scholarly journals C2HEST score for atrial fibrillation risk prediction models: a Diagnostic Accuracy Tests meta-analysis

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Habib Haybar ◽  
Kimia Shirbandi ◽  
Fakher Rahim
Keyword(s):  
Atrial Fibrillation ◽  
Prediction Models ◽  
Meta Analysis ◽  
Area Under The Curve ◽  
Asian Population ◽  
Risk Scores ◽  
Study Region ◽  
Heart Atrium ◽  
Point Increase ◽  
Study Type

Abstract Background This meta-analysis aimed to assess the value of the C2HEST score to facilitate population screening and detection of AF risk in millions of populations and validate risk scores and their composition and discriminatory power for identifying people at high or low risk of AF. We searched major indexing databases, including Pubmed/Medline, ISI web of science, Scopus, Embase, and Cochrane central, using (“C2HEST” OR “risk scoring system” OR “risk score”) AND (“atrial fibrillation (AF)” OR “atrial flutter” OR “tachycardia, supraventricular” OR “heart atrium flutter”) without any language, study region or study type restrictions between 1990 and 2021 years. Analyses were done using Meta-DiSc. The title and abstract screening were conducted by two independent investigators. Results Totally 679 records were found through the initial search, of which ultimately, nine articles were included in the qualitative and quantitative analyses. The risk of AF accompanied every one-point increase of C2HEST score (OR 1.03, 95% CI 1.01–1.05, p < 0.00001), with a high heterogeneity across studies (I2 = 100%). The SROC for C2HEST score in the prediction of AF showed that the overall area under the curve (AUC) was 0.91 (95% CI 0.85–0.96), AUC in Asian population was 0.87 (95% CI: 0.78–0.95) versus non-Asian 0.95 (95% CI 0.91–0.99), and in general population was 0.92 (95% CI 0.85–0.99) versus those with chronic conditions 0.83 (95% CI 0.71–0.95), respectively. Conclusions The results of this research support the idea that this quick score has the opportunity for use as a risk assessment in patients' AF screening strategies.

P663Identifying patients at risk for atrial fibrillation in the community: a systematic review and meta-analysis of prediction models

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
J C L Himmelreich ◽  
L Veelers ◽  
W A M Lucassen ◽  
H C P M Van Weert ◽  
R E Harskamp
Keyword(s):  
Heart Failure ◽  
Atrial Fibrillation ◽  
At Risk ◽  
Prediction Models ◽  
Evidence Synthesis ◽  
Meta Analysis ◽  
Risk Scores ◽  
Risk Models ◽  
C Statistic

Abstract Background Atrial fibrillation (AF) presents a considerable burden on our health care systems. Early detection of AF may prevent AF-associated complications, such as stroke and heart failure. Given our aging populations, the number of new AF cases is expected to double over the next decades. As such, there is renewed interest to screen for AF in the community. To optimise screening efforts, risk prediction models may help us identify at-risk patients. Purpose To identify and evaluate the performance of prediction models for AF that may be applicable for screening in community settings. Methods We searched PubMed, Embase, and CINAHL databases for studies that derived and/or validated AF risk models from population-based cohorts. Three investigators independently assessed risk of bias (CHARMS checklist), and performed data extraction and evidence synthesis. The primary expression of associations in meta-analysis was the C-statistic for discrimination between AF and non-AF cases during follow-up, using a random effects model. We calculated 95% prediction intervals (PI) due to high heterogeneity (I2 >30%) in all analyses. Results We identified 23 studies that presented data on 8 risk models derived from 18 cohorts with a total of 1,4 million participants from across the globe. Average age in these cohorts ranged from 43–76 years and follow-up ranged from 3 to 20 years. Two of the 8 risk models had a sufficient number of validation studies to be included in the meta-analysis. The CHARGE-AF (Cohorts for Heart and Aging Research in Genomic Epidemiology) score had a summary C-statistic of 0.72 (95%-PI: 0.67–0.77; n=7 cohorts, n=53.040 patients). The FHS (Framingham Heart Study) score for AF had a summary C-statistic of 0.71 (95%-PI: 0.59–0.83; n=4 cohorts, n=19.300 patients). Both models include age, height and weight, blood pressure, prevalent heart failure, and antihypertensive medication use as variables. CHARGE-AF additionally includes race, current smoking, and history of diabetes and myocardial infarction. FHS additionally includes sex, PR interval, and significant murmur. Conclusions Currently two risk scores, CHARGE-AF and FHS, have been rigorously tested for predicting atrial fibrillation in general populations. The CHARGE-AF score may present the more promising, user-friendly score for future community screening efforts, as it solely relies on readily available clinical parameters. Acknowledgement/Funding This work was supported by the Netherlands Organisation for Health Research and Development (ZonMw) [80-83910-98-13046]

scholarly journals Individual Prediction of Heart Failure Among Childhood Cancer Survivors

2015 ◽  
Vol 33 (5) ◽  
pp. 394-402 ◽  
Author(s):  
Eric J. Chow ◽  
Yan Chen ◽  
Leontien C. Kremer ◽  
Norman E. Breslow ◽  
Melissa M. Hudson ◽  
...  
Keyword(s):  
Heart Failure ◽  
Childhood Cancer ◽  
Cancer Diagnosis ◽  
Prediction Models ◽  
Wilms Tumor ◽  
Area Under The Curve ◽  
Risk Groups ◽  
Childhood Cancer Survivors ◽  
Risk Scores ◽  
Individual Risk

Purpose To create clinically useful models that incorporate readily available demographic and cancer treatment characteristics to predict individual risk of heart failure among 5-year survivors of childhood cancer. Patients and Methods Survivors in the Childhood Cancer Survivor Study (CCSS) free of significant cardiovascular disease 5 years after cancer diagnosis (n = 13,060) were observed through age 40 years for the development of heart failure (ie, requiring medications or heart transplantation or leading to death). Siblings (n = 4,023) established the baseline population risk. An additional 3,421 survivors from Emma Children's Hospital (Amsterdam, the Netherlands), the National Wilms Tumor Study, and the St Jude Lifetime Cohort Study were used to validate the CCSS prediction models. Results Heart failure occurred in 285 CCSS participants. Risk scores based on selected exposures (sex, age at cancer diagnosis, and anthracycline and chest radiotherapy doses) achieved an area under the curve of 0.74 and concordance statistic of 0.76 at or through age 40 years. Validation cohort estimates ranged from 0.68 to 0.82. Risk scores were collapsed to form statistically distinct low-, moderate-, and high-risk groups, corresponding to cumulative incidences of heart failure at age 40 years of 0.5% (95% CI, 0.2% to 0.8%), 2.4% (95% CI, 1.8% to 3.0%), and 11.7% (95% CI, 8.8% to 14.5%), respectively. In comparison, siblings had a cumulative incidence of 0.3% (95% CI, 0.1% to 0.5%). Conclusion Using information available to clinicians soon after completion of childhood cancer therapy, individual risk for subsequent heart failure can be predicted with reasonable accuracy and discrimination. These validated models provide a framework on which to base future screening strategies and interventions.

scholarly journals Prediction models for atrial fibrillation applicable in the community: a systematic review and meta-analysis

EP Europace ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 684-694 ◽  
Author(s):  
Jelle C L Himmelreich ◽  
Lieke Veelers ◽  
Wim A M Lucassen ◽  
Renate B Schnabel ◽  
Michiel Rienstra ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Systematic Review ◽  
Prediction Models ◽  
Prediction Interval ◽  
Data Extraction ◽  
Meta Analysis ◽  
Aging Research ◽  
Genomic Epidemiology ◽  
C Statistic ◽  
Modelling Studies

Abstract Aims Atrial fibrillation (AF) is a common arrhythmia associated with an increased stroke risk. The use of multivariable prediction models could result in more efficient primary AF screening by selecting at-risk individuals. We aimed to determine which model may be best suitable for increasing efficiency of future primary AF screening efforts. Methods and results We performed a systematic review on multivariable models derived, validated, and/or augmented for AF prediction in community cohorts using Pubmed, Embase, and CINAHL (Cumulative Index to Nursing and Allied Health Literature) through 1 August 2019. We performed meta-analysis of model discrimination with the summary C-statistic as the primary expression of associations using a random effects model. In case of high heterogeneity, we calculated a 95% prediction interval. We used the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) checklist for risk of bias assessment. We included 27 studies with a total of 2 978 659 unique participants among 20 cohorts with mean age ranging from 42 to 76 years. We identified 21 risk models used for incident AF risk in community cohorts. Three models showed significant summary discrimination despite high heterogeneity: CHARGE-AF (Cohorts for Heart and Aging Research in Genomic Epidemiology) [summary C-statistic 0.71; 95% confidence interval (95% CI) 0.66–0.76], FHS-AF (Framingham Heart Study risk score for AF) (summary C-statistic 0.70; 95% CI 0.64–0.76), and CHA2DS2-VASc (summary C-statistic 0.69; 95% CI 0.64–0.74). Of these, CHARGE-AF and FHS-AF had originally been derived for AF incidence prediction. Only CHARGE-AF, which comprises easily obtainable measurements and medical history elements, showed significant summary discrimination among cohorts that had applied a uniform (5-year) risk prediction window. Conclusion CHARGE-AF appeared most suitable for primary screening purposes in terms of performance and applicability in older community cohorts of predominantly European descent.

scholarly journals Performance of Atrial Fibrillation Risk Prediction Models in Over Four Million Individuals

2020 ◽  
Author(s):  
Shaan Khurshid ◽  
Uri Kartoun ◽  
Jeffrey M. Ashburner ◽  
Ludovic Trinquart ◽  
Anthony Philippakis ◽  
...  
Keyword(s):  
Heart Failure ◽  
Atrial Fibrillation ◽  
Risk Prediction ◽  
Proportional Hazards ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Health Management ◽  
Cox Proportional Hazards ◽  
Risk Scores ◽  
Office Visits

Background - Atrial fibrillation (AF) is associated with increased risks of stroke and heart failure. Electronic health record (EHR) based AF risk prediction may facilitate efficient deployment of interventions to diagnose or prevent AF altogether. Methods - We externally validated an EHR atrial fibrillation (EHR-AF) score in IBM Explorys Life Sciences, a multi-institutional dataset containing statistically de-identified EHR data for over 21 million individuals ("Explorys Dataset"). We included individuals with complete AF risk data, ≥2 office visits within two years, and no prevalent AF. We compared EHR-AF to existing scores including CHARGE-AF, C 2 HEST, and CHA 2 DS 2 -VASc. We assessed association between AF risk scores and 5-year incident AF, stroke, and heart failure using Cox proportional hazards modeling, 5-year AF discrimination using c-indices, and calibration of predicted AF risk to observed AF incidence. Results - Of 21,825,853 individuals in the Explorys Dataset, 4,508,180 comprised the analysis (age 62.5, 56.3% female). AF risk scores were strongly associated with 5-year incident AF (hazard ratio [HR] per standard deviation [SD] increase 1.85 using CHA 2 DS 2 -VASc to 2.88 using EHR-AF), stroke (1.61 using C 2 HEST to 1.92 using CHARGE-AF), and heart failure (1.91 using CHA 2 DS 2 -VASc to 2.58 using EHR-AF). EHR-AF (c-index 0.808 [95%CI 0.807-0.809]) demonstrated favorable AF discrimination compared to CHARGE-AF (0.806 [0.805-0.807]), C 2 HEST (0.683 [0.682-0.684]), and CHA 2 DS 2 -VASc (0.720 [0.719-0.722]). Of the scores, EHR-AF demonstrated the best calibration to incident AF (calibration slope 1.002 [0.997-1.007]). In subgroup analyses, AF discrimination using EHR-AF was lower in individuals with stroke (c-index 0.696 [0.692-0.700]) and heart failure (0.621 [0.617-0.625]). Conclusions - EHR-AF demonstrates predictive accuracy for incident AF using readily ascertained EHR data. AF risk is associated with incident stroke and heart failure. Use of such risk scores may facilitate decision-support and population health management efforts focused on minimizing AF-related morbidity.

scholarly journals Individual participant data validation of the PICNICC prediction model for febrile neutropenia

2019 ◽  
Vol 105 (5) ◽  
pp. 439-445 ◽  
Author(s):  
Bob Phillips ◽  
Jessica Elizabeth Morgan ◽  
Gabrielle M Haeusler ◽  
Richard D Riley
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Meta Analysis ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Infectious Complications ◽  
Cancer Therapies ◽  
Individual Participant Data ◽  
Documented Infection ◽  
Individual Participant

BackgroundRisk-stratified approaches to managing cancer therapies and their consequent complications rely on accurate predictions to work effectively. The risk-stratified management of fever with neutropenia is one such very common area of management in paediatric practice. Such rules are frequently produced and promoted without adequate confirmation of their accuracy.MethodsAn individual participant data meta-analytic validation of the ‘Predicting Infectious ComplicatioNs In Children with Cancer’ (PICNICC) prediction model for microbiologically documented infection in paediatric fever with neutropenia was undertaken. Pooled estimates were produced using random-effects meta-analysis of the area under the curve-receiver operating characteristic curve (AUC-ROC), calibration slope and ratios of expected versus observed cases (E/O).ResultsThe PICNICC model was poorly predictive of microbiologically documented infection (MDI) in these validation cohorts. The pooled AUC-ROC was 0.59, 95% CI 0.41 to 0.78, tau2=0, compared with derivation value of 0.72, 95% CI 0.71 to 0.76. There was poor discrimination (pooled slope estimate 0.03, 95% CI −0.19 to 0.26) and calibration in the large (pooled E/O ratio 1.48, 95% CI 0.87 to 2.1). Three different simple recalibration approaches failed to improve performance meaningfully.ConclusionThis meta-analysis shows the PICNICC model should not be used at admission to predict MDI. Further work should focus on validating alternative prediction models. Validation across multiple cohorts from diverse locations is essential before widespread clinical adoption of such rules to avoid overtreating or undertreating children with fever with neutropenia.

scholarly journals Characterizing and Quantifying Performance Heterogeneity in Cardiovascular Risk Prediction Models — A Step Towards Improved Disease Risk Assessment

2021 ◽  
Author(s):  
Uri Kartoun ◽  
Shaan Khurshid ◽  
Bum Chul Kwon ◽  
Aniruddh Patel ◽  
Puneet Batra ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Cardiovascular Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
Disease Risk ◽  
Large Data ◽  
Risk Scores ◽  
Atherosclerotic Cardiovascular Disease ◽  
Genomic Epidemiology ◽  
Performance Heterogeneity

Abstract Prediction models are commonly used to estimate risk for cardiovascular diseases; however, performance may vary substantially across relevant subgroups of the population. Here we investigated the variability of performance and fairness across a variety of subgroups for risk prediction of two common diseases, atherosclerotic cardiovascular disease (ASCVD) and atrial fibrillation (AF). We calculated the Cohorts for Heart and Aging in Genomic Epidemiology Atrial Fibrillation (CHARGE-AF) for AF and the Pooled Cohort Equations (PCE) score for ASCVD in three large data sets: Explorys Life Sciences Dataset (Explorys, n = 21,809,334), Mass General Brigham (MGB, n = 520,868), and the UK Biobank (UKBB, n = 502,521). Our results demonstrate important performance heterogeneity of established cardiovascular risk scores across subpopulations defined by age, sex, and presence of preexisting disease. For example, in CHARGE-AF, discrimination declined with increasing age, with concordance index of 0.72 [ 95% CI, 0.72–0.73 ] for the youngest (45–54y) subgroup to 0.57 [ 0.56–0.58 ], for the oldest (85–90y) subgroup in Explorys. The statistical parity difference (i.e., likelihood of being classified as high risk) was considerable between males and females within the 65–74y subgroup with a value of -0.33 [ 95% CI, -0.33–-0.33 ]. We observed also that large segments of the population suffered from both decreased discrimination (i.e., <0.7) and poor calibration (i.e., calibration slope outside of 0.7–1.3); for example, all individuals 75 or older in Explorys (17.4%). Our findings highlight the need to characterize and quantify how clinical risk models behave and perform within specific subpopulations so they can be used appropriately to facilitate more accurate and equitable assessment of disease risk.

scholarly journals Clinical Utility of the BIWACO Score for Patients With Atrial Fibrillation After Percutaneous Coronary Intervention

2021 ◽  
Author(s):  
Teruki Takeda ◽  
Tomohiro Dohke ◽  
Yoshiki Ueno ◽  
Toshiki Mastui ◽  
Masanori Fujii ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Percutaneous Coronary Intervention ◽  
Area Under The Curve ◽  
Dual Therapy ◽  
Coronary Intervention ◽  
Current Status ◽  
Risk Scores ◽  
Bleeding Events ◽  
Percutaneous Coronary ◽  
Multicenter Survey

Abstract Background: No predictive clinical risk scores for net adverse clinical events (NACE) have been developed in patients with atrial fibrillation (AF) after percutaneous coronary intervention (PCI). Methods: We evaluated the NACE in order to develop clinically applicable risk-stratification scores in the BIWACO study, a multicenter survey which enrolled a total of 7837 patients. We also investigated the current status and time trends for the use of antithrombotic drugs.Results: A total of 188 AF patients who had received PCI were enrolled. At discharge, 65% of patients were prescribed a triple therapy (TT), 6% were prescribed a dual therapy, the remaining 29% of patients received dual-antiplatelet therapy. Over 3 years, the fraction of patients continuing TT decreased by 15%, whereas only 2% received oral anticoagulant alone. NACE developed in 20% of patients, resulting in the deaths of 5% patients, and 13% experiencing bleeding events. We developed risk scores for NACE comprising the five best predictive items, which we designated BIWACO scores. The area under the curve was 0.774 for NACE. Conclusions: Our study explored the differences in treatment practices and guideline recommendations for antithrombotic therapy. We concluded that our BIWACO score is useful for predicting clinical outcomes in AF-patients after PCI.

scholarly journals MO071EXTERNAL VALIDATION OF ISCHEMIC STROKE RISK PREDICTION MODELS IN ATRIAL FIBRILLATION PATIENTS WITH CHRONIC KIDNEY DISEASE: THE STOCKHOLM CREATININE MEASUREMENTS (SCREAM) PROJECT

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Y De Jong ◽  
Edouard Fu ◽  
Juan Jesus Carrero ◽  
Friedo W Dekker ◽  
Merel Van Diepen ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Chronic Kidney Disease ◽  
Ischemic Stroke ◽  
Stroke Risk ◽  
Prediction Models ◽  
Predictive Performance ◽  
Risk Scores ◽  
Risk Models ◽  
Lower Egfr ◽  
Ischemic Stroke Risk

Abstract Background and Aims Ischemic stroke (IS) is a leading cause of morbidity and mortality world-wide. Patients with chronic kidney disease (CKD) have an increased ischemic stroke risk. Stroke risk scores, including the CHA2DS2-VASc, CHADS2, ATRIA, GARFIELD, AFI and the modified-CHADS2, have been developed to identify patients with an increased stroke risk in patients with atrial fibrillation (AF) allowing for personalization of anticoagulant prescription. In the original articles, these risk scores had reasonable predictive abilities. However, the predictive performances of these stroke risk in patients with CKD is unclear. Therefore, the aim of this study was to validate existing stroke risk scores in patients with AF and different stages of CKD. Method The study included subjects with newly-diagnosed AF from the SCREAM (Stockholm CREAtinine Measurements) database, a healthcare utilization cohort of residents from Stockholm, Sweden, during 2007-2011. The performance of prediction models were compared across eGFR strata: normal (KDIGO classification G1-2; eGFR &gt;60) mild (G3; eGFR 30-60) and advanced (G4-5; eGFR &lt;30, not on dialysis) CKD. Model discrimination was evaluated through c-statistics, and calibration by plotting predicted probabilities and observed frequencies for IS. Results A total of 36.004 subjects with AF and known eGFR were identified. During a median follow up of 1.91 years, 2572 (7.1%) suffered an ischemic stroke. The incidence rate of stroke increased across lower eGFR strata: from 6.3% in patients with eGFR&gt;60 to 10.4% and 7.3% in patients with eGFR 30-60 and &lt;30. Predictive performance of the stroke risk models was moderate-to-good in patients normal eGFR, but worsened with lower eGFR strata: C-statistics ranged from 0.68 to 0.77 for patients with normal (G1-2) eGFR, 0.58 to 0.72 for mild (G3) CKD, and 0.53 to 0.70 for patients with advanced (G4-5) CKD (Table). The modified-CHADS2 showed the best predictive performance in all CKD groups. Calibration plots (Figure 1) showed that the predicted risk was higher than the observed risk for almost all stroke risk models, independent of CKD status. The modified-CHADS2 showed slightly higher predicted risks than the observed risks (Figure) and has the most accurate ability (almost equal observed and predicted risks) in CKD patients. Conclusion The predictive performance of models for IS in a large cohort of AF patients decreased with decreasing kidney function. Predictions became less accurate in the clinically more relevant final stages of CKD. Therefore, most commonly used stroke risk models may not be useful for guiding individual decision-making in patients with CKD. The modified-CHADS2 score showed good and consistent discrimination and calibration, in AF patients normal eGFR, as well as in mild and advanced CKD, and would therefore be the preferred option for use in clinical practice in AF patients with CKD.

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