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

Abstract Background Prediction of liver cancer risk is beneficial to define high-risk population of liver cancer and guide clinical decisions. We aimed to review and critically appraise the quality of existing risk-prediction models for liver cancer. Methods This systematic review followed the guidelines of CHARMS (Checklist for Critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies) and Preferred Reporting Items for Systematic Reviews and Meta (PRISMA). We searched for PubMed, Embase, Web of Science, and the Cochrane Library from inception to July 2020. Prediction model Risk Of Bias Assessment Tool was used to assess the risk of bias of all potential articles. A narrative description and meta-analysis were conducted. Results After removal irrespective and duplicated citations, 20 risk prediction publications were finally included. Within the 20 studies, 15 studies performed model derivation and validation process, three publications only conducted developed procedure without validation and two articles were used to validate existing models. Discrimination was expressed as area under curve or C statistic, which was acceptable for most models, ranging from 0.64 to 0.96. Calibration of the predictions model were rarely assessed. All models were graded at high risk of bias. The risk bias of applicability in 13 studies was considered low. Conclusions This systematic review gives an overall review of the prediction risk models for liver cancer, pointing out several methodological issues in their development. No prediction risk models were recommended due to the high risk of bias.Systematic review registration: This systematic has been registered in PROSPERO (International Prospective Register of Systemic Review: CRD42020203244).

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Development and Evaluation of Models to Predict Readmission or Death After Discharge from Intensive Care

10.21203/rs.3.rs-841701/v1 ◽

2021 ◽

Author(s):

Jamie M Boyd ◽

Matthew T James ◽

Danny J Zuege ◽

Henry Thomas Stelfox

Keyword(s):

Risk Factors ◽

Systematic Review ◽

Intensive Care ◽

Risk Prediction ◽

Prediction Models ◽

Meta Analysis ◽

Local Data ◽

Predictive Values ◽

Risk Prediction Models ◽

Icu Discharge

Abstract Background Patients being discharged from the intensive care unit (ICU) have variable risks of subsequent readmission or death; however, there is limited understanding of how to predict individual patient risk. We sought to derive risk prediction models for ICU readmission or death after ICU discharge to guide clinician decision-making. Methods Systematic review and meta-analysis to identify risk factors. Development and validation of risk prediction models using two retrospective cohorts of patients discharged alive from medical-surgical ICUs (n = 3 ICUs, n = 11,291 patients; n = 14 ICUs, n = 11,400 patients). Models were developed using literature and data-derived weighted coefficients. Results Sixteen variables identified from the systematic review were used to develop four risk prediction models. In the validation cohort there were 795 (7%) patients who were re-admitted to ICU and 703 (7%) patients who died after ICU discharge. The area under the curve (AUROC) for ICU readmission for the literature (0.615 [95%CI: 0.593, 0.637]) and data (0.652 [95%CI: 0.631, 0.674]) weighted models showed poor discrimination. The AUROC for death after ICU discharge for the literature (0.708 [95%CI: 0.687, 0.728]) and local data weighted (0.752 [95%CI: 0.733, 0.770]) models showed good discrimination. The negative predictive values for ICU readmission and death after ICU discharge ranged from 94%-98%. Conclusions Identifying risk factors and weighting coefficients using systematic review and meta-analysis to develop prediction models is feasible and can identify patients at low risk of ICU readmission or death after ICU discharge.

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Summarising and synthesising regression coefficients through systematic review and meta-analysis for improving hypertension prediction using metamodelling: protocol

BMJ Open ◽

10.1136/bmjopen-2019-036388 ◽

2020 ◽

Vol 10 (4) ◽

pp. e036388

Author(s):

Mohammad Ziaul Islam Chowdhury ◽

Iffat Naeem ◽

Hude Quan ◽

Alexander A Leung ◽

Khokan C Sikdar ◽

...

Keyword(s):

Risk Factors ◽

Systematic Review ◽

Risk Prediction ◽

Prediction Models ◽

Assessment Tool ◽

Meta Analysis ◽

Grey Literature ◽

Random Effect ◽

Regression Coefficients ◽

Risk Prediction Models

IntroductionHypertension is one of the most common medical conditions and represents a major risk factor for heart attack, stroke, kidney disease and mortality. The risk of progression to hypertension depends on several factors, and combining these risk factors into a multivariable model for risk stratification would help to identify high-risk individuals who should be targeted for healthy behavioural changes and/or medical treatment to prevent the development of hypertension. The risk prediction models can be further improved in terms of accuracy by using a metamodel updating technique where existing hypertension prediction models can be updated by combining information available in existing models with new data. A systematic review and meta-analysis will be performed of hypertension prediction models in order to identify known risk factors for high blood pressure and to summarise the magnitude of their association with hypertension.Methods and analysisMEDLINE, Embase, Web of Science, Scopus and grey literature will be systematically searched for studies predicting the risk of hypertension among the general population. The search will be based on two key concepts: hypertension and risk prediction. The summary statistics from the individual studies will be the regression coefficients of the hypertension risk prediction models, and random-effect meta-analysis will be used to obtain pooled estimates. Heterogeneity and publication bias will be assessed, along with study quality, which will be assessed using the Prediction Model Risk of Bias Assessment Tool checklist.Ethics and disseminationEthics approval is not required for this systematic review and meta-analysis. We plan to disseminate the results of our review through journal publications and presentations at applicable platforms.

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A systematic review of breast cancer incidence risk prediction models with meta-analysis of their performance

Breast Cancer Research and Treatment ◽

10.1007/s10549-011-1818-2 ◽

2011 ◽

Vol 132 (2) ◽

pp. 365-377 ◽

Cited By ~ 97

Author(s):

Catherine Meads ◽

Ikhlaaq Ahmed ◽

Richard D. Riley

Keyword(s):

Breast Cancer ◽

Systematic Review ◽

Risk Prediction ◽

Cancer Incidence ◽

Prediction Models ◽

Meta Analysis ◽

Breast Cancer Incidence ◽

Risk Prediction Models ◽

Incidence Risk

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Risk Prediction Models for Contrast-Induced Acute Kidney Injury Accompanying Cardiac Catheterization: Systematic Review and Meta-analysis

Canadian Journal of Cardiology ◽

10.1016/j.cjca.2017.01.018 ◽

2017 ◽

Vol 33 (6) ◽

pp. 724-736 ◽

Cited By ~ 28

Author(s):

David W. Allen ◽

Bryan Ma ◽

Kelvin C. Leung ◽

Michelle M. Graham ◽

Neesh Pannu ◽

...

Keyword(s):

Systematic Review ◽

Acute Kidney Injury ◽

Risk Prediction ◽

Cardiac Catheterization ◽

Prediction Models ◽

Meta Analysis ◽

Kidney Injury ◽

Risk Prediction Models

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Risk prediction models for mortality in patients with ventilator-associated pneumonia: A systematic review and meta-analysis

Journal of Critical Care ◽

10.1016/j.jcrc.2016.09.003 ◽

2017 ◽

Vol 37 ◽

pp. 112-118 ◽

Cited By ~ 11

Author(s):

Johan Larsson ◽

Theis Skovsgaard Itenov ◽

Morten Heiberg Bestle

Keyword(s):

Systematic Review ◽

Risk Prediction ◽

Prediction Models ◽

Meta Analysis ◽

Ventilator Associated Pneumonia ◽

Risk Prediction Models

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Risk Prediction Models for Postoperative Delirium: A Systematic Review and Meta-Analysis

Journal of the American Geriatrics Society ◽

10.1111/jgs.13138 ◽

2014 ◽

Vol 62 (12) ◽

pp. 2383-2390 ◽

Cited By ~ 62

Author(s):

Laura C. C. van Meenen ◽

David M. P. van Meenen ◽

Sophia E. de Rooij ◽

Gerben ter Riet

Keyword(s):

Systematic Review ◽

Risk Prediction ◽

Postoperative Delirium ◽

Prediction Models ◽

Meta Analysis ◽

Risk Prediction Models

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Can risk be predicted? An umbrella systematic review of current risk prediction models for cardiovascular diseases, diabetes and hypertension

BMJ Open ◽

10.1136/bmjopen-2019-030234 ◽

2019 ◽

Vol 9 (12) ◽

pp. e030234 ◽

Cited By ~ 4

Author(s):

Francesca Lucaroni ◽

Domenico Cicciarella Modica ◽

Mattia Macino ◽

Leonardo Palombi ◽

Alessio Abbondanzieri ◽

...

Keyword(s):

Systematic Review ◽

Cardiovascular Diseases ◽

Risk Prediction ◽

Prediction Models ◽

Meta Analysis ◽

Ankle Brachial Index ◽

Risk Of Bias ◽

Cochrane Library ◽

Eligibility Criteria ◽

Risk Prediction Models

ObjectiveTo provide an overview of the currently available risk prediction models (RPMs) for cardiovascular diseases (CVDs), diabetes and hypertension, and to compare their effectiveness in proper recognition of patients at risk of developing these diseases.DesignUmbrella systematic review.Data sourcesPubMed, Scopus, Cochrane Library.Eligibility criteriaSystematic reviews or meta-analysis examining and comparing performances of RPMs for CVDs, hypertension or diabetes in healthy adult (18–65 years old) population, published in English language.Data extraction and synthesisData were extracted according to the following parameters: number of studies included, intervention (RPMs applied/assessed), comparison, performance, validation and outcomes. A narrative synthesis was performed. Data were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.Study selection3612 studies were identified. After title/abstract screening and removal of duplicate articles, 37 studies met the eligibility criteria. After reading the full text, 13 were deemed relevant for inclusion. Three further papers from the reference lists of these articles were then added.Study appraisalThe methodological quality of the included studies was assessed using the AMSTAR tool.Risk of bias in individual studiesRisk of Bias evaluation was carried out using the ROBIS tool.ResultsSixteen studies met the inclusion criteria: six focused on diabetes, two on hypertension and eight on CVDs. Globally, prediction models for diabetes and hypertension showed no significant difference in effectiveness. Conversely, some promising differences among prediction tools were highlighted for CVDs. The Ankle-Brachial Index, in association with the Framingham tool, and QRISK scores provided some evidence of a certain superiority compared with Framingham alone.LimitationsDue to the significant heterogeneity of the studies, it was not possible to perform a meta-analysis. The electronic search was limited to studies in English and to three major international databases (MEDLINE/PubMed, Scopus and Cochrane Library), with additional works derived from the reference list of other studies; grey literature with unpublished documents was not included in the search. Furthermore, no assessment of potential adverse effects of RPMs was carried out.ConclusionsConsistent evidence is available only for CVD prediction: the Framingham score, alone or in combination with the Ankle-Brachial Index, and the QRISK score can be confirmed as the gold standard. Further efforts should not be concentrated on creating new scores, but rather on performing external validation of the existing ones, in particular on high-risk groups. Benefits could be further improved by supplementing existing models with information on lifestyle, personal habits, family and employment history, social network relationships, income and education.PROSPERO registration numberCRD42018088012.

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