Shehla Baqai, Shazia Tufail, Anam Waheed, Qurat Ul Ain Hanif
{"title":"使用三个参数优化早期子痫前期筛查。","authors":"Shehla Baqai, Shazia Tufail, Anam Waheed, Qurat Ul Ain Hanif","doi":"10.29271/jcpsp.2023.09.995","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>To evaluate the performance of first-trimester preeclampsia-screening algorithm in predicting preeclampsia (PE).</p><p><strong>Study design: </strong>Observational study. Place and Duration of the Study: Department of Obstetrics and Gynaecology, Combined Military Hospitals (CMH) Lahore, Pakistan, between 1st January and 31st August 2022.</p><p><strong>Methodology: </strong>Data of 100 women of any parity aged 18-35 years at gestational age < 13 weeks based on the last menstrual period (LMP), was analysed. First trimester Fetal Medicine Foundation (FMF) screening algorithm for preeclampsia was used entering maternal characteristics, mean arterial pressure and uterine pulsatility index only, for risk calculation. Patients were followed up till delivery for the development of preeclampsia and fetomaternal outcomes. Clinical characteristics of women with and without preeclampsia were compared using the Chi-square and independent samples t-test.</p><p><strong>Results: </strong>The mean age of patients was 29.29±4.56 years and 60% were nullipara. Seventy-eight patients were placed in the low-risk category and 22 patients were in the high-risk category according to the FMF algorithm. Preeclampsia developed in 13 patients. For a risk cut-off of 1 in 100, the FMF algorithm showed a detection rate of 38% with diagnostic accuracy of 75% and a false positive rate (FPR) of 20%.</p><p><strong>Conclusion: </strong>Although the performance of adapted FMF algorithm to predict preeclampsia gestational was low, it was found superior to prediction by maternal risk factors alone. Adjustment for additional factors or ethnicity-specific values may help in further improvement of detection rate.</p><p><strong>Key words: </strong>Blood pressure, Biomarkers, Biological markers, Preeclampsia, Risk assessment.</p>","PeriodicalId":0,"journal":{"name":"","volume":"33 9","pages":"995-1000"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimising Preeclampsia First-Trimester Screening Using Three Parameters.\",\"authors\":\"Shehla Baqai, Shazia Tufail, Anam Waheed, Qurat Ul Ain Hanif\",\"doi\":\"10.29271/jcpsp.2023.09.995\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>To evaluate the performance of first-trimester preeclampsia-screening algorithm in predicting preeclampsia (PE).</p><p><strong>Study design: </strong>Observational study. Place and Duration of the Study: Department of Obstetrics and Gynaecology, Combined Military Hospitals (CMH) Lahore, Pakistan, between 1st January and 31st August 2022.</p><p><strong>Methodology: </strong>Data of 100 women of any parity aged 18-35 years at gestational age < 13 weeks based on the last menstrual period (LMP), was analysed. First trimester Fetal Medicine Foundation (FMF) screening algorithm for preeclampsia was used entering maternal characteristics, mean arterial pressure and uterine pulsatility index only, for risk calculation. Patients were followed up till delivery for the development of preeclampsia and fetomaternal outcomes. Clinical characteristics of women with and without preeclampsia were compared using the Chi-square and independent samples t-test.</p><p><strong>Results: </strong>The mean age of patients was 29.29±4.56 years and 60% were nullipara. Seventy-eight patients were placed in the low-risk category and 22 patients were in the high-risk category according to the FMF algorithm. Preeclampsia developed in 13 patients. For a risk cut-off of 1 in 100, the FMF algorithm showed a detection rate of 38% with diagnostic accuracy of 75% and a false positive rate (FPR) of 20%.</p><p><strong>Conclusion: </strong>Although the performance of adapted FMF algorithm to predict preeclampsia gestational was low, it was found superior to prediction by maternal risk factors alone. Adjustment for additional factors or ethnicity-specific values may help in further improvement of detection rate.</p><p><strong>Key words: </strong>Blood pressure, Biomarkers, Biological markers, Preeclampsia, Risk assessment.</p>\",\"PeriodicalId\":0,\"journal\":{\"name\":\"\",\"volume\":\"33 9\",\"pages\":\"995-1000\"},\"PeriodicalIF\":0.0,\"publicationDate\":\"2023-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.29271/jcpsp.2023.09.995\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.29271/jcpsp.2023.09.995","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimising Preeclampsia First-Trimester Screening Using Three Parameters.
Objective: To evaluate the performance of first-trimester preeclampsia-screening algorithm in predicting preeclampsia (PE).
Study design: Observational study. Place and Duration of the Study: Department of Obstetrics and Gynaecology, Combined Military Hospitals (CMH) Lahore, Pakistan, between 1st January and 31st August 2022.
Methodology: Data of 100 women of any parity aged 18-35 years at gestational age < 13 weeks based on the last menstrual period (LMP), was analysed. First trimester Fetal Medicine Foundation (FMF) screening algorithm for preeclampsia was used entering maternal characteristics, mean arterial pressure and uterine pulsatility index only, for risk calculation. Patients were followed up till delivery for the development of preeclampsia and fetomaternal outcomes. Clinical characteristics of women with and without preeclampsia were compared using the Chi-square and independent samples t-test.
Results: The mean age of patients was 29.29±4.56 years and 60% were nullipara. Seventy-eight patients were placed in the low-risk category and 22 patients were in the high-risk category according to the FMF algorithm. Preeclampsia developed in 13 patients. For a risk cut-off of 1 in 100, the FMF algorithm showed a detection rate of 38% with diagnostic accuracy of 75% and a false positive rate (FPR) of 20%.
Conclusion: Although the performance of adapted FMF algorithm to predict preeclampsia gestational was low, it was found superior to prediction by maternal risk factors alone. Adjustment for additional factors or ethnicity-specific values may help in further improvement of detection rate.