Risk prediction models for acute kidney injury following major noncardiac surgery: systematic review

Background The incidence of Acute Kidney Injury (AKI) and its human and economic cost is increasing steadily. One way to reduce the burden associated with AKI is to prevent the event altogether. An important step in prevention lies in AKI risk prediction. Due to the increasing number of available risk prediction models (RPMs) clinicians need to be able to rely on systematic reviews (SRs) to provide an objective assessment on which RPM can be used in a specific setting. Our aim was to assess the quality of SRs of RPMs in AKI. Methods The protocol for this overview was registered in PROSPERO. MEDLINE and Embase were searched for SRs of RPMs of AKI in any setting from 2003 till August 2020. We used the ROBIS tool to assess the methodological quality of the retrieved SRs. Results Eight SRs were retrieved. All studies were assessed as being at high risk for bias using the ROBIS tool. Eight reviews had a high risk of bias in study eligibility criteria (domain 1), five for study identification and selection (domain 2), seven for data collection and appraisal (domain 3) and seven for synthesis and findings (domain 4). Five reviews were scored at high risk of bias across all four domains. Risk of bias assessment with a formal risk of bias tool was only performed in five reviews. Primary studies were heterogeneous and used a wide range of AKI definitions. Only 19 unique RPM were externally validated, of which 11 had only 1 external validation report. Conclusion The methodological quality of SRs of RPMs of AKI is inconsistent. Most SRs lack a formal risk of bias assessment. SRs ought to adhere to certain standard quality criteria so that clinicians can rely on them to select a RPM for use in an individual patient. Trial registration PROSPERO registration number is CRD 42020204236, available at https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=204236.

Download Full-text

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

Canadian Journal of Cardiology ◽

10.1016/j.cjca.2019.07.488 ◽

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

Download Full-text

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

Value in Health ◽

10.1016/j.jval.2017.01.001 ◽

2017 ◽

Vol 20 (4) ◽

pp. 718-726 ◽

Cited By ~ 8

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

Download Full-text

Risk prediction models for esophageal cancer: A systematic review and critical appraisal

Cancer Medicine ◽

10.1002/cam4.4226 ◽

2021 ◽

Author(s):

He Li ◽

Dianqin Sun ◽

Maomao Cao ◽

Siyi He ◽

Yadi Zheng ◽

...

Keyword(s):

Systematic Review ◽

Esophageal Cancer ◽

Risk Prediction ◽

Critical Appraisal ◽

Prediction Models ◽

Risk Prediction Models

Download Full-text

Polygenic risk prediction models for colorectal cancer: a systematic review

BMC Cancer ◽

10.1186/s12885-021-09143-2 ◽

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.

Download Full-text

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

10.21203/rs.3.rs-948336/v1 ◽

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.

Download Full-text