Predicting intensive care need in women with preeclampsia using machine learning – a pilot study
ABSTRACTBackground Predicting severe preeclampsia with need for intensive care is challenging. To better predict high-risk pregnancies to prevent adverse outcomes such as eclampsia is still an unmet need worldwide. In this study we aimed to develop a prediction model for severe outcomes using routin...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Hypertension in Pregnancy |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/10641955.2024.2312165 |
| _version_ | 1797300231668236288 |
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| author | Camilla Edvinsson Ola Björnsson Lena Erlandsson Stefan R. Hansson |
| author_facet | Camilla Edvinsson Ola Björnsson Lena Erlandsson Stefan R. Hansson |
| author_sort | Camilla Edvinsson |
| collection | DOAJ |
| description | ABSTRACTBackground Predicting severe preeclampsia with need for intensive care is challenging. To better predict high-risk pregnancies to prevent adverse outcomes such as eclampsia is still an unmet need worldwide. In this study we aimed to develop a prediction model for severe outcomes using routine biomarkers and clinical characteristics.Methods We used machine learning models based on data from an intensive care cohort with severe preeclampsia (n=41) and a cohort of preeclampsia controls (n=40) with the objective to find patterns for severe disease not detectable with traditional logistic regression models.Results The best model was generated by including the laboratory parameters aspartate aminotransferase (ASAT), uric acid and body mass index (BMI) with a cross-validation accuracy of 0.88 and an area under the curve (AUC) of 0.91. Our model was internally validated on a test-set where the accuracy was lower, 0.82, with an AUC of 0.85.Conclusion The clinical routine blood parameters ASAT and uric acid as well as BMI, were the parameters most indicative of severe disease. Aspartate aminotransferase reflects liver involvement, uric acid might be involved in several steps of the pathophysiologic process of preeclampsia, and obesity is a well-known risk factor for development of both severe and non-severe preeclampsia likely involving inflammatory pathways..[Figure: see text] |
| first_indexed | 2024-03-07T23:03:18Z |
| format | Article |
| id | doaj.art-902fb8c1b1ce409693b07bc16d50e4be |
| institution | Directory Open Access Journal |
| issn | 1064-1955 1525-6065 |
| language | English |
| last_indexed | 2024-03-07T23:03:18Z |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Hypertension in Pregnancy |
| spelling | doaj.art-902fb8c1b1ce409693b07bc16d50e4be2024-02-22T09:02:12ZengTaylor & Francis GroupHypertension in Pregnancy1064-19551525-60652024-12-0143110.1080/10641955.2024.2312165Predicting intensive care need in women with preeclampsia using machine learning – a pilot studyCamilla Edvinsson0Ola Björnsson1Lena Erlandsson2Stefan R. Hansson3Division of Obstetrics and Gynecology, Department of Clinical Sciences Lund, Lund University, Lund, SwedenDivision of Mathematical Statistics, Centre for Mathematical Sciences, Lund University, Lund, SwedenDivision of Obstetrics and Gynecology, Department of Clinical Sciences Lund, Lund University, Lund, SwedenDivision of Obstetrics and Gynecology, Department of Clinical Sciences Lund, Lund University, Lund, SwedenABSTRACTBackground Predicting severe preeclampsia with need for intensive care is challenging. To better predict high-risk pregnancies to prevent adverse outcomes such as eclampsia is still an unmet need worldwide. In this study we aimed to develop a prediction model for severe outcomes using routine biomarkers and clinical characteristics.Methods We used machine learning models based on data from an intensive care cohort with severe preeclampsia (n=41) and a cohort of preeclampsia controls (n=40) with the objective to find patterns for severe disease not detectable with traditional logistic regression models.Results The best model was generated by including the laboratory parameters aspartate aminotransferase (ASAT), uric acid and body mass index (BMI) with a cross-validation accuracy of 0.88 and an area under the curve (AUC) of 0.91. Our model was internally validated on a test-set where the accuracy was lower, 0.82, with an AUC of 0.85.Conclusion The clinical routine blood parameters ASAT and uric acid as well as BMI, were the parameters most indicative of severe disease. Aspartate aminotransferase reflects liver involvement, uric acid might be involved in several steps of the pathophysiologic process of preeclampsia, and obesity is a well-known risk factor for development of both severe and non-severe preeclampsia likely involving inflammatory pathways..[Figure: see text]https://www.tandfonline.com/doi/10.1080/10641955.2024.2312165Artificial intelligenceclinical prediction modelaspartate aminotransferaseuric acidbody mass index |
| spellingShingle | Camilla Edvinsson Ola Björnsson Lena Erlandsson Stefan R. Hansson Predicting intensive care need in women with preeclampsia using machine learning – a pilot study Hypertension in Pregnancy Artificial intelligence clinical prediction model aspartate aminotransferase uric acid body mass index |
| title | Predicting intensive care need in women with preeclampsia using machine learning – a pilot study |
| title_full | Predicting intensive care need in women with preeclampsia using machine learning – a pilot study |
| title_fullStr | Predicting intensive care need in women with preeclampsia using machine learning – a pilot study |
| title_full_unstemmed | Predicting intensive care need in women with preeclampsia using machine learning – a pilot study |
| title_short | Predicting intensive care need in women with preeclampsia using machine learning – a pilot study |
| title_sort | predicting intensive care need in women with preeclampsia using machine learning a pilot study |
| topic | Artificial intelligence clinical prediction model aspartate aminotransferase uric acid body mass index |
| url | https://www.tandfonline.com/doi/10.1080/10641955.2024.2312165 |
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