promoted, avoiding abuse, by developing medical guidelines for therapeutic use by specialties based on scientific evidence. Awareness should be made of the high cost of production, the permanent existence of residual risks of infectious diseases and the possibility of causing immediate or late post-transfusion reactions in the patient [17]. Understanding the costs associated with blood products requires extensive knowledge about transfusion medicine and this is attracting not only clinicians but also administrative personnel from the health care sector worldwide. To improve both the clinical and the economic situation, the use of blood bank resources should be optimized [17–19]. Estimate the costs of storage, procurement, transfer among others is complex, however they should be minimized and used only when strictly necessary based on clinical judgment and on the use of technology and tools that allow estimating the state of patient oxygenation. With a rapid and accessible examination in many of the hospitals where surgical procedures are performed, we can obtain data about tissue oxygenation and thus be able to decide more effectively the use of blood bank resources.

Conclusion

This study does not find enough evidence to support the correlation of ScVO2 with hemoglobin, that is, there is great variability in venous saturation at different hemoglobin levels, and however, there is a tendency to increase ScvO2 after transfusion of globular packages. In the absent of a mixed venous saturation sample (SvO2) which is obtain via a Swan Ganz catheter, the central venous oxygen saturation (SvcO2) is a precise substitute and a reliable tool that integrates the relationship between the supply and consumption of oxygen in the body. By means of a central venous catheter, it is possible to take blood samples for the measurement of ScvO. We recommend that in patients who have this catheter use it to obtain a sample for gasometry and guide better decision-making regarding blood administration. There is an increase in interest in the use of mixed venous saturation and central venous saturation to guide therapeutic interventions during the intraoperative period. However, an understanding of the physiological principles and venous oximetry are essential for safe use in clinical practice. The venous oxygen saturation reflects the balance between the overall oxygen supply and its consumption, which can be affected by a large number of factors during the intraoperative period.

References

1. Madjdpour C, Spahn DR, Weiskopf RB (2006) Anemia and perioperative red blood cell transfusion: a matter of tolerance. Crit Care Med 34: S102–108. [crossref]
2. Vazquez Flores JA (2006) La seguridad de las reservas sanguíneas en la república mexicana. Revista de Investigación Clínica 58: 101–108.
3. Añón JM, García de Lorenzo A, Quintana M, González E, Bruscas MJ (2010) [Transfusion-related acute lung injury]. Med Intensiva 34: 139–149. [crossref]
4. Walley KR (2011) Use of central venous oxygen saturation to guide therapy. Am J Resp Crit Care 184(5): 514–520.
5. Cain SM (1965) Appearance of excess lactate in anesthetized dogs during anemic and hypoxic hypoxia. Am J Physiol 209: 604–610. [crossref]
6. Vallet B, Emmanuel Robin, Lebuffe G (2010) Venous oxygen saturation as a physiologic transfusion trigger. Critical Care 14: 213.
7. Reinhart K, Kuhn HJ, Hartog C, Bredle DL (2004) Continuous central venous and pulmonary artery oxygen saturation monitoring in the critically ill. Intens Care Med 30: 1572–1578.
8. Adamczyk S, Robin E, Barreau O, Fleyfel M, Tavernier B, et al. (2009) Contribution of central venous oxygen saturation in postoperative blood transfusion decision. Ann Fr Anesth 28: 522–530.
9. Vincent JL (2012) Transfusion triggers: getting it right! Crit Care Med 40: 3308–3309. [crossref]
10. Vallet B, Adamczyk S, Lebuffe G (2007) Physiologic transfusion triggers. Best Pract Res Clin Anaesthesiol. 21: 173–181.
11. Colomina M, Guilabert P (2016) Transfusion according to haemoglobin levels or therapeutic objectives. Rev Esp Anestesiol Reanim 63: 65–68.
12. Shander A, Gross I, Hill S, Javidroozi M, Sledge S (2013) A new perspective on best transfusion practices. Blood Transfus 11: 193–202.
13. Carrillo R, Núñez J (2007) Saturación venosa central. Conceptos actuales. Rev Mex Anestesiol 30: 165–171.
14. Cabrales P, Intaglietta M, Tsai AG (2007) Transfusion restores blood viscosity and reinstates microvascular conditions from hemorrhagic shock independent of oxygen carrying capacity. Resuscitation 75: 124–134. [crossref]
15. Shander A, Hofmann A, Gombotz H, Theusinger OM, Spahn DR (2007) Estimating the cost of blood: past, present, and future directions. Best Pract Res Clin Anaesthesiol 21: 271–289. [crossref]
16. Rojo J (2014) Enfermedades infecciosas transmitidas por transfusión. Panorama internacional y en México. Gac Med Mex 150: 78–83
17. Goodnough LT (2005) Risks of blood transfusion. Anesthesiol Clin North Am 23: 241–252, [crossref].
18. Shepherd SJ1, Pearse RM (2009) Role of central and mixed venous oxygen saturation measurement in perioperative care. Anesthesiology 111: 649–656. [crossref]
19. Park D, Chun B, Kwon S (2012) Red blood cell transfusions are associated with lower mortality in patients with severe sepsis and septic shock: A propensity-matched analysis. Crit Care Med 40: 3140–3145.

Abstract

We present the emerging science of Mind Genomics, to understand people’s responses to health-related issues, specifically pain. Mind Genomics emerge out of short, affordable, scalable, east-to-run experiments. The topic, here pain, is deconstructed into four questions, each with four separate answers (elements.) The answers are combined into vignettes, presented to respondents, who rate the entire vignette. Emerging from the study are the ratings and the response times to the vignettes, both of which are deconstructed into the contributions of the different underlying elements which the vignettes comprise. The answers cannot be gamed, and the data quickly reveal what is important to the individual, as well as revealing the existence of new-to-the-world mind-sets which differ in the pattern of elements that they find important. Mind Genomics  provides the opportunity to understand the person’s needs and wants for specific health as well as other experiential situations where human judgment is relevant.

Introduction

Pain is an inevitable companion in our life’s journey. Pain is defined through its association with actual or potential tissue damage, denoting it as a necessary characteristic of the experience, but also recognizing that events other than tissue damage can serve as determinants, consistent with a bio psychosocial model of pain [1,2]. This definition of pain denotes multiple causal factors underlying pain, beyond the issue pathology.

There is no dearth of studies on pain, whether these studies are report of pain from one’s everyday life [3], a topic dealt with in medicine [4], and a topic of scientific investigation [5]. When we talk about pain, can we probe into the mind of the person beyond simply the report, beyond a simplistic scale? Can we move beyond simple indicators, approaching a more detailed description of one’s pain but yet not forcing the respondent to become a scientist?

Pain, a highly subjective phenomenon, often refers to a sensory experience resulting from actual damage to the body or from non-bodily damage [6]. Pain may be influenced by psychological mechanisms such as: attention, emotion, beliefs and expectations [7].

In general, there are two different classifications of physical pain, visceral and somatic. Visceral pain originates in the internal organs whereas somatic pain stems from skin, muscle, soft tissue, and bone. There are many types of pain which fall under these categories. A person’s pain can also be classified as acute or chronic. Pain can be described as nerve pain, psychogenic pain, muscle pain, abdominal pain, back pain, pelvic pain, etc.

Subjective pain is influenced by its intensity and by interventions to treat the pain. Expectations and attitudes towards pain, may stem from psychological processes that are fundamental to learning across various sensory experiences and affect. Understanding expectations and attitudes towards pain may help us form communication messaging to help individuals deal more effectively with their chronic pain.

The subjective nature of pain makes it difficult to test the actual nature of perceived pain across populations, within a country, and in different countries. There are accepted methods of testing the actual perception of pain, specifically pain thresholds and pain tolerance, as well as psychophysical scaling of pain. One example is measuring the time one can submerge a limb in an ice bath, to test the ability of subjects to tolerate pain under varying conditions, most notable with the testing of analgesics of anesthetics. These methods give a measure of the all-or-none response to pain, and even the qualitative nature of the pain, but do not give a sense of the mind of the person who is undergoing the pain.

Increase in pain accompanies one’s beliefs that a certain treatment will cause pain or increase one’s symptoms overtime [7]. Negative beliefs regarding pain and its effects may occur in some types of chronic pains. To test whether expectations affect pain, studies tested the extent to which expectations influenced physiological responses among individuals. Placebo treatments truly reduced pain intensity [8–12]. These studies also indicated that short-term expectations varied and strongly affected perceptions of pain and pain-evoked responses [13].

Other studies linked differences in expectations regarding pain to the magnitude of responses to pain treatments [14]. Research on the relationship between expectations and pain experiences, showed that expectations about treatments and painful stimuli profoundly influenced behavioral markers of pain perception [7].

Pain treatments also bring positive changes in negative emotions [15]. Expectations affect pain through attention, executive functioning, value learning, anxiety and negative emotions [16]. Attitudes towards pain such as anxiety raised subjective pain. Pain is, thus a complex experience, involving sensory, motivational, and cognitive components. Affect any one of these components may change one’s attitudes towards pain [7].

Whereas studies indicate that beliefs influenced pain experience, it is unclear to what extent psychological processes such as attention, anxiety and emotions affect choice of treatments and what communication messages may mediate the effects of these psychological processes. This study tests communication messaging that affect emotion, attitudes towards pain and choice of treatment for pain.

In his book, Pain: The Gift Nobody Wants, author Paul Brand, MD describes his observations across cultures. Growing up as the child of missionaries in India and then moving to the US, Brand noted the difference in pain and suffering that existed in the East versus the West. He noted that, “as a society gained the ability to limit suffering, it lost the ability to cope with what suffering remains”. He stated that he believed that Easterners have learned to control pain at the level of the mind and spirit whereas, Westerners tend to view pain and suffering as an injustice or failure and an infringement on their right to happiness [17].

In the newly developing science of Mind Genomics we attempt to demonstrate a richer understanding of one’s inner life by presenting the respondent (or ill/healthy pain sufferer, here) with vignettes describing the inner experience, instructing the respondent to rate the fit of the vignettes, one at a time, and then estimating the degree to which each of the elements of the vignette ‘fits’ the respondent.

Method

Mind Genomics as an emerging science has been previously presented [18]. Mind Genomics works by presenting respondents with vignettes, combinations of statements which together tell a story. The respondent is instructed to judge the vignette, rating the vignette as a totality. The rating scale for this study is simply ‘How well does this describe you?’

The statements, elements in the language of Mind Genomics, present simple ideas. The approach requires the construction of four questions which ‘tell a story.’ For each question, the researcher is required to provide four answers, all expressed in simple language.  Table 1 presents the four questions, and the four answers to each question. Ideally, the questions and answers should deal with the topic, here pain, but need not mention pain directly. Rather, the questions and answers should be relevant to the topic.

Table 1. The four questions and the four answers to each question.

The answers in Table 1 are combined by experimental design into a set of 24 vignettes, with each vignette comprising 2–4 elements. Table 2 shows an example of the first six vignettes. The elements appear an equal number of times. Each of the 16 elements is, by design, statistically independent of every other element.

Table 2. The first seven vignettes for the first respondent, created by the experimental design. The table shows the combinations, then the combinations transformed into binary, and then the ratings.

Each respondent evaluates a unique set of 24 vignettes. The underlying mathematical structure of the experimental design is maintained, but the specific combinations are changed, in a permutation scheme which preserves the mathematical properties of the design [19]. The permutation covers many more combinations of elements compared to the standard approach of creating one experimental design and presenting that design to many respondents.  The Mind Genomics achieves stability by testing many combinations, each a single time, but the expanded coverage ensures that a great of the ‘space of combinations’ is covered. It is difficult to be very ‘wrong’ with a Mind Genomics study because the scope. In contrast, traditional research works with a very small experimental design, e.g., equivalent to the combinations tested by one person, but the combinations are tested by many respondents in order to obtain a stable estimate of the value for each combination.

Mind Genomics and traditional statistics are on opposite sides in terms of what generates valid data. Is valid data obtained by sampling a few of the many possible combinations, albeit with stability for each point (traditional), or by sampling a great many of the combinations, albeit with less stability at any point. A good analogy to Mind Genomics is, metaphorically, the MRI, which discovers the configuration of tissue by taking different ‘snapshots’ and integrating them into one picture.  With the permuted experimental one need not ‘be sure’ that the limited number of combinations is the correct set to represent the total set of possible alternatives. With as few as 25 respondents, the number of respondents participating, generating a total of 720 different combinations has covered the space quite well.

Running the Mind Genomics experiment

The experiment is run on the web, typically with respondents from a specific population who have agreed to participate (e.g., those being treated for a condition), or more typically with respondents recruited from the general population, when the objective is a quick ‘scan’ of what is important.  The base sizes of these studies range from 25 for an exploration to 500 for a massive deconstruction of the population into different mind-sets.  The more typical base size of 25–50 respondents reveals quite a bit about the nature of people’s minds with regard to a particular issue.  This study shows the type of learning emerging from this small base size of respondents from the general population, and can be followed with many different studies to follow up on various interesting aspects.

The elements, answers to the questions, are created by experimental design [20]. The 16 elements are combined into 24 combinations or vignettes, similar in structure to the vignettes shown schematically in Table 2. The vignette can be presented on smartphones, tablets, or PC’s.

Although the respondent might feel that the vignettes are created in a random fashion, the reality is just the opposite. The vignettes are created within the framework of the design, which prescribe the exact combinations. The elements are placed one atop the other, centered, without any connectives, making the respondent’s task easier as the respondent ‘grazes for information’.

The experimental design ensures that the elements are statistically independent; appear several times against different backgrounds provided by the other elements in the vignette. Each respondent evaluates a unique set of 24 vignettes, permuted as noted above, so that the design structure is maintained but the specific combinations are new. The permutation system allows a great deal of the design space, or combinations, to be tested, and allows the information to emerge even when the researcher has absolutely no idea what will be important and what won’t. In other words, Mind Genomics is a discovery system, and not a confirmation system. One can learn quickly from a base of zero knowledge, simply by doing 1–4 easy studies of different facets of a topic.

The respondents who participated were US residents, members of a 10+ million world-wide panel of Luc.id Inc., who had previously agreed to participate in these studies for a reward administered by the panel provider. All respondents participated anonymously. The only information about the respondent was age, gender, and the answer to the third question about what type of pain they had.  There were five answers to the third question, three dealing with chronic pain of various sorts, and two saying either ‘no pain,’ or ‘not applicable.’  All respondents were classified by gender, age, and by either pain/yes versus pain/no.

Preparing the data for analysis

The respondent assigns a rating to assess ‘How much does this describe how you feel’. The low anchor, 1, is ‘not at all.’ The high anchor, 9, is ‘very much.’ The Mind Genomics program bifurcates the scale, dividing it into the lower part, ratings of 1–6, transformed to 0, plus a very small random number (<10^–5), and a high part, ratings of 7–9, transformed to 100, plus a very small random number. The bifurcation comes from the decades of experience which suggest that managers and scientists alike do not ‘understand’ the meaning or use of the Likert or category scale, but they easily understand the meaning of a no/yes, binary scale.  The choice of where to bifurcate is left to the researcher. Thirty-five years of experiments suggest that a 2/3 vs 1/3 division seems to work well.  The small random number added to the binary transformed data ensures that when it is time to run the OLS (ordinary least-squares) regression on the data at the level of the individual respondent, there will not be a ‘crash’ of the regression program when the respondent confined the ratings to either the low range (1–6) or to the high range (7–9.) Either of those two cases produces all 0’s or all 1’s, crashing the regression. The small random number ensures that there is variability in the dependent variable, the binary transformed data.

How the different elements drive the binary transformed rating

Table 3 shows the parameters and relevant statistics for the additive model created from the ratings of the total panel, after transformation to a binary scale. The model itself is a simple linear equation of the form: Binary Rating = k₀ + k₁(A1) + k₂(A2) … K₁₆(D4). The experimental design allows us to create the model either at the level of the individual respondent or at the grand level, combining all of the data from the ‘relevant’ respondents, with relevant being

Table 3. Parameters of the model for ‘Fits Me’ after binary transformation. The data come from the Total Panel (720 observations, 24 tested vignettes from each of 30 respondents.) The table is sorted in descending order of coefficient for ‘describes me.’ At the right is the associated coefficient for response time.

The analysis suggests the following:

1. Additive constant, the expected binary value in the absence of elements: Without any elements, the likely response that the vignette will ‘describe me’ is about 46%. By design, all vignettes comprised 2–4 elements, so the additive constant is an estimated parameter.  Thus, the value of 46 for additive constant says that half the time respondents will answer that whatever appears will describe them. It is the elements which must do the work to move beyond this almost 50% agreement rate. It is worthwhile commenting here that this baseline of 46% is modest. When the topic is credit cards and the rating is ‘interested in acquiring this credit card,’ the additive constant plummets to about 10–15. When the topic is pizza and the rating is ‘interested in eating this pizza,’ the additive constant skyrockets to 60–70.
2. There are no very strong elements for the total panel: That is, no element drives the description of ‘me.’ This weakness can either be the result of choosing the wrong elements, or the result of dealing with two or perhaps even three or more different populations, who describe their impressions by different terms, and who may live in quite different worlds of pain.
3. The highest scoring element is C2, I would like exercises and stretches that reduce pain. This element generates a coefficient of only 6, and has a t-statistic of 0.95, with a probability of 0.34 that it came from a distribution with a true mean of 0. That is, it’s quite likely that were we to do this study again, we would come up with a coefficient much lower than 6, probably 0 or thereabouts.
4. The remaining elements do not ‘fit’ the respondent:  It may well be that the elements are simply incorrect and others will fit the respondent better, or more likely that we are dealing with a segmented population of individuals, some of whom feel that an element ‘fits them,’ whereas others feel that the same element ‘does not fit them.’ In such a situation the responses cancel each other, and we are left with a coefficient around 0, denoting ‘no fit.’

Key subgroups

We know three additional things about the respondent based upon the self-profiling questions completed during the study. The first is gender, the second is age, and the third is whether or not they suffer pain on a regular basis. In this computerized application, the respondent is required to select one of two genders (male/female), and required to put in the year of birth, which provides age.  The third question is left to the discretion of the researcher. In this study is the selection of pain, with five options. Two options are defined as ‘no pain’ (actual selection of ‘no pain’ as an answer, selection of not applicable). The remaining three options as pain (i.e. pain in the limbs, back, etc.).  We will look at gender, age, and self-reported pain as the three self-defined subgroups. We will also explore two new subgroups, mind-sets inherent in the population but revealed by understanding patterns of responses, behavioral patterns, rather than self-classification.

The focus of interest in Mind Genomics studies is on the additive constant as the ‘baseline,’ and then on the ‘story’ told by the winning elements.  These elements are operationally defined as having a value of +6.51 or higher, which becomes 7 when rounded to the nearest whole number.

Gender

1. Males show a higher additive constant than do females (57 vs 38). In the absence of elements, men are more likely to say that a vignette ‘describes ME.’  Women are less likely to say that, and require more specification.
2. We get a good sense of what is important by looking at the elements which are most positive (most like me), and most negative (least like me)
3. For men, the single phrase which most describes them is

   C2: I would like exercises and stretches that reduce pain

4. For men, the single phrase which least describes them is

   C1: I would like to have a diet that is tailored to reduce my pain

5. For women, the two phrases phrase which most describe them are

   B2: I try to deal with the pain to work through it,

   A1: Pain bothers me all over my body. The degree of fit is less, however, for these elements than the corresponding best fits for males.

6. For women, the phrase which least describes them is

   B4: I just like taking a pill that deals with the pain.

Age: Under 50 versus 50+

Respondents provided the year of their birth. One respondent did not provide the year and was eliminated from this particular analysis by age.

1. Surprisingly, the additive constant is much higher for the younger respondents versus the for the older respondents (48 vs 31.)
2. For the younger respondents, there are no strong elements which fit them. The two elements which most describe them are those which suggest control over the pain:

   C2: I would like exercises and stretches that reduce pain

   D3: The doctor should set me up with a system for me to follow

3. For the younger respondents, the two elements which least describe them are those which suggest passivity, and no control over the pain.

   B1: The doctor explains to me how to deal with the pain

   B4: I just like taking a pill that deals with the pain.

4. For the older respondents, the two elements which most describe them are actual experience to reduce the pain, as well as a description of the experience.

   A3: The pain radiates and makes it difficult to function

   C2: I would like exercises and stretches that reduce pain

5. For the older respondents, the three elements which least describe them is passivity

   D1: The doctor should give me advice

   C4: I would like a prescription that gives me the medication I need to feel better

   C1: I would like to have a diet that is tailored to reduce my pain

No pain versus pain

As part of the self-profiling classification, the respondents selected the type of pain, if any, afflicting them. The respondents who check any of the three types of pain assigned to the group saying YES. The remaining respondents were assigned to the group saying NO.

1. The additive constant is virtually the same, 46 vs 48, meaning that in the absence of elements in the vignette; a little fewer than 50% of the responses will be ‘describes me.’
2. For those with pain, the phrase which most describes them is

   C2:  I would like exercises and stretches that reduce pain.

3. For those with pain, the element which least describes

   C1:  I would like to have a diet that is tailored to reduce my pain

4. For those with no pain, virtually no element most describes them
5. For those with no pain, many elements least describe. The strong element which least describes is

   C4: I would like a prescription that gives me the medication I need to feel better

Mind-Sets: Dividing respondents by the patterns of their coefficients for a specific topic

We have just seen that there are some differences in terms of ‘describes me’ across genders, and across those who define themselves as having pain versus no pain. These are ways that people describe themselves. People may differ in ways that the researcher cannot describe in simple terms, or even in way that they themselves don’t understand.

A major tenet of Mind Genomics is that within any topic area, such as the description of pain presented here, there are fundamental differences across people, differences that are obvious once demonstrated, but differences limited to a single topic area.  This is the case of the data here. Even within the small sample of 30 respondents we can extract two, possibly three different mind-sets. The method for extracting mind-sets has been previously described [21]. Quite simply, the technique is a matter of clustering the respondents into two or three groups based upon the pattern of their 16 coefficients. The statistical method of clustering is well accepted [22] All that remains is the clustering, extracting the small groups with the property that these mutually exclusive groups represent different ways of thinking about the topic.

Table 4 shows the results for the two mind-set segments emerging from the clustering of the 30 respondents. A base size of 25–30 suffices to reveal the nature of these different mind-sets, especially because the segments are so obviously different and interpretable.

Table 4. Coefficients for the binary-transformed scale ‘Describes me’ across gender, age, pain, and mind-set, respectively. Coefficients of +7 or more are presented in bold, and shaded.

1. Mind-Set 1 (wants a cure) begins with a low additive constant, 37. To them, it’s not the general response which ‘describes me’ but rather the specific phrase. Mind-Set 1 suffers pain, and wants a cure. Here are the elements which Mind-Set 1 feels best describes them:

   A1: Pain bothers me all over my body

   A3: The pain radiates and makes it difficult to function

   C2: I would like exercises and stretches that reduce the pain

2. Mind-Set 1 do not want simple medical treatment which will alleviate their pain. Here is the element which is they feel least describes them:

   B4: I just like taking a pill that deals with the pain.

3. Mind Set 2 (simplicity through the doctor) shows a higher additive constant, 54. Mind-Set 2 is less discriminating among elements. Mind-Set 2 wants simplicity. Here is the one element that they feel best describes them:

   D3: The doctor should set me up with a system for me to follow

4. Mind Set 2 does not want to take responsibility. Here are the elements that they feel least describe them:

   C1: I would like to have a diet that is tailored to reduce my pain

   B1: The doctor explains to me how to deal with the pain

   A3: The pain radiates and makes it difficult to function

Response times as a measure of cognitive processing of information

At the same time that the respondents were reading the vignettes, the response time was being measured. Response time is operationally defined as the time between the appearance of the vignette and the assignment of the rating. The experiment was executed on the internet.

 The respondent was unaware of response time being measured, being instructed simply read the vignette and assign a ‘gut-level’ judgment. Occasionally, in about 10% of the cases, the response time was longer than 10 seconds, suggesting that the respondent was doing something as well, so-called multi-tasking. Those response times of 10 seconds or longer were recoded as 10 seconds. Figure 1 shows the distribution of the 720 response times (30 respondents, each evaluating 24 vignettes)

Figure 1. Distribution of response times for the total panel of 30 respondents, each rating 24 unique vignettes.

Response time patterns for different subgroups

The measurement of response times as a key feature of Mind Genomics began during the summer of 2019. In the studies run since that introduction, the response time data suggests that when the topic deals with an important health issue, the respondents spend a long time reading the vignette, and thus their response times are long, often 1.0 seconds or longer. When the topic deals with something commercial or ‘fun’ the response times are very short, around 0.2 – 0.7 seconds.

Table 5 presents the response time coefficients for the key subgroups. The model for response time is written in the same way as the model for the binary transformed rating, with the key difference being that that the model for response time does not have an additive constant. The ingoing assumption is that the response time is 0 when there are no elements in the vignette.

Table 5. The coefficients for the response time models. The models do not feature an additive constant.

In Table, coefficients of 2.0 or higher are shaded and shown in bold. These are the elements to which the respondent pays attention.  There are some simple patterns which emerge from visual inspection of these elements that are processed ‘more slowly.’

1. For gender, males focus on the description of symptoms.

   A4  The pain is minor but frequent and annoying

   B2   I try to deal with the pain to work through it

   B3   I’m happy when I can use a device that delivers therapeutic solution

   C4  I would like a prescription that gives me the medication I need to feel better

   B4   I just like taking a pill that deals with the pain.

2. For gender, females want a relationship, or at least someone/something external to them.

   D3  The doctor should set me up with a system for me to follow

   B3   I’m happy when I can use a device that delivers therapeutic solution

   D2  The doctor should give me a shot that delivers long term relief

3. For age, those under 50 focus on only one element:

   D3  The doctor should set me up with a system for me to follow

4. For age, those 50+ focus on a number of phrases, most dealing with methods to assure pain reduction

   B3   I’m happy when I can use a device that delivers therapeutic solution

   B1   The doctor explains to me how to deal with the pain

   A4  The pain is minor but frequent and annoying

   B4   I just like taking a pill that deals with the pain.

   D3  The doctor should set me up with a system for me to follow

   B2   I try to deal with the pain to work through it

   D4  The doctor should give me a regular schedule of visits to treat my pain

   D2  The doctor should give me a shot that delivers long term relief

5. For pain, those with PAIN YES, i.e., who say they suffer from one or another pain, the focus is on what stops the pain, i.e., assure pain reduction

   B3   I ‘m happy when I can use a device that delivers therapeutic solution

   B2   I try to deal with the pain to work through it

   D3  The doctor should set me up with a system for me to follow

   C3  I would like regular therapy sessions to reduce my pain

   C4  I would like a prescription that gives me the medication I need to feel better

6. For pain, those with PAIN NO, i.e., who say that they do not suffer from pain, the focus is on descriptions of pain

   A4  The pain is minor but frequent and annoying

   D3  The doctor should set me up with a system for me to follow

   A1  Pain bothers me all over my body

7. For Mind-Sets, Mind-Set 1 (Wants a cure)

   D3  The doctor should set me up with a system for me to follow

   B2   I try to deal with the pain to work through it

   B3   I’m happy when I can use a device that delivers therapeutic solution

8. For Mind-Sets, Mind-Set 2 (Simplicity through the doctor)

   A4  The pain is minor but frequent and annoying

   B3   I’m happy when I can use a device that delivers therapeutic solution

   D4  The doctor should give me a regular schedule of visits to treat my pain

Finding the mind-sets in the population using a PVI (Personal Viewpoint Identifier)

The mind-sets reveal different ways of perceiving the nature of pain.  The mind-sets represent a way to divide what is likely a continuum of feelings and points of view into at least two distinct groups, a division which may provide further understanding, and certain a division that can be used to deal with patients in different, and possibly more appropriate fashion.

Table 6 shows, however, that it’s unlikely to identify mind-sets by their age and gender. It is also quite possible that there are no direct classifications of who a person ‘is’ or what a person ‘experiences’ which can easily assign a person to one of these two mind-sets.

Table 6. How the two emergent mind-sets for pain distribute on the self-profiling classification in terms of age, sex, and experience of pain.

An alternative way to assign new individuals to mind-set has been developed by author Gere. It is called the PVI, the personal viewpoint identifier. The PVI comprises a set of six questions, answered with one of two answers, no or yes.  The pattern of the answers to the six questions assigns the respondent to one of the two mind-sets.  Figure 2 shows the PVI questionnaire at the left, and the response emerging, given either to the physician and/or to the patient/client.  The questions themselves are taken from the actual study. These are the answers or elements, now turned into questions.

The PVI can be deployed along with additional information obtained during the questions. Thus, Figure 2 shows that the respondent, a new person not part of the previous study establishing the PVI, is asked for his or her email. Other questions can be asked, to relate mind-set membership to external variables, whether of a medical/health nature, or of a life-style nature.

Discussion & Conclusions

Since pain is a complex sensation involving sensory, motivational, and cognitive components, and affecting any one of these may change one’s attitudes towards pain [7]; we tested the effect of communication messaging, across mind-set segments towards pain. We tested how each min-set segment we identified emotionally responds to chronic pain, and which treatment choices are preferred by attitudinal mind-sets towards pain.

People who belong to the first mind-set segment feel the pain as radiating and challenging their daily functioning. The pain is very bothersome, but they choose to alleviate it by exercises and stretching. They chose to avoid medical treatment to simply deal with the pain and its ramifications.  People belonging to the second mind-set segment also view their chronic pain as radiating and challenging their daily functioning.  They, however, choose to simply take pain medication their doctor will prescribe.  They expect their doctor to also set them up with a system to follow.  In addition, they do not want to take responsibility for self-managing the illness which causes their pain. They prefer to avoid a diet that is tailored to reduce their pain.

Figure 2. The PVI created for the pain study. The link for the PVI as of this writing (Feb. 2019) is: http://162.243.165.37:3838/TT13/

This study also illustrated how a medical professional may easily identify the mind-set segment to which a patient belongs and accord communication messaging to patient choices and values. Identification of the mind-set to which a patient belongs may assist in building patient-physician trust resulting in higher patient adherence and better implementation of patient-centered care [21].

Mind Genomics provides the ability to segment out populations that share a common mind type and thereby help identify the possibility of determining the types of pain that a person is most likely to experience. It may help answer the question of why people with the same disease experience pain in profoundly different ways. By mind-typing patients who share ailments, Mind Genomics may aid in helping tailor a treatment plan best suited to that individual lying within a disease spectrum.

In light of the current opioid epidemic, it more important, now more than ever, to address how to customize pain treatments to individuals. There are many modalities to treat pain. In the West, pain medications are the first line of treatment. These medications include narcotics/opiates, Non-Steroidal Anti-Inflammatory Drugs (NSAIDs), acetaminophen, certain antidepressants, muscle relaxants, anticonvulsants, corticosteroids, local anesthetics, and most recently medical marijuana. Other modalities such as Transcutaneous Nerve Stimulation (TENS), implantable spinal cord stimulators, meditation and biofeedback are also used to help combat pain. Health care professionals who specialize in pain management use experience and training to try and help tailor treatment regimens to the individual patient. But a tool like Mind Genomics may help the practitioner go beyond the current protocols and prejudices of current practice. Mind Genomics may provide a “cheat sheet” to the patient’s mind and help provide a short cut to success by focusing on pathways that will more likely work for a given patient and eliminating the pathways that will waste time and resources.

References

1. Hadjistavropoulos T, Craig KD, Duck S, Cano A, Goubert, L, et al. (2011) A biopsychosocial formulation of pain communication. Psychological bulletin 137: 9.
2. Williams AC, Craig KD (2016) Updating the definition of pain. Pain 157: 2420– 2423. [crossref]
3. Baker KS, Gibson S, Georgiou-Karistianis N, Roth RM, Giummarra MJ (2016) Everyday executive functioning in chronic pain: specific deficits in working memory and emotion control, predicted by mood, medications, and pain interference. The Clinical Journal of Pain 32: 673–680.
4. Morrison RS, Maroney-Galin C, Kralovec PD, Meier (2005) The growth of palliative care programs in United States hospitals. Journal of Palliative Medicine 8: 1127–1134.
5. Schug SA, Palmer GM, Scott DA, Halliwell R, Trinca J (2016) Acute pain management: scientific evidence, 2015. Medical Journal of Australia 204: 315–317.
6. Loeser JD, Treede RD (2008) The Kyoto protocol of IASP Basic Pain Terminology. Pain 137: 473–477. [crossref]
7. Atlas LY, Wager TD (2012) How expectations shape pain. Neurosci Lett 520:140–148. [crossref]
8. Goffaux P, de Souza JB, Potvin S, Marchand S (2009) Pain relief through expectation supersedes descending inhibitory deficits in fibromyalgia patients. Pain 145: 18–23.
9. Goffaux P, Redmond WJ, Rainville P, Marchand S (2007) Descending analgesia–when the spine echoes what the brain expects. Pain 130: 137–143. [crossref]
10. Matre D, Casey KL, Knardahl S (2006) Placebo-induced changes in spinal cord pain processing. Journal of Neuroscience 26: 559–563.
11. Price DD, Craggs J, Verne GN, Perlstein WM, Robinson ME (2007) Placebo analgesia is accompanied by large reductions in pain-related brain activity in irritable bowel syndrome patients. Pain 127: 63–72.
12. Alkes L. Price, Arti Tandon, Nick Patterson, Kathleen C. Barnes, Nicholas Rafaels, et al (2009) Sensitive Detection of Chromosomal Segments of Distinct Ancestry in Admixed Populations. PLoS Genet 5: 1000519.
13. Atlas LY, Bolger N, Lindquist MA, Wager TD (2010) Brain mediators of predictive cue effects on perceived pain. J Neurosci 30: 12964–12977. [crossref]
14. Watson A, El-Deredy W, Iannetti GD, Lloyd D, Tracey I, et al. (2009) Placebo conditioning and placebo analgesia modulate a common brain network during pain anticipation and perception. PAIN 145: 24–30.
15. Wager TD, Atlas LY, Leotti LA, Rilling JK (2011) Predicting individual differences in placebo analgesia: contributions of brain activity during anticipation and pain experience. Journal of Neuroscience 31: 439–452.
16. Flaten MA, Aslaksen PM, Lyby PS, Bjørkedal E (2011) The relation of emotions to placebo responses. Philosophical Transactions of the Royal Society B: Biological Sciences 366: 1818–1827.
17. Brand PW, Yancey P (1993) Pain: the gift nobody wants. New York, HarperCollins Publishers.
18. Moskowitz HR, Gofman A, Beckley J, Ashman H (2006) Founding a new science: Mind genomics. Journal of sensory studies 21: 266–307.
19. Gofman A, Moskowitz HR (2010) Isomorphic permuted experimental designs and their application in conjoint analysis. Journal of Sensory Studies 25: 127–145.
20. Box GE, Hunter JS, Hunter WG (2005) Statistics for experimenters: design, innovation, and discovery (Vol. 2). New York: Wiley-Interscience.
21. Gabay G, Moskowitz HR, Silcher M, Galanter E (2017) The New Novum Organum: Policies, Perceptions and Emotions in Health. Pardes-Ann Harbor Publishing.
22. Moskowitz HR, Martin DM (1993) How computer aided design and presentation of concepts speeds up the product development process. Paper presented at the ESOMAR Congress, September, 1993, Copenhagen.
23. Eippert F, Finsterbusch J, Bingel U, Büchel C (2009) Direct evidence for spinal cord involvement in placebo analgesia. Science 326: 404. [crossref]
24. Moskowitz, HR (2012) ‘Mind genomics’: The experimental, inductive science of the ordinary, and its application to aspects of food and feeding. Physiology & behavior 107: 606–613.

Abstract

Background: Effective hospital teams can improve outcomes, yet, traditional hospital staffing, leadership, and rounding practices discourage effective teamwork and communication. Under the Accountable Care Unit model, physicians are assigned to units, team members conduct daily structured interdisciplinary bedside rounds, and physicians and nurses are jointly responsible for unit outcomes.

Objectives: To evaluate the impact of ACUs on patient outcomes.

Design: Retrospective, pre-post design with concurrent controls.

Patients: 23,406 patients admitted to ACU and non-ACU medical wards at a large academic medical center between January 1, 2008 and December 31, 2012.

Measures: In-hospital mortality and discharge to hospice, length of stay, 30-day readmission.

Results: Patients admitted to ACUs were less likely to be discharged dead or to hospice (-1.8 percentage point decline [95% CI: -3.3, -0.3; p = .015]) ACUs did not reduce 30 day readmission rates or have a significant effect on length-of-stay.

Conclusions: Results suggest ACUs improved patient outcomes. However, it is difficult to identify the impact of ACUs against a backdrop of low inpatient mortality and the development of a hospice unit during the study period.

Keywords

quality improvement, teamwork, hospital medicine, care standardization

Introduction

Under the traditional model of inpatient staffing, hospitals nurses and allied health professionals are assigned to a unit, while hospital medicine physicians treat patients on multiple units. Care is delivered asynchronously. Physicians see patients when their schedules permit, usually early in the morning or in the late afternoon and update orders at those times. Nurses and other professionals care for patients separately. They may not see the physician during rounds, and their priorities for patient care may be different from those of the physician. In our experience, they often obtain information from second-hand sources or the often difficult-to-decipher notes in patients’ charts.

The traditional, physician-centric model of inpatient care poses significant coordination and incentive problems. Beginning in October 2010, Emory University Hospital re-organized two medical units into Accountable Care Units (ACU® units). In the ACU care model, hospital-based physicians are assigned to a home unit where they can focus on the patients in the unit and work with the same nurse team. By assigning physicians to home units with other unit-based personnel such as nurses and having teams engage in structured interdisciplinary bedside rounds, ACUs enable clinicians to recognize preventable hospital complications and signs of deterioration or diagnostic error that might otherwise have been missed and implement a coordinated response.

Previous publications on the ACU model have been mostly descriptive in nature [1–4]. Using electronic medical records and a pre-post study design with concurrent controls, we retrospectively evaluated the effect of ACUs on patient mortality, length of stay, and readmissions at Emory University Hospital.

Methods

Intervention

Emory University Hospital is a 500 bed teaching hospital in Atlanta, Georgia. Prior to the implementation of ACUs, hospital medicine physicians at Emory University Hospital treated patients in as many as eight units. In the first unit to be organized into an ACU, patients were divided between five physician care teams prior the re-organization. Beginning in October 2010, Emory University Hospital assigned two physician care teams to each of two newly-constituted ACU units. ACUs combine a number of interventions, some of which have been implemented at other hospitals [5–8] , into a single, cohesive bundle.

ACU physician teams were assigned to units and included one hospital medicine attending physician, one internal medicine resident, and three interns. Within an ACU, two teams rotated call schedules over a 24 hour period. The team on-call admitted every patient who arrived at the unit. The same nurse teams continued to staff each unit as before the reorganization.

ACUs standardize communication through a series of brief but highly scripted intra- and inter-professional exchanges to review patients’ conditions and care plans. Each shift change begins with a five minute huddle where the departing staff hands over the unit to the incoming staff. During the huddle, the departing staff alerts the incoming staff to patient- and quality-related issues. After the huddle, nurses hand over individual patients at the bedside using a structured format, highlighting patient-level factors that might indicate patient instability or are outside the expected range. Once a day, each patient’s care team meets for structured interdisciplinary bedside rounds. Structured interdisciplinary bedside rounds bring the bedside nurse, attending physician, and unit-based allied health professionals to the bedside every day with the patient and family members to review the patient’s current condition, response to treatment, care plan, and discharge plan collaboratively [5–8]. Evidence-based actions, such as “bundles” to prevent hospital acquired conditions, are embedded in structured interdisciplinary bedside rounds, and reported on by the patient’s nurse. A scripted, standard communication protocol reduces extraneous communication and focuses the structured interdisciplinary bedside round team’s attention on aspects of patients’ conditions that are responsive to staff attention and effort.

A unit leadership dyad, consisting of a nurse manager and senior physician, set explicit expectations for staff and manage unit process and performance. Physicians operating in the traditional model may be unaware of unit-level quality protocols and outcome measures. As part of the re-organization, a data analyst prepared quarterly unit-level performance reports describing rates of in-hospital mortality, blood stream infections, 30-day readmissions and patient satisfaction scores and length of stay. These reports are used by hospital administrators to set goals for the ACU leadership team and may figure into the performance evaluations of ACU administrators. Readers interested in additional details about the ACU model are urged to consult previous publications [1–4].

Following implementation of ACUs, physician teams assigned to ACUs saw patients on only 1.5 units, with 90% of their patients located in the ACUs, compared to non-ACU physician teams, which cared for patients spread across 6 to 8 units every day.¹ The number of patient encounters per day for the ACU physician teams increased from 11.8 in the year before the ACUs (when the teams were not unit based) to 12.9 in the four years following implementation [1]. No changes were made to nurse staffing levels (1 to 4 or 5 nurses per patient).

During the study period, Emory University Hospital created two ACUs, but medical patients were also admitted to seven other units in the hospital. The units that became ACUs were selected because nearly all the patients were under the care of hospital medicine attending physicians so we could designate them as hospital medicine units. In other units, hospital medicine patients were mixed in with patients from other specialties (for example, cardiology). The assignment of patients to ACUs or other medical units was determined by bed control officers based on a mix of criteria that can include bed availability, relative patient wait times, and individual judgement. Bed managers know patients’ names, medical record number, and admitting diagnosis when they assign patients to units. They do not know have access to other prognostic indicators.

Study Sample

The study sample includes patients ages 18 and older admitted to the medical units of Emory University Hospital between January 1, 2008 and December 31, 2013. Following an intent-to-treat framework, we grouped patients who were transferred into ACUs during their hospital stay with non-ACU patients. Patients admitted to surgical, orthopedic, observation, or other specialty units (e.g. medical oncology) were excluded from the analysis, as were patients with cystic fibrosis who are treated only within one of the two ACUs. Patients in the control group were spread across 38 units, though 70% were in just 8 of these units.

Data and Outcome Variables

All study variables are captured in Emory’s internal electronic medical record and administrative data systems. We evaluated the impact of ACUs on in-hospital mortality, discharge to hospice, length of stay, readmission or emergency department visit to Emory University hospital within 30 days, and hospital-acquired urinary tract infection and deep vein thrombosis and pulmonary embolism. We counted a patient as having hospital-acquired urinary tract infection and deep vein thrombosis and pulmonary embolism if their records listed ICD-9 codes for these condition but not if they were among the present-on-admission ICD-9 codes.

Emory University Hospital opened an on-site hospice during the study period in November 2010, potentially reducing the barriers to transferring patients from the hospital to hospice care. While discharge to hospice is in many cases an indication of appropriate care, the opening of the inpatient hospice complicates efforts to measure trends in in-patient mortality. The opening of the unit may be responsible for changes in the site of death for patients admitted to the hospital over time. For this reason, we highlight the impact of ACUs on the combined outcome of in-hospital death or discharge to hospice.

Statistical Analysis

We compared patient characteristics between ACUs and control units using chi-squared tests. We estimated the impact of ACUs on these outcomes using a difference-in-difference study design (equivalently, a pre/post study with a concurrent control group). The pre period was January 1, 2008 to October 31, 2010. The post period was November 1, 2010 to December 31, 2012. We calculated the change in outcomes between the pre and post periods among patients admitted to the units that became ACUs and the change among patients in the control group. The difference of these changes is the difference-in-difference estimator. It assesses changes in outcomes in the units that became ACUs relative to changes in the control group. It assumes that absent any change in policy (i.e., the implementation of ACUs), trends in outcomes among patients admitted to the ACUs would have mirrored trends among patients in the control group. We calculated 95% confidence intervals for unadjusted estimates using z-tests. We used logistic regression with robust standard errors to estimate adjusted effects for in-hospital mortality and hospice discharges and readmissions. We used Poisson regression with robust standard errors to estimate adjusted effects for length of stay. We calculated standard errors and 95% confidence intervals for the difference-in-difference estimator using the Delta method [9].

In multivariable analysis, we adjusted estimates for patient age group, sex, race, primary payer, admission source (hospital or skilled nursing facility versus other), and Elixhauser comorbidities (based on all diagnosis codes) [10] that were present in at least 2.5% of patients in the sample. About one-third of the sample had missing values for admission source. We included each Elixhauser comorbidities as a separate variable in the model rather than collapsing the conditions into a count to avoid imposing unnecessary restrictions on the relationship between conditions and outcomes. Conditions are not mutually exclusive.

Estimates from difference-in-difference models may be biased if there are pre-existing trends in outcomes that differ between ACU and non-ACU units. We tested for pre-existing trends by estimating a model that included, in addition to the variables described above, indicators for the years in the pre-period (2008 to 2010) and these year indicators interacted with treatment group (ACU versus non ACU). We assessed the significance of the year-group interactions and used a likelihood ratio test to compare the model fit with a model that omitted the year-group interactions [11].

Estimates of the impact of ACUs on in-hospital mortality and hospice discharge rates may be biased by differences in length of stay. An intervention that reduces length of stay but does not affect mortality rates will reduce in-hospital mortality rates by shifting the place of death from the hospital to the community. In a sensitivity analysis we assessed the robustness of logistic regression estimates by estimating a Weibull survival model with robust standard errors of the time to hospice discharge or in-hospital death. Records for patients who were not discharged to hospice or dead are censored.

Results

There were 23,403 patients included in the study sample, of whom 10,639 were admitted to the ACU units (including patients admitted to the units before they became ACUs) and 12,764 patients in the control group. There are significant differences in some of the characteristics of ACU and control group patients in the pre and post periods (Table 1), but most differences are qualitatively small. There are some clinically meaningful differences in patients’ diagnoses. For example, in the pre-ACU period, 8.2% of patients in the control group had a solid tumor compared to 6.7% in the ACU group.

The unadjusted proportion of ACU patients discharged to hospice or dead declined from 7.7% to 5.8% (Figure 1) or -2.0 (95% CI: -2.9, -1.0) percentage points. The unadjusted proportion of patients discharged to hospice and dead both declined. A reduction in in-hospital mortality rates accounted for 70% of the decline (= [2.5–1.1] ÷ 2).

Figure 1. Discharge destination in ACUs and control units

Table 1. Sample characteristics

The unadjusted proportion of patients in the control group discharged to hospice or dead declined from 7.9% to 7.1%, or -0.8 (95% CI: -1.7, 0.1) percentage points. A decline in the proportion of patients discharged dead was offset by an increase in the proportion discharged to hospice.

Adjusted estimates of the impact of ACUs are displayed in the last columns of Table 2. (Full regression results are available in the Appendix Table.) The adjusted estimate of the impact of ACUs on the composite outcome of discharged dead or to hospice is -1.8 (95% CI: -3.3, -0.3; p = .015) percentage points. The adjusted difference-in-difference estimate of the impact of ACUs on length of stay is negative but not statistically significant (-0.5 days [95% CI: -1.2, -0.3; p =.21]). The estimates for 30 day readmissions and hospital-acquired urinary tract infections are close to 0. The estimate of the impact of ACUs on the occurrence of pulmonary embolism/deep vein thrombosis was positive and borderline significant (0.6 percentage points [95% CI: -0.05, 1.3] p = .07).

Table 2. Changes in outcomes among ACU and non-ACU patients