Evidence map›Paper›PMID 34615477›Full record

ArticleBMC pregnancy and childbirth2021

Development and internal validation of a model predicting severe maternal morbidity using pre-conception and early pregnancy variables: a population-based study in Ontario, Canada.

Natalie Dayan, Gabriel D Shapiro, Jin Luo, Jun Guan, Deshayne B Fell, Carl A Laskin, Olga Basso, Alison L Park, Joel G Ray

Open access · goldAbstract read
In one paragraph

Article in BMC pregnancy and childbirth, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
2.0field-weighted citation impact, top 13% 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

5 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
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

9 authors at 3 institutions in 1 country.

Natalie DayanDepartment of Medicine and Research Institute, McGill University Health Centre, 5252 de Maisonneuve West, 2B.40, Montreal, QC, H4A 3S5, Canada. natalie.dayan@mcgill.ca.
Gabriel D ShapiroDepartment of Medicine and Research Institute, McGill University Health Centre, 5252 de Maisonneuve West, 2B.40, Montreal, QC, H4A 3S5, Canada.
Jin LuoICES, 2075 Bayview Ave, Toronto, ON, M4N 3M5, Canada.
Jun GuanICES, 2075 Bayview Ave, Toronto, ON, M4N 3M5, Canada.
Deshayne B FellSchool of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Centre for Practice-Changing Research Building, Room L-1154, 401 Smyth Road, Ottawa, ON, K1H 8L1, Canada.
Carl A LaskinDepartments of Medicine and Obstetrics and Gynecology, University of Toronto, 123 Edward St., suite 1200, Toronto, ON, M5G 1E2, Canada.
Olga BassoDepartment of Epidemiology, Biostatistics and Occupational Health, McGill University, Purvis Hall, 1020 Pine Ave West, Montreal, QC, H3A 1A2, Canada.
Alison L ParkICES, 2075 Bayview Ave, Toronto, ON, M4N 3M5, Canada.
Joel G RayICES, 2075 Bayview Ave, Toronto, ON, M4N 3M5, Canada.
McGill University · CAUniversity of Toronto · CAUniversity of Ottawa · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundImprovement in the prediction and prevention of severe maternal morbidity (SMM) - a range of life-threatening conditions during pregnancy, at delivery or within 42 days postpartum - is a public health priority. Reduction of SMM at a population level would be facilitated by early identification and prediction. We sought to develop and internally validate a model to predict maternal end-organ injury or death using variables routinely collected during pre-pregnancy and the early pregnancy period.

methodsWe performed a population-based cohort study using linked administrative health data in Ontario, Canada, from April 1, 2006 to March 31, 2014. We included women aged 18-60 years with a livebirth or stillbirth, of which one birth was randomly selected per woman. We constructed a clinical prediction model for the primary composite outcome of any maternal end-organ injury or death, arising between 20 weeks' gestation and 42 days after the birth hospital discharge date. Our model included variables collected from 12 months before estimated conception until 19 weeks' gestation. We developed a separate model for parous women to allow for the inclusion of factors from previous pregnancy(ies).

resultsOf 634,290 women, 1969 experienced the primary composite outcome (3.1 per 1000). Predictive factors in the main model included maternal world region of origin, chronic medical conditions, parity, and obstetrical/perinatal issues - with moderate model discrimination (C-statistic 0.68, 95% CI 0.66-0.69). Among 333,435 parous women, the C-statistic was 0.71 (0.69-0.73) in the model using variables from the current (index) pregnancy as well as pre-pregnancy predictors and variables from any previous pregnancy.

conclusionsA combination of factors ascertained early in pregnancy through a basic medical history help to identify women at risk for severe morbidity, who may benefit from targeted preventive and surveillance strategies including appropriate specialty-based antenatal care pathways. Further refinement and external validation of this model are warranted and can support evidence-based improvements in clinical practice.

Indexed as

Maternal MortalityModels, StatisticalMorbidityCohort StudiesFemaleHumansOntarioPregnancyPregnancy ComplicationsReproducibility of ResultsRisk AssessmentRoutinely Collected Health DataEpidemiologyGynaecologyObstetricsWomen’s health

Identifiers

PMID34615477
PMCPMC8496026
OpenAlexW3204172495

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.