A comparison of comorbidity measures for predicting mortality after elective hip and knee replacement: A cohort study of data from the National Joint Registry in England and Wales

• Published in: PloS one Vol. 16; no. 8; p. e0255602
• Main Authors: Penfold, Chris M, Whitehouse, Michael R, Blom, Ashley W, Judge, Andrew, Wilkinson, J. Mark, Sayers, Adrian
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 12.08.2021; Public Library of Science (PLoS)
• Subjects: Biology and Life Sciences; Biomedical materials; Biomedical research; Cohort analysis; Comorbidity; Complications; Complications and side effects; Frailty; Gender; Health risks; Health sciences; Hip; Hospitals; Joints (anatomy); Knee; Medical research; Medical schools; Medicine and Health Sciences; Mortality; Orthopaedic implants; Orthopedics; Patient outcomes; Patients; Physical Sciences; Prediction models; Regression analysis; Regression models; Risk; Society; Surgery; United Kingdom; England; United Kingdom > UK; Northern Ireland; Isle of Man; Wales
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0255602

The risk of mortality following elective total hip (THR) and knee replacements (KR) may be influenced by patients' pre-existing comorbidities. There are a variety of scores derived from individual comorbidities that can be used in an attempt to quantify this. The aims of this study were to a) identify which comorbidity score best predicts risk of mortality within 90 days or b) determine which comorbidity score best predicts risk of mortality at other relevant timepoints (30, 45, 120 and 365 days). We linked data from the National Joint Registry (NJR) on primary elective hip and knee replacements performed between 2011-2015 with pre-existing conditions recorded in the Hospital Episodes Statistics. We derived comorbidity scores (Charlson Comorbidity Index-CCI, Elixhauser, Hospital Frailty Risk Score-HFRS). We used binary logistic regression models of all-cause mortality within 90-days and within 30, 45, 120 and 365-days of the primary operation using, adjusted for age and gender. We compared the performance of these models in predicting all-cause mortality using the area under the Receiver-operator characteristics curve (AUROC) and the Index of Prediction Accuracy (IPA). We included 276,594 elective primary THRs and 338,287 elective primary KRs for any indication. Mortality within 90-days was 0.34% (N = 939) after THR and 0.26% (N = 865) after KR. The AUROC for the CCI and Elixhauser scores in models of mortality ranged from 0.78-0.81 after THR and KR, which slightly outperformed models with ASA grade (AUROC = 0.77-0.78). HFRS performed similarly to ASA grade (AUROC = 0.76-0.78). The inclusion of comorbidities prior to the primary operation offers no improvement beyond models with comorbidities at the time of the primary. The discriminative ability of all prediction models was best for mortality within 30 days and worst for mortality within 365 days. Comorbidity scores add little improvement beyond simpler models with age, gender and ASA grade for predicting mortality within one year after elective hip or knee replacement. The additional patient-specific information required to construct comorbidity scores must be balanced against their prediction gain when considering their utility.