Data Sheet 1_A landmark-based dynamic prediction model for anastomotic leakage after rectal cancer surgery: integrating perioperative and postoperative trajectories.pdf

Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curves, integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA).</p>Results<p>AL occurred in 82 of 815 patients (10.1%). The multi-algorithm consensus strategy identified 16 core baseline variables. Model 1 showed strong discrimination, with an AUC of 0.825 (95% CI, 0.774–0.875).

After incorporating early postoperative dynamic parameters, including longitudinal neutrophil-to-lymphocyte ratio trajectory clusters and acute hypoalbuminemia, Model 2 improved the AUC to 0.889 (95% CI, 0.848–0.930; DeLong test, P < 0.001). Model 2 also improved risk reclassification, with an IDI of 0.116 (P < 0.001) and a net NRI of 0.129 (P = 0.006). The Brier score decreased to 0.057, and DCA showed consistently greater clinical net benefit across a broad range of threshold probabilities.</p>Conclusions<p>This two-stage landmark-based dynamic prediction framework improves early warning for AL by combining baseline risk factors with postoperative inflammatory dynamics.

The model may support more precise perioperative risk stratification and earlier individualized intervention after radical rectal cancer surgery.</p>