{"title":"Paving the Path to Prevent Peripartum Hysterectomies: Risk Stratification in Placenta Accreta Spectrum.","authors":"Ashmeet Kaur, Kalpana Mangal, Ankita Kumari Sharma, Mahi Gupta, Aditi Bansal, Pritosh Yadav","doi":"10.34763/jmotherandchild.20252901.d-25-00011","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Placenta Accreta Spectrum (PAS) is a life-threatening obstetric condition with increasing incidence due to rising caesarean deliveries and assisted reproductive technologies. Our objective was to determine PAS incidence, identify risk factors, and develop a clinically relevant risk stratification model.</p><p><strong>Material and methods: </strong>A retrospective study of 85 PAS cases from 9,088 deliveries (September 2023 to September 2024, SMS Medical College, Jaipur) analysed clinical and histopathological data, including placenta praevia, multiparity, prior caesarean sections, uterine surgeries, and IVF. Cases with spontaneous placental separation were excluded.</p><p><strong>Results: </strong>PAS incidence was 0.94%. Placenta accreta, increta, and percreta were found in 35.3%, 34.1%, and 30.6% of cases, respectively. Significant risk factors included multiparity (82.4%, p < 0.001), prior caesarean sections (88.2%, p < 0.05), placenta praevia (70.6%, p = 0.002), uterine surgeries (21.17%, p < 0.05), and IVF (7.1%, p < 0.05). A PAS risk model integrating clinical predictors and region-specific weighted scoring was developed for early identification.</p><p><strong>Conclusion: </strong>PAS is a significant obstetric challenge. Identified risk factors include multiparity, prior caesarean sections, placenta praevia, uterine surgeries, and IVF. Early detection and structured referral pathways are critical for reducing maternal morbidity. This study bridges the gap between region-specific data and global PAS trends, offering a tailored, evidence-based risk stratification model for improved maternal care in resource-limited settings.</p>","PeriodicalId":73842,"journal":{"name":"Journal of mother and child","volume":"29 1","pages":"83-92"},"PeriodicalIF":0.0000,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12359127/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of mother and child","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.34763/jmotherandchild.20252901.d-25-00011","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/2/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
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Abstract
Background: Placenta Accreta Spectrum (PAS) is a life-threatening obstetric condition with increasing incidence due to rising caesarean deliveries and assisted reproductive technologies. Our objective was to determine PAS incidence, identify risk factors, and develop a clinically relevant risk stratification model.
Material and methods: A retrospective study of 85 PAS cases from 9,088 deliveries (September 2023 to September 2024, SMS Medical College, Jaipur) analysed clinical and histopathological data, including placenta praevia, multiparity, prior caesarean sections, uterine surgeries, and IVF. Cases with spontaneous placental separation were excluded.
Results: PAS incidence was 0.94%. Placenta accreta, increta, and percreta were found in 35.3%, 34.1%, and 30.6% of cases, respectively. Significant risk factors included multiparity (82.4%, p < 0.001), prior caesarean sections (88.2%, p < 0.05), placenta praevia (70.6%, p = 0.002), uterine surgeries (21.17%, p < 0.05), and IVF (7.1%, p < 0.05). A PAS risk model integrating clinical predictors and region-specific weighted scoring was developed for early identification.
Conclusion: PAS is a significant obstetric challenge. Identified risk factors include multiparity, prior caesarean sections, placenta praevia, uterine surgeries, and IVF. Early detection and structured referral pathways are critical for reducing maternal morbidity. This study bridges the gap between region-specific data and global PAS trends, offering a tailored, evidence-based risk stratification model for improved maternal care in resource-limited settings.