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January 24, 20260 citationsOpen Access

Establishment and validation of a machine learning model to stratify malnutrition risk in hospitalized older patients with chronic heart failure.

QSQiang SunJCJing Che

Key Result

The CAT-based model effectively stratifies malnutrition risk in hospitalized older patients with chronic heart failure, aiding in early intervention.

Key Points

  • The study aims to validate a machine learning model for assessing malnutrition risk in older patients with chronic heart failure.
  • Developed a CAT-based machine learning model
  • Stratified malnutrition risk in hospitalized older patients
  • Validated through initial assessments
  • The model effectively stratifies malnutrition risk
  • Offers a tool for early intervention
  • Highlights the need for multicenter studies to confirm real-world applicability

Structured PICO

Does a CAT-based machine learning model effectively stratify malnutrition risk in hospitalized older patients with chronic heart failure?

P
Population
Hospitalized older patients with chronic heart failure (CHF)
I
Intervention
CAT-based machine learning model
O
Outcome
Malnutrition risk stratification

A CAT-based machine learning model can effectively stratify malnutrition risk in older hospitalized patients with chronic heart failure, potentially guiding early intervention.

Limitations

  • Lacks multicenter prospective validation for real-world applicability

Abstract

The CAT-based model effectively stratifies malnutrition risk in older hospitalized CHF patients, offering a tool for early intervention to improve outcomes. Further multicenter prospective studies are needed to validate its real-world applicability.

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Cite This Study

Sun et al. (2026) studied this question. The CAT-based model effectively stratifies malnutrition risk in hospitalized older patients with chronic heart failure, aiding in early intervention.

synapsesocial.com/papers/69746149bb9d90c67120b18bhttps://doi.org/10.6133/apjcn.202602_35(1).0014
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