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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
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01. History of Cardiology
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05. Atrial Fibrillation
06. Supraventricular Tachycardia (non-AF)
07. Syncope and Bradycardia
08. Ventricular Arrhythmias and Sudden Cardiac Death (SCD)
09. Device Therapy
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31. Pharmacology and Pharmacotherapy
32. Cardiovascular Nursing
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34. Public Health and Health Economics
35. Research Methodology
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Can Machine learning help us find the fountain of youth for elderly acute coronary syndrome patients?
Session:
Posters 3 - Écran 5 - Doença CV em populações especiais
Speaker:
Sofia S. Martinho
Congress:
CPC 2019
Topic:
K. Cardiovascular Disease In Special Populations
Theme:
30. Cardiovascular Disease in Special Populations
Subtheme:
30.5 Cardiovascular Disease in the Elderly
Session Type:
Posters
FP Number:
---
Authors:
Sofia S. Martinho; He M; Simpson C; Zaslavskiy M.; Li, L; Balazard F; Rousset A; Schopf S; D. Dellamonica; J Almeida; J Milner; J Ferreira; P Monteiro; S Monteiro
Abstract
<p><u>Background:</u> Acute coronary syndromes (ACS), remain an important cause of morbidity and mortality, especially amongst elderly patients. Therefore, new risk prediction tools are important to better identify and risk stratify high risk patients within this important ACS subpopulation. Previous studies already showed that high GRACE score was significant predictor of mortality of ACS.</p> <p><u>Aim:</u> The goal of this analisys was, using machine learning and artificial intelligence, in a single center database of acute coronary syndrome (ACS), to identify the best predictors of a new ACS and to compare its relevance for risk discrimination in a general ACS population versus a given subpopulation of interest.</p> <p><u>Methods</u>: This study was conducted using the data of 5977 patients, admitted in a single center for ACS between 2004 and 2017 and discharged alive. In the subpopulation of elderly patients (n=3323), each covariate present in the database was ?analysed 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 elderly patients and the risk of a new ACS, compared to the one in the general population. Kaplan Meier curve represents how ACS free-survival depends on the covariate and elderly patients. In the general population and in this group of interest, the covariate was used to further create 3 groups, of which, only the 2 extremes are shown. The solid lines represent KM inside the elderly patients, the dotted lines in the general population. Pink or grey color of the curves represent the stratification level of the covariate (can be 0 or 1 in the case of binary variables or intervals).</p> <p><u>Results:</u> In our model, GRACE score was found to be a better discriminator of risk of further ACS in elderly patients than in the general ACS population. We saw that higher GRACE score was associated with lower risk of recurrent ACS.</p> <p><u>Conclusions:</u> This may be explained by the fact that elderly patients with higher GRACE score dyed during the index ACS hospitalization or out of hospital. Based on this finding, we now can better risk stratify elderly post-ACS patients, and mare 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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