基于心肺运动测试构建并验证 NSCLC 12 个月内长期预后的提名图

IF 1.9 4区医学 Q3 RESPIRATORY SYSTEM Clinical Respiratory Journal Pub Date : 2024-08-08 DOI:10.1111/crj.13806

Xinyu Wang, Jin Li, Jingjie Zhou, Min Gao, Bin Wang, Yiman Tong, Yuhan Cao, Wei Chen

{"title":"基于心肺运动测试构建并验证 NSCLC 12 个月内长期预后的提名图","authors":"Xinyu Wang, Jin Li, Jingjie Zhou, Min Gao, Bin Wang, Yiman Tong, Yuhan Cao, Wei Chen","doi":"10.1111/crj.13806","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Objective</h3>\n \n <p>Construction nomogram was to effectively predict long-term prognosis in patients with non-small cell lung cancer (NSCLC).</p>\n </section>\n \n <section>\n \n <h3> Materials and Methods</h3>\n \n <p>The nomogram is developed by a retrospective study of 347 patients with NSCLC who underwent cardiopulmonary exercise testing (CPET) before surgery from May 2019 to February 2022. Cross-validation divided the data into a training cohort and validation cohort. The discrimination and accuracy ability of the nomogram were proofed by concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve, the area under the curve (AUC), and time-dependent ROC in validation cohort.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Age, intraoperative blood loss, VO<sub>2</sub> peak, and VE/VCO<sub>2</sub> slope were included in the model of nomogram. The model demonstrated good discrimination and accuracy with C-index of 0.770 (95% CI: 0.712–0.822). AUC of 6 (AUC: 0.789, 95% CI: 0.726–0.851) and 12 months (AUC: 0.787, 95% CI: 0.724–0.850) were shown in ROC. Time-independent ROC maintains a good effect within 12 months.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>We developed a nomogram based on CPET. This model has a good ability of discrimination and accuracy. It could help clinicians to make treatment decision in clinical decision.</p>\n </section>\n </div>","PeriodicalId":55247,"journal":{"name":"Clinical Respiratory Journal","volume":"18 8","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11310092/pdf/","citationCount":"0","resultStr":"{\"title\":\"Based on Cardiopulmonary Exercise Testing to Construct and Validate Nomogram of Long-Term Prognosis Within 12 Months for NSCLC\",\"authors\":\"Xinyu Wang, Jin Li, Jingjie Zhou, Min Gao, Bin Wang, Yiman Tong, Yuhan Cao, Wei Chen\",\"doi\":\"10.1111/crj.13806\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Objective</h3>\\n \\n <p>Construction nomogram was to effectively predict long-term prognosis in patients with non-small cell lung cancer (NSCLC).</p>\\n </section>\\n \\n <section>\\n \\n <h3> Materials and Methods</h3>\\n \\n <p>The nomogram is developed by a retrospective study of 347 patients with NSCLC who underwent cardiopulmonary exercise testing (CPET) before surgery from May 2019 to February 2022. Cross-validation divided the data into a training cohort and validation cohort. The discrimination and accuracy ability of the nomogram were proofed by concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve, the area under the curve (AUC), and time-dependent ROC in validation cohort.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>Age, intraoperative blood loss, VO<sub>2</sub> peak, and VE/VCO<sub>2</sub> slope were included in the model of nomogram. The model demonstrated good discrimination and accuracy with C-index of 0.770 (95% CI: 0.712–0.822). AUC of 6 (AUC: 0.789, 95% CI: 0.726–0.851) and 12 months (AUC: 0.787, 95% CI: 0.724–0.850) were shown in ROC. Time-independent ROC maintains a good effect within 12 months.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusion</h3>\\n \\n <p>We developed a nomogram based on CPET. This model has a good ability of discrimination and accuracy. It could help clinicians to make treatment decision in clinical decision.</p>\\n </section>\\n </div>\",\"PeriodicalId\":55247,\"journal\":{\"name\":\"Clinical Respiratory Journal\",\"volume\":\"18 8\",\"pages\":\"\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2024-08-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11310092/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical Respiratory Journal\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/crj.13806\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"RESPIRATORY SYSTEM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical Respiratory Journal","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/crj.13806","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"RESPIRATORY SYSTEM","Score":null,"Total":0}

引用次数: 0

摘要

目的：构建提名图以有效预测非小细胞肺癌（NSCLC）患者的长期预后：构建提名图以有效预测非小细胞肺癌（NSCLC）患者的长期预后：通过对2019年5月至2022年2月期间347名术前接受心肺运动测试（CPET）的NSCLC患者进行回顾性研究，建立了提名图。交叉验证将数据分为训练队列和验证队列。在验证队列中，通过一致性指数（C-index）、校准曲线、接收者操作特征曲线（ROC）、曲线下面积（AUC）和时间依赖性ROC来证明提名图的区分度和准确性：结果：年龄、术中失血量、VO2 峰值和 VE/VCO2 斜率均被纳入提名图模型。该模型具有良好的区分度和准确性，C 指数为 0.770（95% CI：0.712-0.822）。在 ROC 中显示了 6 个月（AUC：0.789，95% CI：0.726-0.851）和 12 个月（AUC：0.787，95% CI：0.724-0.850）的 AUC。与时间无关的 ROC 在 12 个月内保持良好的效果：我们建立了一个基于 CPET 的提名图。结论：我们开发了一种基于 CPET 的提名图，该模型具有良好的辨别能力和准确性。结论：我们建立了基于 CPET 的提名图，该模型具有良好的区分能力和准确性，可以帮助临床医生在临床决策中做出治疗决定。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

摘要图片

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

Based on Cardiopulmonary Exercise Testing to Construct and Validate Nomogram of Long-Term Prognosis Within 12 Months for NSCLC

Objective

Construction nomogram was to effectively predict long-term prognosis in patients with non-small cell lung cancer (NSCLC).

Materials and Methods

The nomogram is developed by a retrospective study of 347 patients with NSCLC who underwent cardiopulmonary exercise testing (CPET) before surgery from May 2019 to February 2022. Cross-validation divided the data into a training cohort and validation cohort. The discrimination and accuracy ability of the nomogram were proofed by concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve, the area under the curve (AUC), and time-dependent ROC in validation cohort.

Results

Age, intraoperative blood loss, VO₂ peak, and VE/VCO₂ slope were included in the model of nomogram. The model demonstrated good discrimination and accuracy with C-index of 0.770 (95% CI: 0.712–0.822). AUC of 6 (AUC: 0.789, 95% CI: 0.726–0.851) and 12 months (AUC: 0.787, 95% CI: 0.724–0.850) were shown in ROC. Time-independent ROC maintains a good effect within 12 months.

Conclusion

We developed a nomogram based on CPET. This model has a good ability of discrimination and accuracy. It could help clinicians to make treatment decision in clinical decision.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Clinical Respiratory Journal 医学-呼吸系统

CiteScore

3.70

自引率

0.00%

发文量

104

审稿时长

>12 weeks

期刊介绍： Overview Effective with the 2016 volume, this journal will be published in an online-only format. Aims and Scope The Clinical Respiratory Journal (CRJ) provides a forum for clinical research in all areas of respiratory medicine from clinical lung disease to basic research relevant to the clinic. We publish original research, review articles, case studies, editorials and book reviews in all areas of clinical lung disease including: Asthma Allergy COPD Non-invasive ventilation Sleep related breathing disorders Interstitial lung diseases Lung cancer Clinical genetics Rhinitis Airway and lung infection Epidemiology Pediatrics CRJ provides a fast-track service for selected Phase II and Phase III trial studies. Keywords Clinical Respiratory Journal, respiratory, pulmonary, medicine, clinical, lung disease, Abstracting and Indexing Information Academic Search (EBSCO Publishing) Academic Search Alumni Edition (EBSCO Publishing) Embase (Elsevier) Health & Medical Collection (ProQuest) Health Research Premium Collection (ProQuest) HEED: Health Economic Evaluations Database (Wiley-Blackwell) Hospital Premium Collection (ProQuest) Journal Citation Reports/Science Edition (Clarivate Analytics) MEDLINE/PubMed (NLM) ProQuest Central (ProQuest) Science Citation Index Expanded (Clarivate Analytics) SCOPUS (Elsevier)