Informed LASSO machine learning method in postoperative survival analysis of supra-cardiac total anomalous pulmonary venous connection
- VernacularTitle:Informed LASSO 机器学习方法在心上型完全性肺静脉异位引流术后生存分析中的应用
- Author:
Xiaobing LIU
1
,
2
;
Furong LIU
1
,
2
;
Zeyu CHEN
3
;
Guangzheng XU
4
;
Hailong QIU
1
,
2
;
Erchao JI
1
,
2
;
Xiaohua LI
1
,
2
;
Shusheng WEN
1
,
2
;
Tao LIU
3
;
Jian ZHUANG
1
,
2
Author Information
1. Department of Cardiovascular Surgery, Guangdong Provincial People'
2. s Hospital, Guangdong Academy of Medical Sciences, Guangdong Cardiovascular Institute, Guangzhou, 510080, P. R. China
3. Department of Biostatistics School, Public Health Brown University, Rhode Island, 02912, USA
4. School of Social Science, Tsinghua University, Beijing, 100084, P. R. China
- Publication Type:Journal Article
- Keywords:
Total anomalous pulmonary venous connection;
risk factors;
machine learning;
LASSO
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2022;29(07):848-853
- CountryChina
- Language:Chinese
-
Abstract:
Objective To characterize surgical outcomes of supra-cardiac total anomalous pulmonary venous connection (TAPVC), investigate risk factors for postoperative death, and explore informed LASSO machine learning methods to solve "small sample size problem" in research of rare congenital heart diseases. Methods A retrospective analysis of 241 patients with supra-cardiac TAPVC who underwent surgical repair in Guangdong Provincial People's Hospital from 2009 to 2019 was conducted, including 179 males and 62 females with a median surgical age of 71 (33, 232) d. Detailed clinical data of the postoperative death-related factors were extracted. Univariable Cox proportional hazard models were used to initially screen potential risk factors for postoperative death. Factors with P鈮?.05 were retained. To solve the limitation of small sample size and the "P>n" problem, we proposed a novel LASSO method for conducting multivariable Cox regression analysis that was capable of bringing in findings of related studies to improve analysis power and to reduce false-negative findings. Results 聽 聽Univariable Cox analyses showed several potential clinical risk factors, among which highly significant factors (P<0.001) included: surgical weight鈮?.5 kg (HR=16.00), main pulmonary artery diameter (HR=0.78), prolonged cardiopulmonary bypass time (HR=1.21), aortic block time (HR=1.28), and postoperative ventilator-assisted time (HR=1.13/d). LASSO multivariable analysis revealed that independent risk factors for postoperative death included cardiopulmonary bypass time (aHR=1.308/30 min), age (aHR=0.898), postoperative ventilator-assisted time (aHR=1.023/d), weight鈮?.5 kg (aHR=2.545), right vertical venous return (aHR=1.977), preoperative pulmonary venous obstruction (aHR=1.633) and emergency surgery (aHR=1.383). Conclusion 聽 聽Our proposed informed LASSO method can use previous studies' results to improve the power of analysis and effectively solve the "P>n" and small sample size limitation. Cardiopulmonary bypass time, surgical age, postoperative ventilator-assisted time, weight, right vertical venous return, preoperative pulmonary venous obstruction, and emergency surgery are risk factors for postoperative death of supra-cardiac TAPVC.
