Current preeclampsia prediction model and biomarker
Preeclampsia (PE) is a serious hypertensive disorder that occurs during pregnancy and is often accompanied by proteinuria (excessive protein in the urine), posing significant risks to both maternal and neonatal health worldwide. PE is a leading cause of maternal and neonatal morbidity and mortality...
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| Format: | Article |
| Language: | English |
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Universitas Airlangga
2024-11-01
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| Series: | Majalah Obstetri dan Ginekologi |
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| Online Access: | https://e-journal.unair.ac.id/MOG/article/view/42835 |
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| _version_ | 1841557017116803072 |
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| author | Anak Agung Ngurah Jaya Kusuma |
| author_facet | Anak Agung Ngurah Jaya Kusuma |
| author_sort | Anak Agung Ngurah Jaya Kusuma |
| collection | DOAJ |
| description | Preeclampsia (PE) is a serious hypertensive disorder that occurs during pregnancy and is often accompanied by proteinuria (excessive protein in the urine), posing significant risks to both maternal and neonatal health worldwide. PE is a leading cause of maternal and neonatal morbidity and mortality and is notably challenging to predict due to its unpredictable nature and steadily rising incidence rates globally. As a result, substantial efforts have been directed toward developing predictive models and identifying biomarkers to assess the risk and progression of PE. However, existing models vary widely in their design, methodologies, and efficacy. Current prediction models recommended by notable organizations, including the National Institute for Health and Care Excellence (NICE), the American College of Obstetricians and Gynecologists (ACOG), the Fetal Medicine Foundation (FMF), and the World Health Organization (WHO), generally involve screening based on maternal characteristics and known risk factors. These include parameters such as maternal age, body mass index (BMI), number of pregnancies and births, blood pressure, and uterine arterial pulse index (UtA-PI). Additionally, biomarkers like mean arterial pressure (MAP), UtA-PI, and the ratio of soluble fms-like tyrosine kinase-1 to placental growth factor (sFlt-1/PlGF) are employed to improve predictive accuracy. Despite the diversity of predictive models and biomarkers, there is no consensus on the optimal model for PE prediction, largely due to the limitations in comparative studies and the challenges involved in cross-study comparisons. However, literature suggests that the FMF model demonstrates superior detection capacity compared to other predictive models. |
| format | Article |
| id | doaj-art-632bcf591007464eaf49cdd5adb8876f |
| institution | Kabale University |
| issn | 0854-0381 2598-1013 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Universitas Airlangga |
| record_format | Article |
| series | Majalah Obstetri dan Ginekologi |
| spelling | doaj-art-632bcf591007464eaf49cdd5adb8876f2025-01-07T03:23:16ZengUniversitas AirlanggaMajalah Obstetri dan Ginekologi0854-03812598-10132024-11-0132321422210.20473/mog.V32I32024.214-22240917Current preeclampsia prediction model and biomarkerAnak Agung Ngurah Jaya Kusuma0https://orcid.org/0000-0002-2934-0223Department of Obstetrics and Gynecology, Prof. Dr. I.G.N.G. Ngoerah Hospital/ Facuty of Medicine, Universitas Udayana, BaliPreeclampsia (PE) is a serious hypertensive disorder that occurs during pregnancy and is often accompanied by proteinuria (excessive protein in the urine), posing significant risks to both maternal and neonatal health worldwide. PE is a leading cause of maternal and neonatal morbidity and mortality and is notably challenging to predict due to its unpredictable nature and steadily rising incidence rates globally. As a result, substantial efforts have been directed toward developing predictive models and identifying biomarkers to assess the risk and progression of PE. However, existing models vary widely in their design, methodologies, and efficacy. Current prediction models recommended by notable organizations, including the National Institute for Health and Care Excellence (NICE), the American College of Obstetricians and Gynecologists (ACOG), the Fetal Medicine Foundation (FMF), and the World Health Organization (WHO), generally involve screening based on maternal characteristics and known risk factors. These include parameters such as maternal age, body mass index (BMI), number of pregnancies and births, blood pressure, and uterine arterial pulse index (UtA-PI). Additionally, biomarkers like mean arterial pressure (MAP), UtA-PI, and the ratio of soluble fms-like tyrosine kinase-1 to placental growth factor (sFlt-1/PlGF) are employed to improve predictive accuracy. Despite the diversity of predictive models and biomarkers, there is no consensus on the optimal model for PE prediction, largely due to the limitations in comparative studies and the challenges involved in cross-study comparisons. However, literature suggests that the FMF model demonstrates superior detection capacity compared to other predictive models.https://e-journal.unair.ac.id/MOG/article/view/42835biomarkerpreeclampsiaprediction modelmaternal health |
| spellingShingle | Anak Agung Ngurah Jaya Kusuma Current preeclampsia prediction model and biomarker Majalah Obstetri dan Ginekologi biomarker preeclampsia prediction model maternal health |
| title | Current preeclampsia prediction model and biomarker |
| title_full | Current preeclampsia prediction model and biomarker |
| title_fullStr | Current preeclampsia prediction model and biomarker |
| title_full_unstemmed | Current preeclampsia prediction model and biomarker |
| title_short | Current preeclampsia prediction model and biomarker |
| title_sort | current preeclampsia prediction model and biomarker |
| topic | biomarker preeclampsia prediction model maternal health |
| url | https://e-journal.unair.ac.id/MOG/article/view/42835 |
| work_keys_str_mv | AT anakagungngurahjayakusuma currentpreeclampsiapredictionmodelandbiomarker |