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Disabling disease codes predict worse outcomes for acute medical admissions S. H. Chotirmall, S. Picardo, J. Lyons, M. D’Alton, D. O’Riordan and B. Silke Department of Internal Medicine, Saint James’s Hospital, Dublin, Ireland
Key words disability, mortality, patient admission, emergency, comorbidity. Correspondence Bernard Silke, Department of Internal Medicine, Saint James’s Hospital, James’s Street, Dublin 8, Ireland. Email: [email protected] Received 29 November 2013; accepted 9 March 2014. doi:10.1111/imj.12440
Abstract Background: Concurrent with an extension in longevity, a prodrome of ill-health (‘disability’ identifiable by certain International Classification of Disease (ICD) 9/ICD10 codes) predates the acute emergency presentation. To date, no study has assessed the effect of such ‘disability’ on outcomes of emergency medical admissions. Aim: To devise a new method of scoring the burden of ‘disability’ and assess its relevance to outcomes of acute hospital admissions. Methods: All emergency admissions (67 971 episodes in n = 37 828 patients) to St James’ Hospital, Dublin, Ireland over an 11-year period (2002–2012) were studied, and 30-day in-hospital mortality and length of stay were assessed as objective end-points. Patients were classified according to a validated ‘disability’ classification method and scored from 0 to 4+ (5 classes), dependent on number of ICD9/ICD10 ‘hits’ in hospital episode codes. Results: A disabling score of zero was present in 10.6% of patients. Scores of 1, 2, 3 and 4+ (classified by the number of organ systems involved) occurred with frequencies of 23.3%, 28.7%, 21.9% and 15.5% respectively. The ‘disability’ score was strongly driven by age. The 30-day mortality rates were 0.9% (no score), 2.6%, 4.1%, 6.3% and 10.9%. Surviving patients remained in hospital for medians of 1.8 (no score), 3.9, 6.1, 8.1 and 9.7 days respectively. High ‘disability’ and illness severity predicted a particularly bad outcome. Conclusion: Disability burden, irrespective of organ system at emergency medical admission, independently predicts worse outcomes and a longer in-hospital stay.
Introduction Acute medicine focuses on the immediate management of patients requiring emergency admission.1 The process of care delivery influences patient outcomes, and as such, care delivery in an acute medical admissions unit (AMAU) has been a priority of healthcare reform. St James’s Hospital (SJH), Dublin, Ireland established an AMAU in 2003 and has reported shortand long-term outcomes.2–4 Changes in demographic combined with complex medical needs of an ageing population necessitates investment in the delivery of acute care, intrinsically linked to emergency medical admissions. Age-related functional decline contributes to increased morbidity, mortality and institutionalisation.5–7 Acute illness is critical, but remains challenging to address
Funding: None. Conflict of interest: None.
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quantitatively in formal study. Multiple elements influence outcome during hospital admission including individual factors such as age, nutrition and comorbidity, health service system factors including availability and environmental factors such as deprivation.8 What has not been addressed to date is whether ‘disability’, predating admission to hospital, can be quantitatively assessed in terms of meaningful clinical outcomes. Health trends suggest increased life expectancies with shifts from acute to chronic ‘disabling’ conditions. Where life expectancy has increased by 4.0 and 2.6 years for males and females, respectively, from 1970 to 2010, time spent with a chronic ‘disabling’ condition increased by 9.2 and 9.4 years for males and females, respectively, in the same time period.9 These findings were based on an operational definition of a ‘chronic disabling condition’ proposed by the US Department of Health and Human Services.10 In this context, disabling disease (or ‘disability’) refers to an impairment of an individual’s ability to function during routine daily tasks. While quantity of life has improved owing to better living standards, © 2014 The Authors Internal Medicine Journal © 2014 Royal Australasian College of Physicians
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prevention programmes and medical innovation, ‘true’ quality of life during these extended years cannot be assumed.11 Existing score systems for disability, such as the Charlson12 or Elixhauser,13 measure disease burden; however, these constructs are weighted scoring methods based on observed mortality of particular disease codes. Inherently, therefore, they overrepresent particular disease groups with undue adverse outcomes – for instance neoplastic and cardiovascular illnesses. One can debate how best to define ‘disability’ or more precisely the ability to measure it objectively. Our disability construct is hypothesis-driven and based on the expectation that cumulative burden of ‘disability’-coded diagnoses more broadly represents the gamut of disease and may therefore translate into an impact on both mortality and length of stay (LOS) clinical outcomes. Limited work to date has assessed ‘disability’ and none in the context of acute medical admissions. We sought to assess if specific disease ‘codes’ (International Classification of Disease (ICD) 9/ICD10 codes based on a hospital admission), deemed to reflect ‘disability’, would predict the outcomes of acute medical admissions in terms of 30-day in-hospital mortality and LOS.
Methods Background SJH is a secondary care centre in its catchment area of 270 000 adults, operating a continuous acute medical ‘take’. All unselected emergency medical admissions between 2002 and 2012 (11-year period) were prospectively recorded into an anonomysed database maintained through the AMAU. The operation and outcome of the AMAU have been described elsewhere.2,3
Data collection The information collated within the anonomysed database prospectively assembles core information about each clinical episode and is described within the Supporting Information Appendix S1.
Disability and disabling scores Discharge codes were interrogated to construct a ‘disabling score’. To devise the score, we searched ICD9 codes (back-mapping ICD10 codes to ICD9 as appropriate because Stata routines support ICD9 and not ICD10 ‘calls’) and matched ‘chronic disabling’ codes based on the definition proposed by the US Department of Health and Human Services for a ‘chronic disabling condition’.10 This is defined as ‘a limitation, caused by one or more © 2014 The Authors Internal Medicine Journal © 2014 Royal Australasian College of Physicians
chronic physical or mental health conditions, in performing activities that people are generally expected to be able to perform’. The ICD codes were initially grouped by system into the following eight groups: (i) cardiovascular, (ii) respiratory, (iii) neurological, (iv) gastrointestinal, (v) diabetes, (vi) renal insufficiency, (vii) neoplastic disease and (viii) others (including rheumatological disabilities). ICD codes assigned to each of these groups are detailed in Supporting Information Table S1. In addition, code searches were refined by supplementation from additional hospital databases detailed in the Supporting Information Appendix S1 for groups 2, 5 and 6 respectively. To determine the ‘disabling score’ for each individual’s clinical episode during the study, we counted the number of systems involved where a code ‘hit’ was detected. Thereafter, we summed the scores from each disabling category (with minimum and maximum scores 0 and 8 respectively) for each clinical episode. Zero score meant that none of the defined ‘disability’ codes was present. For each episode, there could be only one point awarded, whatever the number of within-system ‘hits’. Therefore, a code of 1, 2, 3, or 4+ meant one, two, three, and four or more codes from different organ systems, as previously defined. This study had no interventional component, used anonymised routinely collected data, complied with data protection legislation and was undertaken with the approval of hospital authorities, hence did not require approval from our institutional ethics committee.
Adjustment for underlying acute illness severity Acute illness severity predicts clinical outcomes and must be accounted for when evaluating the impact of a ‘disability’ score on hospital admission. Deranged haemodynamic and physiological admission parameters may be utilised to derive an acute illness severity score (AISS).14–16 We previously reported an AISS and utilised ‘laboratory’ data to derive it. We subsequently then employed this validated AISS to adjust for the underlying acute illness severity on presentation during this study as detailed in the Supporting Information Appendix S1.15 The AISS, Charlson Comorbidity Index,12 Manchester Triage Category (Emergency Department Urgency Scale),17 primary disease classification (Major Disease Category) of respiratory (MDC4), cardiovascular (MDC5) or neurological (MDC1) type and evidence of sepsis (positive blood culture) during the hospital admission have all been included in a multiple stepwise logistic regression model as described later using 30-day in-hospital mortality (i.e. patients died or discharged by day 30 following a hospital admission) as the primary end-point for this study. While 547
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Table 1 Disabling diagnoses in emergency medical admissions (St James’s Hospital: 2002–2012) Variable Gender Male Female Total Age (years) Median (IQR) LOS (days), median (IQR) Charlson index 0 1 2 MDC class Respiratory Cardiac Neurological Gastrointestinal
None
3727 (52.0%) 3443 (48.0%) 7170 (100%) 41.1 (28.6, 58.6) 1.8 (0.5, 5.0)
1–2 points
3+ points
17 816 (50.7%) 17 353 (49.3%) 35 169 (100%)
11 472 (45.4%) 13 774 (54.6%) 25 246 (100%)
56.5 (38.2, 74.0) 5.0 (2.1, 10.4)
74.9 (62.6, 82.5) 8.8 (4.4, 19.0)
P< 0.001
0.001 0.001
7053 (98.4%) 101 (2.4%) 16 (0.2%)
18 389 (52.3%) 10 445 (29.7%) 6335 (18.0%)
4156 (16.5%) 7780 (30.8%) 13 310 (52.7%)
0.001
1038 (6.0%) 1492 (13.7%) 1812 (16.2%) 779 (10.9%)
8831 (50.7%) 5203 (47.7%) 5704 (50.9%) 3703 (51.8%)
7543 (43.4%) 4218 (38.7%) 3692 (32.9%) 2666 (37.3%)
0.001 0.001 0.001 0.001
Baseline demographic and clinical data comparing all patients in accordance with increasing disabling scores. One point allocated per category of disabling diagnoses in one of eight groups – cardiovascular, respiratory, neurological, gastrointerstinal, diabetes, renal, neoplastic and other. IQR, interquartile range; LOS, length of stay; MDC, major disease category.
total in-hospital mortality might be a superior end-point, approximately 9.9% of our patients stay >30 days with a median LOS of 54.8 days (interquartile range (IQR) 38.8, 97.2). Consequently, the LOS data are a highly skewed distribution. Although the clinical episode is complete for the majority, some patients remain for social reasons related to the lack of long-term care facilities. The latter analysis (total length rather than the 30-day episode endpoint) would, in our view, be problematic because of the introduction of additional confounders.
covariates, and averaging or otherwise over the remaining covariates. Adjusted odds ratios (OR) and 95% confidence intervals (CI) were calculated for those predictors that significantly entered the model (P < 0.10). Statistical significance was assumed at a P-value < 0.05. Stata v.12.1 (Stata Corporation, College Station, TX, USA) software was used for analysis.
Results Patient demographics and ‘disability’ score
Statistical methods Descriptive statistics were calculated for background demographic data, including means/standard deviations, medians/IQR or percentages. Comparisons between categorical variables and mortality were made using chi-squared testing. We examined 30-day in-hospital mortality and hospital LOS as major outcomes. Using generalised estimating equations, an extension of generalised linear models permitting adjustment for correlated observations (hospital readmissions), we employed a logistic model with a robust estimate to allow for clustering. The correlation matrix reflected the average dependence among the specified correlated observations. We used the margins command in Stata 12.1 to estimate and interpret adjusted predictions for interactions of key predictors, while controlling for other variables such as illness severity, using computations of average marginal effects. Margins are statistics calculated from predictions of a previously fitted model at fixed values of some 548
A total of 67 971 episodes was recorded in n = 37 828 unique patients admitted as medical emergencies over the study period. These episodes represented all emergency medical admissions including patients admitted directly into the intensive care unit or high dependency unit respectively. The proportion of males was 49.1%. Median (IQR) LOS was 7.0 (2.0, 9.8) days. The median (IQR) age was 62.0 (41.8, 76.9) years, with the upper 10% boundary at 84.3. The Charlson Comorbidity Score of 0, 1 or 2 was present in 45.5%, 27.2% and 27.3% respectively. The MDC were respiratory (26.1%), cardiovascular (16.5%), neurological (16.0%), gastrointestinal (10.9%), hepatobilary (5.1%) and renal (4.2%). A disabling score (one point per disabling group) of zero was recorded in 10.6% of patients. The frequency of scores for groups 1, 2, 3 and 4+ were 23.3%, 28.7%, 21.9% and 15.5% respectively. Patients with disabling scores (Table 1) were older, female, and had more respiratory, neurological and gastrointestinal disease. © 2014 The Authors Internal Medicine Journal © 2014 Royal Australasian College of Physicians
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Table 3 Disabling score and hospital length of stay
Table 2 Disabling score and 30-day mortality Disabling score 0 1 2 3 4+
n
Mortality (%)
OR (95% CI)
P-value
6923 14 888 17 704 12 737 8612
0.9 2.6 4.1 6.3 10.9
0.16 (0.12–0.20) 3.09 (2.35–4.05) 4.83 (3.71–6.30) 7.73 (5.93–10.1) 14.0 (10.8–18.2)
0.001 0.001 0.001 0.001 0.001
Univariate analysis for prediction of an in-hospital death by day 30 stratified by disabling score with odds ratios (OR). CI, confidence interval.
Figure 1 Mortality by ‘Disability’ score. Data were derived following logistic regression of mortality against predictor variables of disability codes and illness severity. The marginal effects of disability score were then calculated from predictions of the fitted model at fixed values of the abscissa covariate and averaging over the remaining covariates. The disabling disease score is as described in the Methods and Results. CI, con), 95% Cl; ( ), fitted values; ( ), mean group value. fidence interval. (
Impact of ‘disability’ score on 30-day mortality and LOS The 30-day in-hospital mortality by episode, over the 11-year period, averaged 5.8% (95% CI 5.6–5.9%); however, there was a relative risk reduction of 35.0% between 2002 and 2012 from 7.0% to 4.6% (P = 0.001). The mortality related to the ‘disability’ score progressively increased (Table 2; Fig. 1). The 10.6% of patients with absence of any ‘disability’ score were at low risk with an 84% lower 30-day in-hospital mortality risk compared with patients with ‘disability’ scores – OR 0.16 (95% CI 0.12–0.20). The calculated univariate risk of a death by day 30 was very high with OR of 3.1, 4.8, 7.7 and 14.0 with 1, 2, 3 and 4+ ‘disability’ scores respectively (Table 2). © 2014 The Authors Internal Medicine Journal © 2014 Royal Australasian College of Physicians
Disabling score
Mean
Median (IQR)
OR (95% CI)
P-value
0 1 2 3 4+
6.4 10.0 13.7 19.5 23.7
1.8 (0.5, 5.0) 3.9 (1.6, 8.1) 6.1 (2.8,12.5) 8.1 (4.0,17.2) 9.7 (5.0,21.2)
0.23 (0.22–0.25) 2.09 (1.96–2.24) 3.91 (3.67–4.16) 6.40 (5.99–6.83) 8.61 (8.03–9.24)
0.001 0.001 0.001 0.001 0.001
Univariate analysis for prediction of a length of stay beyond median stratified by disabling score with odds ratios (OR). CI, confidence interval; IQR, interquartile range.
Figure 2 Length of stay by ‘Disability’ score. Data were derived following zero truncated Poisson regression of hospital length of stay against predictor variables of disability codes and illness severity. The marginal effects of disability score were then calculated from predictions of the fitted model at fixed values of the abscissa covariate and averaging over the remaining covariates. The disabling disease score is as described in ), 95% Cl; ( ), the Methods and Results. CI, confidence interval. ( fitted values; ( ), mean group value.
In terms of hospital LOS, patients without no ‘disability’ burden (i.e. zero score) had a much shorter LOS – median 1.8 days (IQR 0.5, 5.0) and OR predicting a stay below the median LOS 0.23 (0.22–0.25) (Table 3). There was progressive increase in LOS (Fig. 2) related to a higher burden of ‘disability’ score with OR predicting a longer than median stay of 2.09 (95% CI 1.96–2.24), 3.91 (95% CI 3.67–4.16), 6.40 (95% CI 5.99–6.83) and 8.61 (95% CI 8.03–9.24) with 1, 2, 3 and 4+ ‘disability’ scores respectively (Table 3).
Risk estimates of disability adjusted for major outcome predictors The major predictors of a poor outcome (30-day in-hospital mortality) were the acute illness severity 549
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Table 4 Multivariate logistic regression model for a 30-day in-hospital death Variable Illness severity score
Charlson index Triage category Blood culture Primary respiratory Primary cardiac Primary neurology Disabling diagnosis
Variable
Odds ratio
95% CI
P > |z|
Group 0 versus 1 Group 0 versus 2 Group 0 versus 3 Group 0 versus 4 Group 0 versus 5 Group 0 versus 1 Group 0 versus 2 Group 3 versus 2 Group 3 versus 1 None versus neg None versus pos NA NA NA None versus 1 None versus 2 None versus 3 None versus 4+
0.60 3.23 5.46 13.1 55.6 1.26 2.40 1.69 14.4 2.61 4.67 1.35 1.49 1.84 1.89 1.76 1.71 2.22
0.10–3.50 0.78–13.3 1.35–22.0 3.29–52.2 14.0–220.6 1.10–1.43 2.12–2.71 1.55–1.84 11.9–17.4 2.39–2.85 4.10–5.33 1.22–1.49 1.32–1.68 1.60–2.10 1.41–2.54 1.32–2.36 1.27–2.30 1.65–2.99
0.57 0.11 0.02 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001
Outcomes of multivariate logistic regression analysis for 30-day in-hospital death including ‘disabling diagnoses’ with odds ratios (OR) and confidence intervals (CI). NA, not applicable.
(biochemical laboratory score at admission), Charlson Comorbidity Index,12 Manchester Triage Category at admission,17 sepsis status (based on blood culture result), and major diagnoses of cardiovascular, respiratory or neurological categories (Table 4). The full model predicted any in-hospital death by day 30 with an area under receiver operating characteristic of 0.91 (95% CI 0.90–0.92) (Supporting Information Fig. S1). The univariate estimates of ‘disability’ score (compared with no ‘disability’ score) were 3.02 (95% CI 2.28–4.00), 4.65 (95% CI 3.54–6.11), 7.23 (95% CI 5.51–9.49) and 13.2 (95% CI 10.0–17.3), respectively, for 1, 2, 3 and 4+ scores. In the full model, the extent of the adjustment indicated that these patients had a very high degree of acuity; their respective adjusted risk estimates were 1.89 (95% CI 1.41–2.54), 1.76 (95% CI 1.32–2.36), 1.71 (95% CI 1.27–2.30) and 2.22 (95% CI 1.65–2.99) (Table 4). The extent of the downwards adjustment of these risk estimates was however not proportionate. It follows that as the ‘disability’ score increased, the overall risk of mortality concurrently increased but disproportionately. The impact of disability (irrespective of score) on mortality is minimal when considered in the lower illness severity groups (0–3). However, when considering patients in highest illness severity groups (4–5), preexisting ‘disability’ score independently impacted on mortality over the acute medical admission (Fig. 3). Therefore, when the marginal effect of ‘disability’ score is considered and adjusted for illness severity, the outcomes 550
Figure 3 Average marginal effect of increasing illness severity on the predicted 30-day mortality by disabling score. Outcomes appear consistently worse only for patients with disabling scores in high acute illness severity groups. The risk groups are as described in the Methods and ), No score; ( ), one or two codes; Results. CI, confidence interval. ( ), three or more codes. (
can be explained and are directly related to acute illness severity on presentation (i.e. calculated risk related to ‘laboratory’ score). However, patients with high disabling scores coupled with high laboratory scores have much greater risks of a poorer outcome (Fig. 3). Therefore, ‘disability’ score is an independent factor for a poor outcome most evident in the ‘sickest’ patients.
Discussion Our study is the largest to date assessing the impact of disability on outcomes in acute medical admissions over © 2014 The Authors Internal Medicine Journal © 2014 Royal Australasian College of Physicians
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more than a decade. Up to 10% of those evaluated did not have prior disability preceding hospitalisation; however, patients with increasing disabling scores were older, female and with more comorbidity. This is interesting in the context of recognised inequalities in disability-free survival between genders.18 In essence, it appears that while females live longer that this time includes disability that impacts upon quality of life. While an overall risk reduction in 30-day in-hospital mortality was observed over the study period, mortality related to disability progressively increased. This relative risk reduction in mortality reflects improved medical diagnostic and therapeutic advances. Increased burden of disability translates to increased mortality and longer hospital stays. A single disability increased one’s hospital LOS to that above median. Multivariate logistic regression modelling for determinants of mortality illustrated that while a major contributor was acute illness severity on presentation, that disability plays an independent role but mainly in the sickest patients. Patients with both disability and AISS in the highest bracket had the worst outcomes and a poorer prognosis following admission. As ‘disability score’ increased, overall mortality risk disproportionately increased, likely explained by complex body system interactions. As disability contributes to hospital outcomes, it is prudent to consider its effects on healthcare resources, budgets and population health outcomes. Patients presenting with disabling illness consume greater resources and have poorer outcomes. These are proportionate to both cumulative score and the nature of the underlying disability. It is critical to distinguish between severe (chronic disabling illness) and moderate (activity limiting) disability; however, this was beyond the scope of this work. Differing degrees of disability impact patients, hospitals and healthcare budgets proportionally; however, quantifying disability is challenging and requires consistency and transparency. Our study should positively influence the development of the care process for people with long-term conditions that inhibit routine functioning. This would prevent additional burdens on secondary and tertiary care hospitals. To achieve this, a change in care delivery may be necessary from a standardised hospital approach to an increased community-based adaptable service. Such fundamental changes extend beyond locational adjustments but include system and behavioural change. The major challenge remains assessing disability from a clinical and research context. A ‘gold standard’ definition is lacking as is a clear delineation from morbidity. Such difficulties lead to mixed findings in the literature making the subject difficult to interpret in the context of everyday clinical practice.19 Disadvantages of subjective self© 2014 The Authors Internal Medicine Journal © 2014 Royal Australasian College of Physicians
reported data have precluded a reliable comparison of trends for both disability and morbidity over time, populations and locations. Our proposed ‘disability’ scoring system differs from the previously described Charlson and Elixhauser methods12,13 in that the latter two are weighted and reverse engineered, count mortalities and examine contributing factors. Neither method considers all disease ‘codes’ nor treats them as equally as we have applied. Arguably, we have adopted a more representative hypothesis-driven view on the subject as we consider all disabling ‘codes’ applied over a prolonged period. Comorbidity differs from disability. While they are intrinsically linked, each exerts an individual effect. One may have multiple comorbidities with moderate disability or alternatively a single comorbidity with severe disability. Our study findings mirror that within the literature for comorbidity.20–24 Risks of hospitalisation increase exponentially with each additional comorbid condition and the number, type and severity are all determinants.25 A focus of future work should evaluate optimal methods to measure disability, its severity and how this applies longitudinally to assess hospital care delivery and outcomes. An important population concept to consider in the context of disabling disease is ageing. Ageing has been examined from various perspectives, each by a differing methodology, making integrative synthesis of literature challenging. Proposed notions of ‘healthy ageing’ are challenged if our data are typical as it illustrates increasing disability with advancing age. While life expectancies are increasing, quality of life in extra lived years is decreasing owing to increasing time spent with chronic disabling illness. We may be trading off quality for an extended quantity of life. Our findings are in agreement with an Australian study that utilised similar hospitalbased morbidity and mortality data to explore theories of compression, expansion and dynamic equilibrium.9 The expansion of morbidity theory suggesting increased disease burden and time spent with a chronic disabling disease with age was supported. Taking our findings together, this suggests an increase in the medicalisation of more serious morbidity that has profound implications for health costs and future capacity to provide healthcare. The emergence of disability as a genuine entity affecting frontline services suggests that health service system factors be examined to best manage disability effectively. Three important features for consideration include the provision of health services,26,27 integrated and coordinated care,28 and self-management supports.29 Our study possesses important strengths and limitations. We have a large and comprehensive dataset. Reflecting real-world clinical practice, our dataset identifies disability as a contributing factor to hospital admission outcome. Our proposed scoring system can be 551
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applied routinely as it is simple, inexpensive and identifies patients ‘most at risk’ of negative outcome including those likely to ‘stay longest’. Limitations to our approach include the observational nature of our work and challenges defining disability. The latter was overcome using standardised ICD coding that strongly associates with mortality and LOS to classify illness and duration; however, improved methods applicable longitudinally are necessary. It is important to highlight that the ‘disabling score’ assessed does not quantitate the severity of disease; the latter cannot be determined from the routine interrogation of hospital discharge codes.
Conclusion Our work is the first and largest to date assessing disability in the context of outcomes in acute medical admissions. It highlights the impact of this concept to acute
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Acknowledgements We wish to recognise the contribution of our consultant medical colleagues and the non-consultant members of the ‘on-call’ teams without which the AMAU initiative could not have been progressed. The dedicated contribution of Sr S. Donnelly (St James’s Hospital), her Clinical Nurse Managers and the ancillary professions related to medicine (SCOPE) are gratefully acknowledged. In addition, we thank Tom Clemens (University of St Andrews, Birmingham) for providing the computer code to distinguish ICD coded disabling and limiting health conditions (2011) that we subsequently modified and adapted for use within this publication.
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21 Niewoehner DE, Lokhnygina Y, Rice K, Kuschner WG, Sharafkhaneh A, Sarosi GA et al. Risk indexes for exacerbations and hospitalizations due to COPD. Chest 2007; 131: 20–8. 22 Brameld KJ, Holman CD. Demographic factors as predictors for hospital admission in patients with chronic disease. Aust N Z J Public Health 2006; 30: 562–6. 23 Wolff JL, Starfield B, Anderson G. Prevalence, expenditures, and complications of multiple chronic conditions in the elderly. Arch Intern Med 2002; 162: 2269–76.
24 Ahern MM, Hendryx M. Avoidable hospitalizations for diabetes: comorbidity risks. Dis Manag 2007; 10: 347–55. 25 O’Malley AS, Pham HH, Schrag D, Wu B, Bach PB. Potentially avoidable hospitalizations for COPD and pneumonia: the role of physician and practice characteristics. Med Care 2007; 45: 562–70. 26 Basu J, Friedman B, Burstin H. Primary care, HMO enrollment, and hospitalization for ambulatory care sensitive conditions: a new approach. Med Care 2002; 40: 1260–9. 27 Morris RD, Munasinghe RL. Geographic variability in hospital admission rates for
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Supporting information Additional Supporting Information may be found in the online version of this article at the publisher’s web-site: Figure S1 Area under receiver operating characteristic (AUROC) curve for the multivariate logistic regression model employed to determine risk estimates of disability adjusted for major outcome predictors. The full model (Table 4, main manuscript) predicted any in-hospital death by day 30 with an AUROC of 0.91 (95% CI 0.90–0.92). Table S1 ICD-9-CM and ICD-10-CM codes grouped according to systems for the purpose of constructing a ‘disability score’. Appendix S1 Supplementary methods.
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Disabling disease codes predict worse outcomes for acute medical admissions S. H. Chotirmall, S. Picardo, J. Lyons, M. D’Alton, D. O’Riordan and B. Silke Department of Internal Medicine, Saint James’s Hospital, Dublin, Ireland
Key words disability, mortality, patient admission, emergency, comorbidity. Correspondence Bernard Silke, Department of Internal Medicine, Saint James’s Hospital, James’s Street, Dublin 8, Ireland. Email: [email protected] Received 29 November 2013; accepted 9 March 2014. doi:10.1111/imj.12440
Abstract Background: Concurrent with an extension in longevity, a prodrome of ill-health (‘disability’ identifiable by certain International Classification of Disease (ICD) 9/ICD10 codes) predates the acute emergency presentation. To date, no study has assessed the effect of such ‘disability’ on outcomes of emergency medical admissions. Aim: To devise a new method of scoring the burden of ‘disability’ and assess its relevance to outcomes of acute hospital admissions. Methods: All emergency admissions (67 971 episodes in n = 37 828 patients) to St James’ Hospital, Dublin, Ireland over an 11-year period (2002–2012) were studied, and 30-day in-hospital mortality and length of stay were assessed as objective end-points. Patients were classified according to a validated ‘disability’ classification method and scored from 0 to 4+ (5 classes), dependent on number of ICD9/ICD10 ‘hits’ in hospital episode codes. Results: A disabling score of zero was present in 10.6% of patients. Scores of 1, 2, 3 and 4+ (classified by the number of organ systems involved) occurred with frequencies of 23.3%, 28.7%, 21.9% and 15.5% respectively. The ‘disability’ score was strongly driven by age. The 30-day mortality rates were 0.9% (no score), 2.6%, 4.1%, 6.3% and 10.9%. Surviving patients remained in hospital for medians of 1.8 (no score), 3.9, 6.1, 8.1 and 9.7 days respectively. High ‘disability’ and illness severity predicted a particularly bad outcome. Conclusion: Disability burden, irrespective of organ system at emergency medical admission, independently predicts worse outcomes and a longer in-hospital stay.
Introduction Acute medicine focuses on the immediate management of patients requiring emergency admission.1 The process of care delivery influences patient outcomes, and as such, care delivery in an acute medical admissions unit (AMAU) has been a priority of healthcare reform. St James’s Hospital (SJH), Dublin, Ireland established an AMAU in 2003 and has reported shortand long-term outcomes.2–4 Changes in demographic combined with complex medical needs of an ageing population necessitates investment in the delivery of acute care, intrinsically linked to emergency medical admissions. Age-related functional decline contributes to increased morbidity, mortality and institutionalisation.5–7 Acute illness is critical, but remains challenging to address
Funding: None. Conflict of interest: None.
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quantitatively in formal study. Multiple elements influence outcome during hospital admission including individual factors such as age, nutrition and comorbidity, health service system factors including availability and environmental factors such as deprivation.8 What has not been addressed to date is whether ‘disability’, predating admission to hospital, can be quantitatively assessed in terms of meaningful clinical outcomes. Health trends suggest increased life expectancies with shifts from acute to chronic ‘disabling’ conditions. Where life expectancy has increased by 4.0 and 2.6 years for males and females, respectively, from 1970 to 2010, time spent with a chronic ‘disabling’ condition increased by 9.2 and 9.4 years for males and females, respectively, in the same time period.9 These findings were based on an operational definition of a ‘chronic disabling condition’ proposed by the US Department of Health and Human Services.10 In this context, disabling disease (or ‘disability’) refers to an impairment of an individual’s ability to function during routine daily tasks. While quantity of life has improved owing to better living standards, © 2014 The Authors Internal Medicine Journal © 2014 Royal Australasian College of Physicians
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prevention programmes and medical innovation, ‘true’ quality of life during these extended years cannot be assumed.11 Existing score systems for disability, such as the Charlson12 or Elixhauser,13 measure disease burden; however, these constructs are weighted scoring methods based on observed mortality of particular disease codes. Inherently, therefore, they overrepresent particular disease groups with undue adverse outcomes – for instance neoplastic and cardiovascular illnesses. One can debate how best to define ‘disability’ or more precisely the ability to measure it objectively. Our disability construct is hypothesis-driven and based on the expectation that cumulative burden of ‘disability’-coded diagnoses more broadly represents the gamut of disease and may therefore translate into an impact on both mortality and length of stay (LOS) clinical outcomes. Limited work to date has assessed ‘disability’ and none in the context of acute medical admissions. We sought to assess if specific disease ‘codes’ (International Classification of Disease (ICD) 9/ICD10 codes based on a hospital admission), deemed to reflect ‘disability’, would predict the outcomes of acute medical admissions in terms of 30-day in-hospital mortality and LOS.
Methods Background SJH is a secondary care centre in its catchment area of 270 000 adults, operating a continuous acute medical ‘take’. All unselected emergency medical admissions between 2002 and 2012 (11-year period) were prospectively recorded into an anonomysed database maintained through the AMAU. The operation and outcome of the AMAU have been described elsewhere.2,3
Data collection The information collated within the anonomysed database prospectively assembles core information about each clinical episode and is described within the Supporting Information Appendix S1.
Disability and disabling scores Discharge codes were interrogated to construct a ‘disabling score’. To devise the score, we searched ICD9 codes (back-mapping ICD10 codes to ICD9 as appropriate because Stata routines support ICD9 and not ICD10 ‘calls’) and matched ‘chronic disabling’ codes based on the definition proposed by the US Department of Health and Human Services for a ‘chronic disabling condition’.10 This is defined as ‘a limitation, caused by one or more © 2014 The Authors Internal Medicine Journal © 2014 Royal Australasian College of Physicians
chronic physical or mental health conditions, in performing activities that people are generally expected to be able to perform’. The ICD codes were initially grouped by system into the following eight groups: (i) cardiovascular, (ii) respiratory, (iii) neurological, (iv) gastrointestinal, (v) diabetes, (vi) renal insufficiency, (vii) neoplastic disease and (viii) others (including rheumatological disabilities). ICD codes assigned to each of these groups are detailed in Supporting Information Table S1. In addition, code searches were refined by supplementation from additional hospital databases detailed in the Supporting Information Appendix S1 for groups 2, 5 and 6 respectively. To determine the ‘disabling score’ for each individual’s clinical episode during the study, we counted the number of systems involved where a code ‘hit’ was detected. Thereafter, we summed the scores from each disabling category (with minimum and maximum scores 0 and 8 respectively) for each clinical episode. Zero score meant that none of the defined ‘disability’ codes was present. For each episode, there could be only one point awarded, whatever the number of within-system ‘hits’. Therefore, a code of 1, 2, 3, or 4+ meant one, two, three, and four or more codes from different organ systems, as previously defined. This study had no interventional component, used anonymised routinely collected data, complied with data protection legislation and was undertaken with the approval of hospital authorities, hence did not require approval from our institutional ethics committee.
Adjustment for underlying acute illness severity Acute illness severity predicts clinical outcomes and must be accounted for when evaluating the impact of a ‘disability’ score on hospital admission. Deranged haemodynamic and physiological admission parameters may be utilised to derive an acute illness severity score (AISS).14–16 We previously reported an AISS and utilised ‘laboratory’ data to derive it. We subsequently then employed this validated AISS to adjust for the underlying acute illness severity on presentation during this study as detailed in the Supporting Information Appendix S1.15 The AISS, Charlson Comorbidity Index,12 Manchester Triage Category (Emergency Department Urgency Scale),17 primary disease classification (Major Disease Category) of respiratory (MDC4), cardiovascular (MDC5) or neurological (MDC1) type and evidence of sepsis (positive blood culture) during the hospital admission have all been included in a multiple stepwise logistic regression model as described later using 30-day in-hospital mortality (i.e. patients died or discharged by day 30 following a hospital admission) as the primary end-point for this study. While 547
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Table 1 Disabling diagnoses in emergency medical admissions (St James’s Hospital: 2002–2012) Variable Gender Male Female Total Age (years) Median (IQR) LOS (days), median (IQR) Charlson index 0 1 2 MDC class Respiratory Cardiac Neurological Gastrointestinal
None
3727 (52.0%) 3443 (48.0%) 7170 (100%) 41.1 (28.6, 58.6) 1.8 (0.5, 5.0)
1–2 points
3+ points
17 816 (50.7%) 17 353 (49.3%) 35 169 (100%)
11 472 (45.4%) 13 774 (54.6%) 25 246 (100%)
56.5 (38.2, 74.0) 5.0 (2.1, 10.4)
74.9 (62.6, 82.5) 8.8 (4.4, 19.0)
P< 0.001
0.001 0.001
7053 (98.4%) 101 (2.4%) 16 (0.2%)
18 389 (52.3%) 10 445 (29.7%) 6335 (18.0%)
4156 (16.5%) 7780 (30.8%) 13 310 (52.7%)
0.001
1038 (6.0%) 1492 (13.7%) 1812 (16.2%) 779 (10.9%)
8831 (50.7%) 5203 (47.7%) 5704 (50.9%) 3703 (51.8%)
7543 (43.4%) 4218 (38.7%) 3692 (32.9%) 2666 (37.3%)
0.001 0.001 0.001 0.001
Baseline demographic and clinical data comparing all patients in accordance with increasing disabling scores. One point allocated per category of disabling diagnoses in one of eight groups – cardiovascular, respiratory, neurological, gastrointerstinal, diabetes, renal, neoplastic and other. IQR, interquartile range; LOS, length of stay; MDC, major disease category.
total in-hospital mortality might be a superior end-point, approximately 9.9% of our patients stay >30 days with a median LOS of 54.8 days (interquartile range (IQR) 38.8, 97.2). Consequently, the LOS data are a highly skewed distribution. Although the clinical episode is complete for the majority, some patients remain for social reasons related to the lack of long-term care facilities. The latter analysis (total length rather than the 30-day episode endpoint) would, in our view, be problematic because of the introduction of additional confounders.
covariates, and averaging or otherwise over the remaining covariates. Adjusted odds ratios (OR) and 95% confidence intervals (CI) were calculated for those predictors that significantly entered the model (P < 0.10). Statistical significance was assumed at a P-value < 0.05. Stata v.12.1 (Stata Corporation, College Station, TX, USA) software was used for analysis.
Results Patient demographics and ‘disability’ score
Statistical methods Descriptive statistics were calculated for background demographic data, including means/standard deviations, medians/IQR or percentages. Comparisons between categorical variables and mortality were made using chi-squared testing. We examined 30-day in-hospital mortality and hospital LOS as major outcomes. Using generalised estimating equations, an extension of generalised linear models permitting adjustment for correlated observations (hospital readmissions), we employed a logistic model with a robust estimate to allow for clustering. The correlation matrix reflected the average dependence among the specified correlated observations. We used the margins command in Stata 12.1 to estimate and interpret adjusted predictions for interactions of key predictors, while controlling for other variables such as illness severity, using computations of average marginal effects. Margins are statistics calculated from predictions of a previously fitted model at fixed values of some 548
A total of 67 971 episodes was recorded in n = 37 828 unique patients admitted as medical emergencies over the study period. These episodes represented all emergency medical admissions including patients admitted directly into the intensive care unit or high dependency unit respectively. The proportion of males was 49.1%. Median (IQR) LOS was 7.0 (2.0, 9.8) days. The median (IQR) age was 62.0 (41.8, 76.9) years, with the upper 10% boundary at 84.3. The Charlson Comorbidity Score of 0, 1 or 2 was present in 45.5%, 27.2% and 27.3% respectively. The MDC were respiratory (26.1%), cardiovascular (16.5%), neurological (16.0%), gastrointestinal (10.9%), hepatobilary (5.1%) and renal (4.2%). A disabling score (one point per disabling group) of zero was recorded in 10.6% of patients. The frequency of scores for groups 1, 2, 3 and 4+ were 23.3%, 28.7%, 21.9% and 15.5% respectively. Patients with disabling scores (Table 1) were older, female, and had more respiratory, neurological and gastrointestinal disease. © 2014 The Authors Internal Medicine Journal © 2014 Royal Australasian College of Physicians
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Table 3 Disabling score and hospital length of stay
Table 2 Disabling score and 30-day mortality Disabling score 0 1 2 3 4+
n
Mortality (%)
OR (95% CI)
P-value
6923 14 888 17 704 12 737 8612
0.9 2.6 4.1 6.3 10.9
0.16 (0.12–0.20) 3.09 (2.35–4.05) 4.83 (3.71–6.30) 7.73 (5.93–10.1) 14.0 (10.8–18.2)
0.001 0.001 0.001 0.001 0.001
Univariate analysis for prediction of an in-hospital death by day 30 stratified by disabling score with odds ratios (OR). CI, confidence interval.
Figure 1 Mortality by ‘Disability’ score. Data were derived following logistic regression of mortality against predictor variables of disability codes and illness severity. The marginal effects of disability score were then calculated from predictions of the fitted model at fixed values of the abscissa covariate and averaging over the remaining covariates. The disabling disease score is as described in the Methods and Results. CI, con), 95% Cl; ( ), fitted values; ( ), mean group value. fidence interval. (
Impact of ‘disability’ score on 30-day mortality and LOS The 30-day in-hospital mortality by episode, over the 11-year period, averaged 5.8% (95% CI 5.6–5.9%); however, there was a relative risk reduction of 35.0% between 2002 and 2012 from 7.0% to 4.6% (P = 0.001). The mortality related to the ‘disability’ score progressively increased (Table 2; Fig. 1). The 10.6% of patients with absence of any ‘disability’ score were at low risk with an 84% lower 30-day in-hospital mortality risk compared with patients with ‘disability’ scores – OR 0.16 (95% CI 0.12–0.20). The calculated univariate risk of a death by day 30 was very high with OR of 3.1, 4.8, 7.7 and 14.0 with 1, 2, 3 and 4+ ‘disability’ scores respectively (Table 2). © 2014 The Authors Internal Medicine Journal © 2014 Royal Australasian College of Physicians
Disabling score
Mean
Median (IQR)
OR (95% CI)
P-value
0 1 2 3 4+
6.4 10.0 13.7 19.5 23.7
1.8 (0.5, 5.0) 3.9 (1.6, 8.1) 6.1 (2.8,12.5) 8.1 (4.0,17.2) 9.7 (5.0,21.2)
0.23 (0.22–0.25) 2.09 (1.96–2.24) 3.91 (3.67–4.16) 6.40 (5.99–6.83) 8.61 (8.03–9.24)
0.001 0.001 0.001 0.001 0.001
Univariate analysis for prediction of a length of stay beyond median stratified by disabling score with odds ratios (OR). CI, confidence interval; IQR, interquartile range.
Figure 2 Length of stay by ‘Disability’ score. Data were derived following zero truncated Poisson regression of hospital length of stay against predictor variables of disability codes and illness severity. The marginal effects of disability score were then calculated from predictions of the fitted model at fixed values of the abscissa covariate and averaging over the remaining covariates. The disabling disease score is as described in ), 95% Cl; ( ), the Methods and Results. CI, confidence interval. ( fitted values; ( ), mean group value.
In terms of hospital LOS, patients without no ‘disability’ burden (i.e. zero score) had a much shorter LOS – median 1.8 days (IQR 0.5, 5.0) and OR predicting a stay below the median LOS 0.23 (0.22–0.25) (Table 3). There was progressive increase in LOS (Fig. 2) related to a higher burden of ‘disability’ score with OR predicting a longer than median stay of 2.09 (95% CI 1.96–2.24), 3.91 (95% CI 3.67–4.16), 6.40 (95% CI 5.99–6.83) and 8.61 (95% CI 8.03–9.24) with 1, 2, 3 and 4+ ‘disability’ scores respectively (Table 3).
Risk estimates of disability adjusted for major outcome predictors The major predictors of a poor outcome (30-day in-hospital mortality) were the acute illness severity 549
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Table 4 Multivariate logistic regression model for a 30-day in-hospital death Variable Illness severity score
Charlson index Triage category Blood culture Primary respiratory Primary cardiac Primary neurology Disabling diagnosis
Variable
Odds ratio
95% CI
P > |z|
Group 0 versus 1 Group 0 versus 2 Group 0 versus 3 Group 0 versus 4 Group 0 versus 5 Group 0 versus 1 Group 0 versus 2 Group 3 versus 2 Group 3 versus 1 None versus neg None versus pos NA NA NA None versus 1 None versus 2 None versus 3 None versus 4+
0.60 3.23 5.46 13.1 55.6 1.26 2.40 1.69 14.4 2.61 4.67 1.35 1.49 1.84 1.89 1.76 1.71 2.22
0.10–3.50 0.78–13.3 1.35–22.0 3.29–52.2 14.0–220.6 1.10–1.43 2.12–2.71 1.55–1.84 11.9–17.4 2.39–2.85 4.10–5.33 1.22–1.49 1.32–1.68 1.60–2.10 1.41–2.54 1.32–2.36 1.27–2.30 1.65–2.99
0.57 0.11 0.02 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001
Outcomes of multivariate logistic regression analysis for 30-day in-hospital death including ‘disabling diagnoses’ with odds ratios (OR) and confidence intervals (CI). NA, not applicable.
(biochemical laboratory score at admission), Charlson Comorbidity Index,12 Manchester Triage Category at admission,17 sepsis status (based on blood culture result), and major diagnoses of cardiovascular, respiratory or neurological categories (Table 4). The full model predicted any in-hospital death by day 30 with an area under receiver operating characteristic of 0.91 (95% CI 0.90–0.92) (Supporting Information Fig. S1). The univariate estimates of ‘disability’ score (compared with no ‘disability’ score) were 3.02 (95% CI 2.28–4.00), 4.65 (95% CI 3.54–6.11), 7.23 (95% CI 5.51–9.49) and 13.2 (95% CI 10.0–17.3), respectively, for 1, 2, 3 and 4+ scores. In the full model, the extent of the adjustment indicated that these patients had a very high degree of acuity; their respective adjusted risk estimates were 1.89 (95% CI 1.41–2.54), 1.76 (95% CI 1.32–2.36), 1.71 (95% CI 1.27–2.30) and 2.22 (95% CI 1.65–2.99) (Table 4). The extent of the downwards adjustment of these risk estimates was however not proportionate. It follows that as the ‘disability’ score increased, the overall risk of mortality concurrently increased but disproportionately. The impact of disability (irrespective of score) on mortality is minimal when considered in the lower illness severity groups (0–3). However, when considering patients in highest illness severity groups (4–5), preexisting ‘disability’ score independently impacted on mortality over the acute medical admission (Fig. 3). Therefore, when the marginal effect of ‘disability’ score is considered and adjusted for illness severity, the outcomes 550
Figure 3 Average marginal effect of increasing illness severity on the predicted 30-day mortality by disabling score. Outcomes appear consistently worse only for patients with disabling scores in high acute illness severity groups. The risk groups are as described in the Methods and ), No score; ( ), one or two codes; Results. CI, confidence interval. ( ), three or more codes. (
can be explained and are directly related to acute illness severity on presentation (i.e. calculated risk related to ‘laboratory’ score). However, patients with high disabling scores coupled with high laboratory scores have much greater risks of a poorer outcome (Fig. 3). Therefore, ‘disability’ score is an independent factor for a poor outcome most evident in the ‘sickest’ patients.
Discussion Our study is the largest to date assessing the impact of disability on outcomes in acute medical admissions over © 2014 The Authors Internal Medicine Journal © 2014 Royal Australasian College of Physicians
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more than a decade. Up to 10% of those evaluated did not have prior disability preceding hospitalisation; however, patients with increasing disabling scores were older, female and with more comorbidity. This is interesting in the context of recognised inequalities in disability-free survival between genders.18 In essence, it appears that while females live longer that this time includes disability that impacts upon quality of life. While an overall risk reduction in 30-day in-hospital mortality was observed over the study period, mortality related to disability progressively increased. This relative risk reduction in mortality reflects improved medical diagnostic and therapeutic advances. Increased burden of disability translates to increased mortality and longer hospital stays. A single disability increased one’s hospital LOS to that above median. Multivariate logistic regression modelling for determinants of mortality illustrated that while a major contributor was acute illness severity on presentation, that disability plays an independent role but mainly in the sickest patients. Patients with both disability and AISS in the highest bracket had the worst outcomes and a poorer prognosis following admission. As ‘disability score’ increased, overall mortality risk disproportionately increased, likely explained by complex body system interactions. As disability contributes to hospital outcomes, it is prudent to consider its effects on healthcare resources, budgets and population health outcomes. Patients presenting with disabling illness consume greater resources and have poorer outcomes. These are proportionate to both cumulative score and the nature of the underlying disability. It is critical to distinguish between severe (chronic disabling illness) and moderate (activity limiting) disability; however, this was beyond the scope of this work. Differing degrees of disability impact patients, hospitals and healthcare budgets proportionally; however, quantifying disability is challenging and requires consistency and transparency. Our study should positively influence the development of the care process for people with long-term conditions that inhibit routine functioning. This would prevent additional burdens on secondary and tertiary care hospitals. To achieve this, a change in care delivery may be necessary from a standardised hospital approach to an increased community-based adaptable service. Such fundamental changes extend beyond locational adjustments but include system and behavioural change. The major challenge remains assessing disability from a clinical and research context. A ‘gold standard’ definition is lacking as is a clear delineation from morbidity. Such difficulties lead to mixed findings in the literature making the subject difficult to interpret in the context of everyday clinical practice.19 Disadvantages of subjective self© 2014 The Authors Internal Medicine Journal © 2014 Royal Australasian College of Physicians
reported data have precluded a reliable comparison of trends for both disability and morbidity over time, populations and locations. Our proposed ‘disability’ scoring system differs from the previously described Charlson and Elixhauser methods12,13 in that the latter two are weighted and reverse engineered, count mortalities and examine contributing factors. Neither method considers all disease ‘codes’ nor treats them as equally as we have applied. Arguably, we have adopted a more representative hypothesis-driven view on the subject as we consider all disabling ‘codes’ applied over a prolonged period. Comorbidity differs from disability. While they are intrinsically linked, each exerts an individual effect. One may have multiple comorbidities with moderate disability or alternatively a single comorbidity with severe disability. Our study findings mirror that within the literature for comorbidity.20–24 Risks of hospitalisation increase exponentially with each additional comorbid condition and the number, type and severity are all determinants.25 A focus of future work should evaluate optimal methods to measure disability, its severity and how this applies longitudinally to assess hospital care delivery and outcomes. An important population concept to consider in the context of disabling disease is ageing. Ageing has been examined from various perspectives, each by a differing methodology, making integrative synthesis of literature challenging. Proposed notions of ‘healthy ageing’ are challenged if our data are typical as it illustrates increasing disability with advancing age. While life expectancies are increasing, quality of life in extra lived years is decreasing owing to increasing time spent with chronic disabling illness. We may be trading off quality for an extended quantity of life. Our findings are in agreement with an Australian study that utilised similar hospitalbased morbidity and mortality data to explore theories of compression, expansion and dynamic equilibrium.9 The expansion of morbidity theory suggesting increased disease burden and time spent with a chronic disabling disease with age was supported. Taking our findings together, this suggests an increase in the medicalisation of more serious morbidity that has profound implications for health costs and future capacity to provide healthcare. The emergence of disability as a genuine entity affecting frontline services suggests that health service system factors be examined to best manage disability effectively. Three important features for consideration include the provision of health services,26,27 integrated and coordinated care,28 and self-management supports.29 Our study possesses important strengths and limitations. We have a large and comprehensive dataset. Reflecting real-world clinical practice, our dataset identifies disability as a contributing factor to hospital admission outcome. Our proposed scoring system can be 551
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applied routinely as it is simple, inexpensive and identifies patients ‘most at risk’ of negative outcome including those likely to ‘stay longest’. Limitations to our approach include the observational nature of our work and challenges defining disability. The latter was overcome using standardised ICD coding that strongly associates with mortality and LOS to classify illness and duration; however, improved methods applicable longitudinally are necessary. It is important to highlight that the ‘disabling score’ assessed does not quantitate the severity of disease; the latter cannot be determined from the routine interrogation of hospital discharge codes.
Conclusion Our work is the first and largest to date assessing disability in the context of outcomes in acute medical admissions. It highlights the impact of this concept to acute
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Acknowledgements We wish to recognise the contribution of our consultant medical colleagues and the non-consultant members of the ‘on-call’ teams without which the AMAU initiative could not have been progressed. The dedicated contribution of Sr S. Donnelly (St James’s Hospital), her Clinical Nurse Managers and the ancillary professions related to medicine (SCOPE) are gratefully acknowledged. In addition, we thank Tom Clemens (University of St Andrews, Birmingham) for providing the computer code to distinguish ICD coded disabling and limiting health conditions (2011) that we subsequently modified and adapted for use within this publication.
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21 Niewoehner DE, Lokhnygina Y, Rice K, Kuschner WG, Sharafkhaneh A, Sarosi GA et al. Risk indexes for exacerbations and hospitalizations due to COPD. Chest 2007; 131: 20–8. 22 Brameld KJ, Holman CD. Demographic factors as predictors for hospital admission in patients with chronic disease. Aust N Z J Public Health 2006; 30: 562–6. 23 Wolff JL, Starfield B, Anderson G. Prevalence, expenditures, and complications of multiple chronic conditions in the elderly. Arch Intern Med 2002; 162: 2269–76.
24 Ahern MM, Hendryx M. Avoidable hospitalizations for diabetes: comorbidity risks. Dis Manag 2007; 10: 347–55. 25 O’Malley AS, Pham HH, Schrag D, Wu B, Bach PB. Potentially avoidable hospitalizations for COPD and pneumonia: the role of physician and practice characteristics. Med Care 2007; 45: 562–70. 26 Basu J, Friedman B, Burstin H. Primary care, HMO enrollment, and hospitalization for ambulatory care sensitive conditions: a new approach. Med Care 2002; 40: 1260–9. 27 Morris RD, Munasinghe RL. Geographic variability in hospital admission rates for
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Supporting information Additional Supporting Information may be found in the online version of this article at the publisher’s web-site: Figure S1 Area under receiver operating characteristic (AUROC) curve for the multivariate logistic regression model employed to determine risk estimates of disability adjusted for major outcome predictors. The full model (Table 4, main manuscript) predicted any in-hospital death by day 30 with an AUROC of 0.91 (95% CI 0.90–0.92). Table S1 ICD-9-CM and ICD-10-CM codes grouped according to systems for the purpose of constructing a ‘disability score’. Appendix S1 Supplementary methods.
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