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 Liver Cancer in Adults: A Systematic Review and Meta-Analysis

2021 ◽  
Author(s):  
Maomao Cao ◽  
He Li ◽  
Dianqin Sun ◽  
Siyi He ◽  
Yadi Zheng ◽  
...  
Keyword(s):  
Systematic Review ◽  
High Risk ◽  
Liver Cancer ◽  
Risk Prediction ◽  
Prediction Models ◽  
Meta Analysis ◽  
Risk Of Bias ◽  
Risk Models ◽  
Risk Prediction Models ◽  
Prediction Risk

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).

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

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.

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

BMJ Open ◽  
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.

P3429A novel prediction model for mortality after cardiac surgery using institutional case volume

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
S Lee ◽  
J B Park ◽  
Y J Cho ◽  
H G Ryu ◽  
E J Jang
Keyword(s):  
Cardiac Surgery ◽  
Hospital Mortality ◽  
Risk Prediction ◽  
Prediction Models ◽  
Validation Cohort ◽  
Mortality Data ◽  
P Value ◽  
Case Volume ◽  
Risk Prediction Models ◽  
Derivation Cohort

Abstract Purpose A number of risk prediction models have been developed to identify short term mortality after cardiovascular surgery. Most models include patient characteristics, laboratory data, and type of surgery, but no consideration for the amount of surgical experience. With numerous reports on the impact of case volume on patient outcome after high risk procedures, we attempted to develop a risk prediction models for in-hospital and 1-year mortality that takes institutional case volume into account. Methods We identified adult patients who underwent cardiac surgery from January 2008 to December 2017 from the National Health Insurance Service (NHIS) database by searching for patients with procedure codes of coronary artery bypass grafting, valve surgery, and surgery on thoracic aorta during the hospitalization. Study subjects were randomly assigned to either the derivation cohort or the validation cohort. In-hospital mortality and 1-year mortality data were collected using the NHIS database. Risk prediction models were developed from the derivation cohort using Cox proportional hazards regression. The prediction performances of models were evaluated in the validation cohort. Results The models developed in this study demonstrated fair discrimination for derivation cohort (N=22,004, c-statistics, 0.75 for in-hospital mortality; 0.73 for 1-year mortality) and acceptable calibration in the validation cohort. (N=22,003, Hosmer-Lemeshow χ2-test, P=0.08 and 0.16, respectively). Case volume was the key factor of mortality prediction models after cardiac surgery. (50≤ x <100 case per year. 100≤ x <200 case per year, ≥200 case per year are correlated with OR 3.29, 2.49, 1.85 in in-hospital mortality, 2.76, 1.99, 1.69 in 1-year mortality respectively, P value <0.001.) Annual case volume as risk factor Variables In-hospital mortality 1-year mortality OR (95% CI) p-value OR (95% CI) p-value Annual case-volume (reference: ≥200) – – 100–200 1.69 (1.48, 1.93) <0.001 1.85 (1.58, 2.18) <0.001 50–100 1.99 (1.75, 2.25) <0.001 2.49 (2.15, 2.89) <0.001 <50 2.76 (2.44, 3.11) <0.001 3.29 (2.85, 3.79) <0.001 OR: Odds ratio; CI: confidence interval; Ref: Reference. Discrimination and calibration Conclusion We developed and validated new risk prediction models for in-hospital and 1-year mortality after cardiac surgery using the NHIS database. These models may provide useful guides to predict mortality risks of patients with basic information and without laboratory findings.

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