ORIGINAL ARTICLES

 

Use of comorbidity measures to predict the risk of death in Brazilian in-patients

 

Uso de medidas de comorbilidades para predicción de riesgo de óbito en pacientes brasileros hospitalizados

 

 

Monica Martins

Departamento de Administração e Planejamento em Saúde. Escola Nacional de Saúde Pública Sérgio Arouca. Fundação Oswaldo Cruz. Rio de Janeiro, RJ, Brasil

Correspondence

 

 


ABSTRACT

OBJECTIVE: To assess the use of comorbidity measures to predict the risk of death in Brazilian in-patients.
METHODS: Data from the Sistema de Informações Hospitalares do Sistema Único de Saúde (Unified Health System Hospital Information System) were used, which enables only one secondary diagnosis to be recorded. A total of 1,607,697 hospitalizations were selected, all of which occurred in Brazil, between 2003 and 2004, and whose main diagnoses were: ischemic heart disease, congestive cardiac failure, stroke and pneumonia. Charlson Index and Elixhauser comorbidities were the comorbidity measures used. In addition, the simple record of a certain secondary diagnosis was also used. Logistic regression was applied to assess the impact of comorbidity measures on the estimate of risk of death. The baseline model included the following variables: age, sex and main diagnosis. Models to predict death were assessed, based on C-statistic and Hosmer-Lemeshow test.
RESULTS: Hospital mortality rate was 10.4% and mean length of stay was 5.7 days. The majority (52%) of hospitalizations occurred among men and mean age was 62.6 years. Of all hospitalizations, 5.4% included a recorded secondary diagnosis, although the odds ratio between death and presence of comorbidity was 1.93. The baseline model showed a discriminatory capacity (C-statistic) of 0.685. The improvement in the models, attributed to the introduction of comorbidity indices, was poor, equivalent to zero when C-statistic with only two digits was considered.
CONCLUSIONS: Although the introduction of three comorbidity measures in distinct models to predict death improved the predictive capacity of the baseline model, the values obtained are still considered insufficient. The accuracy of this type of measure is influenced by the completeness of the source of information. In this sense, high underreporting of secondary diagnosis, in addition to the well-known lack of space to note down this type of information in the Sistema de Informações Hospitalares, are the main explanatory factors for the results found.

Descriptors: Comorbidity. Hospital Mortality. Risk Adjustment. Health Services Evaluation.


RESUMEN

OBJETIVO: Evaluar el uso de medidas de comorbilidad para predecir el riesgo de óbito en pacientes brasileros.
MÉTODOS: Fueron utilizados datos de internaciones obtenidos del Sistema de Informaciones Hospitalarias del Sistema Único de Salud, que permite el registro de solamente un diagnóstico secundario. Fueron seleccionadas 1.607.697 internaciones ocurridas en Brasil en 2003 y 2004, cuyos diagnósticos principales fueron enfermedad isquémica del corazón, insuficiencia cardiaca congestiva, enfermedades cerebro-vasculares y neumonía. El Índice de Charlson y las comorbilidades de Elixhauser fueron las medidas de comorbilidad utilizadas; el simple registro de algún diagnóstico secundario fue también empleado. La regresión logística fue aplicada para evaluar el impacto de las medidas de comorbilidad en la estimativa de la probabilidad de óbito. El modelo de base incluyó las siguientes variables: edad, sexo y diagnóstico principal. Los modelos de predicción de óbitos fueron evaluados con base en la estadística C y en la prueba de Hosmer-Lemeshow.
RESULTADOS: La tasa de mortalidad hospitalaria fue 10,4% y el tiempo promedio de permanencia fue 5,7 días. La mayoría (52%) de las internaciones ocurrió en hombres y la edad promedio fue 62,6 años. Del total de internaciones, 5,4% presentaba un diagnóstico secundario registrado, pero el odds ratio entre óbito y presencia de comorbilidad fue de 1,93. El modelo de base presentó una capacidad de discriminación (estadística C) de 0,685. La mejoría en los modelos atribuida a la introducción de los índices de comorbilidad fue débil - equivalió a cero cuando se consideró la estadística C con solamente dos dígitos.
CONCLUSIONES: A pesar de la introducción de las tres medidas de comorbilidad en los distintos modelos de predicción de óbito haya mejorado la capacidad predictiva del modelo de base, los valores obtenidos aún son considerados insuficientes. La precisión de este tipo de medida es influenciada por la completitud de la fuente de información. En ese sentido, el alto sub-registro de diagnóstico secundario, aliado a la conocida insuficiencia de espacio para anotación de este tipo de información en el Sistema de Informaciones Hospitalarias, son los principales elementos explicativos de los resultados encontrados.

Descriptores: Comorbilidad. Mortalidad Hospitalaria. Ajuste de Riesgo. Evaluación de Servicios de Salud.


 

 

INTRODUCTION

Concerns about quality of care have triggered comparative analyses of health service performance indicators, especially hospital care. In several countries, governmental agencies, hospital associations, health insurance companies and consumer associations perform and publish comparative assessments of hospital performance, using mortality rates and other indicators.4 The availability of large computerized administrative databases has promoted this type of approach.17 It is necessary to consider the difference in prevalence of risk factors that change the prognosis and therapeutic response in inpatient care to assess quality of performance.6

The risk of a patient is associated with the severity of the case, and greater severity means higher risk or probability of occurrence of an undesirable outcome. Risk is a multidimensional concept that includes several attributes of a patient, such as age, sex, clinical instability, primary diagnosis, extension and severity of comorbidities and patient attitudes and preferences.6

Several methods to measure the severity of cases have been developed to enable the comparison of indicators from the case mix adjustment. The intensity (number and severity) of coexisting pathologies is one of the predictive factors of unfavorable outcomes and complications in in-patients.6 Methodologies that use comorbidities to weigh their effect on patient prognosis can be applied to administrative databases, once they usually include diagnostic information exclusively.5

However, the quality and value of this type of method depend on the completeness and accuracy of diagnostic codes. With such characteristics, the Charlson Comorbidity Index (CCI)2 and the methodology developed by Elixhauser et al3 are used as the approach to risk adjustment in several studies.5 These two methods differ from each other mostly in terms of the number of comorbidities included and attribution of weights to weigh their prognostic effect. This weighing is present in 19 clinical conditions comprising the CCI.2 Elixhauser's methodology does not attribute any weight to the 30 comorbidities defined, focusing exclusively on the number of pathologies present.3 Indications of the validity of such comorbidity indices to measure the severity of cases have been reported in the literature.5,11

The use of risk measures to adjust performance indicators is uncommon in Brazil, as are studies on the validity of such measures.7,12,13 Results from studies that used the Sistema de Informação Hospitalar do Sistema Único de Saúde (SIH/SUS - National Health System Hospital Information System) were limited to either the analysis of hospitalizations in specific cities (Rio de Janeiro)7 or to the surgical procedure selected (myocardial revascularization).12,13 The SIH/SUS enables the record of only one secondary diagnosis, which is not mandatory for payment of hospital services.

The objective of the present study was to assess the use of comorbidity measures to predict the risk of death in in-patients, using the SIH/SUS databases and methodologies proposed by Charlson2 and Elixhauser.3

 

METHODS

The SIH/SUS includes anonymous information about the following variables: demographic profile of patients (sex and age); primary and secondary diagnoses; surgical, therapeutic and diagnostic procedures; medical specialty of the case treated (general or specialized surgery and obstetrics, among others); days of stay; discharge status and hospital unit. First, 4,086,329 hospitalizations resulting from respiratory and circulatory problems, based on the International Classification of Diseases, 10th revision (ICD-10), and funded by the Sistema Único de Saúde (SUS - National Health System) in Brazil, between 2003 and 2004, were included to define the universe of study. These two health problems were selected according to the following criteria: volume of hospitalizations in the period higher than 500,000, hospital mortality rate higher than 4.9%, and volume of deaths in the period higher than 99,000. The mean value of reimbursement for hospitalization and length of stay were used as secondary criteria, once they show the importance of hospitalizations in terms of use of hospital resources. For this selection, tabulations based on the SUS hospital morbidity were constructed, according to information available on the website of the Sistema de Informática do SUS (DATASUS - SUS Information Technology System).ª

The primary diagnosis of a patient is an essential dimension to risk adjustment, once severity may differ considerably among diagnostic categories. However, also at this stage, specific reasons for admission were selected to comprise the universe of study, considering the volume of hospitalizations and deaths per pathology as selection criteria. The hospitalizations selected were those whose ICD-10 codes were registered as primary diagnosis: ischemic heart disease (ICD-10: I21 and I25); congestive cardiac failure (ICD-10: I50); stroke (ICD-10: I60-I62, I64, I67, I69) and pneumonia (J15, J18). Hospitalizations of patients aged less than 18 years and those who provided incorrect information about sex, i.e. who used inexistent SIH/SUS codes, and had a length of stay above 30 days were excluded. The result of this process totaled 1,607,697 hospitalizations.

Frequency measures and bivariate and multivariate analyses were used to assess the use of comorbidity measures. Logistic regression was employed to assess the impact of comorbidity measures on the estimate of risk of death. The deaths occurred during hospitalization were the dependent dichotomous variable. The impact of introduction of each of the comorbidity measures was tested to predict death in the baseline model. Considering the information available in the SIH/SUS databases, the baseline model included the following variables: age, sex and primary diagnosis. Age was treated as a continuous variable; the sex variable as a dichotomous variable and the male sex was the reference category. The primary diagnosis variable was considered a categorical variable with 11 groups and the reference category was chronic ischemic heart disease (ICD-10: I25), as it showed the lowest mortality rate.

The algorithm developed by Quan et al,14 which defines the ICD-10 codes for each comorbidity included in Charlson's2 and Elixhauser's3 methodologies, was used to calculate the severity score. This choice is justified because it is a proposal that aims to adopt a standardized coding for international use. These authors reviewed the different adaptations for ICD-10 available in the literature at that moment and made them compatible.

A total of three comorbidity measures were analyzed: (1) the CCI,2 codified according to Quan's14 algorithm for ICD-10; (2) Elixhauser comorbidities,3 also codified according to Quan's14 algorithm; and (3) the presence of comorbidity (secondary diagnoses - yes/no). Comorbidity measures were introduced in the models tested as an independent categorical variable and regrouped according to the distribution of frequency, based on CCI weighing. Weights were grouped into the following categories: category (1) weight equal to 0; category (2) weight equal to 1; category (3) weight equal to or higher than 2. Weight equal to zero (category 1) was used as reference category, because a score equal to zero means absence of severity. The other two comorbidity measures were considered dichotomous variables (0 = absence and 1 = presence).

The adequacy of the death prediction model was assessed based on the capacity to discriminate and on the adjustment of models. The statistics used were the percentage of improvement of the model in relation to the initial likelihood (cð2), C-statistic and Hosmer-Lemeshow goodness of fit test. Statistical analyses were processed in the Stata software, version 10.0.

 

RESULTS

In the period of study, 1,607,697 hospitalizations occurred due to ischemic heart disease, congestive cardiac failure, stroke and pneumonia. Mean age of patients was 62.6 years and the percentage of hospitalizations in men was 51.9% (Table 1). The majority of hospitalizations occurred in private hospitals (63.9%). Patients remained hospitalized for 5.7 days and surgical interventions totaled 5.5% of cases (Table 1). Hospitalizations with a recorded secondary diagnosis (comorbidity) corresponded to 5.4% (Table 1). State-owned hospitals were those that showed the highest percentage of recorded secondary diagnosis (18.7%). For the diagnoses selected, a hospital mortality rate of 10.4% was observed, of which 14.8% were associated with acute myocardial infarction (I21) and 7.2% with congestive cardiac failure (I50). As regards stroke, mortality varied between 6.4% and 32.0%, according to the diagnostic category. In cases of pneumonia, this variation was lower, between 6% and 8% (Table 1).

Tables 2 and 3 show the odds ratio (OR) between death and comorbidity measures. Of all clinical conditions that comprise the Charlson Index, 14 showed a statistically significant OR. However, the OR was below 1.20 for four clinical conditions. Cases with ulcer showed an OR equal to 3.15 (Table 2). As regards the 30 comorbidities defined by Elixhauser, at least one third had an OR significantly associated with the occurrence of deaths (Table 3). Of all these 30 comorbidities, 13 showed an OR higher than 1.50; of these, coagulopathies, weight loss, hydro-electrolytic imbalance and alcohol abuse are not included in the CCI (Table 3).

The percentage of cases with a score different from zero, i.e. with a certain level of severity, was low for both the CCI and Elixhauser comorbidities (Table 4). Mortality rates increased and were statistically significant, indicating an association between these two comorbidity measures and the risk of death (Table 4). The mortality rate is higher due to the recording of comorbidity - patients without a comorbidity showed a mortality rate of 10%, whereas this rate was 17.6% among patients with one comorbidity (Table 4).

OR between comorbidity and death was 1.93 (95% CI: 1.89;1.96, p < 0.000). As regards other measures, OR was 1.68 (95% CI: 1.63;1.73) for CCI, and 1.63 (95% CI: 1.58;1.68) for Elixhauser comorbidities. All OR were statistically significant (p < 0.001).

Of all models for hospital death prediction tested, model 4 showed the best discriminatory capacity (C-statistic = 0.691), incorporating the simple presence of comorbidity (Table 5). The effect on the discriminatory capacity of the baseline model (model 1), attributed to the incorporation of comorbidity measures, was insignificant in all models tested. Finally, all models tested showed calibration problems (Table 5).

 

DISCUSSION

The present study used information about secondary diagnosis recorded in the SIH/SUS to assess the severity of cases, based on comorbidity measures. In this assessment, the use of Elixhauser comorbidities,3 which includes other pathologies previously excluded from the CCI, did not increase the predictive capacity, which was even lower than that observed for the CCI. The improvement in the predictive capacity of the baseline model (C-statistic = 0.685), attributed to the comorbidity measures, was poor - equal to zero, when considering C-statistic with only two digits, i.e. all models showed C-statistic equal to 0.69. The record of any secondary diagnosis (C-statistic of 0.691) was more important than other comorbidity measures assessed. In addition, an OR equal to 1.93 was found between presence of comorbidity and death, a value higher than that obtained for the other two comorbidity measures.

The validity of use of severity score measures, such as the CCI,2 or those that use the presence or not of a pathology for admission, such as Elixhauser comorbidities,3 depends on the completeness and accuracy of diagnostic codes recorded in the databases. Underreporting also interferes with the discriminatory capacity of these measures. In the data analyzed, the percentage of recording of secondary diagnosis in national hospitalizations was low (5%). The results found seem to indicate disregard for or unawareness of the importance of this type of information. A previous study9 applied the CCI to hospitalizations occurred in 1993-1994, in the city of Rio de Janeiro, Southeastern Brazil, and obtained a score equal to zero in 94.3% of cases. This percentage is similar to that observed in this study for the CCI and Elixhauser comorbidities, whose values were higher than 95%.

As regards the completeness of diagnoses, the SIH/SUS databases enable only one secondary diagnosis to be recorded. In addition, this information is irrelevant for hospitalizations payments, resulting in underreporting. The main impact of this situation is reflected in the results of comparison of death prediction models - all of them showed a discriminatory capacity lower than 0.70, which is considered insufficient.1 A Brazilian study that assessed the validity of use of the CCI with hospital data from the city of Ribeirão Preto, Southeastern Brazil, which recorded one primary diagnosis and two secondary diagnoses at that time, showed and compared models to predict death with a higher predictive capacity (C-statistic of 0.72).8 However, although the result was comparatively better when data from Ribeirão Preto were used, rather than SIH/SUS data, the C-statistics obtained were still lower than those reported in international studies. These studies tested the effect of the CCI, using databases with records of up to 15 diagnoses, and obtained better discriminatory capacity of death prediction models (C-statistics higher than 0.80).11,16

Among the study limitations, although including SUS-funded Brazilian hospitalizations, the population studied is restricted to specific diseases of the respiratory and circulatory systems. Even considering that the discriminatory capacity (C-statistic) is more important when the model is constructed to predict individual results,6 all models showed adjustment problems, assessed by the Hosmer-Lemeshow test. The high number of hospitalizations analyzed may be the main explanation for this problem of adjustment of models, once previous Brazilian studies did not report this type of finding.8,9,12 In view of the previously known limitations of the SIH/SUS, which encouraged researchers to perform the present study, and the magnitude of underreporting of secondary diagnoses found in the population analyzed, other studies are necessary. Future studies should be aimed at finding out the magnitude of underreporting, using medical records as source of information. Analyses of this type could help to assess the potential measurement bias resulting from the use of an administrative database. Considering the lack of studies on validity of comorbidity measures in the Brazilian population,8 initiatives in this area are also important, especially in terms of Elixhauser et al's methodology,3 which has not yet been validated in Brazil.

The results obtained do not promote the use of comorbidity measures, such as the proposals by Charlson2 and Elixhauser,3 based on the SIH/SUS. However, despite the limited effect on the capacity to predict death, probably associated with the quality of diagnostic information available in the national databases, the use of measures based on the presence of comorbidities is still recommended to adjust the risk of death or other performance indicators, as done in other countries. Thus, the CCI2 and Elixhauser comorbidities3 are still considered useful, especially with the adaptations to the ICD-10 performed by Quan et al.14 The diagnostic information available in the Brazilian databases, particularly that from the SIH/SUS, need to be enriched. In other countries, discharge summaries enable secondary diagnoses to be recorded, including between 15 and 25 spaces6,16 for this purpose. In addition, educational strategies for clinical professionals should be developed, aiming to promote recording of such information. Together with educational actions, the creation of mechanisms to monitor the quality of information recorded in different sources is important, whether in the patient's medical chart or in specific information system forms, such as the administrative database of hospital production.

In conclusion, findings from the present study indicate the importance of allowing a higher number of spaces to record comorbidities of Brazilian in-patients, as reported by previous studies.8,10,15 Although the SIH/SUS was conceived in the 1980s, opportunities for improvement have not been identified or implemented, differently from the hospital information systems of other countries. This situation partly results from the difficult debate between type of payment for hospital services and the information system. Until now, there has not been an information system in Brazil that enables a complete description of hospital morbidity, when compared to those present in other countries, thus limiting the use of such information to assess the performance of services, among other things. This will require human and financial investments, but it will result in the construction of more valid performance indicators, which enable monitoring and improvement of the health care provided by the Brazilian health system.

 

REFERENCES

1. Aylin P, Bottle A, Majeed A. Use of administrative data or clinical databases as preditors of risk of death in hospital: comparison of models. BMJ. 2007;334(7602):1044. DOI: 10.1136/bmj.39168.496366.55        

2. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-83. DOI: 10.1016/0021-9681(87)90171-8        

3. Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Medical Care. 1998;36(1):8-27. DOI: 10.1097/00005650-199801000-00004        

4. Fung CH, Lim YW, Mattke S, Damberg C, Shekelle PG. Systematic review: the evidence that publishing patient care performance data improves quality of care. Ann Internal Med. 2008;148(2):111-23.         

5. Groot V, Becherman H, Lankhorst G, Bouter LM. How to measure comorbidity: a critical review of available methods. J Clin Epidemiol. 2003;56(3): 221-9. DOI: 10.1016/S0895-4356(02)00585-1        

6. Iezzoni LI, editor. Risk adjustment for measuring health care outcomes. 3. ed. Ann Arbor: Health Administration; 2003.         

7. Martins M, Blais R, Leite IC. Mortalidade hospitalar e tempo de permanência: comparação entre hospitais públicos e privados na região de Ribeirão Preto, São Paulo, Brasil. Cad Saude Publica. 2004;20(suppl. 2):268-82. DOI: 10.1590/S0102-311X2004000800021        

8. Martins M, Blais R. Evaluation of comorbidity indices for inpatient mortality prediction models. J Clin Epidemiol. 2006;59(7):665-9. DOI: 10.1016/j.jclinepi.2005.11.017        

9. Martins M, Travassos C, Noronha JC. Sistema de Informações Hospitalares como ajuste de risco de índices de desempenho. Rev Saude Publica. 2001;35(2):185-92. DOI: 10.1590/S0034-89102001000200013        

10. Martins MS, Travassos C. Assessing the availability of casemix information in hospital database systems in Rio de Janeiro, Brazil. Int J Qual Health Care. 1998;10(2):125-33. DOI: 10.1093/intqhc/10.2.125        

11. Needham DM, Scales DC, Laupacis A, Pronovost PJ. A systematic review of the Charlson comorbidity index using administrative databases: a perspective on risk adjustment in critical care research. J Crit Care. 2005;20(1):12-9. DOI: 10.1016/j.jcrc.2004.09.007        

12. Noronha JC, Martins M, Travassos C, Campos MR, Maia M, Panezzuti R. Aplicação da mortalidade hospitalar após a realização de cirurgia de revascularização do miocárdio para monitoramento do cuidado hospitalar. Cad Saude Publica. 2004;20 (suppl.2):322-39. DOI: 10.1590/S0102-311X2004000800025        

13. Noronha JC, Travassos C, Martins M, Campos MR, Maia P, Panezzuti R. Avaliação da relação entre volume de procedimentos e a qualidade do cuidado: o caso de cirurgia coronariana no Brasil. Cad Saude Publica. 2003;19(6):1781-9. DOI: 10.1590/S0102-311X2003000600022        

14. Quan H, Sundarajan V, Halfon P, Fong A, Burnand B, Luthi JC, et al. Coding algoritms for defining comorbities in ICD-9-CM and ICD-10 administrative data. Medical Care. 2005;43(11):1130-9. DOI: 10.1097/01.mlr.0000182534.19832.83        

15. Souza RC, Pinheiro RS, Coeli CM, Camargo Junior KR. The Charlson comorbidity index (CCI) for adjustment of hip fracture mortality in the elderly: analysis of the importance of recording secondary diagnoses. Cad Saude Publica. 2008;24(2):315-22. DOI: 10.1590/S0102-311X2008000200010        

16. Sundararajan V, Quan H, Halfon P, Fushimi K, Luthi JC, Burnand B, et al. Cross-national comparative performance of three version of the ICD-10 Charlson index. Medical Care. 2007;45(12):1210-5. DOI: 10.1097/MLR.0b013e3181484347        

17. Wray NP, Hollingsworth J, Peterson N, Ashton CM. Case-mix adjustment using administrative databases: a paradigm to guide future research. Medical Care Res Rev. 1997;54(3):326-56. DOI: 10.1177/107755879705400306        

 

 

Correspondence:
Monica Martins
R. Leopoldo Bulhões, 1480 sala 725
Manguinhos
21041-210 Rio de Janeiro, RJ, Brasil
E-mail: martins@ensp.fiocruz.br

Received: 5/15/2009
Approved: 9/29/2009

 

 

Research funded by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq - National Council for Scientific and technological Development - Process 400106/2006-0; PAPES IV - FIOCRUZ/Papes B).
The authors declare that there are no conflicts of interest.
a Ministério da Saúde. Datasus. Morbidade hospitalar do SUS por local de internação - Brasil [internet]. [citado 2007 jan 16]. Disponível em: http://tabnet.datasus.gov.br/cgi/tabcgi.exe?sih/cnv/miuf.def

Faculdade de Saúde Pública da Universidade de São Paulo São Paulo - SP - Brazil
E-mail: revsp@org.usp.br