Costs, effects and implementation of routine data emergency admission risk prediction models in primary care for patients with, or at risk of, chronic conditions: a systematic review protocol

ObjectiveTo identify risk prediction models for prostate cancer (PCa) that can be used in the primary care and community health settings.DesignSystematic review.Data sourcesMEDLINE and Embase databases combined from inception and up to the end of January 2019.EligibilityStudies were included based on satisfying all the following criteria: (i) presenting an evaluation of PCa risk at initial biopsy in patients with no history of PCa, (ii) studies not incorporating an invasive clinical assessment or expensive biomarker/genetic tests, (iii) inclusion of at least two variables with prostate-specific antigen (PSA) being one of them, and (iv) studies reporting a measure of predictive performance. The quality of the studies and risk of bias was assessed by using the Prediction model Risk Of Bias ASsessment Tool (PROBAST).Data extraction and synthesisRelevant information extracted for each model included: the year of publication, source of data, type of model, number of patients, country, age, PSA range, mean/median PSA, other variables included in the model, number of biopsy cores to assess outcomes, study endpoint(s), cancer detection, model validation and model performance.ResultsAn initial search yielded 109 potential studies, of which five met the set criteria. Four studies were cohort-based and one was a case-control study. PCa detection rate was between 20.6% and 55.8%. Area under the curve (AUC) was reported in four studies and ranged from 0.65 to 0.75. All models showed significant improvement in predicting PCa compared with being based on PSA alone. The difference in AUC between extended models and PSA alone was between 0.06 and 0.21.ConclusionOnly a few PCa risk prediction models have the potential to be readily used in the primary healthcare or community health setting. Further studies are needed to investigate other potential variables that could be integrated into models to improve their clinical utility for PCa testing in a community setting.

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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

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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

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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

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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.

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The influence of therapeutic alliance on adult obesity interventions in primary care: A systematic review protocol

10.31128/ajgp-03-18-4538 ◽

2018 ◽

Vol 47 (9) ◽

pp. 646-649

Author(s):

Elizabeth Sturgiss ◽

Nicholas Elmitt ◽

Jason Agostino ◽

Kirsty Douglas ◽

Alexander M Clark

Keyword(s):

Systematic Review ◽

Primary Care ◽

Therapeutic Alliance ◽

Adult Obesity ◽

Systematic Review Protocol ◽

Obesity Interventions ◽

Review Protocol

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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.

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