Development and validation of patient-level prediction models for adverse outcomes following total knee arthroplasty

BackgroundElective total knee replacement (TKR) is a safe and cost-effective surgical procedure for treating severe knee osteoarthritis (OA). Although complications following surgery are rare, prediction tools could help identify those patients who are at particularly high risk who could then be targeted with preventative interventions. We aimed to develop a simple model to help inform treatment choices. MethodsWe trained and externally validated adverse event prediction models for patients with TKR using electronic health records (EHR) and claims data from the US (OPTUM, CCAE, MDCR, and MDCD) and general practice data in the UK (IQVIA Medical Research Database ([IMRD], incorporating data from The Health Improvement Network [THIN], a Cegedim database). The target population consisted of patients undergoing a primary TKR, aged [≥]40 years and registered in any of the contributing data sources for [≥]1 year before surgery. LASSO logistic regression models were developed for four adverse outcomes: post-operative (90-day) mortality, venous thromboembolism (VTE), readmission, and long-term (5-year) revision surgery. A second model was developed with a reduced feature set to increase interpretability and usability. FindingsA total of 508,082 patients were included, with sample size per data source ranging from 1,853 to 158,549 patients. Overall, 90-day mortality, VTE, and readmission prevalence occurred in a range of 0.20%-0.32%, 1.7%-3.0% and 2.2%-4.8%, respectively. Five-year revision surgery was observed in 1.5%-3.1% of patients. The full model predicting 90-day mortality yielded AUROC of 0.78 when trained in OPTUM and yielded an AUROC of 0.70 when externally validated on THIN. We then developed a 12 variable model which achieved internal AUROC of 0.77 and external AUROC of 0.71 in THIN. The discriminative performances of the models predicting 90-day VTE, readmission, and 5-year revision were consistently poor across the datasets (AUROC<0.7). InterpretationWe developed and externally validated a simple prediction model based on sex, age, and 10 comorbidities that can identify patients at high risk of short-term mortality following TKR. Our model had a greater discriminative ability than the Charlson Comorbidity Index in predicting 90-day mortality. The 12-feature mortality model is easily implemented and the performance suggests it could be used to inform evidence based shared decision-making prior to surgery and for appropriate precautions to be taken for those at high risk. The other outcomes examined had low performance. FundingThis activity under the European Health Data & Evidence Network (EHDEN) has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 806968. This Joint Undertaking receives support from the European Unions Horizon 2020 research and innovation programme and EFPIA. The sponsor of the study did not have any involvement in the writing of the manuscript or the decision to submit it for publication. The research was supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC). DPA is funded by a National Institute for Health Research Clinician Scientist award (CS-2013-13-012). TDS is funded by the Department of Health of the Generalitat de Catalunya under the Strategic Plan for Research and Innovation in Health (PERIS; SLT002/16/00308). The views expressed in this publication are those of the authors and not those of the NHS, the National Institute for Health Research or the Department of Health. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication. Key PointsO_ST_ABSQuestionC_ST_ABSIs it possible to predict adverse events following total knee replacement? FindingsMortality was the only adverse event studied that we were able to predict with adequate performance. We produced a 12 variable prediction model for 90-day post-operative mortality that achieved an AUROC of 0.77 on internal test validation (Optum) and 0.71 when externally validated in THIN. The model also showed adequate calibration. MeaningPatients can now be presented with an accurate risk assessment for short term mortality such that they are well-informed before the decision for surgery is taken. ImportanceTotal Knee Replacement is generally a safe, effective procedure that is performed on thousands of patients each year. However, a small number of those patients will experience adverse events. Due to the surgerys elective nature, a well calibrated, high performing risk model could pre-emptively inform the patient and clinician decision making process and help to guide preventative treatment.