NutriSighT: Interpretable Transformer Model for Dynamic Prediction of Underfeeding Enteral Nutrition in Mechanically Ventilated Patients

Achieving adequate enteral nutrition among mechanically ventilated patients is challenging, yet critical. We develop NutriSighT, a transformer model using learnable positional encodings to predict which patients would be underfed (receive less than 70% daily caloric requirements) between days 3-7 of mechanical ventilation and compared its performance against XGBoost. Using retrospective data from two ICU databases (3284 patients from AmsterdamUMCdb for development and 6456 from MIMIC-IV for external validation), we included adults mechanically ventilated for at least 72 h. NutriSighT achieved AUROC of 0.81 (95% CI: 0.81 - 0.82) and AUPRC of 0.70 (95% CI: 0.70 - 0.72) internally. External validation yielded AUROC of 0.76 (95% CI: 0.75 - 0.76) and an AUPRC of 0.70 (95% CI: 0.69 - 0.70). In comparison, XGBoost achieved AUROC of 0.58 (95% CI: 0.58 - 0.59) and AUPRC of 0.48 (95% CI: 0.46 - 0.50). This approach may help clinicians personalize nutritional therapy in critical care.

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Publication Details

Journal
Nature Communications
Published
2025-12-17
DOI
https://doi.org/10.1038/s41467-025-66200-1
Primary Topic
Clinical Nutrition and Gastroenterology
Type
article
Field-Weighted Citation Impact
0.00

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article

NutriSighT: Interpretable Transformer Model for Dynamic Prediction of Underfeeding Enteral Nutrition in Mechanically Ventilated Patients

Jayshil J. Patel, Jacob Desman, Paul McCarthy, Nidhi Kavi et al.
Nature Communications
Clinical Nutrition and Gastroenterology
article

NutriSighT: Interpretable Transformer Model for Dynamic Prediction of Underfeeding Enteral Nutrition in Mechanically Ventilated Patients

Jayshil J. Patel, Jacob Desman, Paul McCarthy, Nidhi Kavi, Eyal Klang, Akhil Vaid, Dhruv Patel, Girish N. Nadkarni, Lili Chan, Jaskirat Gill, Rohit Gupta, Robert Freeman, Mayte Suárez‐Fariñas, Ankit Sakhuja, Roopa Kohli‐Seth, Anthony Manasia, Alexander W. Charney, Mateen Jangda, John Oropello, S Bakare
article en

Abstract

Achieving adequate enteral nutrition among mechanically ventilated patients is challenging, yet critical. We develop NutriSighT, a transformer model using learnable positional encodings to predict which patients would be underfed (receive less than 70% daily caloric requirements) between days 3-7 of mechanical ventilation and compared its performance against XGBoost. Using retrospective data from two ICU databases (3284 patients from AmsterdamUMCdb for development and 6456 from MIMIC-IV for external validation), we included adults mechanically ventilated for at least 72 h. NutriSighT achieved AUROC of 0.81 (95% CI: 0.81 - 0.82) and AUPRC of 0.70 (95% CI: 0.70 - 0.72) internally. External validation yielded AUROC of 0.76 (95% CI: 0.75 - 0.76) and an AUPRC of 0.70 (95% CI: 0.69 - 0.70). In comparison, XGBoost achieved AUROC of 0.58 (95% CI: 0.58 - 0.59) and AUPRC of 0.48 (95% CI: 0.46 - 0.50). This approach may help clinicians personalize nutritional therapy in critical care.

Nature CommunicationsVol. 16(1)
West Virginia University (US), Medical College of Wisconsin (US), Icahn School of Medicine at Mount Sinai (US)
National Institutes of Health
Openalex Percentile: Top 52%
Clinical Nutrition and Gastroenterology
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