Dataset Information

Risk prediction models for maternal mortality: A systematic review and meta-analysis.

ABSTRACT:

Purpose

Pregnancy-related critical illness leads to death for 3-14% of affected women. Although identifying patients at risk could facilitate preventive strategies, guide therapy, and help in clinical research, no prior systematic review of this literature exploring the validity of risk prediction models for maternal mortality exists. Therefore, we have systematically reviewed and meta-analyzed risk prediction models for maternal mortality.

Methods

Search strategy: MEDLINE, EMBASE and Scopus, from inception to May 2017. Selection criteria: Trials or observational studies evaluating risk prediction models for maternal mortality. Data collection and analysis: Two reviewers independently assessed studies for eligibility and methodological quality, and extracted data on prediction performance.

Results

Thirty-eight studies that evaluated 12 different mortality prediction models were included. Mortality varied across the studies, with an average rate 10.4%, ranging from 0 to 41.7%. The Collaborative Integrated Pregnancy High-dependency Estimate of Risk (CIPHER) model and the Maternal Severity Index had the best performance, were developed and validated from studies of obstetric population with a low risk of bias. The CIPHER applies to critically ill obstetric patients (discrimination: area under the receiver operating characteristic curve (AUC) 0.823 (0.811-0.835), calibration: graphic plot [intercept-0.09, slope 0.92]). The Maternal Severity Index applies to hospitalized obstetric patients (discrimination: AUC 0.826 [0.802-0.851], calibration: standardized mortality ratio 1.02 [0.86-1.20]).

Conclusions

Despite the high heterogeneity of the study populations and the limited number of studies validating the finally eligible prediction models, the CIPHER and the Maternal Severity Index are recommended for use among critically ill and hospitalized pregnant and postpartum women for risk adjustment in clinical research and quality improvement studies. Neither index has sufficient discrimination to be applicable for clinical decision making at the individual patient level.

SUBMITTER: Aoyama K

PROVIDER: S-EPMC6279047 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Risk prediction models for maternal mortality: A systematic review and meta-analysis.

Aoyama Kazuyoshi K D'Souza Rohan R Pinto Ruxandra R Ray Joel G JG Hill Andrea A Scales Damon C DC Lapinsky Stephen E SE Seaward Gareth R GR Hladunewich Michelle M Shah Prakesh S PS Fowler Robert A RA

PloS one 20181204 12

<h4>Purpose</h4>Pregnancy-related critical illness leads to death for 3-14% of affected women. Although identifying patients at risk could facilitate preventive strategies, guide therapy, and help in clinical research, no prior systematic review of this literature exploring the validity of risk prediction models for maternal mortality exists. Therefore, we have systematically reviewed and meta-analyzed risk prediction models for maternal mortality.<h4>Methods</h4>Search strategy: MEDLINE, EMBASE ...[more]

PMID: 30513118

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Project description:ContextEarly detection of kidney cancer improves survival; however, low prevalence means that population-wide screening may be inefficient. Stratification of the population into risk categories could allow for the introduction of a screening programme tailored to individuals.ObjectiveThis review will identify and compare published models that predict the risk of developing kidney cancer in the general population.Evidence acquisitionA search identified primary research reporting or validating models predicting the risk of kidney cancer in Medline and EMBASE. After screening identified studies for inclusion, we extracted data onto a standardised form. The risk models were classified using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines and evaluated using the PROBAST assessment tool.Evidence synthesisThe search identified 15 281 articles. Sixty-two satisfied the inclusion criteria; performance measures were provided for 11 models. Some models predicted the risk of prevalent undiagnosed disease and others future incident disease. Six of the models had been validated, two using external populations. The most commonly included risk factors were age, smoking status, and body mass index. Most of the models had acceptable-to-good discrimination (area under the receiver-operating curve >0.7) in development and validation. Many models also had high specificity; however, several had low sensitivity. The highest performance was seen for the models using only biomarkers to detect kidney cancer; however, these were developed and validated in small case-control studies.ConclusionsWe identified a small number of risk models that could be used to stratify the population according to the risk of kidney cancer. Most exhibit reasonable discrimination, but a few have been validated externally in population-based studies.Patient summaryIn this review, we looked at mathematical models predicting the likelihood of an individual developing kidney cancer. We found several suitable models, using a range of risk factors (such as age and smoking) to predict the risk for individuals. Most of the models identified require further testing in the general population to confirm their usefulness.

Project description:ObjectivesTo look at the available literature on validated prediction models for contrast induced nephropathy and describe their characteristics.DesignSystematic review.Data sourcesMedline, Embase, and CINAHL (cumulative index to nursing and allied health literature) databases.Review methodsDatabases searched from inception to 2015, and the retrieved reference lists hand searched. Dual reviews were conducted to identify studies published in the English language of prediction models tested with patients that included derivation and validation cohorts. Data were extracted on baseline patient characteristics, procedural characteristics, modelling methods, metrics of model performance, risk of bias, and clinical usefulness. Eligible studies evaluated characteristics of predictive models that identified patients at risk of contrast induced nephropathy among adults undergoing a diagnostic or interventional procedure using conventional radiocontrast media (media used for computed tomography or angiography, and not gadolinium based contrast).Results16 studies were identified, describing 12 prediction models. Substantial interstudy heterogeneity was identified, as a result of different clinical settings, cointerventions, and the timing of creatinine measurement to define contrast induced nephropathy. Ten models were validated internally and six were validated externally. Discrimination varied in studies that were validated internally (C statistic 0.61-0.95) and externally (0.57-0.86). Only one study presented reclassification indices. The majority of higher performing models included measures of pre-existing chronic kidney disease, age, diabetes, heart failure or impaired ejection fraction, and hypotension or shock. No prediction model evaluated its effect on clinical decision making or patient outcomes.ConclusionsMost predictive models for contrast induced nephropathy in clinical use have modest ability, and are only relevant to patients receiving contrast for coronary angiography. Further research is needed to develop models that can better inform patient centred decision making, as well as improve the use of prevention strategies for contrast induced nephropathy.

Project description:BackgroundMaternal infection with cholera may negatively affect pregnancy outcomes. The objective of this research is to systematically review the literature and determine the risk of fetal, neonatal and maternal death associated with cholera during pregnancy.Materials and methodsMedline, Global Health Library, and Cochrane Library databases were searched using the key terms cholera and pregnancy for articles published in any language and at any time before August 2013 to quantitatively summarize estimates of fetal, maternal, and neonatal mortality. 95% confidence intervals (CIs) were calculated for each selected study. Random-effect non-linear logistic regression was used to calculate pooled rates and 95% CIs by time period. Studies from the recent period (1991-2013) were compared with studies from 1969-1990. Relative risk (RR) estimates and 95% CIs were obtained by comparing mortality of selected recent studies with published national normative data from the closest year.ResultsThe meta-analysis included seven studies that together involved 737 pregnant women with cholera from six countries. The pooled fetal death rate for 4 studies during 1991-2013 was 7.9% (95% CIs 5.3-10.4), significantly lower than that of 3 studies from 1969-1990 (31.0%, 95% CIs 25.2-36.8). There was no difference in fetal death rate by trimester. The pooled neonatal death rate for 1991-2013 studies was 0.8% (95% CIs 0.0-1.6), and 6.4% (95% CIs 0.0-20.8) for 1969-1990. The pooled maternal death rate for 1991-2013 studies was 0.2% (95% CIs 0.0-0.7), and 5.0% (95% CIs 0.0-16.0) for 1969-1990. Compared with published national mortality estimates, the RR for fetal death of 5.8 (95% CIs 2.9-11.3) was calculated for Haiti (2013), 1.8 (95% CIs 0.3-10.4) for Senegal (2007), and 2.6 (95% CIs 0.5-14.9) for Peru (1991); there were no significant differences in the RR for neonatal or maternal death.ConclusionResults are limited by the inconsistencies found across included studies but suggest that maternal cholera is associated with adverse pregnancy outcomes, particularly fetal death. These findings can inform a research agenda on cholera in pregnancy and guidance for the timely management of pregnant women with cholera.

Dataset Information

Risk prediction models for maternal mortality: A systematic review and meta-analysis.

Purpose

Methods

Results

Conclusions

Publications

Risk prediction models for maternal mortality: A systematic review and meta-analysis.

Similar Datasets

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