Why the study?
Reliable risk stratification tools tailored to high-risk European populations undergoing total hip arthroplasty remain limited as fast-track and outpatient protocols expand.
Can a simple clinical score reliably estimate the risk of in-hospital adverse events after elective primary total hip arthroplasty in a high-risk population?
Can a simple clinical score reliably estimate the risk of in-hospital adverse events after elective primary total hip arthroplasty in a high-risk population?
A newly developed, simple clinical score (AUC 0.80) can effectively stratify patients by risk of in-hospital adverse events following elective total hip arthroplasty, aiding in the selection of candidates for fast-track pathways.
May aid preoperative risk stratification in high-risk THA; leaves open prospective validation before changing practice.
INTRODUCTION: Patients undergoing total hip arthroplasty (THA) in tertiary centres often present with complex comorbidities that increase the risk of perioperative adverse events (AE). While fast-track and outpatient protocols are expanding, reliable risk stratification tools tailored to high-risk European populations remain limited. This study aimed to (1) compare comorbidity burden in a high-risk population to national data, (2) determine incidence and risk factors for in-hospital AE and (3) develop a simple, pragmatic score to identify patients at elevated AE risk. MATERIALS AND METHODS: We retrospectively analyzed 4,101 elective primary THA cases from a German tertiary care centre (2010-2019). Comorbidity burden was quantified using the Elixhauser Comorbidities (EC) and benchmarked against national registry data (EPRD). Independent predictors of in-hospital AE were identified using multivariate logistic regression. These variables were then used to develop a pragmatic preoperative clinical risk score via LASSO regression, internally validated with 10-fold cross-validation and bootstrapping. RESULTS: Compared to the national registry, our cohort showed significantly higher rates of major comorbidities, including cardiac valvular disease, diabetes, and fluid/electrolyte disorders. The overall in-hospital AE rate was 2.6%. Six comorbidities-including pulmonary circulation disorders (OR 10.7, 95% CI: 3.6-31.8)-were independently associated with AE. The derived LASSO model demonstrated strong discrimination (AUC 0.80; 95% CI: 0.75-0.84) and calibration (Brier score 0.07). A cutoff score ≥ 2 identified patients with an AE rate of > 7%, while scores < 2 corresponded to an NPV of 0.99, supporting its utility in identifying low-risk patients for fast-track pathways. CONCLUSIONS: Patients treated in tertiary centres exhibit elevated comorbidity burden but maintain acceptable perioperative AE rate. A simple, validated clinical score can flag patients at substantially increased risk of in-hospital AE who may benefit from closer in-hospital surveillance and effectively identify low-risk candidates for fast-track THA pathways. Further external validation is warranted.
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Wolf et al. (2025) studied this question.
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