A Risk Stratification Model for Postoperative Complications following Video-Assisted Thoracic Surgery Lobectomy

Background: More accurate risk knowledge of significant morbidity following VATS lobectomy (VL) patients is fundamental since this might influence treatment choice. Aims and Objectives: Development/validation risk stratification model to assess incidence of postoperative complications (PC) following VL. Methods: Retrospective analysis on prospectively data of 193 VATS lobectomy for lung cancer (2014–2017) from 2-centres:44(22.7%)left upper, 31(16.5%)left lower, 68(35.1%)right upper, 10(5.1%)middle, 40(20.6%)right lower. Model was built from derivation set(independent predictors of PC), validation set(evaluate constructed model).Potential predictors:age, FEV1%, DLCO%, Charlson’s Comorbidity Index. To develop model only patients with PC were randomly divided(60:40 ratio)into derivation/validation set. Logistic regression analysis identified(derivation set)significant independent PC predictors. We constructed prognostic index using regression analysis coefficients. Appropriate score cutoff was assessed;validation sample was used to evaluate score diagnostic accuracy. Results: 193 VL were included (derivation set=116, validation set=77).DLCO%, Charlson’s Index were independent PC predictors. Using simplified scoring system(natural logs of odds ratios from multivariable analysis on derivation sample), patients can be stratified into high/low risk of PC.16/18 (derivation set), 5/6 (validation set) had PC proven and, respectively, 82/98 & 60/71 had no PC. Negative predictive value of model for derivation/validation set was 96.2% & 93.4%. Conclusions: Model could predict PC risk for VL and can be used as decision aid tool during multidisciplinary team discussion to decide best therapeutic strategy.