PROGNOSTIC VALUE OF GRACE VERSUS TIMI SCORE FOR IN-HOSPITAL OUTCOMES AFTER NON-ST-ELEVATION ACUTE CORONARY SYNDROME

Objectives: To compare the predictive value of TIMI and GRACE score for predicting in-hospital outcomes after non-ST elevation acute coronary syndrome (NSTE-ACS). Methodology: This study included prospectively recruited cohort of patients presented to a tertiary care cardiac center of Karachi, Pakistan who were diagnosed with NSTE-ACS. GRACE and TIMI score were obtained and in-hospital mortality was recorded. The receiver operating characteristic (ROC) curves analysis was performed and area under the curve (AUC) was obtained as indicative of predictive value for both scores. Results: A total of 300 patients were included, out of which 76.7%(230) were male and mean age was 58.04±10.71 years. Risk profile comprises of 84.3%(253) hypertensive, 42.0%(126) diabetic, 27.3%(82) smokers, 9.0%(27) obese, 15.3%(46) dyslipidemic, and 31%(93) with sedentary lifestyle. Mean GRACE and TIMI score were 120.19±33.17 and 3.18±0.85 respectively. In-hospital mortality rate was 5.3%(16). AUC for the GRACE score was 0.851 [0.767 - 0.934] with the optimal cut-off value of 150 with sensitivity of 68.8% and specificity of 84.9%. The AUC for the TIMI score was 0.781[0.671 - 0.891] with the optimal cut-off value of 4 with sensitivity of 75.0% and specificity of 67.6%. Conclusion: The GRACE score has high discriminating strength for predicting in-hospital mortality after NSTE-ACS. GRACE score should be used as risk stratification modality in clinical decision making for the management of NSTE-ACS.

Electrocardiography Score for Left Ventricular Systolic Dysfunction in Non-ST Segment Elevation Acute Coronary Syndrome

Background: Few studies have characterized electrocardiography (ECG) patterns correlated with left ventricular (LV) systolic dysfunction in patients with non-ST segment elevation acute coronary syndrome (NSTE-ACS).Objectives: This study aims to develop ECG pattern-derived scores to predict LV systolic dysfunction in NSTE-ACS patients.Methods: A total of 466 patients with NSTE-ACS were retrospectively enrolled. LV ejection fraction (LVEF) was assessed by echocardiography within 72 h after the first triage ECG acquisition; there was no coronary intervention in between. ECG score was developed to predict LVEF < 40%. Performance of LVEF, the Global Registry of Acute Coronary Events (GRACE), Thrombolysis in Myocardial Infarction (TIMI) and ECG scores to predict 24-month all-cause mortality were analyzed. Subgroups with varying LVEF, GRACE and TIMI scores were stratified by ECG score to identify patients at high risk of mortality.Results: LVEF < 40% was present in 20% of patients. We developed the PQRST score by multivariate logistic regression, including poor R wave progression, QRS duration > 110 ms, heart rate > 100 beats per min, and ST-segment depression ≥ 1 mm in ≥ 2 contiguous leads, ranging from 0 to 6.5. The score had an area under the curve (AUC) of 0.824 in the derivation cohort and 0.899 in the validation cohort for discriminating LVEF < 40%. A PQRST score ≥ 3 could stratify high-risk patients with LVEF ≥ 40%, GRACE score > 140, or TIMI score ≥ 3 regarding 24-month all-cause mortality.Conclusions: The PQRST score could predict LVEF < 40% in NSTE-ACS patients and identify patients at high risk of mortality in the subgroups of patients with LVEF ≥ 40%, GRACE score > 140 or TIMI score ≥ 3.

MORTALITY OF ACUTE MYOCARDIAL INFARCTION IN RELATION WITH TIMI RISK SCORE

Background: The Thrombolysis in Myocardial Infarction (TIMI) risk score is said to be an important factor in predicting mortality risk in fibrinolysis-eligible STEMI patients. An attempt was made to assess the situation by comparing risk stratification based on the TIMI score with the hospital outcome of such individuals. Methods: 145 STEMI patients were included in this srudy , TIMI risk scores were calculated and analysed vis-Ã -vis various relevant parameters.. Based on their TIMI scores, the patients were placed into three risk groups: low-risk,moderate-risk, and high-risk. All patients received standard anti-ischemic medication, were thrombolyzed, monitored in the ICCU, and monitored throughout their hospital stay for post-MI sequelae. Results: According to the TIMI risk score, 79 patients (54.5%) had low-risk , 48 (33.1%) to the moderate-risk , and 18 (12.4%) to the high-risk . The highest mortality rate (total 17 deaths) was found in the high-risk group (55.6%), followed by moderate-risk (12.2%) and low-risk (1.28%) groups, respectively. Killips categorization grade 2-4 had the highest relative risk (RR-15.85) of the seven potentially dubious variables evaluated, followed by systolic BP 100mmHg (RR-10.48), diabetes mellitus (RR-2.79), and age >65 years (RR- 2.59). Conclusions: In patients with STEMI, the TIMI risk scoring system appears to be a straightforward, valid, and practical bedside tool for quantitative risk classification and short-term prognosis prediction.

Investigating performance of deep learning and machine learning risk stratification of Asian in-hospital patients after ST-elevation myocardial infarction

Background Machine learning (ML) algorithm support vector machine (SVM) performed better than Thrombolysis in Myocardial Infarction (TIMI) score for ASIAN STEMI patients. However, Deep Learning (DL) effectiveness in the multiethnic ASIAN population has yet to be determined. DL has automatic learning of the feature from a given dataset without the need to conduct feature selection. However, the selected features by the algorithm is black box. Identifying features associated with mortality is essential to recognize characteristics of patients with high risk for better patient management. Purpose To develop a DL algorithm for in-hospital mortality in multiethnic STEMI patients using predictors identified from the SVM algorithm. To investigate DL performance constructed using predictors from SVM feature extraction and expert-recommended predictors. Methods We constructed four algorithms; a) DL and SVM algorithms with predictors identified from the SVM variable importance b) DL and SVM using predictors based on expert recommendation. We used registry data from the National Cardiovascular Disease Database of 11397 patient's. Fifty parameters including demographics, cardiovascular risk, medications and clinical variables were considered. The Area under the curve (AUC) is the performance evaluation metric. Algorithms were validated against the TIMI and tested using the same validation data. SVM variable importance with backward elimination was used to select and rank important variables. Results DL algorithms outperform SVM and TIMI on the validation dataset; i) DL with SVM selected predictors (15 predictors, AUC = 0.97), ii) DL with expert-recommended predictors (16 predictors, AUC = 0.96), iii) SVM with selected predictors (15 predictors, AUC = 0.92), iv) SVM with expert-recommended predictors (AUC = 0.89) and TIMI (AUC = 0.82). Common predictors across SVM feature selection, expert-recommendation and TIMI are: age, heart rate, Killip class, fasting blood glucose, systolic blood pressure, comorbid diseases and ST-elevation. SVM feature selection also identified diuretics, PCI and pharmacotherapy drugs as predictors that improve mortality prediction in STEMI patients. Our findings suggest that the TIMI score underestimates patients risk of mortality. DL algorithm using selected predictors classified 35% of nonsurvival patients as high risk (risk probabilities >50%) compared to only 12.7% nonsurvival patients by TIMI (score >5) (Figure below). Conclusions In the ASIAN population, patients with STEMI can be better classified using the DL algorithm compared to the ML and TIMI score. Combining ML feature selection with DL allows the identification of distinct factors in a unique ASIAN population for better mortality prediction than relying solely on an expert recommendation as it is a very subjective approach. Continuous validation on population-specific algorithms using DL and ML is needed before implementing in a real clinical setting. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Technology Development Fund 1 TIMI performance on validation set DL performance on validation set

Time to think beyond door to balloon time: significance of total ischemic time in STEMI

Background Significance of total ischemic time (TIT) in the context of ST-segment elevation myocardial infarction (STEMI) is still controversial. Therefore, in this study, we have evaluate the association of TIT with immediate outcomes in STEMI patients in whom recommended door to balloon (DTB) time of less than 90 min was achieved. Results A total of 5730 patients were included in this study, out of which 80.9% were male and median age was 55 [61–48] years. The median DTB was observed to be 60 [75–45] min and onset of chest pain to emergency room (ER) arrival time was 180 [300–120] min. Prolonged TIT was associated with poor pre-procedure thrombolysis in myocardial infarction (TIMI) flow grade (p = 0.022), number of diseased vessels (p = 0.002), use of intra-aortic balloon pump (p = 0.003), and in-hospital mortality (p = 0.002). Mortality rate was 4.5%, 5.7%, and 7.8% for the patients with TIT of ≤ 120 min, 121 to 240 min, and > 240 min, respectively. Thirty days’ risk of mortality on TIMI score was 4.97 ± 7.09%, 5.01 ± 6.99%, and 7.12 ± 8.64% for the patients with TIT of ≤ 120 min, 121 to 240 min, and > 240 min, respectively. Conclusions Prolonged total ischemic was associated with higher in-hospital mortality. Therefore, TIT can also be considered in the matrix of focus, along with DTB time and other clinical determinants to improve the survival from STEMI.

The Added Value of ST-Elevation in Lead aVR to Clinical TIMI Score in Predicting the Angiographic Severity of Coronary Artery Disease in Patients with Non ST-Elevation Myocardial Infarction

Background Atherosclerosis is the ongoing process of plaque formation involving primarily the intima of large and medium-sized arteries. The condition progresses relentlessly throughout a person’s lifetime, before finally manifesting itself as an acute ischemic event. TIMI score is a tool of 7 points for patients with NSTE-ACS to detect the risk according to the score. The term acute coronary syndrome (ACS) includes unstable angina (UA), non STsegment elevation myocardial infarction (NSTEMI), and ST-segment elevation myocardial infarction (STEMI). Aim To investigate the added value of the presence of ST-segment elevation in lead avR on admission electrocardiogram to the (TIMI) clinical scoring system in predicting the angiographic severity of coronary artery disease in patients admitted with NSTE-ACS. Patients and Methods 150 patients with Non ST-segment elevation acute coronary syndrome was included from Cardiology Department, Ain Shams University Hospital, Cairo, Egypt. Results From those patients, 137 patients (91.3%) diagnosed by coronary angiography to have significant CAD and 93 patients (62%) had ST-Elevation in lead aVR . These 137 patients were divided into 3 groups according to TIMI risk score to 16 patients (10.6%) had low risk score, 63 patients (42.0%) had intermediate risk & lastly 58 patients (38.7%) had high risk score. . Although being useful in prediction of multi-vessel & LM involvement among high risk group, TIMI score failed to predict the same in intermediate & low risk groups where multi-vessel involvement was found in 46 patients (30.6%) & 7 patients (4.6%) of intermediate & low risk groups respectively. Also LM involvement was found in 15 patients (10%) & 2 patients (1.3%) of intermediate & low risk groups respectively. Conclusion ST-segment elevation (STE) in lead avR had an adding predicting value in NSTEACS patients especially those with low to intermediate TIMI score. Adding the value of STE in lead aVR to TIMI risk score may improve the early stratification & management of those patients at high risk coronary artery disease, with subsequent impact on morbidity & mortality.

Association of neutrophil to lymphocyte ratio (NLR) with angiographic SYNTAX score in patients with non-ST-Segment elevation acute coronary syndrome (NSTE-ACS)

Introduction: Considering the role of inflammation in pathogenesis of atherosclerosis, we aimed to investigate the association of presentation neutrophil to lymphocyte ratio (NLR) with complexity of coronary artery lesions determined by SYNTAX score in patients with non-ST-elevation acute coronary syndrome (NSTE-ACS). Methods: From March 2018 to March 2019, we recruited 202 consecutive patients, who were hospitalized for NSTE-ACS and had undergone percutaneous coronary intervention in our hospital. The association of presentation NLR with SYNTAX score was determined in univariate and multivariate linear regression analysis. Results: Higher NLR was significantly associated with higher SYNTAX score (beta= 0.162, P=0.021). In addition, older age, having hypertension, higher TIMI score, and lower ejection fraction on echocardiographic examination were significantly associated with higher SYNTAX score. TIMI score had the largest beta coefficient among the studied variables (TIMI score beta=0.302, P<0.001). In two separate multivariate linear regression models, we assessed the unique contribution of NLR in predicting SYNTAX score in patients with NSTE-ACS. In the first model, NLR was significantly contributed to predicting SYNTAX score after adjustment for age, sex, and hypertension as covariates available on patient presentation (beta=0.142, P=0.040). In the second model, NLR was not an independent predictor of SYNTAX score after adjustment for TIMI score (beta=0.121, P=0.076). Conclusion: In NSTE-ACS, presentation NLR is associated with SYNTAX score. However, NLR does not contribute significantly to the prediction of SYNTAX score after adjustment for TIMI score. TIMI risk score might be a better predictor of the SYNTAX score in comparison to NLR.

Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach

Background Conventional risk score for predicting short and long-term mortality following an ST-segment elevation myocardial infarction (STEMI) is often not population specific. Objective Apply machine learning for the prediction and identification of factors associated with short and long-term mortality in Asian STEMI patients and compare with a conventional risk score. Methods The National Cardiovascular Disease Database for Malaysia registry, of a multi-ethnic, heterogeneous Asian population was used for in-hospital (6299 patients), 30-days (3130 patients), and 1-year (2939 patients) model development. 50 variables were considered. Mortality prediction was analysed using feature selection methods with machine learning algorithms and compared to Thrombolysis in Myocardial Infarction (TIMI) score. Invasive management of varying degrees was selected as important variables that improved mortality prediction. Results Model performance using a complete and reduced variable produced an area under the receiver operating characteristic curve (AUC) from 0.73 to 0.90. The best machine learning model for in-hospital, 30 days, and 1-year outperformed TIMI risk score (AUC = 0.88, 95% CI: 0.846–0.910; vs AUC = 0.81, 95% CI:0.772–0.845, AUC = 0.90, 95% CI: 0.870–0.935; vs AUC = 0.80, 95% CI: 0.746–0.838, AUC = 0.84, 95% CI: 0.798–0.872; vs AUC = 0.76, 95% CI: 0.715–0.802, p < 0.0001 for all). TIMI score underestimates patients’ risk of mortality. 90% of non-survival patients are classified as high risk (>50%) by machine learning algorithm compared to 10–30% non-survival patients by TIMI. Common predictors identified for short- and long-term mortality were age, heart rate, Killip class, fasting blood glucose, prior primary PCI or pharmaco-invasive therapy and diuretics. The final algorithm was converted into an online tool with a database for continuous data archiving for algorithm validation. Conclusions In a multi-ethnic population, patients with STEMI were better classified using the machine learning method compared to TIMI scoring. Machine learning allows for the identification of distinct factors in individual Asian populations for better mortality prediction. Ongoing continuous testing and validation will allow for better risk stratification and potentially alter management and outcomes in the future.

Hypofunction of Circulating Endothelial Progenitor Cells and Aggravated Severity in Elderly Male Patients With Non-ST Segment Elevation Myocardial Infarction: Its Association With Systemic Inflammation

Background: Aging patients easily suffer from non-ST segment elevation myocardial infarction (NSTEMI). Our previous studies revealed declined function of endothelial progenitor cells (EPCs) in the elderly. However, the impact of aging on EPC function and severity in male NSTEMI patients and its possible mechanism is unclear until now.Methods: We measured the circulating EPC function including migration, proliferation, and adhesion in aging or young male patients with NSTEMI. The GRACE and TIMI risk score were evaluated. Plasma levels of interleukin-6 (IL-6) and interleukin-17 (IL-17) were also detected in all patients.Results: Compared with the young group, the old male patients with NSTEMI had higher GRACE score and TIMI score and decreased function of circulating EPCs. EPC function was negatively correlated with GRACE score and TIMI score. IL-6 and IL-17 level were higher in the old group than those in the young group. There was a significant negative correlation between EPC function and IL-6 or IL-17. Moreover, IL-6 and IL-17 positively correlated with GRACE and TIMI score. Age was positively related with GRACE or TIMI score and plasma level of IL-6 or IL-17, but inversely correlated with EPC function.Conclusions: The current study firstly illustrates that the age-related decrement in EPC function is related to the severity of NSTEMI in male patients, which may be connected with systemic inflammation. These findings provide novel insights into the pathogenetic mechanism and intervention target of aging NSTEMI.