scholarly journals Systematic review of perioperative mortality risk prediction models for adults undergoing inpatient non‐cardiac surgery

2020 ◽  
Author(s):  
Jennifer R. Reilly ◽  
Belinda J. Gabbe ◽  
Wendy A. Brown ◽  
Carol L. Hodgson ◽  
Paul S. Myles
Keyword(s):  
Systematic Review ◽  
Cardiac Surgery ◽  
Risk Prediction ◽  
Mortality Risk ◽  
Prediction Models ◽  
Perioperative Mortality ◽  
Risk Prediction Models

Systematic review of mortality risk prediction models in the era of endovascular abdominal aortic aneurysm surgery

2017 ◽  
Vol 104 (8) ◽  
pp. 964-976 ◽  
Author(s):  
N. Lijftogt ◽  
T. W. F. Luijnenburg ◽  
A. C. Vahl ◽  
E. D. Wilschut ◽  
V. J. Leijdekkers ◽  
...  
Keyword(s):  
Systematic Review ◽  
Abdominal Aortic Aneurysm ◽  
Aortic Aneurysm ◽  
Risk Prediction ◽  
Mortality Risk ◽  
Prediction Models ◽  
Aneurysm Surgery ◽  
Risk Prediction Models ◽  
Abdominal Aortic ◽  
Aortic Aneurysm Surgery

scholarly journals 1657 Systematic Review and Meta-Analysis of Mortality Risk Prediction Models in Adult Cardiac Surgery

2021 ◽  
Vol 108 (Supplement_6) ◽  
Author(s):  
S Sinha ◽  
A Dimagli ◽  
L Dixon ◽  
M Gaudino ◽  
M Caputo ◽  
...  
Keyword(s):  
Cardiac Surgery ◽  
Risk Prediction ◽  
Mortality Risk ◽  
Prediction Models ◽  
Meta Analysis ◽  
Group Analysis ◽  
Risk Prediction Models ◽  
Geographical Variations ◽  
Significant Difference ◽  
Adult Cardiac Surgery

Abstract Background The most used mortality risk prediction models in cardiac surgery are the European System for Cardiac Operative Risk Evaluation(EuroSCORE)(ES) and Society of Thoracic Surgeons(STS) score. There is no agreement on which score should be considered more accurate nor which score should be utilised in each population sub-group. We sought to provide a thorough quantitative assessment of these 2 models. Method We performed a systematic literature review and captured information on discrimination, as quantified by the area under the receiver operator curve(AUC), and calibration, as quantified by the ratio of observed-to-expected mortality(O:E). We performed random effects meta-analysis of the performance of the individual models as well as pairwise comparisons and sub-group analysis by procedure type, time and continent. Results The ES2(AUC 0.783[95%CI 0.765-0.800];O:E 1.102[95%CI 0.943-1.289]) and STS(AUC 0.757[95%CI 0.727-0.785];O:E 1.111[95%CI 0.853-1.447]) both showed good overall discrimination and calibration. There was no significant difference in the discrimination of the two models(Difference in AUC -0.016; 95%CI -0.034 to -0.002;p0.09). However, the calibration of ES2 showed significant geographical variations(p < 0.001) and a trend towards miscalibration with time(p0.0057). This was not seen with STS. Conclusions ES2 and STS are both reliable predictors of short-term mortality following adult cardiac surgery in the populations from which they were derived. STS may have broader applications when comparing outcomes across continents and time periods as compared to ES2.

scholarly journals RISK PREDICTION MODELS FOR SURVIVAL AFTER HEART TRANSPLANTATION: A SYSTEMATIC REVIEW

2019 ◽  
Vol 35 (10) ◽  
pp. S94-S95
Author(s):  
N. Aleksova ◽  
A. Alba ◽  
V. Molinero ◽  
K. Connolly ◽  
A. Orchanian-Cheff ◽  
...  
Keyword(s):  
Systematic Review ◽  
Heart Transplantation ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Prediction Models

scholarly journals Systematic Review of Health Economic Impact Evaluations of Risk Prediction Models: Stop Developing, Start Evaluating

2017 ◽  
Vol 20 (4) ◽  
pp. 718-726 ◽  
Author(s):  
Anoukh van Giessen ◽  
Jaime Peters ◽  
Britni Wilcher ◽  
Chris Hyde ◽  
Carl Moons ◽  
...  
Keyword(s):  
Systematic Review ◽  
Health Economic ◽  
Economic Impact ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Prediction Models

scholarly journals Polygenic risk prediction models for colorectal cancer: a systematic review

BMC Cancer ◽  
2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Michele Sassano ◽  
Marco Mariani ◽  
Gianluigi Quaranta ◽  
Roberta Pastorino ◽  
Stefania Boccia
Keyword(s):  
Colorectal Cancer ◽  
Risk Factors ◽  
Systematic Review ◽  
Prediction Model ◽  
Risk Prediction ◽  
Genetic Variants ◽  
Prediction Models ◽  
Risk Prediction Models ◽  
Discriminatory Accuracy ◽  
Traditional Risk Factors

Abstract Background Risk prediction models incorporating single nucleotide polymorphisms (SNPs) could lead to individualized prevention of colorectal cancer (CRC). However, the added value of incorporating SNPs into models with only traditional risk factors is still not clear. Hence, our primary aim was to summarize literature on risk prediction models including genetic variants for CRC, while our secondary aim was to evaluate the improvement of discriminatory accuracy when adding SNPs to a prediction model with only traditional risk factors. Methods We conducted a systematic review on prediction models incorporating multiple SNPs for CRC risk prediction. We tested whether a significant trend in the increase of Area Under Curve (AUC) according to the number of SNPs could be observed, and estimated the correlation between AUC improvement and number of SNPs. We estimated pooled AUC improvement for SNP-enhanced models compared with non-SNP-enhanced models using random effects meta-analysis, and conducted meta-regression to investigate the association of specific factors with AUC improvement. Results We included 33 studies, 78.79% using genetic risk scores to combine genetic data. We found no significant trend in AUC improvement according to the number of SNPs (p for trend = 0.774), and no correlation between the number of SNPs and AUC improvement (p = 0.695). Pooled AUC improvement was 0.040 (95% CI: 0.035, 0.045), and the number of cases in the study and the AUC of the starting model were inversely associated with AUC improvement obtained when adding SNPs to a prediction model. In addition, models constructed in Asian individuals achieved better AUC improvement with the incorporation of SNPs compared with those developed among individuals of European ancestry. Conclusions Though not conclusive, our results provide insights on factors influencing discriminatory accuracy of SNP-enhanced models. Genetic variants might be useful to inform stratified CRC screening in the future, but further research is needed.

scholarly journals Risk Prediction Models for Patients With Acute Heart Failure: A Protocol for Systematic Review, Critical Appraisal, and Meta-Analysis

2021 ◽  
Author(s):  
Xuecheng Zhang ◽  
Kehua Zhou ◽  
Jingjing Zhang ◽  
Ying Chen ◽  
Hengheng Dai ◽  
...  
Keyword(s):  
Heart Failure ◽  
Systematic Review ◽  
Prediction Model ◽  
Acute Heart Failure ◽  
Risk Prediction ◽  
Methodological Quality ◽  
Critical Appraisal ◽  
Prediction Models ◽  
Cochrane Library ◽  
Risk Prediction Models

Abstract Background Nearly a third of patients with acute heart failure (AHF) die or are readmitted within three months after discharge, accounting for the majority of costs associated with heart failure-related care. A considerable number of risk prediction models, which predict outcomes for mortality and readmission rates, have been developed and validated for patients with AHF. These models could help clinicians stratify patients by risk level and improve decision making, and provide specialist care and resources directed to high-risk patients. However, clinicians sometimes reluctant to utilize these models, possibly due to their poor reliability, the variety of models, and/or the complexity of statistical methodologies. Here, we describe a protocol to systematically review extant risk prediction models. We will describe characteristics, compare performance, and critically appraise the reporting transparency and methodological quality of risk prediction models for AHF patients. Method Embase, Pubmed, Web of Science, and the Cochrane Library will be searched from their inception onwards. A back word will be searched on derivation studies to find relevant external validation studies. Multivariable prognostic models used for AHF and mortality and/or readmission rate will be eligible for review. Two reviewers will conduct title and abstract screening, full-text review, and data extraction independently. Included models will be summarized qualitatively and quantitatively. We will also provide an overview of critical appraisal of the methodological quality and reporting transparency of included studies using the Prediction model Risk of Bias Assessment Tool(PROBAST tool) and the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis(TRIPOD statement). Discussion The result of the systematic review could help clinicians better understand and use the prediction models for AHF patients, as well as make standardized decisions about more precise, risk-adjusted management. Systematic review registration : PROSPERO registration number CRD42021256416.

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