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Enhancing Predictive Modeling for Respiratory Support with LLM-Driven Guideline Adherence.

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Summary
This summary is machine-generated.

Integrating a large language model (LLM) with a deep counterfactual model improved respiratory support recommendations for intensive care unit (ICU) patients, reducing invasive mechanical ventilation (IMV) rates and mortality.

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Causal inferenceguideline adherencehigh-flow nasal cannulaindividualized treatment effectlarge language modelsnoninvasive ventilation

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Area of Science:

  • Critical Care Medicine
  • Artificial Intelligence in Healthcare
  • Respiratory Therapy

Background:

  • Optimal selection between high-flow nasal cannula (HFNC) and noninvasive ventilation (NIV) for intensive care unit (ICU) patients at risk of invasive mechanical ventilation (IMV) is unclear.
  • Previous deep counterfactual models (RepFlow-CFR) lacked interpretability and clinical guideline alignment.
  • This study addresses these challenges by integrating a clinical guideline-driven LLM.

Purpose of the Study:

  • To develop and integrate a clinical guideline-driven LLM to enhance deep counterfactual model recommendations for NIV versus HFNC.
  • To improve interpretability and clinical guideline adherence for respiratory support decisions in high-risk ICU patients.
  • To assess the impact of LLM-enhanced recommendations on patient outcomes and clinical practice.

Main Methods:

  • Enhanced the RepFlow-CFR model by incorporating a large language model (LLM, Claude 3.5 Sonnet) for guideline adherence and explainable recommendations.
  • Configured the LLM in a HIPAA-compliant AWS environment, using structured patient data, clinical notes, and guideline criteria for prompting.
  • Compared LLM-enhanced recommendations with actual treatment decisions, evaluating invasive mechanical ventilation (IMV) and mortality/hospice rates. Conducted a chart review for clinical validity and safety.

Main Results:

  • Treatments concordant with LLM-enhanced recommendations were associated with significantly lower IMV rates (24.47% vs. 52.94%) and reduced odds of mortality or hospice discharge (OR=0.670, p=0.046).
  • In a chart review of 20 cases, 95% of LLM recommendations aligned with clinical guidelines, and physicians agreed with 65% of final recommendations.
  • Identified errors in 11/20 cases, with most deemed low or moderate risk; only 2 were rated as potentially causing severe harm.

Conclusions:

  • Integrating LLMs enhances interpretability and clinical alignment of counterfactual models for respiratory support decision-making.
  • This hybrid framework improves concordance with real-world practice and shows potential for better patient outcomes.
  • Future work will focus on refining contraindication detection and expanding validation through prospective clinical trials.