BACKGROUND: Comorbidity measures, such as the Charlson Comorbidity Index (CCI) and Elixhauser Method (EM), are frequently used for risk-adjustment by healthcare researchers. This study sought to create CCI and EM lists of Read codes, which are standard terminology used in some large primary care databases. It also aimed to describe and compare the predictive properties of the CCI and EM amongst patients with hip fracture (and matched controls) in a large primary care administrative dataset.
METHODS: Two researchers independently screened 111,929 individual Read codes to populate the 17 CCI and 31 EM comorbidity categories. Patients with hip fractures were identified (together with age- and sex-matched controls) from UK primary care practices participating in the Clinical Practice Research Datalink (CPRD). The predictive properties of both comorbidity measures were explored in hip fracture and control populations using logistic regression models fitted with 30- and 365-day mortality as the dependent variables together with tests of equality for Receiver Operating Characteristic (ROC) curves.
RESULTS: There were 5832 CCI and 7156 EM comorbidity codes. The EM improved the ability of a logistic regression model (using age and sex as covariables) to predict 30-day mortality (AUROC 0.744 versus 0.686). The EM alone also outperformed the CCI (0.696 versus 0.601).
Capturing comorbidities over a prolonged period only modestly improved the predictive value of either index: EM 1-year look-back 0.645 versus 5-year 0.676 versus complete record 0.695 and CCI 0.574 versus 0.591 versus 0.605.
CONCLUSIONS: The comorbidity code lists may be used by future researchers to calculate CCI and EM using records from Read coded databases. The EM is preferable to the CCI but only marginal gains should be expected from incorporating comorbidities over a period longer than 1 year.
|投稿者||Metcalfe, David; Masters, James; Delmestri, Antonella; Judge, Andrew; Perry, Daniel; Zogg, Cheryl; Gabbe, Belinda; Costa, Matthew|
|組織名||Oxford Trauma, Kadoorie Centre for Critical Care Research and Education, Nuffield;Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS),;John Radcliffe Hospital, Headley Way, Oxford, OX3 9BU, UK.;firstname.lastname@example.org.;Centre for Statistics in Medicine, NDORMS, Nuffield Orthopaedic Centre,;University of Oxford, Windmill Road, Oxford, OX3 7LD, UK.;Musculoskeletal Research Unit, Translational Health Sciences, Bristol Medical;School, University of Bristol, Learning and Research Building, Level 1, Southmead;Hospital, Bristol, BS10 5NB, UK.;National Institute for Health Research Bristol Biomedical Research Centre (NIHR;Bristol BRC), University Hospitals Bristol NHS Foundation Trust, University of;Bristol, Southmead Hospital, Bristol, BS10 5NB, UK.;MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton General;Hospital, Southampton, SO16 6YD, UK.;Yale School of Medicine, 333 Cedar Street, New Haven, CT, 06510, USA.;School of Public Health and Preventive Medicine, Monash University, Level 3, 553;St Kilda Road, Melbourne, VIC, 3004, Australia.|