Analysis of Influencing Factors and Construction of Predictive Model for Persistent Cough After Lung Cancer Resection Under Thoracoscopy.

IF 2.8 3区医学 Q1 Pharmacology, Toxicology and Pharmaceutics

Therapeutics and Clinical Risk Management Pub Date : 2024-10-01 eCollection Date: 2024-01-01 DOI:10.2147/TCRM.S464307

Jingling Lan, Xia Lin, Li Liu

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引用次数: 0

Abstract

Objective: This study aims to explore the influencing factors of cough after pulmonary resection (CAP) after thoracoscopic lung resection in lung cancer patients and to develop a predictive model.

Methods: A total of 374 lung cancer patients who underwent lung resection in our hospital from March 2020 to October 2023 were randomly divided into a modeling group (n=262) and a validation group (n=112). Based on the occurrence of CAP in the modeling group, the patients were divided into a CAP group (n=85) and a non-CAP group (n=177). Multivariate Logistic regression analysis was used to identify the influencing factors of CAP in lung cancer patients. A nomogram model for predicting the risk of CAP was constructed using R4.3.1. The consistency of the model's predictions was evaluated, and a clinical decision curve (DCA) was drawn to assess the clinical utility of the nomogram. The predictive performance of the model was evaluated using ROC curves and the Hosmer-Lemeshow test.

Results: Multivariate Logistic regression analysis showed that smoking history (OR=6.285, 95% CI: 3.031-13.036), preoperative respiratory function training (OR=20.293, 95% CI: 7.518-54.779), surgical scope (OR=20.667, 95% CI: 7.734-55.228), and peribronchial lymph node dissection (OR=5.883, 95% CI: 2.829-12.235) were significant influencing factors of CAP in lung cancer patients (P<0.05). ROC curves indicated good discriminatory power of the model, and the Hosmer-Lemeshow test showed a high degree of agreement between predicted and actual probabilities. The DCA curve revealed that the nomogram model had high clinical value when the high-risk threshold was between 0.08 and 0.98.

Conclusion: The nomogram model based on smoking history, preoperative respiratory function training, surgical scope, and peribronchial lymph node dissection has high predictive performance for CAP in lung cancer patients. It is useful for clinical prediction, guiding preoperative preparation, and postoperative care.

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胸腔镜下肺癌切除术后持续咳嗽的影响因素分析与预测模型构建

研究目的本研究旨在探讨肺癌患者胸腔镜肺切除术后咳嗽（CAP）的影响因素，并建立预测模型：方法：将2020年3月至2023年10月在我院接受肺切除术的374例肺癌患者随机分为建模组（n=262）和验证组（n=112）。根据建模组中 CAP 的发生率，将患者分为 CAP 组（n=85）和非 CAP 组（n=177）。多变量逻辑回归分析用于确定肺癌患者 CAP 的影响因素。使用 R4.3.1 建立了预测 CAP 风险的提名图模型。对模型预测的一致性进行了评估，并绘制了临床决策曲线（DCA）以评估提名图的临床实用性。使用 ROC 曲线和 Hosmer-Lemeshow 检验对模型的预测性能进行了评估：多变量逻辑回归分析显示，吸烟史（OR=6.285，95% CI：3.031-13.036）、术前呼吸功能训练（OR=20.293，95% CI：7.518-54.779）、手术范围（OR=20.667，95% CI：7.734-55.228）、支气管周围淋巴结清扫（OR=5.883，95% CI：2.829-12.235）是肺癌患者 CAP 的显著影响因素（PConclusion：基于吸烟史、术前呼吸功能训练、手术范围和支气管周围淋巴结清扫的提名图模型对肺癌患者的 CAP 具有较高的预测能力。它有助于临床预测、指导术前准备和术后护理。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Therapeutics and Clinical Risk Management HEALTH CARE SCIENCES & SERVICES-

CiteScore

5.30

自引率

3.60%

发文量

139

审稿时长

16 weeks

期刊介绍： Therapeutics and Clinical Risk Management is an international, peer-reviewed journal of clinical therapeutics and risk management, focusing on concise rapid reporting of clinical studies in all therapeutic areas, outcomes, safety, and programs for the effective, safe, and sustained use of medicines, therapeutic and surgical interventions in all clinical areas. The journal welcomes submissions covering original research, clinical and epidemiological studies, reviews, guidelines, expert opinion and commentary. The journal will consider case reports but only if they make a valuable and original contribution to the literature. As of 18th March 2019, Therapeutics and Clinical Risk Management will no longer consider meta-analyses for publication. The journal does not accept study protocols, animal-based or cell line-based studies.