Predicting the Risk of Adverse Events in Pregnant Women With Congenital Heart Disease
Background Women with congenital heart disease are considered at high risk for adverse events. Therefore, we aim to establish 2 prediction models for mothers and their offspring, which can predict the risk of adverse events occurred in pregnant women with congenital heart disease. Methods and Result...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-07-01
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| Series: | Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease |
| Subjects: | |
| Online Access: | https://www.ahajournals.org/doi/10.1161/JAHA.120.016371 |
| _version_ | 1819175157275557888 |
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| author | Ran Chu Wei Chen Guangmin Song Shu Yao Lin Xie Li Song Yue Zhang Lijun Chen Xiangli Zhang Yuyan Ma Xia Luo Yuan Liu Ping Sun Shuquan Zhang Yan Fang Taotao Dong Qing Zhang Jin Peng Lu Zhang Yuan Wei Wenxia Zhang Xuantao Su Xu Qiao Kun Song Xingsheng Yang Beihua Kong |
| author_facet | Ran Chu Wei Chen Guangmin Song Shu Yao Lin Xie Li Song Yue Zhang Lijun Chen Xiangli Zhang Yuyan Ma Xia Luo Yuan Liu Ping Sun Shuquan Zhang Yan Fang Taotao Dong Qing Zhang Jin Peng Lu Zhang Yuan Wei Wenxia Zhang Xuantao Su Xu Qiao Kun Song Xingsheng Yang Beihua Kong |
| author_sort | Ran Chu |
| collection | DOAJ |
| description | Background Women with congenital heart disease are considered at high risk for adverse events. Therefore, we aim to establish 2 prediction models for mothers and their offspring, which can predict the risk of adverse events occurred in pregnant women with congenital heart disease. Methods and Results A total of 318 pregnant women with congenital heart disease were included; 213 women were divided into the development cohort, and 105 women were divided into the validation cohort. Least absolute shrinkage and selection operator was used for predictor selection. After validation, multivariate logistic regression analysis was used to develop the model. Machine learning algorithms (support vector machine, random forest, AdaBoost, decision tree, k‐nearest neighbor, naïve Bayes, and multilayer perceptron) were used to further verify the predictive ability of the model. Forty‐one (12.9%) women experienced adverse maternal events, and 93 (29.2%) neonates experienced adverse neonatal events. Seven high‐risk factors were discovered in the maternal model, including New York Heart Association class, Eisenmenger syndrome, pulmonary hypertension, left ventricular ejection fraction, sinus tachycardia, arterial blood oxygen saturation, and pregnancy duration. The machine learning–based algorithms showed that the maternal model had an accuracy of 0.76 to 0.86 (area under the receiver operating characteristic curve=0.74–0.87) in the development cohort, and 0.72 to 0.86 (area under the receiver operating characteristic curve=0.68–0.80) in the validation cohort. Three high‐risk factors were discovered in the neonatal model, including Eisenmenger syndrome, preeclampsia, and arterial blood oxygen saturation. The machine learning–based algorithms showed that the neonatal model had an accuracy of 0.75 to 0.80 (area under the receiver operating characteristic curve=0.71–0.77) in the development cohort, and 0.72 to 0.79 (area under the receiver operating characteristic curve=0.69–0.76) in the validation cohort. Conclusions Two prenatal risk assessment models for both adverse maternal and neonatal events were established, which might assist clinicians in tailoring precise management and therapy in pregnant women with congenital heart disease. |
| first_indexed | 2024-12-22T20:50:24Z |
| format | Article |
| id | doaj.art-7d6c5a4d75504f4f86d56326eb3f86c5 |
| institution | Directory Open Access Journal |
| issn | 2047-9980 |
| language | English |
| last_indexed | 2024-12-22T20:50:24Z |
| publishDate | 2020-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease |
| spelling | doaj.art-7d6c5a4d75504f4f86d56326eb3f86c52022-12-21T18:13:06ZengWileyJournal of the American Heart Association: Cardiovascular and Cerebrovascular Disease2047-99802020-07-0191410.1161/JAHA.120.016371Predicting the Risk of Adverse Events in Pregnant Women With Congenital Heart DiseaseRan Chu0Wei Chen1Guangmin Song2Shu Yao3Lin Xie4Li Song5Yue Zhang6Lijun Chen7Xiangli Zhang8Yuyan Ma9Xia Luo10Yuan Liu11Ping Sun12Shuquan Zhang13Yan Fang14Taotao Dong15Qing Zhang16Jin Peng17Lu Zhang18Yuan Wei19Wenxia Zhang20Xuantao Su21Xu Qiao22Kun Song23Xingsheng Yang24Beihua Kong25Department of Obstetrics and Gynecology Qilu Hospital Cheeloo College of Medicine Shandong University Jinan Shandong ChinaSchool of Control Science and Engineering Shandong University Jinan Shandong ChinaDepartment of Cardiovascular Surgery Qilu Hospital Cheeloo College of Medicine Shandong University Jinan Shandong ChinaDepartment of Obstetrics and Gynecology Qilu Hospital Cheeloo College of Medicine Shandong University Jinan Shandong ChinaDepartment of Obstetrics and Gynecology Qilu Hospital Cheeloo College of Medicine Shandong University Jinan Shandong ChinaDepartment of Obstetrics and Gynecology Qilu Hospital Cheeloo College of Medicine Shandong University Jinan Shandong ChinaDepartment of Obstetrics and Gynecology Qilu Hospital Cheeloo College of Medicine Shandong University Jinan Shandong ChinaDepartment of Obstetrics and Gynecology Qilu Hospital Cheeloo College of Medicine Shandong University Jinan Shandong ChinaDepartment of Obstetrics and Gynecology Qilu Hospital Cheeloo College of Medicine Shandong University Jinan Shandong ChinaDepartment of Obstetrics and Gynecology Qilu Hospital Cheeloo College of Medicine Shandong University Jinan Shandong ChinaDepartment of Obstetrics and Gynecology Qilu Hospital Cheeloo College of Medicine Shandong University Jinan Shandong ChinaDepartment of Obstetrics and Gynecology Qilu Hospital Cheeloo College of Medicine Shandong University Jinan Shandong ChinaDepartment of Obstetrics and Gynecology Qilu Hospital Cheeloo College of Medicine Shandong University Jinan Shandong ChinaDepartment of Obstetrics and Gynecology Qilu Hospital Cheeloo College of Medicine Shandong University Jinan Shandong ChinaDepartment of Obstetrics and Gynecology Qilu Hospital Cheeloo College of Medicine Shandong University Jinan Shandong ChinaDepartment of Obstetrics and Gynecology Qilu Hospital Cheeloo College of Medicine Shandong University Jinan Shandong ChinaDepartment of Obstetrics and Gynecology Qilu Hospital Cheeloo College of Medicine Shandong University Jinan Shandong ChinaDepartment of Obstetrics and Gynecology Qilu Hospital Cheeloo College of Medicine Shandong University Jinan Shandong ChinaDepartment of Obstetrics and Gynecology Qilu Hospital Cheeloo College of Medicine Shandong University Jinan Shandong ChinaDepartment of Obstetrics and Gynecology Qilu Hospital Cheeloo College of Medicine Shandong University Jinan Shandong ChinaDepartment of Obstetrics and Gynecology Qilu Hospital Cheeloo College of Medicine Shandong University Jinan Shandong ChinaSchool of Control Science and Engineering Shandong University Jinan Shandong ChinaSchool of Control Science and Engineering Shandong University Jinan Shandong ChinaDepartment of Obstetrics and Gynecology Qilu Hospital Cheeloo College of Medicine Shandong University Jinan Shandong ChinaDepartment of Obstetrics and Gynecology Qilu Hospital Cheeloo College of Medicine Shandong University Jinan Shandong ChinaDepartment of Obstetrics and Gynecology Qilu Hospital Cheeloo College of Medicine Shandong University Jinan Shandong ChinaBackground Women with congenital heart disease are considered at high risk for adverse events. Therefore, we aim to establish 2 prediction models for mothers and their offspring, which can predict the risk of adverse events occurred in pregnant women with congenital heart disease. Methods and Results A total of 318 pregnant women with congenital heart disease were included; 213 women were divided into the development cohort, and 105 women were divided into the validation cohort. Least absolute shrinkage and selection operator was used for predictor selection. After validation, multivariate logistic regression analysis was used to develop the model. Machine learning algorithms (support vector machine, random forest, AdaBoost, decision tree, k‐nearest neighbor, naïve Bayes, and multilayer perceptron) were used to further verify the predictive ability of the model. Forty‐one (12.9%) women experienced adverse maternal events, and 93 (29.2%) neonates experienced adverse neonatal events. Seven high‐risk factors were discovered in the maternal model, including New York Heart Association class, Eisenmenger syndrome, pulmonary hypertension, left ventricular ejection fraction, sinus tachycardia, arterial blood oxygen saturation, and pregnancy duration. The machine learning–based algorithms showed that the maternal model had an accuracy of 0.76 to 0.86 (area under the receiver operating characteristic curve=0.74–0.87) in the development cohort, and 0.72 to 0.86 (area under the receiver operating characteristic curve=0.68–0.80) in the validation cohort. Three high‐risk factors were discovered in the neonatal model, including Eisenmenger syndrome, preeclampsia, and arterial blood oxygen saturation. The machine learning–based algorithms showed that the neonatal model had an accuracy of 0.75 to 0.80 (area under the receiver operating characteristic curve=0.71–0.77) in the development cohort, and 0.72 to 0.79 (area under the receiver operating characteristic curve=0.69–0.76) in the validation cohort. Conclusions Two prenatal risk assessment models for both adverse maternal and neonatal events were established, which might assist clinicians in tailoring precise management and therapy in pregnant women with congenital heart disease.https://www.ahajournals.org/doi/10.1161/JAHA.120.016371congenital heart diseasemachine learningprediction modelpregnancy |
| spellingShingle | Ran Chu Wei Chen Guangmin Song Shu Yao Lin Xie Li Song Yue Zhang Lijun Chen Xiangli Zhang Yuyan Ma Xia Luo Yuan Liu Ping Sun Shuquan Zhang Yan Fang Taotao Dong Qing Zhang Jin Peng Lu Zhang Yuan Wei Wenxia Zhang Xuantao Su Xu Qiao Kun Song Xingsheng Yang Beihua Kong Predicting the Risk of Adverse Events in Pregnant Women With Congenital Heart Disease Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease congenital heart disease machine learning prediction model pregnancy |
| title | Predicting the Risk of Adverse Events in Pregnant Women With Congenital Heart Disease |
| title_full | Predicting the Risk of Adverse Events in Pregnant Women With Congenital Heart Disease |
| title_fullStr | Predicting the Risk of Adverse Events in Pregnant Women With Congenital Heart Disease |
| title_full_unstemmed | Predicting the Risk of Adverse Events in Pregnant Women With Congenital Heart Disease |
| title_short | Predicting the Risk of Adverse Events in Pregnant Women With Congenital Heart Disease |
| title_sort | predicting the risk of adverse events in pregnant women with congenital heart disease |
| topic | congenital heart disease machine learning prediction model pregnancy |
| url | https://www.ahajournals.org/doi/10.1161/JAHA.120.016371 |
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