Malnutrition and Clinical Factors as Predictors of Extended Hospital Stay After Total Hip Arthroplasty: Development of a Predictive Nomogram.

BACKGROUND

Extended lengths of stay (eLOS) after total hip arthroplasty (THA) increase health-care costs and adverse outcomes. Nutritional status is important in postoperative recovery, but its impact on eLOS remains underexplored.

METHODS

This study included 805 THA patients, and the prolonged hospitalization group (PHG) was defined as a length of stay (LOS) ≥ 10 days. Nutritional status was evaluated using the controlling nutritional status (CONUT), geriatric nutritional risk index (GNRI), and prognostic nutritional index (PNI) scores, and their predictive value for eLOS was assessed via receiver operating characteristic (ROC) analysis. Independent predictors of eLOS in THA patients were identified using logistic regression analyses, upon which a nomogram was developed for risk prediction.

RESULTS

The prolonged hospitalization group had lower GNRI and PNI scores than the nonprolonged hospitalization group (NPHG) (P < 0.001), while CONUT scores showed no difference (P = 0.153). After adjusting for age and sex, GNRI (r = -0.195, P = 0.008) and PNI (r = -0.08, P = 0.024) were negatively correlated with eLOS. The receiver operating characteristic analysis indicated that GNRI had superior predictive accuracy for eLOS [area under the curve (AUC) = 0.643] compared to PNI, and combining multiple nutritional scores did not enhance predictive performance. Multivariate regression identified GNRI, HuaXi fall risk score (HXFS), age-adjusted Charlson comorbidity index (ACCI), visual analog scale (VAS), and admission type as independent risk factors for eLOS (all P < 0.05). A nomogram incorporating these variables demonstrated the highest predictive value (area under the curve = 0.750) with a sensitivity of 71.4% and specificity of 72.1%.

CONCLUSIONS

The GNRI score is an independent risk factor for eLOS in THA patients. A predictive model incorporating other variables demonstrated the highest diagnostic value, and the application of this model for the early identification of high-risk patients who have eLOS may facilitate targeted interventions, optimize preoperative management, and improve clinical outcomes.