Paper
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Authors: Jonathan Schor, Adesh Kadambi, Isabel Fulcher, Kartik Venkatesh, Mark Clapp, Senan Ebrahim, Ali Ebrahim, Timothy Wen*
Journal: AJOG Global Reports
Key Findings: The objective of the paper was to develop and internally validate a machine learning prediction model for hypertensive disorders of pregnancy (HDP) when initiating prenatal care. Among individuals in the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b) cohort, 21% (n=1,927) developed HDP. The prediction model for HDP had satisfactory discrimination with an area under the curve (AUC) of 0.73 (95% CI: 0.70, 0.75). Sensitivity analysis noted superior sensitivity (AUC 0.80 vs 0.65) and specificity (0.65 vs 0.53) of the model compared to the “Clinical Risk Assessment for Preeclampsia” recommended by the American College of Obstetrics and Gynecology and U.S. Preventive Services Task Force.
Paper
Authors: Jonathan Schor, Adesh Kadambi, Isabel Fulcher, Kartik Venkatesh, Mark Clapp, Senan Ebrahim, Ali Ebrahim, Timothy Wen*
Journal: AJOG Global Reports
Key Findings: The objective of the paper was to develop and internally validate a machine learning prediction model for hypertensive disorders of pregnancy (HDP) when initiating prenatal care. Among individuals in the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b) cohort, 21% (n=1,927) developed HDP. The prediction model for HDP had satisfactory discrimination with an area under the curve (AUC) of 0.73 (95% CI: 0.70, 0.75). Sensitivity analysis noted superior sensitivity (AUC 0.80 vs 0.65) and specificity (0.65 vs 0.53) of the model compared to the “Clinical Risk Assessment for Preeclampsia” recommended by the American College of Obstetrics and Gynecology and U.S. Preventive Services Task Force.
Paper
Authors: Jonathan Schor, Adesh Kadambi, Isabel Fulcher, Kartik Venkatesh, Mark Clapp, Senan Ebrahim, Ali Ebrahim, Timothy Wen*
Journal: AJOG Global Reports
Key Findings: The objective of the paper was to develop and internally validate a machine learning prediction model for hypertensive disorders of pregnancy (HDP) when initiating prenatal care. Among individuals in the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b) cohort, 21% (n=1,927) developed HDP. The prediction model for HDP had satisfactory discrimination with an area under the curve (AUC) of 0.73 (95% CI: 0.70, 0.75). Sensitivity analysis noted superior sensitivity (AUC 0.80 vs 0.65) and specificity (0.65 vs 0.53) of the model compared to the “Clinical Risk Assessment for Preeclampsia” recommended by the American College of Obstetrics and Gynecology and U.S. Preventive Services Task Force.
Paper
Authors: Jonathan Schor, Adesh Kadambi, Isabel Fulcher, Kartik Venkatesh, Mark Clapp, Senan Ebrahim, Ali Ebrahim, Timothy Wen*
Journal: AJOG Global Reports
Key Findings: The objective of the paper was to develop and internally validate a machine learning prediction model for hypertensive disorders of pregnancy (HDP) when initiating prenatal care. Among individuals in the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b) cohort, 21% (n=1,927) developed HDP. The prediction model for HDP had satisfactory discrimination with an area under the curve (AUC) of 0.73 (95% CI: 0.70, 0.75). Sensitivity analysis noted superior sensitivity (AUC 0.80 vs 0.65) and specificity (0.65 vs 0.53) of the model compared to the “Clinical Risk Assessment for Preeclampsia” recommended by the American College of Obstetrics and Gynecology and U.S. Preventive Services Task Force.
Paper
Authors: Jonathan Schor, Adesh Kadambi, Isabel Fulcher, Kartik Venkatesh, Mark Clapp, Senan Ebrahim, Ali Ebrahim, Timothy Wen*
Journal: AJOG Global Reports
Key Findings: The objective of the paper was to develop and internally validate a machine learning prediction model for hypertensive disorders of pregnancy (HDP) when initiating prenatal care. Among individuals in the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b) cohort, 21% (n=1,927) developed HDP. The prediction model for HDP had satisfactory discrimination with an area under the curve (AUC) of 0.73 (95% CI: 0.70, 0.75). Sensitivity analysis noted superior sensitivity (AUC 0.80 vs 0.65) and specificity (0.65 vs 0.53) of the model compared to the “Clinical Risk Assessment for Preeclampsia” recommended by the American College of Obstetrics and Gynecology and U.S. Preventive Services Task Force.
Paper
