Chenyan Huang, Xi Long, Myrthe van der Ven, Maurits Kaptein, S Guid Oei, Edwin van den Heuvel
{"title":"Predicting preterm birth using electronic medical records from multiple prenatal visits.","authors":"Chenyan Huang, Xi Long, Myrthe van der Ven, Maurits Kaptein, S Guid Oei, Edwin van den Heuvel","doi":"10.1186/s12884-024-07049-y","DOIUrl":null,"url":null,"abstract":"<p><p>This study aimed to predict preterm birth in nulliparous women using machine learning and easily accessible variables from prenatal visits. Elastic net regularized logistic regression models were developed and evaluated using 5-fold cross-validation on data from 8,830 women in the Nulliparous Pregnancy Outcomes Study: New Mothers-to-Be (nuMoM2b) dataset at three prenatal visits: <math><msup><mn>6</mn> <mn>0</mn></msup> </math> - <math><msup><mn>13</mn> <mn>6</mn></msup> </math> , <math><msup><mn>16</mn> <mn>0</mn></msup> </math> - <math><msup><mn>21</mn> <mn>6</mn></msup> </math> , and <math><msup><mn>22</mn> <mn>0</mn></msup> </math> - <math><msup><mn>29</mn> <mn>6</mn></msup> </math> weeks of gestational age (GA). The models' performance, assessed using Area Under the Curve (AUC), sensitivity, specificity, and accuracy, consistently improved with the incorporation of data from later prenatal visits. AUC scores increased from 0.6161 in the first visit to 0.7087 in the third visit, while sensitivity and specificity also showed notable improvements. The addition of ultrasound measurements, such as cervical length and Pulsatility Index, substantially enhanced the models' predictive ability. Notably, the model achieved a sensitivity of 0.8254 and 0.9295 for predicting very preterm and extreme preterm births, respectively, at the third prenatal visit. These findings highlight the importance of ultrasound measurements and suggest that incorporating machine learning-based risk assessment and routine late-pregnancy ultrasounds into prenatal care could improve maternal and neonatal outcomes by enabling timely interventions for high-risk women.</p>","PeriodicalId":9033,"journal":{"name":"BMC Pregnancy and Childbirth","volume":"24 1","pages":"843"},"PeriodicalIF":2.8000,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Pregnancy and Childbirth","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12884-024-07049-y","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OBSTETRICS & GYNECOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
This study aimed to predict preterm birth in nulliparous women using machine learning and easily accessible variables from prenatal visits. Elastic net regularized logistic regression models were developed and evaluated using 5-fold cross-validation on data from 8,830 women in the Nulliparous Pregnancy Outcomes Study: New Mothers-to-Be (nuMoM2b) dataset at three prenatal visits: - , - , and - weeks of gestational age (GA). The models' performance, assessed using Area Under the Curve (AUC), sensitivity, specificity, and accuracy, consistently improved with the incorporation of data from later prenatal visits. AUC scores increased from 0.6161 in the first visit to 0.7087 in the third visit, while sensitivity and specificity also showed notable improvements. The addition of ultrasound measurements, such as cervical length and Pulsatility Index, substantially enhanced the models' predictive ability. Notably, the model achieved a sensitivity of 0.8254 and 0.9295 for predicting very preterm and extreme preterm births, respectively, at the third prenatal visit. These findings highlight the importance of ultrasound measurements and suggest that incorporating machine learning-based risk assessment and routine late-pregnancy ultrasounds into prenatal care could improve maternal and neonatal outcomes by enabling timely interventions for high-risk women.
期刊介绍:
BMC Pregnancy & Childbirth is an open access, peer-reviewed journal that considers articles on all aspects of pregnancy and childbirth. The journal welcomes submissions on the biomedical aspects of pregnancy, breastfeeding, labor, maternal health, maternity care, trends and sociological aspects of pregnancy and childbirth.