Login
Search
Search
0 Dates
2024
2023
2022
2021
2020
2019
2018
0 Events
CPC 2018
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
CPC 2020
CPC 2021
CPC 2022
CPC 2023
CPC 2024
0 Topics
A. Basics
B. Imaging
C. Arrhythmias and Device Therapy
D. Heart Failure
E. Coronary Artery Disease, Acute Coronary Syndromes, Acute Cardiac Care
F. Valvular, Myocardial, Pericardial, Pulmonary, Congenital Heart Disease
G. Aortic Disease, Peripheral Vascular Disease, Stroke
H. Interventional Cardiology and Cardiovascular Surgery
I. Hypertension
J. Preventive Cardiology
K. Cardiovascular Disease In Special Populations
L. Cardiovascular Pharmacology
M. Cardiovascular Nursing
N. E-Cardiology / Digital Health, Public Health, Health Economics, Research Methodology
O. Basic Science
P. Other
0 Themes
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
14. Acute Cardiac Care
15. Valvular Heart Disease
16. Infective Endocarditis
17. Myocardial Disease
18. Pericardial Disease
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
36. Basic Science
37. Miscellanea
0 Resources
Abstract
Slides
Vídeo
Report
CLEAR FILTERS
The quest for GRACE 3.0: improving our beloved risk score with machine learning
Session:
CO 04 - Doença coronária-genética-avaliação funcional
Speaker:
José Pedro Sousa
Congress:
CPC 2021
Topic:
E. Coronary Artery Disease, Acute Coronary Syndromes, Acute Cardiac Care
Theme:
13. Acute Coronary Syndromes
Subtheme:
13.2 Acute Coronary Syndromes – Epidemiology, Prognosis, Outcome
Session Type:
Comunicações Orais
FP Number:
---
Authors:
José Pedro Sousa; Afonso Lima; Paulo Gil; Jorge Henriques; Lino Gonçalves
Abstract
<p style="text-align:justify"><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif">Background: </span></span><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif">Although widely recommended for risk assessment of patients with acute coronary syndrome (ACS), the Global Registry of Acute Coronary Events (GRACE) score famously lacks discriminative power. On the other hand, in-hospital serum hemoglobin levels (HG) have been shown to simultaneously forecast both thrombotic and hemorrhagic hazards.</span></span></p> <p style="text-align:justify"><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif">Purpose: </span></span><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif">To ascertain the extent to which the incorporation of HG in the GRACE score is able to increase its predictive ability.</span></span></p> <p style="text-align:justify"><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif">Methods: </span></span><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif">Retrospective single-center study encompassing ACS patients consecutively admitted to a Cardiac Intensive Care Unit. Inclusion criteria comprised the acquaintance of GRACE score, HG and vital status on a 6-month follow-up, which served as the outcome. 3 discriminative models were first created: (standard) GRACE score (model 1); GRACE score plus HG, by means of logistic regression (model 2); GRACE score plus HG, by means of multilayer perceptron (a class of feedforward artificial neural network) (model 3). Hereafter, if models 2 and/or 3 were to be found significantly more discriminative than model 1, a correction factor would be calculated, also allowing for the conception of the most predictive model possible (model 4). The discriminative ability was estimated by both the area under the receiver-operating characteristic curve (AUC), and the dyad sensitivity/specificity.</span></span></p> <p style="text-align:justify"><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif">Results: </span></span><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif">Between April 2009 and December 2016, 1468 patients met study inclusion criteria. Mean age was 68.0±13.2 years and 29.8% were female, while 36.9% presented with ST-segment elevation myocardial infarction. Mean GRACE score was 145.5±47.0 and mean HG was 13.5±2.0. All-cause mortality reached 10.5%, at 6 months. Predictive power for models 1, 2 and 3 may be quantified as follows: AUC 0.6998, sensitivity 77.7% and specificity 62.5%; AUC 0.7818, sensitivity 36.3% and specificity 92.2%; AUC 0.7851, sensitivity 47.7% and specificity 88.5%, respectively. Both models 2 and 3 exhibited more discriminative ability than model 1 (p<0.001), due to their higher specificity. As such, a correction factor was computed (y = -7.8556x + 86.4117) and model 4 was created, displaying a sensitivity of 65.9% and a specificity of 76.5%.</span></span></p> <p style="text-align:justify"><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif">Conclusion: </span></span><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif">HG single-handedly provides incremental predictive value – namely more specificity – to the GRACE score. In particular, the latter seems to overestimate ACS patients’ risk if HG is normal or close to normal.</span></span></p>
Our mission: To reduce the burden of cardiovascular disease
Visit our site