Affiliation:
1. Department of Thoracic Fifth, Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011 Hebei, China
Abstract
This study was aimed at exploring the risk factors for thoracotomy in patients undergoing thoracoscopic resection of lung cancer and further analyzing the factors affecting the prognosis of patients. Ninety-six patients with non-small-cell lung cancer who underwent thoracoscopic pulmonary resection were recruited as the subjects, and they were enrolled into the thoracoscopic group (
) and the thoracotomy group (
) according to whether thoracotomy was performed. Univariate analysis and logistic multivariate regression were performed to analyze the risk factors for conversion to thoracotomy, and nomogram prediction model was employed to analyze the prognostic factors. The results revealed that the proportion of patients over 65 years old, with history of coronary heart disease, diabetes, and pulmonary tuberculosis, etc., in the thoracotomy group and the thoracoscopic group was significantly different (
). There were statistically significant differences in the development of interlobular cleft, pleural adhesion, tumor
cm, vascular and lymph node invasion, and tumor TNM stage between the thoracotomy group and the thoracoscopic group (
). Overall, the age of
years old, tumor
cm, hypoplasia of interlobular fissure, history of pulmonary tuberculosis, pleural adhesion, and TNM stage IIIa were all independent risk factors for thoracoscopic resection of lung cancer to thoracotomy. Cox model and nomogram prediction model analysis showed that surgery methods, tumor
cm, chemotherapy
, chemotherapy, and TNM stage IIIa were all independent factors influencing the prognosis of patients undergoing thoracoscopic lung cancer resection. This nomogram prediction model had high application value in patient prognosis prediction.
Funder
Key Research Projects of Medical Science in Hebei Province
Subject
Applied Mathematics,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,Modeling and Simulation,General Medicine
Cited by
2 articles.
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