Development of a simple clinical score to estimate in-hospital adverse event risk after elective hip arthroplasty: a retrospective cohort study in a high-risk population

TL;DR

This study created a simple clinical score to predict in-hospital complications after hip surgery in high-risk patients, helping identify those needing closer monitoring or suitable for fast-track recovery.

Contribution

A validated, pragmatic clinical score for risk stratification in high-risk hip arthroplasty patients using LASSO regression.

Findings

High-risk patients had more comorbidities like cardiac and diabetes compared to national data.

A clinical score with a cutoff ≥2 identified patients with >7% AE risk, while scores <2 had 99% negative predictive value.

The model showed strong discrimination (AUC 0.80) and calibration (Brier score 0.07).

Abstract

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.

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.

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.

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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