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Related Concept Videos

Heart Failure IV: Classification and Diagnostic Evaluation01:30

Heart Failure IV: Classification and Diagnostic Evaluation

162
Heart failure can be classified in various ways, with the most common classifications based on physical activity limitations, disease progression, severity, and treatment strategies.The Functional Classification of Heart Failure divides patients into four categories based on physical activity limitation due to symptom burden.Class I: Patients in this class have cardiac disease but no physical activity limitations. Ordinary activities like walking, climbing stairs, or routine tasks do not cause...
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Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

423
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
423
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

425
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
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Heart Failure I: Introduction01:27

Heart Failure I: Introduction

408
Heart failure refers to a clinical syndrome caused by structural or functional cardiac disorders that prevent the heart from pumping an adequate amount of blood to meet the body's metabolic needs. This condition often arises from myocardial infarction or ischemia, leading to decreased cardiac output, reduced tissue perfusion, impaired gas exchange, fluid volume imbalance, and decreased functional ability.Heart failure can result from disruptions in the mechanisms that regulate cardiac output...
408

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Machine learning vs. conventional statistical models for predicting heart failure readmission and mortality.

Sheojung Shin1, Peter C Austin1, Heather J Ross1

  • 1University of Toronto, ICES, Rm G-106, 2075 Bayview Ave., Toronto, ON, M4G2E1, Canada.

ESC Heart Failure
|November 18, 2020
PubMed
Summary

Machine learning (ML) models show superior performance over conventional statistical models (CSMs) for predicting heart failure (HF) readmission and mortality. Further research should focus on external validation and quality assessment of ML prediction models.

Keywords:
DeathHeart failureHospitalizationMachine learningMortalityPrognosisReadmissionStatistical models

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Area of Science:

  • Biomedical Informatics
  • Cardiology
  • Machine Learning

Background:

  • Heart failure (HF) poses a significant challenge in healthcare, with patient readmission and mortality being key indicators of disease severity and treatment effectiveness.
  • Accurate prediction of HF readmission and mortality is crucial for timely intervention and improved patient outcomes.
  • Machine learning (ML) methods offer potential advancements over conventional statistical models (CSMs) in complex predictive tasks.

Purpose of the Study:

  • To systematically review and compare the performance of ML methods against CSMs in predicting readmission and mortality in HF patients.
  • To develop and present a framework for evaluating the quality of studies employing ML algorithms for prediction modeling in HF.

Main Methods:

  • A systematic literature search was conducted across multiple databases (MEDLINE, EMBASE, etc.) for studies published between January 2000 and July 2020.
  • Eligible studies compared ML and CSMs for HF mortality and readmission prediction, with data extracted and quality assessed using a modified CHARMS checklist.
  • Twenty articles involving 686,842 patients were included, analyzing various ML techniques (e.g., random forests, neural networks) and CSMs (e.g., logistic regression).

Main Results:

  • ML methods demonstrated superior predictive discrimination compared to CSMs in 16 out of 21 readmission comparisons and 7 out of 9 mortality comparisons.
  • ML-derived c-indices were consistently higher, particularly in studies using random survival forests for mortality prediction.
  • A significant limitation was the lack of external validation in most ML studies; however, the single externally validated study showed ML superiority (c-indices 0.913 vs. 0.835).

Conclusions:

  • ML algorithms generally outperform CSMs in predicting HF readmission and mortality.
  • There is a critical need for external validation and adherence to clinical quality standards in ML-based prediction modeling research for HF.
  • Implementing robust evaluation methods will enhance the reliability and clinical utility of ML predictions in cardiology.