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Related Experiment Video

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Improving clinical trial efficiency using a machine learning-based risk score to enrich study populations.

Karola S Jering1, Claudio Campagnari2, Brian Claggett1

  • 1Cardiovascular Division, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.

European Journal of Heart Failure
|May 4, 2022
PubMed
Summary

Using the Machine learning Assessment of RisK and EaRly mortality in Heart Failure (MARKER-HF) score can improve clinical trial efficiency. This prognostic tool helps identify high-risk patients, reducing the required sample size for mortality studies.

Keywords:
Clinical trial efficiencyHeart failureMachine learningPrognostic enrichmentRisk scoresTrial enrolment strategies

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Area of Science:

  • Cardiology
  • Clinical Trials
  • Machine Learning

Background:

  • Prognostic enrichment strategies can enhance clinical trial efficiency but may impact external validity.
  • The effectiveness of using risk scores to identify high-mortality populations for improved trial efficiency is not well-established.

Purpose of the Study:

  • To evaluate if the Machine learning Assessment of RisK and EaRly mortality in Heart Failure (MARKER-HF) score can improve clinical trial efficiency.
  • To assess the association of the MARKER-HF score with 1-year all-cause mortality in heart failure cohorts.

Main Methods:

  • Evaluated mortality rates and MARKER-HF score association with 1-year all-cause death in community and clinical trial heart failure cohorts.
  • Calculated the sample size needed for mortality-reducing therapy trials under varying MARKER-HF risk and treatment effect scenarios.

Main Results:

  • MARKER-HF score strongly predicted 1-year mortality in both community (HR 1.48) and clinical trial cohorts (HFrEF HR 1.41, HFpEF HR 1.74).
  • Community-based HF patients exhibited higher mortality and MARKER-HF scores than clinical trial patients.
  • Utilizing MARKER-HF for patient enrichment in hypothetical trials reduced the necessary sample size to demonstrate mortality benefits.

Conclusions:

  • The MARKER-HF score reliably predicts mortality in heart failure patients.
  • Employing MARKER-HF to enrich clinical trial populations is a viable strategy for enhancing trial efficiency.
  • This approach allows for smaller sample sizes, potentially accelerating the demonstration of clinical benefits.