Background: The Society of Thoracic Surgeons (STS) General Thoracic Surgery Database (GTSD) has been used to develop risk models for patients undergoing pulmonary resection for cancer. Leveraging a contemporary and more inclusive cohort, this study sought to refine these models.

Methods: The study population consisted of adult patients in the STS GTSD who underwent pulmonary resection for cancer between 2015 and 2022. Unlike in previous models, nonelective operations were included. Separate risk models were derived for operative mortality, major morbidity, and composite morbidity or mortality. Logistic regression with backward selection was used with predictors retained in models if P < .10. All derived models were validated using 9-fold cross-validation. Model discrimination and calibration were assessed for the overall cohort and for surgical procedure, demographic, and risk factor subgroups.

Results: Data from 140,927 patients at 337 participating centers were included in the study. Overall operative mortality rate was 1.1%, major morbidity was 7.3%, and composite morbidity or mortality was 7.6%. Novel predictors of short-term outcomes included interstitial lung disease, diffusing capacity of lung for carbon monoxide, and payer status. Overall discrimination was superior to previous STS pulmonary resection models for operative mortality (C-statistic = 0.80) and for composite morbidity or mortality (C-statistic = 0.70). Model discrimination was comparable and model calibration was excellent across all procedure- and demographic-specific subcohorts.

Conclusions: Among STS GTSD participants, major morbidity and operative mortality rates remained low after pulmonary resection. The newly derived pulmonary resection risk models demonstrate superior performance compared with previous models, with broader real-life applicability and clinical face validity.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.athoracsur.2024.07.047DOI Listing

Publication Analysis

Top Keywords

pulmonary resection
20
risk models
16
operative mortality
16
major morbidity
12
composite morbidity
12
morbidity mortality
12
models
9
society thoracic
8
thoracic surgeons
8
resection cancer
8

Similar Publications

Background: Most patients undergoing pancreaticoduodenectomy (PD) for pancreatic ductal adenocarcinoma (PDAC) develop recurrence. No previous studies have investigated predictors of local-only recurrence following PD for PDAC. Our study aimed to determine timing, pattern and predictors of any-site and local-only recurrence following PD for PDAC.

View Article and Find Full Text PDF

The widespread adoption of high-resolution computed tomography (CT) screening has led to increased detection of small pulmonary nodules, necessitating accurate localization techniques for surgical resection. This review examines the evolution, efficacy, and safety of various localization methods for small pulmonary nodules. Studies focusing on localization techniques for pulmonary nodules ≤30 mm in diameter were included, with emphasis on technical success rates and complication profiles.

View Article and Find Full Text PDF

Extensive congenital pulmonary airway malformation (CPAM) of the left fetal lung and associated marked dextroposition of the fetal heart were noted at 21 weeks' gestation. The right fetal lung appeared compressed with the cardiomediastinal shift angle measuring approximately 20 degrees. Potential subsequent right pulmonary hypoplasia was considered.

View Article and Find Full Text PDF

Papillary fibroelastomas (PFEs) are rare, benign, primary cardiac tumors, typically found on the valve surfaces and more commonly on the left side of the heart, with occurrences in the right atrium even rarer. In this case, a highly mobile tumor was incidentally detected in the right atrium of an 83-year-old woman with advanced right lung cancer during preoperative transthoracic echocardiography and magnetic resonance imaging. Although the patient was asymptomatic and of advanced age, the tumor's high mobility warranted resection.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!