Development and internal validation of a nomogram and machine-learning models for postoperative recurrence in adult patients with chronic rhinosinusitis with nasal polyps.

Background: Practical preoperative tools for estimating postoperative recurrence in chronic rhinosinusitis with nasal polyps (CRSwNP) remain limited. We developed and internally validated a conventional nomogram and multiple machine-learning models and evaluated the systemic coagulation-inflammation index (SCI) as a deterministic nonlinear composite of routine laboratory measurements. Methods: The analysis included 245 adults. To preserve the integrity of the originally locked internal validation, the original outcome-stratified assignments were retained after age exclusions, leaving 170 patients in the training set and 75 in the same-center internal held-out set. Centered VIFs were calculated with an intercept, component-versus-composite SCI models were compared, and ten algorithms were tuned by five-fold stratified cross-validation in the training set. A Firth logistic sensitivity analysis forced hypertension and diabetes into the primary model. Results: Recurrence occurred in 95/245 patients (38.8%). SCI was the only initial candidate with VIF > 10 (10.259); after its structural exclusion, all remaining VIFs were ≤1.515. In the five-variable multivariable screening model, WBC was inversely associated with recurrence (OR 0.437, 95% CI 0.307-0.623), whereas PLT (OR 1.024, 95% CI 1.013-1.035) and FIB (OR 4.480, 95% CI 2.346-8.557) were positively associated (all Conclusion: A nomogram based on WBC, PLT, and FIB showed promising same-center internal performance in adults. SCI should be interpreted as a deterministic nonlinear composite rather than unique biological information. External validation in cohorts with standardized tissue histopathology and comprehensive comorbidity data is required before clinical use.