Ann M Bruno, Grecio J Sandoval, Brenna L Hughes, William A Grobman, George R Saade, Tracy A Manuck, Monica Longo, Hyagriv N Simhan, Dwight J Rouse, Hector Mendez-Figueroa, Cynthia Gyamfi-Bannerman, Jennifer L Bailit, Maged M Costantine, Harish M Sehdev, Alan T N Tita
{"title":"用于预测严重产妇发病率的扩展产妇共病指数的验证。","authors":"Ann M Bruno, Grecio J Sandoval, Brenna L Hughes, William A Grobman, George R Saade, Tracy A Manuck, Monica Longo, Hyagriv N Simhan, Dwight J Rouse, Hector Mendez-Figueroa, Cynthia Gyamfi-Bannerman, Jennifer L Bailit, Maged M Costantine, Harish M Sehdev, Alan T N Tita","doi":"10.1097/AOG.0000000000005971","DOIUrl":null,"url":null,"abstract":"<p><p>The expanded maternal comorbidity index developed by Leonard et al uses pre-existing maternal health conditions (eg, hypertension, asthma) to produce a risk score that predicts severe maternal morbidity (SMM). This tool has been adopted into clinical and research use without external validation in a data source not reliant on administrative codes. We assessed the validity of the maternal comorbidity index to predict SMM in a modern obstetric cohort using data derived from detailed medical record abstraction. In this secondary analysis of a multicenter cohort of patients delivering at 17 U.S. hospitals (2019-2020), the maternal comorbidity index risk score was applied to all individuals and the performance of the score to predict SMM was assessed using the area under the receiver operating curve (AUC). Of 20,898 individuals in this cohort, 668 (3.2%) experienced SMM. The AUC for the maternal comorbidity index was 0.72 (95% CI, 0.70-0.74) to predict SMM and 0.83 (95% CI, 0.79-0.86) to predict SMM without transfusion. The expanded maternal comorbidity index for prediction of SMM was externally valid, and findings support the ongoing use of this tool.</p>","PeriodicalId":19483,"journal":{"name":"Obstetrics and gynecology","volume":" ","pages":""},"PeriodicalIF":5.7000,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Validation of an Extended Maternal Comorbidity Index for Prediction of Severe Maternal Morbidity.\",\"authors\":\"Ann M Bruno, Grecio J Sandoval, Brenna L Hughes, William A Grobman, George R Saade, Tracy A Manuck, Monica Longo, Hyagriv N Simhan, Dwight J Rouse, Hector Mendez-Figueroa, Cynthia Gyamfi-Bannerman, Jennifer L Bailit, Maged M Costantine, Harish M Sehdev, Alan T N Tita\",\"doi\":\"10.1097/AOG.0000000000005971\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The expanded maternal comorbidity index developed by Leonard et al uses pre-existing maternal health conditions (eg, hypertension, asthma) to produce a risk score that predicts severe maternal morbidity (SMM). This tool has been adopted into clinical and research use without external validation in a data source not reliant on administrative codes. We assessed the validity of the maternal comorbidity index to predict SMM in a modern obstetric cohort using data derived from detailed medical record abstraction. In this secondary analysis of a multicenter cohort of patients delivering at 17 U.S. hospitals (2019-2020), the maternal comorbidity index risk score was applied to all individuals and the performance of the score to predict SMM was assessed using the area under the receiver operating curve (AUC). Of 20,898 individuals in this cohort, 668 (3.2%) experienced SMM. The AUC for the maternal comorbidity index was 0.72 (95% CI, 0.70-0.74) to predict SMM and 0.83 (95% CI, 0.79-0.86) to predict SMM without transfusion. The expanded maternal comorbidity index for prediction of SMM was externally valid, and findings support the ongoing use of this tool.</p>\",\"PeriodicalId\":19483,\"journal\":{\"name\":\"Obstetrics and gynecology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":5.7000,\"publicationDate\":\"2025-06-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Obstetrics and gynecology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1097/AOG.0000000000005971\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"OBSTETRICS & GYNECOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Obstetrics and gynecology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/AOG.0000000000005971","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OBSTETRICS & GYNECOLOGY","Score":null,"Total":0}
Validation of an Extended Maternal Comorbidity Index for Prediction of Severe Maternal Morbidity.
The expanded maternal comorbidity index developed by Leonard et al uses pre-existing maternal health conditions (eg, hypertension, asthma) to produce a risk score that predicts severe maternal morbidity (SMM). This tool has been adopted into clinical and research use without external validation in a data source not reliant on administrative codes. We assessed the validity of the maternal comorbidity index to predict SMM in a modern obstetric cohort using data derived from detailed medical record abstraction. In this secondary analysis of a multicenter cohort of patients delivering at 17 U.S. hospitals (2019-2020), the maternal comorbidity index risk score was applied to all individuals and the performance of the score to predict SMM was assessed using the area under the receiver operating curve (AUC). Of 20,898 individuals in this cohort, 668 (3.2%) experienced SMM. The AUC for the maternal comorbidity index was 0.72 (95% CI, 0.70-0.74) to predict SMM and 0.83 (95% CI, 0.79-0.86) to predict SMM without transfusion. The expanded maternal comorbidity index for prediction of SMM was externally valid, and findings support the ongoing use of this tool.
期刊介绍:
"Obstetrics & Gynecology," affectionately known as "The Green Journal," is the official publication of the American College of Obstetricians and Gynecologists (ACOG). Since its inception in 1953, the journal has been dedicated to advancing the clinical practice of obstetrics and gynecology, as well as related fields. The journal's mission is to promote excellence in these areas by publishing a diverse range of articles that cover translational and clinical topics.
"Obstetrics & Gynecology" provides a platform for the dissemination of evidence-based research, clinical guidelines, and expert opinions that are essential for the continuous improvement of women's health care. The journal's content is designed to inform and educate obstetricians, gynecologists, and other healthcare professionals, ensuring that they stay abreast of the latest developments and best practices in their field.