Related Experiment Video
Updated: Jun 17, 2025
07:15
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
Published on: August 16, 2020
6.8K
PARCCS: A Machine Learning Risk-Prediction Model for Acute Peripartum Cardiovascular Complications During Delivery
Salman Zahid1, Shikha Jha2, Gurleen Kaur3
1Division of Cardiovascular Medicine, Knight Cardiovascular Institute, Oregon Health and Science University, Portland, Oregon, USA.
JACC. Advances
|August 13, 2024
Summary
A new score, PARCCS (Prediction of Acute Risk for Cardiovascular Complications in the Peripartum Period Score), identifies pregnant individuals at high risk for cardiovascular complications during delivery. This tool aids in early risk assessment for improved maternal outcomes.
Area of Science:
- Obstetrics and Gynecology
- Cardiology
- Public Health
Background:
- Maternal mortality in the US is high, with cardiovascular complications being a primary driver.
- Identifying at-risk pregnancies for cardiovascular events during delivery is crucial for improving maternal health outcomes.
Purpose of the Study:
- To develop the Prediction of Acute Risk for Cardiovascular Complications in the Peripartum Period Score (PARCCS).
- To create a machine learning-based risk prediction model for acute cardiovascular complications during delivery.
Main Methods:
- Utilized data from the National Inpatient Sample (2016-2020) for delivery admissions.
- Defined acute cardiovascular/renal complications as a composite outcome.
- Developed the PARCCS model using machine learning with 14 predictive variables.
Main Results:
- Analyzed 2,371,661 deliveries; 7.0% experienced acute cardiovascular complications.
- Identified key risk factors including pre-existing heart failure, prior stroke, obesity, and Black race.
- The PARCCS model demonstrated an area under the receiver-operating characteristic curve of 0.68 for predicting complications.
Conclusions:
- The PARCCS score shows potential as a valuable tool for identifying pregnant individuals at risk of peripartum cardiovascular complications.
- Further validation studies are recommended to confirm its utility in improving patient outcomes.
