What question did this study set out to answer?

The study aims to develop machine learning models that predict early PCA discontinuation following TKA using perioperative variables.

June 4, 2026Open Access

Machine Learning-Based Prediction of Early Patient-Controlled Analgesia Discontinuation After Total Knee Arthroplasty: A Retrospective Cohort Study

Puntos clave

The study aims to develop machine learning models that predict early PCA discontinuation following TKA using perioperative variables.
Single-center retrospective observational study involving 1188 patients undergoing primary TKA.
Development of interpretable machine learning models using perioperative variables for predicting PCA discontinuation.
Evaluation of the predictive value of hierarchical feature sets.
Early PCA discontinuation occurred in approximately 10% of patients.
Random forest model showed AUC of 0.77 and good calibration accuracy with a Brier score of 0.065.
Key factors for discontinuation included prior intolerance to nausea/vomiting and female sex.

Resumen

Background: Patient-controlled analgesia (PCA) is widely used for postoperative pain control after total knee arthroplasty (TKA). Although postoperative nausea and vomiting (PONV) has been extensively studied, early discontinuation of PCA—representing failure to sustain an opioid-based analgesic strategy—has not been adequately investigated as a distinct, decision-relevant outcome. Methods: We conducted a single-center retrospective observational study of 1188 patients undergoing primary TKA who received PCA for postoperative pain management. The primary aim was to develop interpretable machine learning models for predicting early PCA discontinuation using routinely available perioperative variables. A secondary aim was to evaluate the incremental predictive value of hierarchical feature sets reflecting progressively available clinical information. Results: Early PCA discontinuation occurred in approximately 10% of patients, reflecting a relatively low-frequency clinical event associated with class imbalance. Female sex and PONV-related susceptibility factors, including prior nausea/vomiting intolerance, were more common among patients with early PCA discontinuation. The random forest Step 3 model demonstrated acceptable discriminative performance (AUC: 0.77; PR-AUC: 0.38), good calibration accuracy (Brier score: 0.065), and favorable clinical utility on decision curve analysis. Explainable analyses showed that patient-level susceptibility factors—such as prior intolerance to nausea or vomiting and baseline clinical characteristics—contributed more strongly to early PCA discontinuation than perioperative management or procedural variables. Conclusions: Early PCA discontinuation after TKA represents a distinct, decision-based clinical endpoint that is not captured by symptom-focused outcomes such as PONV. Interpretable machine learning models may help identify patients at increased risk of early PCA discontinuation after TKA, which may support informed counseling and proactive planning of individualized postoperative pain management strategies. However, external validation is required before routine clinical implementation.

Leer artículo completoexternamente

Preguntar a la IA

Me gusta

Guardar

Ver artículo completo