Establishment and validation of a cough prediction model for lung cancer survivors after pulmonary resection based on a systematic review and meta-analysis of 23 cohort studies.

BACKGROUND

Postoperative cough (POC) is a common complication following pulmonary resection, with an incidence of 25-50%. However, it often receives insufficient attention from clinicians. This study aimed to systematically identify the key risk factors associated with POC in lung cancer survivors and to develop and validate a predictive model to assess the likelihood of POC following pulmonary resection.

METHODS

A systematic review and meta-analysis were conducted by searching PubMed, Embase, Cochrane Library, Web of Science, WANFANG DATA, and CNKI to identify relevant risk factors for POC. Additionally, a cohort of 5,570 patients who underwent pulmonary resection was used to develop and validate the predictive model. Statistically significant independent variables from the meta-analysis were incorporated into the model, with risk factors weighted based on their pooled odds ratios (ORs) and β-coefficients. Model performance was evaluated using the area under the curve (AUC), calibration curves, and decision curve analysis (DCA).

RESULTS

The meta-analysis included 23 cohorts with 5,360 patients, of whom 33.8% experienced POC. Significant risk factors identified were age ≥60 years, body mass index (BMI) <24 kg/m, preoperative cough, right lung surgery, lobectomy, subcarinal and peritracheal lymph node dissection, postoperative acid reflux, and preoperative respiratory training. The predictive model demonstrated robust performance, with AUCs of 0.772 [95% confidence interval (CI): 0.757-0.786] in the training cohort and 0.782 (95% CI: 0.761-0.803) in the validation cohort. Calibration curves showed high accuracy, and DCA confirmed clinical utility across a threshold range of 0 to 0.8.

CONCLUSIONS

This study provides a comprehensive, evidence-based predictive model for identifying lung cancer survivors at high risk of POC, offering valuable insights into its risk factors.