دورية أكاديمية
Prediction of clinical outcomes in women with placenta accreta spectrum using machine learning models: an international multicenter study
| العنوان: | Prediction of clinical outcomes in women with placenta accreta spectrum using machine learning models: an international multicenter study |
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| المؤلفون: | Shazly, S.A., Hortu, I., Shih, J.-C., Melekoğlu, R., Fan, S., Ahmed, F.U.A., Karaman, E. |
| بيانات النشر: | Taylor and Francis Ltd. |
| سنة النشر: | 2022 |
| المجموعة: | Ege University Institutional Repository |
| مصطلحات موضوعية: | cesarean hysterectomy, machine learning, morbidly adherent placenta, Obstetric hemorrhage, placenta accreta spectrum, placenta praevia, hemoglobin, adult, area under the curve, Article, bleeding, blood transfusion, cesarean section, clinical feature, clinical outcome, cohort analysis, computer language, controlled study, demographics, diagnostic test accuracy study, disseminated intravascular clotting, female, follow up, hospital admission, hospitalization, human, hysterectomy, intensive care unit, morbidity, multicenter study (topic) |
| الوصف: | Introduction: Placenta accreta spectrum is a major obstetric disorder that is associated with significant morbidity and mortality. The objective of this study is to establish a prediction model of clinical outcomes in these women Materials and methods: PAS-ID is an international multicenter study that comprises 11 centers from 9 countries. Women who were diagnosed with PAS and were managed in the recruiting centers between 1 January 2010 and 31 December 2019 were included. Data were reanalyzed using machine learning (ML) models, and 2 models were created to predict outcomes using antepartum and perioperative features. ML model was conducted using python® programing language. The primary outcome was massive PAS-associated perioperative blood loss (intraoperative blood loss ?2500 ml, triggering massive transfusion protocol, or complicated by disseminated intravascular coagulopathy). Other outcomes include prolonged hospitalization >7 days and admission to the intensive care unit (ICU). Results: 727 women with PAS were included. The area under curve (AUC) for ML antepartum prediction model was 0.84, 0.81, and 0.82 for massive blood loss, prolonged hospitalization, and admission to ICU, respectively. Significant contributors to this model were parity, placental site, method of diagnosis, and antepartum hemoglobin. Combining baseline and perioperative variables, the ML model performed at 0.86, 0.90, and 0.86 for study outcomes, respectively. Ethnicity, pelvic invasion, and uterine incision were the most predictive factors in this model. Discussion: ML models can be used to calculate the individualized risk of morbidity in women with PAS. Model-based risk assessment facilitates a priori delineation of management. © 2021 Informa UK Limited, trading as Taylor & Francis Group. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 1476-7058 34233555 |
| العلاقة: | Journal of Maternal-Fetal and Neonatal Medicine; Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı; https://doi.org/10.1080/14767058.2021.1918670Test; https://hdl.handle.net/11454/86599Test; 35; 25; 6644; 6653; 2-s2.0-85111741739 |
| DOI: | 10.1080/14767058.2021.1918670 |
| الإتاحة: | https://doi.org/10.1080/14767058.2021.1918670Test https://hdl.handle.net/11454/86599Test |
| حقوق: | info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.42FC3E19 |
| قاعدة البيانات: | BASE |
| تدمد: | 14767058 34233555 |
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| DOI: | 10.1080/14767058.2021.1918670 |