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A clinical prediction model for prolonged air leak after pulmonary resection.

ABSTRACT: OBJECTIVE:Prolonged air leak increases costs and worsens outcomes after pulmonary resection. We aimed to develop a clinical prediction tool for prolonged air leak using pretreatment and intraoperative variables. METHODS:Patients who underwent pulmonary resection for lung cancer/nodules (from January 2009 to June 2014) were stratified by prolonged parenchymal air leak (>5 days). Using backward stepwise logistic regression with bootstrap resampling for internal validation, candidate variables were identified and a nomogram risk calculator was developed. RESULTS:A total of 2317 patients underwent pulmonary resection for lung cancer/nodules. Prolonged air leak (8.6%, n = 200) was associated with significantly longer hospital stay (median 10 vs 4 days; P < .001). Final model variables associated with increased risk included low percent forced expiratory volume in 1 second, smoking history, bilobectomy, higher annual surgeon caseload, previous chest surgery, Zubrod score >2, and interaction terms for right-sided thoracotomy and wedge resection by thoracotomy. Wedge resection, higher body mass index, and unmeasured percent forced expiratory volume in 1 second were protective. Derived nomogram discriminatory accuracy was 76% (95% confidence interval [CI], 0.72-0.79) and facilitated patient stratification into low-, intermediate- and high-risk groups with monotonic increase in observed prolonged air leaks (2.0%, 8.9%, and 19.2%, respectively; P < .001). Patients at intermediate and high risk were 4.80 times (95% CI, 2.86-8.07) and 11.86 times (95% CI, 7.21-19.52) more likely to have prolonged air leak compared with patients at low risk. CONCLUSIONS:Using readily available candidate variables, our nomogram predicts increasing risk of prolonged air leak with good discriminatory ability. Risk stratification can support surgical decision making, and help initiate proactive, patient-specific surgical management.

SUBMITTER: Attaar A

PROVIDER: S-EPMC5651171 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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A clinical prediction model for prolonged air leak after pulmonary resection.

Attaar Adam A Winger Daniel G DG Luketich James D JD Schuchert Matthew J MJ Sarkaria Inderpal S IS Christie Neil A NA Nason Katie S KS

The Journal of thoracic and cardiovascular surgery 20161014 3

<h4>Objective</h4>Prolonged air leak increases costs and worsens outcomes after pulmonary resection. We aimed to develop a clinical prediction tool for prolonged air leak using pretreatment and intraoperative variables.<h4>Methods</h4>Patients who underwent pulmonary resection for lung cancer/nodules (from January 2009 to June 2014) were stratified by prolonged parenchymal air leak (>5 days). Using backward stepwise logistic regression with bootstrap resampling for internal validation, candidate ...[more]

PMID: 27912898

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Project description:Background Prolonged air leak (PAL) remains one of the most frequent postoperative complications after pulmonary resection. This study aimed to develop a predictive nomogram to estimate the risk of PAL for individual patients after minimally invasive pulmonary resection. Methods Patients who underwent minimally invasive pulmonary resection for either benign or malignant lung tumors between January 2020 and December 2021 were included. All eligible patients were randomly assigned to the training cohort or validation cohort at a 3:1 ratio. Univariate and multivariate logistic regression were performed to identify independent risk factors. All independent risk factors were incorporated to establish a predictive model and nomogram, and a web-based dynamic nomogram was then built based on the logistic regression model. Nomogram discrimination was assessed using the receiver operating characteristic (ROC) curve. The calibration power was evaluated using the Hosmer-Lemeshow test and calibration curves. The nomogram was also evaluated for clinical utility by the decision curve analysis (DCA). Results A total of 2213 patients were finally enrolled in this study, among whom, 341 cases (15.4%) were confirmed to have PAL. The following eight independent risk factors were identified through logistic regression: age, body mass index (BMI), smoking history, percentage of the predicted value for forced expiratory volume in 1 second (FEV1% predicted), surgical procedure, surgical range, operation side, operation duration. The area under the ROC curve (AUC) was 0.7315 [95% confidence interval (CI): 0.6979–0.7651] for the training cohort and 0.7325 (95% CI: 0.6743–0.7906) for the validation cohort. The P values of the Hosmer-Lemeshow test were 0.388 and 0.577 for the training and validation cohorts, respectively, with well-fitted calibration curves. The DCA demonstrated that the nomogram was clinically useful. An operation interface on a web page (https://lirongyangql.shinyapps.io/PAL_DynNom/) was built to improve the clinical utility of the nomogram. Conclusion The nomogram achieved good predictive performance for PAL after minimally invasive pulmonary resection. Patients at high risk of PAL could be identified using this nomogram, and thus some preventive measures could be adopted in advance. Supplementary Information The online version contains supplementary material available at 10.1186/s12957-022-02716-w.

Dataset Information

A clinical prediction model for prolonged air leak after pulmonary resection.

Publications

A clinical prediction model for prolonged air leak after pulmonary resection.

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