Validation of the 6-Month GRACE Score in Predicting 1-Year Mortality of Patients With Acute Coronary Syndrome Admitted to the Arabian Gulf Hospitals

Thalib, Lukman; Furuya‐Kanamori, Luis; AlHabib, Khalid F.; AlFaleh, Hussam; Alshamiri, Mostafa Q; Amin, Haitham; Suwaidi, Jassim Al; Sulaiman, Kadhim; Almahmeed, Wael; Alsheikh‐Ali, Alawi A.; Al‐Motarreb, Ahmed; Doi, Suhail A.R.

doi:10.1177/0003319716659179

Cited by 11 publications

(11 citation statements)

References 15 publications

(13 reference statements)

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“…Although there are several tools 3,4 available for determining the prognosis of NSTE-ACS patients, such as the Global Registry of Acute Coronary Events (GRACE) score which is recommended in clinical guidelines, 5 the establishment of more simple and more effective tools is still required. The GRACE risk score has been validated in several studies [6][7][8][9][10] ; however, the score does not include measurement of some important biomarkers. Therefore, a novel more simple and effective tool established using important biomarkers may provide more accurate risk estimation in ACS patients.…”

Section: Introductionmentioning

confidence: 99%

A Novel ABC Score Predicts Mortality in Non-ST-Segment Elevation Acute Coronary Syndrome Patients Who underwent Percutaneous Coronary Intervention

et al. 2020

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Objective In the present study, we aimed to establish a novel score to predict long-term mortality of non-ST-segment elevation acute coronary syndrome (NSTE-ACS) patients who underwent percutaneous coronary intervention (PCI). Methods A total of 2,174 NSTE-ACS patients from the CORFCHD-ZZ study were enrolled as the derivation cohort. The validation cohort including 1,808 NSTE-ACS patients were from the CORFCHD-PCI study. Receiver operating characteristic analysis and area under the curve (AUC) evaluation were used to select the candidate variables. The model performance was validated internally and externally. The primary outcome was cardiac mortality (CM). We also explored the model performance for all-cause mortality (ACM). Results Initially, 28 risk factors were selected and ranked according to their AUC values. Finally, we selected age, N-terminal pro-B-type natriuretic peptide, and creatinine to develop a novel prediction model named “ABC” model. The ABC model had a high discriminatory ability for both CM (C-index: 0.774, p < 0.001) and ACM (C-index: 0.758, p < 0.001) in the derivation cohort. In the validation cohort, the C-index of CM was 0.802 (p < 0.001) and that of ACM was 0.797 (p < 0.001), which suggested good discrimination. In addition, this model had adequate calibration in both the derivation and validation cohorts. Furthermore, the ABC score outperformed the GRACE score to predict mortality in NSTE-ACS patients who underwent PCI. Conclusion In the present study, we developed and validated a novel model to predict mortality in patients with NSTE-ACS who underwent PCI. This model can be used as a credible tool for risk assessment and management of NSTE-ACS after PCI.

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Section: Introductionmentioning

confidence: 99%

A Novel ABC Score Predicts Mortality in Non-ST-Segment Elevation Acute Coronary Syndrome Patients Who underwent Percutaneous Coronary Intervention

et al. 2020

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“…Thrombolysis in Myocardial Infarction (TIMI) and Global Registry of Acute Coronary Events (GRACE) are the most commonly used risk scores in this regard. 5 –7…”

Section: Introductionmentioning

confidence: 99%

The Predictive Value of PRECISE-DAPT Score for In-Hospital Mortality in Patients With ST-Elevation Myocardial Infarction Undergoing Primary Percutaneous Coronary Intervention

Tanık

Çınar²,

Aruğaslan

et al. 2018

Angiology

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The PREdicting bleeding Complications In patients undergoing Stent implantation and subsEquent Dual Anti-Platelet Therapy (PRECISE-DAPT) score predicts the bleeding risk in patients treated with dual antiplatelet treatment after primary percutaneous coronary intervention (pPCI). This study aimed to determine the predictive value of the admission PRECISE-DAPT score for in-hospital mortality in patients with ST elevation myocardial infarction (STEMI) treated with pPCI. Of the 1418 patients enrolled, the study population was divided into 2 groups: PRECISE-DAPT score ≥25 and PRECISE-DAPT score <25. The primary goal was to determine the incidence of in-hospital all-cause mortality. In-hospital mortality was significantly higher in patients whose PRECISE-DAPT score ≥25 compared with the patients whose PRECISE-DAPT score <25 (9.4 vs 0.9%; P < .001, respectively). Both univariate and multivariate Cox proportional hazard analyses showed that the PRECISE-DAPT score is independently associated with in-hospital mortality (hazards ratio [HR]: 1.043, 95% confidence interval [CI]: 1.003-1.084; P = .035; and HR: 1.026, 95% CI: 1.004-1.048; P = .021, respectively). A pairwise comparison of receiver operating characteristic curves showed that the predictive value of the PRECISE-DAPT score with regard to in-hospital mortality was noninferior compared with the Thrombolysis in Myocardial Infarction risk score. The PRECISE-DAPT score may be a significant independent predictor of in-hospital mortality in patients with STEMI treated with pPCI.

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“…In this section, we detail the processing and cleaning done in Stage I of our proposed methodology. The data were collected from a second Gulf Registry of Acute Coronary Events (Tang, Wong, & Herbison, ; Thalib et al, ) including consecutive patients with ACS from six Arabian Gulf Countries (Bahrain, Saudi Arabia, Qatar, Oman, United Arab Emirates, and Yemen). These patients belonged to 65 different hospitals during the period of data collection during the period October 2008 to June 2009.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, the current study is designed to validate the GRACE risk score in predicting mortality at 1‐year post discharge amongst Arabian Gulf patients. Thalib et al () noted that GRACE has not been studied in connection with Arabian Gulf patients for propensity (survival) analysis and conduct one of the first studies with Arabian Gulf GRACE dataset. Granger et al () analyse in‐hospital mortality for GRACE data for hospitals in Western Europe, Oceania, and North America.…”

Section: Introductionmentioning

confidence: 99%

Identifying mortality risk factors amongst acute coronary syndrome patients admitted to Arabian Gulf hospitals using machine‐learning methods

et al. 2019

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Acute coronary syndrome (ACS) is a leading cause of mortality and morbidity in the Arabian Gulf. In this study, the in‐hospital mortality amongst patients admitted with ACS to Arabian Gulf hospitals is predicted using a comprehensive modelling framework that combines powerful machine‐learning methods such as support‐vector machine (SVM), Naïve Bayes (NB), artificial neural networks (NN), and decision trees (DT). The performance of the machine‐learning methods is compared with that of the performance of a commonly used statistical method, namely, logistic regression (LR). The study follows the current practise of computing mortality risk using risk scores such as the Global Registry of Acute Coronary Events (GRACE) score, which has not been validated for Arabian Gulf patients. Cardiac registry data of 7,000 patients from 65 hospitals located in Arabian Gulf countries are used for the study. This study is unique as it uses a contemporary data analytics framework. A k‐fold (k = 10) cross‐validation is utilized to generate training and validation samples from the GRACE dataset. The machine‐learning‐based predictive models often incur prejudgments for imbalanced training data patterns. To mitigate the data imbalance due to scarce observations for in‐hospital mortalities, we have utilized specialized methods such as random undersampling (RUS) and synthetic minority over sampling technique (SMOTE). A detailed simulation experimentation is carried out to build models with each of the five predictive methods (LR, NN, NB, SVM, and DT) for the each of the three datasets k‐fold subsamples generated. The predictive models are developed under three schemes of the k‐fold samples that include no data imbalance, RUS, and SMOTE. We have implemented an information fusion method rooted in computing weighted impact scores obtain for an individual medical history attributes from each of the predictive models simulated for a collective recommendation based on an impact score specific to a predictor. Finally, we grouped the predictors using fuzzy c‐mean clustering method into three categories, high‐, medium‐, and low‐risk factors for in‐hospital mortality due to ACS. Our study revealed that patients with medical history related to the presences of peripheral artery disease, congestive heart failure, cardiovascular transient ischemic attack valvular disease, and coronary artery bypass grafting amongst others have the most risk for in‐hospital mortality.

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Validation of the 6-Month GRACE Score in Predicting 1-Year Mortality of Patients With Acute Coronary Syndrome Admitted to the Arabian Gulf Hospitals

Cited by 11 publications

References 15 publications

A Novel ABC Score Predicts Mortality in Non-ST-Segment Elevation Acute Coronary Syndrome Patients Who underwent Percutaneous Coronary Intervention

A Novel ABC Score Predicts Mortality in Non-ST-Segment Elevation Acute Coronary Syndrome Patients Who underwent Percutaneous Coronary Intervention

The Predictive Value of PRECISE-DAPT Score for In-Hospital Mortality in Patients With ST-Elevation Myocardial Infarction Undergoing Primary Percutaneous Coronary Intervention

Identifying mortality risk factors amongst acute coronary syndrome patients admitted to Arabian Gulf hospitals using machine‐learning methods

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