Navigating severe maternal morbidity using big data: Green, yellow, and red flags for researchers

Davidson, Alexander J. F.; Park, Alison L.; Ray, Joel G.

doi:10.1177/1753495x19872880

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…In developing and refining our study’s model in external cohorts, investigators should consider adding first-trimester placental biomarkers and other maternal biomarkers alongside routinely measured clinical variables, such as blood pressure and weight. The incorporation of such variables may facilitate prediction of the whole of severe morbidity as well as cause-specific outcomes, and better inform individualized and targeted prevention [ 38 ].…”

Section: Discussionmentioning

confidence: 99%

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

Dayan

Shapiro

Luo

et al. 2021

BMC Pregnancy Childbirth

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Background Improvement 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. Methods We 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). Results Of 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. Conclusions A 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.

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Section: Discussionmentioning

confidence: 99%

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

Dayan

Shapiro

Luo

et al. 2021

BMC Pregnancy Childbirth

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“…The availability of such variables might facilitate prediction of the whole of severe morbidity as well as cause-specific outcomes, and better inform individualized and targeted prevention. 35…”

Section: Discussionmentioning

confidence: 99%

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

Dayan

Shapirio

Luo

et al. 2020

Preprint

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Objective: To improve the prediction of maternal end-organ injury or death using routinely-collected variables from the pre-pregnancy and the early pregnancy period. Design: Population-based cohort study using linked administrative health data. Setting: Ontario, Canada, April 1, 2006 to March 31, 2014. Sample: Women aged 18-60 years with a livebirth or stillbirth, of which one birth was randomly selected per woman. Methods and main outcome measures: We constructed a CPM 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 CPM included variables collected from 12 months before estimated conception until 19 weeks’ gestation. We developed a separate CPM for parous women to allow for the inclusion of factors from previous pregnancy(ies). Results: Of 634,290 women, 1969 experienced the primary composite outcome (3.1 per 1000). Predictive factors in the main CPM 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. Conclusions: A 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 of this model would enable clinical use.

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“…Such blunting may heighten a woman’s susceptibility to preeclampsia and acute kidney injury—both established causes of maternal morbidity. 2 - 4 Data are lacking on the effect of blunted glomerular hyperfiltration on the risk of severe maternal morbidity (SMM), especially among women without known antecedent kidney disease. The current study evaluated the association between blunted glomerular hyperfiltration and subsequent SMM or death.…”

Section: Introductionmentioning

confidence: 99%

Association Between Blunted Glomerular Hyperfiltration in Pregnancy and Severe Maternal Morbidity—A Research Letter

Harel

Park²,

Ray

2021

Can J Kidney Health Dis

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Background: Glomerular hyperfiltration is one physiological adaptation to pregnancy, marked by a decline in serum creatinine (SCr) concentration by 16 weeks’ gestation. It is not known whether blunted glomerular hyperfiltration leads to adverse maternal outcomes, including severe maternal morbidity (SMM). Objective: To evaluate the association between blunted glomerular hyperfiltration and subsequent SMM or death. Design: Population-based cohort study Setting: Ontario, Canada, from 2008 to 2019. Participants: Included were births among women who had ≥ 1 SCr measured as an outpatient within 10 weeks before conception (“preconception”), and again, at 110/7 to 206/7 weeks’ gestation (“in-pregnancy”). Excluded were women who died before birth, who had end-stage renal disease or kidney transplantation before conception, or whose pre-pregnancy SCr was 125 μmol/L. Exposure: Net glomerular hyperfiltration defined as the preconception minus the in-pregnancy SCr. Measures: The primary study outcome was SMM or death arising from 23 weeks’ gestation up to 42 days after the index birth. Methods: Adjusted relative risks (aRRs) were calculated using Modified Poisson regression per 1-SD net blunting of glomerular hyperfiltration adjusting for important covariates. Results: A total of 10,323 births met all inclusion criteria. The mean (SD) SCr was 61.7 (11.0) μmol/L preconception, 48.0 (9.2) μmol/L in-pregnancy, and the mean net difference 13.6 (8.2) μmol/L. Among these births, the adjusted RR of SMM or death from 23 weeks’ gestation up to 42 days post-partum was 1.16 (95% confidence interval 1.14-1.30) per 1-SD (8.2 μmol/L) net blunting of glomerular hyperfiltration. Limitations: As SCr assessment is not a routine part of pregnancy care, its measurement could have been for a specific health condition thereby imparting selection bias. Conclusions: Blunted glomerular hyperfiltration in pregnancy may identify some women at higher risk of SMM. Further prospective research is needed about the implications of glomerular hyperfiltration in early pregnancy.

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Navigating severe maternal morbidity using big data: Green, yellow, and red flags for researchers

Cited by 3 publications

References 18 publications

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

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

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

Association Between Blunted Glomerular Hyperfiltration in Pregnancy and Severe Maternal Morbidity—A Research Letter

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