# Developing a refined comorbidity index for use in obstetric patients

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2024 · $547,931

## Abstract

Project Summary
Addressing the rising trends in maternal mortality and severe maternal morbidity (SMM) is a critical priority in
the United States. About half of adverse maternal health outcomes were found to be attributable to preventable
harm or unintended consequences arising from clinical practice and the system of delivering perinatal care.
Significant resources are currently being invested to implement quality improvement (QI) initiatives in birthing
hospitals across the country. There is great need to evaluate these efforts and demonstrate their effectiveness
to reducing the burden of preventable SMM and maternal deaths. Virtually all QI initiatives in birthing hospitals
use SMM as an outcome measure, but their evaluation is hindered by the need to risk-adjust SMM rates to
control for differences in patient composition within and between hospitals. To date, 3 different research groups
proposed obstetric comorbidity indices, yet all have significant limitations. The overarching goal of this study is
to develop and validate a refined comorbidity index for obstetric patients that allows SMM rate comparisons
across hospitals and adequate monitoring of QI initiatives in obstetrics. We will use Maryland’s unique, gold-
standard, hospital-based, state-representative SMM Surveillance and Review data to identify a comprehensive
list of comorbidities in patients with SMM events. Using electronic health record data from the Johns Hopkins
Health System, we will employ variable importance estimation with machine learning techniques to develop the
comorbidity index. Subsequently, we will ascertain its accuracy using receiver operating characteristic
(ROC)/precision-recall (PR) curves and areas under the curve (AUC) for outcome discrimination and lowess-
smoothed calibration plots. Also, we will compare the performance of the refined comorbidity index to predict
SMM against that of previously published comorbidity indices. To further validate our refined comorbidity index
and assesses its performance consistency across various sociodemographic groups, we will use national
hospital discharge data from the Healthcare Cost and Utilization Project’s National Inpatient Sample. A
Technical Advisory Group comprised of clinicians, community partners, patient safety experts, and certified
medical coders will meet quarterly for data interpretation sessions. At the end of the study, we expect to have a
refined comorbidity index developed in gold-standard data, with superior psychometric properties than the
previously published comorbidity indices and validated in both EHR and national hospital discharge data. Our
results will be disseminated in the peer-reviewed literature and through presentations at scientific meetings.

## Key facts

- **NIH application ID:** 10930089
- **Project number:** 5R01HD112442-02
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Andreea Alina Creanga
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $547,931
- **Award type:** 5
- **Project period:** 2023-09-15 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10930089

## Citation

> US National Institutes of Health, RePORTER application 10930089, Developing a refined comorbidity index for use in obstetric patients (5R01HD112442-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10930089. Licensed CC0.

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