The latest medical research on Obstetrics And Gynecology

To identify which components of maternal vascular malperfusion (MVM) pathology are associated with adverse pregnancy outcomes and to investigate the morphological phenotypes of MVM placental pathology and their relationship with distinct clinical presentations of pre-eclampsia and/or fetal growth restriction (FGR).

Preterm delivery <34+0 weeks of gestation, early onset pre-eclampsia with delivery <34+0 weeks of gestation, birthweight <10th percentile (small for gestational age, SGA) and stillbirth.

The diagnostic features of MVM most strongly associated with delivery <34+0 weeks of gestation were: infarction, accelerated villous maturation, distal villous hypoplasia and decidual vasculopathy. Two dominant phenotypic clusters of MVM pathology were identified. The largest cluster (n = 104) was characterised by both reduced placental mass and hypoxic ischaemic injury (infarction and accelerated villous maturation), and was associated with combined pre-eclampsia and SGA. The second dominant cluster (n = 59) was characterised by infarction and accelerated villous maturation alone, and was associated with pre-eclampsia and average birthweight for gestational age.

Patients with placental MVM disease are at high risk of pre-eclampsia and FGR, and distinct pathological findings correlate with different clinical phenotypes, suggestive of distinct subtypes of MVM disease.

To develop and validate a predictive model for postpartum hemorrhage that can be deployed in clinical care using automated, real-time electronic health record (EHR) data and to compare performance of the model with a nationally published risk prediction tool.

A multivariable logistic regression model was developed from retrospective EHR data from 21,108 patients delivering at a quaternary medical center between January 1, 2018, and April 30, 2022. Deliveries were divided into derivation and validation sets based on an 80/20 split by date of delivery. Postpartum hemorrhage was defined as blood loss of 1,000 mL or more in addition to postpartum transfusion of 1 or more units of packed red blood cells. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC) and was compared with a postpartum hemorrhage risk assessment tool published by the CMQCC (California Maternal Quality Care Collaborative). The model was then programmed into the EHR and again validated with prospectively collected data from 928 patients between November 7, 2023, and January 31, 2024.

Postpartum hemorrhage occurred in 235 of 16,862 patients (1.4%) in the derivation cohort. The predictive model included 21 risk factors and demonstrated an AUC of 0.81 (95% CI, 0.79-0.84) and calibration slope of 1.0 (Brier score 0.013). During external temporal validation, the model maintained discrimination (AUC 0.80, 95% CI, 0.72-0.84) and calibration (calibration slope 0.95, Brier score 0.014). This was superior to the CMQCC tool (AUC 0.69 [95% CI, 0.67-0.70], P<.001). The model maintained performance in prospective, automated data collected with the predictive model in real time (AUC 0.82 [95% CI, 0.73-0.91]).

We created and temporally validated a postpartum hemorrhage prediction model, demonstrated its superior performance over a commonly used risk prediction tool, successfully coded the model into the EHR, and prospectively validated the model using risk factor data collected in real time. Future work should evaluate external generalizability and effects on patient outcomes; to facilitate this work, we have included the model coefficients and examples of EHR integration in the article.