Evidence map›Paper›PMID 29132349›Full record

SynthesisBMC pregnancy and childbirth2017

Measurement properties of comorbidity indices in maternal health research: a systematic review.

Kazuyoshi Aoyama, Rohan D'Souza, Eiichi Inada, Stephen E Lapinsky, Robert A Fowler

Open access · goldAbstract readSystematic Review
In one paragraph

Synthesis in BMC pregnancy and childbirth, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
4.1field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 1 synthesis or guideline pooled it, 66 citations in OpenAlex.

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  10. Anesthesia practice for Cesarean delivery in Japan: a retrospective cohort study.Canadian journal of anaesthesia = Journal canadien d'anesthesie · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 4 institutions in 2 countries.

Kazuyoshi AoyamaDepartment of Anesthesia and Pain Medicine, The Hospital for Sick Children, University of Toronto, Toronto, Canada. kazu.aoyama@utoronto.ca.
Rohan D'SouzaDepartment of Obstetrics and Gynaecology, Division of Maternal-Fetal Medicine, Mount Sinai Hospital, University of Toronto, Toronto, Canada.
Eiichi InadaDepartment of Anesthesiology and Pain Medicine, School of Medicine, Juntendo University, Tokyo, Japan.
Stephen E LapinskyIntensive Care Unit, Mount Sinai Hospital and University Health Network, University of Toronto, Toronto, Canada.
Robert A FowlerDepartment of Critical Care Medicine, Sunnybrook Health Science Centre, University of Toronto, Toronto, Canada.
Mount Sinai Hospital · CAJuntendo University · JPSickKids Foundation · CASunnybrook Health Science Centre · CA

Funding

CIHR Transitional Open Operating Grant 342397Institute of Gender and Health 276196
6 · The paper itself

Abstract

backgroundMaternal critical illness occurs in 1.2 to 4.7 of every 1000 live births in the United States and approximately 1 in 100 women who become critically ill will die. Patient characteristics and comorbid conditions are commonly summarized as an index or score for the purpose of predicting the likelihood of dying; however, most such indices have arisen from non-pregnant patient populations. We sought to systematically review comorbidity indices used in health administrative datasets of pregnant women, in order to critically appraise their measurement properties and recommend optimal tools for clinicians and maternal health researchers.

methodsWe conducted a systematic search of MEDLINE and EMBASE to identify studies published from 1946 and 1947, respectively, to May 2017 that describe predictive validity of comorbidity indices using health administrative datasets in the field of maternal health research. We applied a methodological PubMed search filter to identify all studies of measurement properties for each index.

resultsOur initial search retrieved 8944 citations. The full text of 61 articles were identified and assessed for final eligibility. Finally, two eligible articles, describing three comorbidity indices appropriate for health administrative data remained: The Maternal comorbidity index, the Charlson comorbidity index and the Elixhauser Comorbidity Index. These studies of identified indices had a low risk of bias. The lack of an established consensus-building methodology in generating each index resulted in marginal sensibility for all indices. Only the Maternal Comorbidity Index was derived and validated specifically from a cohort of pregnant and postpartum women, using an administrative dataset, and had an associated c-statistic of 0.675 (95% Confidence Interval 0.647-0.666) in predicting mortality.

conclusionsOnly the Maternal Comorbidity Index directly evaluated measurement properties relevant to pregnant women in health administrative datasets; however, it has only modest predictive ability for mortality among development and validation studies. Further research to investigate the feasibility of applying this index in clinical research, and its reliability across a variety of health administrative datasets would be incrementally helpful. Evolution of this and other tools for risk prediction and risk adjustment in pregnant and post-partum patients is an important area for ongoing study.

Indexed as

Maternal MortalityRisk AdjustmentFemaleHumansMaternal HealthPregnancyReproducibility of ResultsResearchUnited StatesVital StatisticsCharlson comorbidity indexComorbidity indexElixhauser comorbidity indexMaternal comorbidity indexMaternal Health ResearchMeasurement propertiesSystematic review

Identifiers

PMID29132349
PMCPMC5683518
OpenAlexW2767832767

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.