The obstetrics comorbidity index as a predictive tool for risk-appropriate maternal care

OBCMI was demonstrated to discriminate for SMM in a population of obstetrics transfers to a Level IV maternal care facility. When stratifying for maternal indicated transfers, the ability of OBCMI as a predictive tool decreased. The obtained cutoff OBCMI value of ≥ 4 had high specificity but may miss a significant population that would benefit from transfer. Use of the OBCMI may be too crude of a measure to provide a comprehensive risk assessment to predict SMM and adverse obstetrics outcomes. Further studies that may include newer tools such as machine learning may be necessary to develop a more clinically useful tool.

Authors

Institutions

Publication Details

Journal
BMC Pregnancy and Childbirth
Published
2024-11-27
DOI
https://doi.org/10.1186/s12884-024-06992-0
Citations
1
Primary Topic
Maternal and fetal healthcare
Type
article
Field-Weighted Citation Impact
1.11
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The obstetrics comorbidity index as a predictive tool for risk-appropriate maternal care

Rebecca Smith, Allison R. McCarter, Emily E. Sharpe, Regan N. Theiler et al.
1 citations
BMC Pregnancy and Childbirth
Maternal and fetal healthcare
1.11
article

The obstetrics comorbidity index as a predictive tool for risk-appropriate maternal care

Rebecca Smith, Allison R. McCarter, Emily E. Sharpe, Regan N. Theiler, Megan E. Branda, Vanessa E. Torbenson
article en
1 citations

Abstract

OBCMI was demonstrated to discriminate for SMM in a population of obstetrics transfers to a Level IV maternal care facility. When stratifying for maternal indicated transfers, the ability of OBCMI as a predictive tool decreased. The obtained cutoff OBCMI value of ≥ 4 had high specificity but may miss a significant population that would benefit from transfer. Use of the OBCMI may be too crude of a measure to provide a comprehensive risk assessment to predict SMM and adverse obstetrics outcomes. Further studies that may include newer tools such as machine learning may be necessary to develop a more clinically useful tool.

BMC Pregnancy and ChildbirthVol. 24(1)
Mayo Clinic (US), Mayo Clinic in Arizona (US), Mayo Clinic in Florida (US)
Openalex Percentile: Top 16%
Maternal and fetal healthcare
1.11
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.