Objectives: To determine clinical application of GRACE risk score in patients with acute coronary syndrome (ACS).Patients and Methods: It was an observational analytical study conducted in the Cardiology ward of Mayo hospital, Lahore from April to July 2015. Patients with Acute STEMI, NSTEMI or Unstable angina (UA) were selected on the basis of typical chest pain, ECG changes or cardiac biomarkers .For all eligible cases, at presentation GRS was calculated using online calculator. Also, GRACE risk categories and predicted in-hospital mortality were determined. Patients with previous episodes of STEMI/ NSTEMI, old Left Bundle Branch Block (LBBB), stable angina pectoris, acute pericarditis, myocarditis, acute rheumatic fever or pulmonary embolism were excluded. Data was analyzed on SPSS 20 and the R project for statistical computing. Individual components of GRS were compared among discharged and expired cases using t-test. A p-value of <0.05 was considered significant.Results: A total of 165 patients with STEMI and ACS were included. The mean GRS among males andfemales was 137.4 ± 39 and 151.5 ± 50.6. The observed in-hospital mortality was 12.12% with 60% patients of STEMI. Among expired cases, 90% patients had high GRS, predominantly from STEMI group. Important determinants of adverse outcome were advanced age, tachycardia, low systolic blood pressure and presence of cardiac failure.Conclusion: STEMI was the major acute cardiac event. The mean GRS of expired patients was significantly higher than discharged group. GRS accurately identified low risk cases with low probability of in-hospital death. GRS over estimate probability of in-hospital death among STEMI high risk cases that had higher scores and discharged uneventfully. Grace Risk Score is a reliable predictor of risk category and adverse outcomes and its use by clinicians should be strongly recommended.
Survival prediction of a patient is a critical task in clinical medicine for physicians and patients to make an informed decision. Several survival and risk scoring methods have been developed to estimate the survival score of patients using clinical information. For instance, the Global Registry of Acute Coronary Events (GRACE) and Thrombolysis in Myocardial Infarction (TIMI) risk scores are developed for the survival prediction of heart patients. Recently, state-of-the-art medical imaging and analysis techniques have paved the way for survival prediction of cancer patients by understanding key features extracted from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scanned images with the help of image processing and machine learning techniques. However, survival prediction is a challenging task due to the complexity in benchmarking of image features, feature selection methods, and machine learning models. In this article, we evaluate the performance of 156 visual features from radiomic and hand-crafted feature classes, six feature selection methods, and 10 machine learning models to benchmark their performance. In addition, MRI scanned Brain Tumor Segmentation (BraTS) and CT scanned non-small cell lung cancer (NSCLC) datasets are used to train classification and regression models. Our results highlight that logistic regression outperforms for the classification with 66 and 54% accuracy for BraTS and NSCLC datasets, respectively. Moreover, our analysis of best-performing features shows that age is a common and significant feature for survival prediction. Also, gray level and shape-based features play a vital role in regression. We believe that the study can be helpful for oncologists, radiologists, and medical imaging researchers to understand and automate the procedure of decision-making and prognosis of cancer patients.
Coronary artery disease particularly myocardial infarction remains the leading cause of mortality and morbidity worldwide, There are few modifiable and un-modifiable risk factors of MI. Myocardial infarction is diagnosed on the basis of typical chest pain raised cardiac enzymes and ECG changes, any two of them lead to the diagnosis, In this study old patients of MI, patients with complicated heart disease and the patients who underwent cardiac surgery were excluded, Results: Amongst 50 patients 36(72%) were males and 14(28%) were females. 70% presented with typical chest pain, 48% had anterior wall MI, 32% had anterolateral MI, 16% had anteroseptal MI and 21% had inferior wall MI, whereas 8% had global Ml, Conclusion: Anterior wall is commonest area involved in MI and 70% of all patients presented with typical chest pain and the most important risk factor is smoking.
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