Original ArticleCharlson scores based on ICD-10 administrative data were valid in assessing comorbidity in patients undergoing urological cancer surgery
Introduction
Case-mix adjustment is an essential component of any study that aims to evaluate outcomes of care, in that differences in baseline patient characteristics may account for many of the observed differences [1], [2]. These characteristics include demographic features such as patient age, sex, and socioeconomic status, but also include differences in both the presence and severity of comorbid disease and the severity of the primary disease for which medical and surgical treatment is taking place. Practical constraints, however, often limit the extent of case-mix adjustment within administrative databases [3], [4], [5], [6].
Comorbid disease may be defined as preexisting disease or illness that affects a patient in addition to but not as a result of a primary diagnosis [2]. In an attempt to adjust for the presence or absence of comorbid disease, several comorbidity scoring systems have been previously developed [7]. One of the most widely used and validated systems was developed in 1987 by Charlson and coworkers [8] from risk factors that predicted 1-year survival in a cohort of medical inpatients and then validated in a population of patients undergoing surgery for breast cancer. This scoring system was subsequently adapted for use with administrative databases using diagnostic codes from the International Classification of Diseases, 9th revision, Clinical Modification (ICD-9-CM) by Deyo et al. and then further adapted by a collaboration of workers at Dartmouth and Manitoba Universities [9], [10], [11], [12], [13], [14]. The Charlson Score consists of 19 different disease comorbidity categories, each allocated a weight of 1 to 6 based on the adjusted relative risk of 1-year mortality and summed to provide a total score [8]. A number of previous studies using administrative data to examine outcomes following surgery have derived comorbidity scores not only from records of the hospital admission in which surgery took place but also considering records of admissions preceding the admission for surgery, in an attempt to capture more complete comorbidity data [15], [16], [17].
Administrative databases are increasingly being used to study patient outcomes following medical and surgical care [2], [18], [19]. Advantages of the use of such databases include ready availability, relatively low cost, large population coverage, and ability to assess trends over time. Disadvantages include coding errors or omissions and a lack of detailed clinical information [20], [21].
The Hospital Episode Statistics (HES) database of the Department of Health in England records medical, demographic and administrative data relating to all inpatient admissions to hospitals in England and was established in 1989 [22], [23]. More than 12 million records are collected each year. This database was established to support policy development, to identify variations in healthcare delivery with time and between geographic areas, to be used in medical research, and to assess performance [24]. Until 2002, the HES database contained seven diagnosis fields, using codes defined from the 10th revision of the International Classification of Diseases (ICD-10), and four operative procedure fields, using codes defined from the U.K. Tabular List of the Classification of Surgical Operations and Procedures, version 4, of the U.K. Offices of Populations, Censuses, and Surveys (OPCS-4) [25], [26]. The number of diagnosis and procedure fields increased to 14 and 12, respectively, after 2002.
We translated the Deyo and Dartmouth–Manitoba ICD-9-CM adaptations of the Charlson score for use with ICD-10 administrative databases, such as the English HES database (Fig. 1). For a comorbidity index to be clinically valid, it would be expected that increasing comorbidity scores would be related to risk factors for comorbidity as well as to clinical outcomes. Our objective was therefore to evaluate the validity of the ICD-10 Charlson scores within the English HES database based on these expectations.
We considered patients undergoing radical urological cancer surgery, as part of a program of work investigating the quality of care for these patients. We used data from both the index surgical admission and also from admissions over the year preceding surgery. Although previous groups have adapted the Deyo version of the Charlson score for use with ICD-10 data, to the best of our knowledge, this is the first time that in addition the validity of the Dartmouth–Manitoba version of the Charlson score has been evaluated for ICD-10 administrative data [27], [28]. This has considerable practical significance, given the expected implementation of the ICD-10-CM coding system within the United States [29].
Section snippets
Data
Data were extracted from the HES database for the data years 1998/1999 to 2001/2002 for all patients recorded as having undergone a radical prostatectomy (RP), radical cystectomy (RC), or radical nephrectomy (RN). Patients were included in the study if, first, an ICD-10 code representing the cancer as reason for treatment was present in any of the seven diagnosis fields and, second, an OPCS-4 code representing the corresponding surgical procedure was present in any of the four operative
Results
Demographic characteristics, method of admission, length of hospital stay, in-hospital mortality rates, and the number of days spent in hospital over the year prior to surgery are presented in Table 1 for each of the three cohorts of patients.
Table 2 shows the prevalence of each of the 17 comorbid disease categories using the ICD-10/OPCS-4 translations of the Deyo and Dartmouth–Manitoba ICD-9-CM codes. The observed disagreement between the two methods of coding for each comorbidity varied from
Discussion
The Deyo and Dartmouth–Manitoba ICD-9-CM adaptations of the Charlson score were translated into ICD-10 codes and subsequently applied to a cohort of patients undergoing radical urological cancer surgery within a large English administrative database. The obtained Charlson scores were higher in older patients, in men, in those admitted to hospital on an emergency basis; the scores were also significant predictors of short-term outcome. Addition of either adaptation of the Charlson score to
Conclusions
Charlson scores derived from ICD-10 administrative data represent a valid approach to adjust for comorbidity. Charlson Deyo and Dartmouth–Manitoba scores derived from ICD-10 data performed similarly in risk models predicting hospital mortality following urological cancer surgery. Both scores can continue to be used to adjust for comorbidity following the anticipated implementation of the ICD-10 coding system in the United States. However, adjusting for comorbidity does not seem to have a large
Acknowledgments
M.N. was supported by the Bob Young Research Fellowship and the Research Fellowship Scheme of The Royal College of Surgeons of England. J. vd M. received a National Health Service Public Health Career Scientist Award. The funding sources had no involvement in the production of or the decision to submit this manuscript for publication. We would like to thank the Department of Health in England for providing the extract from the Hospital Episode Statistics database used in this study.
References (53)
- et al.
Adjusting surgical mortality rates for patient comorbidities: more harm than good?
Surgery
(2002) - et al.
Searching for an improved clinical comorbidity index for use with ICD-9-CM administrative data
J Clin Epidemiol
(1996) - et al.
A new method of classifying prognostic comorbidity in longitudinal studies: development and validation
J Chronic Dis
(1987) - et al.
Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases
J Clin Epidemiol
(1992) - et al.
Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: differing perspectives
J Clin Epidemiol
(1993) Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: a response
J Clin Epidemiol
(1993)- et al.
Further evidence concerning the use of a clinical comorbidity index with ICD-9-CM administrative data
J Clin Epidemiol
(1993) - et al.
The development and validation of a comorbidity index for prostate cancer among black men
J Clin Epidemiol
(2003) - et al.
Using administrative data to describe casemix: a comparison with the medical record
J Clin Epidemiol
(1994) - et al.
Measuring potentially avoidable hospital readmissions
J Clin Epidemiol
(2002)
New ICD-10 version of the Charlson Comorbidity Index predicted in-hospital mortality
J Clin Epidemiol
Evaluation of two competing methods for calculating Charlson's comorbidity index when analyzing short-term mortality using administrative data
J Clin Epidemiol
Cleveland health quality choice: a model for collaborative community-based outcomes assessment
Jt Comm J Qual Improv
Comorbidity assessment in men with localised prostate cancer: a review of currently available techniques
Eur Urol
A study of the morbidity, mortality and long-term survival following radical cystectomy and radical radiotherapy in the treatment of invasive bladder cancer in Yorkshire
Eur Urol
Accuracy of administrative data to assess comorbidity in patients with heart disease: an Australian perspective
J Clin Epidemiol
Co-morbidity data in outcomes research: are clinical data derived from administrative databases a reliable alternative to chart review
J Clin Epidemiol
The completeness and accuracy of health authority and cancer registry records according to a study of ovarian neoplasms
Public Health
Explaining variations in hospital death rates
JAMA
Comorbidity measures for use with administrative data
Med Care
Assessing quality using administrative data
Ann Intern Med
Severity of illness measures: comments and caveats
Med Care
Risk adjusting health care outcomes: a methodologic review
Med Care Rev
The international classification of diseases. 9th rev. ed. Clinical modification, ICD-9-CM
International classification of diseases
Surgeon volume and operative mortality in the United States
N Engl J Med
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