Abstract
OBJECTIVE: This study aimed to determine the predictors of postoperative pulmonary complications (PPCs) in patients undergoing secondary pulmonary resection for multiple primary lung cancers (MPLC), thus facilitating targeted clinical management strategies. METHODS: Clinical and computed tomography (CT) imaging data from MPLC patients treated at the Third Affiliated Hospital of Kunming Medical University between January 2022 and June 2023 were retrospectively analyzed. Participants were categorized into PPC and non-PPC cohorts. Initially, univariate analyses were conducted to assess clinical characteristics and CT parameters that significantly differed between groups. Subsequently, Independent predictors were identified via multivariate logistic regression. Receiver operating characteristic (ROC) curve analysis was performed to evaluate diagnostic performance of the identified variables. Internal validation was performed using bootstrap resampling (1,000 resamples), and calibration was assessed using a calibration plot with goodness-of-fit testing. In addition, two prediction models were developed, including a pre-/intraoperative model (Model 1) and an early postoperative augmented model (Model 2); sensitivity analyses were conducted by excluding minor PPC events. RESULTS: A total of 170 patients were included, with postoperative pulmonary complications (PPCs) occurring in 52 cases. Univariate analysis revealed no significant differences between the PPC and non-PPC groups in terms of gender, smoking index, preoperative PaO(2), operative duration, single-lung ventilation time, moderate postoperative pain at 48 hours, closed thoracic drainage duration, FEV1%pred, low attenuation volume percentage (LAV%), resected functional lung volume (RFLV), PPO-FVC, PPO-FEV1, PPO-MVV, PPO-FEV1%pred, PPO-MVV%pred, and PPO-DLCO%pred (P<0.05). Multivariate logistic regression analysis indicated that FEV1%pred (OR = 0.86, 95% CI: 0.789-0.938), unilateral ventilation duration (OR = 1.009, 95% CI: 1.000-1.019), LAV% (OR = 1.057, 95% CI: 1.011-1.106), moderate pain at 48 hours postoperatively (OR = 12.32, 95% CI: 3.903-38.898), and PPO-FEV1%pred (OR = 0.86, 95% CI: 0.789-0.938) were independent predictors of PPCs. In single-factor ROC analysis, PPO-FEV1%pred demonstrated optimal discriminatory ability (AUC = 0.801, cutoff value 79.45%). Regarding model construction: - The preoperative/intraoperative model (Model 1) demonstrated an AUC of 0.86 (95% CI: 0.80-0.92) after bootstrap internal validation (1,000 iterations), with a calibration slope of 0.93 and a Hosmer-Lemeshow test P = 0.466; The early postoperative enhancement model (Model 2) yielded an AUC of 0.91 (95% CI: 0.87-0.96), calibrated slope of 0.90, and Hosmer-Lemeshow test P = 0.203. Sensitivity analysis (excluding minor events) demonstrated that PPO-FEV1%pred retained the strongest discriminatory capacity (AUC = 0.780, 95% CI: 0.696-0.863; cutoff value 78.94%; sensitivity 80.95%; specificity 67.86%), indicating robustness of the primary outcome. CONCLUSIONS: This study identified FEV1%pred, one-lung ventilation time, LAV%, PPO-FEV1%pred, and moderate pain at 48 h postoperatively as independent predictors for PPCs. PPO-FEV1%pred demonstrated the highest diagnostic accuracy in predicting PPCs after secondary pulmonary resection, facilitating personalized clinical decision-making and patient management. Findings remained robust in sensitivity analyses.