Dataset Information

A nomogram for preoperative prediction of prolonged air leak after pulmonary malignancy resection.

ABSTRACT:

Background

Prolonged air leak (PAL) is one of the most common postoperative complications after lung surgery. This study aimed to identify risk factors of PAL after lung resection and develop a preoperative predictive model to estimate its risk for individual patients.

Methods

Patients with pulmonary malignancies or metastasis who underwent pulmonary resection between January 2014 and January 2018 were included. PAL was defined as an air leak more than 5 days after surgery, risk factors were analyzed. Forward stepwise multivariable logistic regression analysis was performed to identify independent risk factors, and a derived nomogram was built. Data from February 2018 to September 2018 were collected for internal validation.

Results

A total of 1,511 patients who met study criteria were enrolled in this study. The overall incidence of PAL was 9.07% (137/1,511). Age, percent forced expiratory volume in 1 second, surgical type, surgical approach and smoking history were included in the final model. A nomogram was developed according to the multivariable logistic regression results. The C-index of the predictive model was 0.70, and the internal validation value was 0.77. The goodness-of-fit test was non-significant for model development and internal validation.

Conclusions

The predictive model and derived nomogram achieved satisfied preoperative prediction of PAL. Using this nomogram, the risk for an individual patient can be estimated, and preventive measures can be applied to high-risk patients.

SUBMITTER: Jin R

PROVIDER: S-EPMC8435390 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Publications

A nomogram for preoperative prediction of prolonged air leak after pulmonary malignancy resection.

Jin Runsen R Zheng Yuyan Y Gao Taotao T Zhang Yajie Y Wang Bingshun B Hang Junbiao J Li Hecheng H

Translational lung cancer research 20210801 8

<h4>Background</h4>Prolonged air leak (PAL) is one of the most common postoperative complications after lung surgery. This study aimed to identify risk factors of PAL after lung resection and develop a preoperative predictive model to estimate its risk for individual patients.<h4>Methods</h4>Patients with pulmonary malignancies or metastasis who underwent pulmonary resection between January 2014 and January 2018 were included. PAL was defined as an air leak more than 5 days after surgery, risk f ...[more]

PMID: 34584861

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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 nomogram for preoperative prediction of prolonged air leak after pulmonary malignancy resection.

Background

Methods

Results

Conclusions

Publications

A nomogram for preoperative prediction of prolonged air leak after pulmonary malignancy resection.

Similar Datasets

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