scholarly journals V2 Evaluation of risk prediction models for postoperative pulmonary complications in adults undergoing major abdominal surgery: A systematic review and external validation study of the REspiratory COmplications after abdomiNal surgery (RECON) cohort

BJS Open ◽  
2021 ◽  
Vol 5 (Supplement_1) ◽  
Author(s):  
◽  
Omar Kouli
Keyword(s):  
Systematic Review ◽  
Abdominal Surgery ◽  
Risk Prediction ◽  
Prediction Models ◽  
External Validation ◽  
Pulmonary Complications ◽  
Major Abdominal Surgery ◽  
Postoperative Pulmonary Complications ◽  
Risk Prediction Models ◽  
Prediction Tools

Abstract Background Postoperative pulmonary complications (PPCs) following major abdominal surgery result in substantial morbidity and mortality, yet stratifying patients for risk-modifying interventions remains challenging. This study aimed to identify and externally validate PPC risk prediction models in an international, prospective cohort. Methods A systematic review was conducted to identify risk prediction models for PPC following abdominal surgery. External validation was performed using data from a prospective dataset of adult patients undergoing major abdominal surgery from January to April 2019 in the UK, Ireland and Australia. The primary outcome was identification of PPC within 30-days (StEP-COMPAC criteria definition). Model discrimination and diagnostic accuracy were compared. Results Six unique risk prediction models were eligible from 2819 records. These were validated across 11,591 patients, with an overall PPC rate of 7.8% (n = 903). The Assess Respiratory Risk in Surgical Patients in Catalonia (ARISCAT) score provided the best discrimination (AUC: 0.709 (95% CI: 0.692-0.727), yet no risk prediction model demonstrated good discrimination (AUC >0.7). Conclusion The risk of PPC for patients following major abdominal surgery in the pre-covid era is not well described by existing prediction tools. New prediction tools are required to account for additional variation introduced for patients affected by SARS-CoV-2 infection.

scholarly journals 702 Evaluation of Risk Prediction Models for Postoperative Pulmonary Complications in Adults Undergoing Major Abdominal Surgery: A Systematic Review and External Validation Study in The Respiratory Complications After Abdominal Surgery (RECON) Cohort

2021 ◽  
Vol 108 (Supplement_2) ◽  
Author(s):  
Keyword(s):  
Systematic Review ◽  
Abdominal Surgery ◽  
Risk Prediction ◽  
Prediction Models ◽  
External Validation ◽  
Pulmonary Complications ◽  
Major Abdominal Surgery ◽  
Postoperative Pulmonary Complications ◽  
Risk Prediction Models ◽  
Prediction Tools

Abstract Introduction Postoperative pulmonary complications (PPCs) following major abdominal surgery result in substantial morbidity and mortality, yet stratifying patients for risk-modifying interventions remains challenging. This study aimed to identify and externally validate PPC risk prediction models in an international, prospective cohort. Method A systematic review was conducted to identify risk prediction models for PPC following abdominal surgery. External validation was performed using data from a prospective dataset of adult patients undergoing major abdominal surgery from January to April 2019 in the UK, Ireland, and Australia. The primary outcome was identification of PPC within 30-days (StEP-COMPAC criteria definition). Model discrimination and diagnostic accuracy were compared. Results Six unique risk prediction models were eligible from 2819 records (112 full texts). These were validated across 11,591 patients, with an overall PPC rate of 7.8% (n = 903). The Assess Respiratory Risk in Surgical Patients in Catalonia (ARISCAT) score provided the best discrimination (AUROC: 0.709 (95% CI: 0.692-0.727), yet no risk prediction model demonstrated good discrimination (AUROC >0.7). Conclusions The risk of PPC for patients following major abdominal surgery in the pre-covid era is not well described by existing prediction tools. New prediction tools are required to account for additional variation introduced for patients affected by SARS-CoV-2 infection.

scholarly journals O42 Evaluation of risk prediction models for postoperative pulmonary complications in adults undergoing major abdominal surgery: a systematic review and external validation study in the respiratory complications after abdominal surgery (RECON) cohort

2021 ◽  
Vol 108 (Supplement_5) ◽  
Author(s):  
Keyword(s):  
Abdominal Surgery ◽  
Risk Prediction ◽  
Prediction Models ◽  
External Validation ◽  
Pulmonary Complications ◽  
Major Abdominal Surgery ◽  
Postoperative Pulmonary Complications ◽  
Respiratory Complications ◽  
Risk Prediction Models ◽  
Prediction Tools

Abstract Introduction Postoperative pulmonary complications (PPCs) following major abdominal surgery result in substantial morbidity and mortality, yet stratifying patients for risk-modifying interventions remains challenging. This study aimed to systematically review and externally validate existing PPC risk prediction models in an international, prospective cohort. Method A systematic search of the MEDLINE and EMBASE databases according to PRISMA guidelines was performed to identify original risk prediction models for PPC following abdominal surgery. Subsequent external validation was performed based on a prospective dataset (REspiratory COmplications after abdomiNal surgery) which encompassed adult patients undergoing major abdominal surgery from January to April 2019 across the UK, Ireland and Australia. The primary outcome was 30-day PPC (StEP-COMPAC criteria definition), and multivariable logistic regression model discrimination were compared (with an area under the curve (AUC) ≥0.7 considered “good”). Result Thirty original risk prediction models were identified from 2819 records, with notable heterogeneity in risk factors considered. Within the validation dataset, the 30-day PPC rate was 7.8% (n = 903/11591), and 6 scores had all variables represented to enable external validation. No score demonstrated statistically significant “good” discrimination for identifying pulmonary complications, with no significant differences between scores. However, the Assess Respiratory Risk in Surgical Patients in Catalonia (ARISCAT) score achieved the highest discrimination (AUC = 0.709, 95% CI = 0.692–0.727). Conclusion The risk of PPC for patients following major abdominal surgery in the pre-covid era is not well described by existing prediction tools. New prediction tools are required to account for additional variation introduced for patients affected by SARS-CoV-2 infection. Take-home Message The risk of PPC for patients following major abdominal surgery in the pre-covid era is not well described by existing prediction tools. New prediction tools are required to account for additional variation introduced for patients affected by SARS-CoV-2 infection.

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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