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Acute Coronary Syndrome IV: Interprofessional Care01:28

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IntroductionThe management of Acute Coronary Syndrome (ACS) aims to minimize myocardial damage, preserve myocardial function, and prevent complications.Initial ManagementInpatient management involves continuous cardiac monitoring, preferably in an ICU, focusing on blood pressure, serum sodium, potassium, and creatinine levels, and urine output. Ongoing pharmacologic management is crucial for stabilizing the patient.Supplemental Oxygen: Administer supplemental oxygen if oxygen saturation is...
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Diagnosing acute coronary syndrome or ACS begins with a thorough patient history. Notable symptoms include central, crushing chest pain radiating to the left arm, neck, jaw, or back, along with shortness of breath, sweating (diaphoresis), nausea, vomiting, dizziness, and palpitations.It is crucial to note any history of cardiac illnesses and assess risk factors, including age, gender, smoking, hypertension, diabetes, hyperlipidemia, and a sedentary lifestyle.During physical examination, vital...
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Acute Coronary Syndrome (ACS) encompasses a spectrum of heart conditions caused by sudden obstruction of coronary arteries, typically resulting from the rupture of an atherosclerotic plaque and subsequent thrombus (blood clot) formation. This obstruction can lead to partial or complete blockage of blood flow, causing varying degrees of myocardial ischemia or infarction.ACS includes the following clinical entities:Unstable Angina (UA)Non-ST-Elevation Myocardial Infarction (NSTEMI)ST-Elevation...
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Updated: Nov 5, 2025

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Improving 1-year mortality prediction in ACS patients using machine learning.

Sebastian Weichwald1,2, Alessandro Candreva3, Rebekka Burkholz1

  • 1Department of Computer Science, Institute for Machine Learning, ETH Zurich, Switzerland.

European Heart Journal. Acute Cardiovascular Care
|May 20, 2021
PubMed
Summary

A new SPUM-ACS Score predicts 1-year mortality in acute coronary syndrome (ACS) patients better than the GRACE 2.0 Score. This score incorporates age, glucose, NT-proBNP, LVEF, and comorbidities for improved risk stratification.

Keywords:
Acute Coronary SyndromesGRACE 2.0 ScoreMachine LearningNT-proBNPage

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Area of Science:

  • Cardiology
  • Clinical Risk Stratification
  • Biomarkers

Background:

  • The Global Registry of Acute Coronary Events (GRACE) score is a standard tool for assessing risk in acute coronary syndromes (ACS).
  • A need exists for improved prediction models for 1-year all-cause mortality in ACS patients.

Purpose of the Study:

  • To develop and internally validate a novel risk stratification model for predicting 1-year all-cause mortality in ACS patients.
  • To compare the performance of the new model against the established GRACE 2.0 Score.

Main Methods:

  • Analysis of 2,168 ACS patients from the Swiss SPUM-ACS Cohort (2009-2012).
  • Evaluation of numerous linear models using combinations of 8 out of 56 variables.
  • Determination of 1-year all-cause mortality in 95.8% of patients, with a mortality rate of 4.3%.

Main Results:

  • The novel SPUM-ACS Score, incorporating age, plasma glucose, NT-proBNP, LVEF, Killip class, PAD, malignancy, and CPR, outperformed the GRACE 2.0 Score.
  • The SPUM-ACS Score demonstrated superior predictive accuracy compared to the GRACE 2.0 Score's 5-fold cross-validated AUC of 0.81 (95% CI 0.78-0.84).
  • Key predictors identified include age, trimethylamine N-oxide, creatinine, PAD/malignancy history, LVEF, and hemoglobin.

Conclusions:

  • The SPUM-ACS Score effectively highlights the importance of age, heart failure markers, and comorbidities in predicting mortality after ACS.
  • External validation and refinement in larger cohorts are necessary before clinical application of the SPUM-ACS Score.