FEV₁ and DL_CO predicting general complications but not prolonged air leaks in pulmonary segmentectomy

Pulmonary segmentectomy is increasingly recognized as a viable alternative to lobectomy for early stage non-small-cell lung cancer (NSCLC), offering comparable oncological outcomes with potentially reduced morbidity. Identifying reliable predictors for postoperative complications and prolonged air leak (PAL) is crucial for optimizing patient selection. While multifactorial scoring systems exist, their complexity limits clinical utility and the predictive value of single factors, such as forced expiratory volume in 1s (FEV₁) and diffusing capacity for carbon monoxide (DL_CO), remains underexplored.

This study aimed to evaluate the ability of preoperative FEV₁ and DL_CO to predict complications (Clavien-Dindo ⩾ 3a) and PAL in patients undergoing pulmonary segmentectomy.

A retrospective, single-center study compared outcomes between patients undergoing segmentectomy (n = 33) and lobectomy (n = 126) for NSCLC.

Patient characteristics, complication rates, and PAL incidence were analyzed. Logistic regression and ROC curve analyses assessed the predictive accuracy of FEV₁ and DL_CO for complications and PAL.

Baseline characteristics, including FEV₁ and DL_CO, were comparable between the segmentectomy and lobectomy groups (p > 0.05). FEV₁ was identified as a significant predictor of complications, with lower values associated with increased risk. DL_CO exhibited an even stronger predictive value for complications in the segmentectomy cohort, with an AUC of 0.924, indicating excellent predictive accuracy. In contrast, neither FEV₁ nor DL_CO demonstrated significant predictive value for PAL, which occurred in 30% of segmentectomy and 20% of lobectomy patients (p > 0.05).

Preoperative FEV₁ and DL_CO are valuable predictors of complications (Clavien-Dindo ⩾ 3a) in pulmonary segmentectomy, with DL_CO showing high predictive accuracy. However, their inability to reliably predict PAL highlights the need for multifactorial models to enhance risk assessment. Despite the limited sample size, our findings align with larger studies and reinforce the clinical utility of FEV₁ and DL_CO for preoperative risk stratification in segmentectomy patients.