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CPC 2019
Curso de Atualização em Medicina Cardiovascular 2019
Reunião Anual Conjunta dos Grupos de Estudo de Cirurgia Cardíaca, Doenças Valvulares e Ecocardiografia da SPC
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A. Basics
B. Imaging
C. Arrhythmias and Device Therapy
D. Heart Failure
E. Coronary Artery Disease, Acute Coronary Syndromes, Acute Cardiac Care
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01. History of Cardiology
02. Clinical Skills
03. Imaging
04. Arrhythmias, General
05. Atrial Fibrillation
06. Supraventricular Tachycardia (non-AF)
07. Syncope and Bradycardia
08. Ventricular Arrhythmias and Sudden Cardiac Death (SCD)
09. Device Therapy
10. Chronic Heart Failure
11. Acute Heart Failure
12. Coronary Artery Disease (Chronic)
13. Acute Coronary Syndromes
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19. Tumors of the Heart
20. Congenital Heart Disease and Pediatric Cardiology
21. Pulmonary Circulation, Pulmonary Embolism, Right Heart Failure
22. Aortic Disease
23. Peripheral Vascular and Cerebrovascular Disease
24. Stroke
25. Interventional Cardiology
26. Cardiovascular Surgery
27. Hypertension
28. Risk Factors and Prevention
29. Rehabilitation and Sports Cardiology
30. Cardiovascular Disease in Special Populations
31. Pharmacology and Pharmacotherapy
32. Cardiovascular Nursing
33. e-Cardiology / Digital Health
34. Public Health and Health Economics
35. Research Methodology
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Can machine learning help us improve risk stratification of diabetic patients with Acute Coronary Syndromes? The answer will blow your mind
Session:
Posters 4 - Écran 7 - Prevenção
Speaker:
James Milner
Congress:
CPC 2019
Topic:
J. Preventive Cardiology
Theme:
28. Risk Factors and Prevention
Subtheme:
28.7 Diabetes and the Heart
Session Type:
Posters
FP Number:
---
Authors:
James Milner; Sílvia Monteiro; Pedro Monteiro; He M; Simpson C.; Zaslavskiy M.; Balazard F; L. Li; Rousset A; Schopf S; D. Dellamonica; Lino Gonçalves
Abstract
<p>Background: Risk stratification following an acute coronary syndrome (ACS) is of utmost importance, in order to identify patients at higher risk of subsequent cardiovascular events. Diabetic patients have a significantly worse prognosis, so new risk prediction tools are important to better identify and risk stratify high risk patients within this important ACS subpopulation. </p> <p>Aim: The aim of this study was to identify the best predictors of a new ACS, in a single-center database of ACS, resorting to machine learning and artificial intelligence, and to compare the Global Registry of Acute Coronary Events (GRACE) risk score’s relevance for risk discrimination in a general ACS population versus a subpopulation of diabetic patients. </p> <p>Methods: In a single center, 5977 patients admitted due to ACS between 2004 and 2017 and alive at discharge were studied. In the subpopulation of diabetic patients (n=3429), each covariate present in the database was analyzed separately with a Cox proportional hazard model with three terms – subpopulation belonging indicator, covariate, interaction term. The p-value of the interaction term was used to rank variables. The more significant the interaction term, the stronger the change in relationship between patients in the subpopulation and the risk of a new ACS, compared to the one in the general population. </p> <p>Results: During long term follow-up, 13% of patients (n=771) experienced a second event. Kaplan-Meier curve represents how ACS free-survival depends on the GRACE risk score and group of interest. In the general population and in the subpopulation of diabetic patients, the GRACE score was used to further divide patients into 3 terciles, of which only the lower and upper tercile are shown (GRACE £ 113 and GRACE > 144, respectively). The solid lines represent Kaplan-Meier curves for diabetic patients, and the dotted lines in the general population. Pink or grey colour of the curves represent the stratification level of the covariate. </p> <p>Conclusions: In our model, the GRACE risk score was found to be a better discriminator of risk of futher ACS in diabetic patients than in the general ACS population. Strikingly, a higher GRACE score predicts a lower rate of readmission, probably because many patients will die in the index hospitalization or out of hospital. This finding reinforces the usefulness of the GRACE score in high risk patients and may improve risk stratisfication in diabetic post-ACS patients, making sure that they are closely followed and submitted to optimal risk factor management, in order to improve their post-ACS prognosis.</p>
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