PARCCS: A Machine Learning Risk-Prediction Model for Acute Peripartum Cardiovascular Complications During Delivery Admissions.

ABSTRACT:

Background

Maternal mortality in the United States remains high, with cardiovascular (CV) complications being a leading cause.

Objectives

The purpose of this paper was to develop the PARCCS (Prediction of Acute Risk for Cardiovascular Complications in the Peripartum Period Score) for acute CV complications during delivery.

Methods

Data from the National Inpatient Sample (2016-2020) and International Classification of Diseases, Tenth Revision codes to identify delivery admissions were used. Acute CV/renal complications were defined as a composite of pre-eclampsia/eclampsia, peripartum cardiomyopathy, renal complications, venous thromboembolism, arrhythmias, and pulmonary edema. A risk prediction model, PARCCS, was developed using machine learning consisting of 14 varia

SUBMITTER: Zahid S 

PROVIDER: S-EPMC11318475 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature