Risk factors and construction of risk prediction model for patients with atelectasis after thoracoscopic segmental resection.
Objective: To analyze the risk factors of lung cancer complicated with atelectasis after thoracoscopic segmental resection, and to construct a prediction model. Methods: 585 patients with lung cancer who underwent thoracoscopic segmental resection in our hospital from February 2021 to February 2023...
| Publicado en: | Chinese Nursing Research Vol. 38; no. 21; pp. 3812 - 3818 |
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| Autores principales: | , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Chinese Nursing Research Editorial Office
Nov2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=181706338&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 181706338 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10096493 YV6 jtl: Chinese Nursing Research issn: 10096493 maglogo: N pubinfo: dt: Nov2024 vid: 38 iid: 21 pid: 37375 pub: Chinese Nursing Research Editorial Office artinfo: ui: 181706338 181706338 181706338 10.12102/j.issn.1009-6493.2024.21.008 181706338 ppf: 3812 ppct: 6 formats: tig: atl: Risk factors and construction of risk prediction model for patients with atelectasis after thoracoscopic segmental resection. aug: au: CHEN Qianqian PENG Jifang LIU Han affil: Jiangsu Provincial People's Hospital/The First Affiliated Hospital of Nanjing Medical University, Jiangsu 210029 China sug: subj: Risk Assessment Prediction Models Pulmonary Atelectasis Risk Factors Thoracic Surgery, Video-Assisted Adverse Effects Lung Neoplasms Surgery Human Hospitals Retrospective Design Logistic Regression Data Analysis Software Random Forest Descriptive Statistics Multivariate Analysis Body Mass Index Smoking History Lung Physiology Time Factors Adenocarcinoma Complications ROC Curve Algorithms ab: Objective: To analyze the risk factors of lung cancer complicated with atelectasis after thoracoscopic segmental resection, and to construct a prediction model. Methods: 585 patients with lung cancer who underwent thoracoscopic segmental resection in our hospital from February 2021 to February 2023 were retrospectively selected as the study objects, and they were divided into atelectasis group and non-atelectasis group according to whether they developed atelectasis after surgery. Clinical data of patients with lung cancer were collected, and multi-factor Logistic regression was used to screen the risk factors affecting atelectasis after thoracoscopic segmental resection of lung cancer. R software was used to establish a random forest model for predicting atelectasis, and the effectiveness of the model was verified. Results: The incidence of atelectasis was 8. 55% in 585 patients with lung cancer after thoracoscopic segmental resection. Multivariate results showed that BMI≥24 kg/m², smoking history, underlying diseases, poor preoperative pulmonary function, operative time ≥2 h and adenocarcinoma were independent risk factors for atatasis after thoracoscopic lung resection (P<C0. 05). The relative important predictors of lung atatasis after thoracoscopic segmental resection in random forest were preoperative lung function, BMI, smoking history, operation time, underlying disease, and pathological type of tumor. ROC results showed that the AUC of random forest algorithm in predicting the occurrence of atatasis was slightly higher than that of multivariate Logistic regression model(0. 841 vs 0. 834). Conclusion: BMI ≥24 kg/m², smoking history, underlying diseases, poor preoperative pulmonary function, operative time ≥2 h, and adenocarcarcinoma are independent risk factors for atectasis after thoracoscopic segmental resection. Based on these factors, the random forest model for predicting atectasis has good risk prediction efficacy. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: Chinese refInfo: holdings: @attributes: islocal: N |
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