Achieving adequate enteral nutrition among mechanically ventilated patients is challenging, yet critical. We developed NutriSighT, a transformer model using learnable positional coding to predict which patients would achieve hypocaloric nutrition between days 3-7 of mechanical ventilation. Using retrospective data from two large ICU databases (3,284 patients from AmsterdamUMCdb – development set, and 6,456 from MIMIC-IV – external validation set), we included adult patients intubated for at least 72 hours. NutriSighT achieved AUROC of 0.81 (95% CI: 0.81 – 0.82) and an AUPRC of 0.70 (95% CI: 0.70 – 0.72) on internal test set. External validation with MIMIC-IV data yielded a AUROC of 0.76 (95% CI: 0.75 – 0.76) and an AUPRC of (95% CI: 0.69 – 0.70). At a threshold of 0.5, the model achieved a 75.16% sensitivity, 60.57% specificity, 58.30% positive predictive value, and 76.88% negative predictive value. This approach may help clinicians personalize nutritional therapy among critically ill patients, improving patient outcomes.

GNN is a founder of Renalytix, Pensieve, Verici and provides consultancy services to AstraZeneca, Reata, Renalytix, Siemens Healthineer and Variant Bio, serves a scientific advisory board member for Renalytix and Pensieve. He also has equity in Renalytix, Pensieve and Verici. LC is a consultant for Vifor Pharma INC and has received honorarium from Fresenius Medical Care. All remaining authors have declared no conflicts of interest.

This work was supported by the National Institutes of Health (NIH) grants K08DK131286 awarded to Ankit Sakhuja. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Study used publicly available datasets - AmsterdamUMCdb available at https://amsterdammedicaldatascience.nl; and MIMIC-IV available at https://physionet.org/content/mimiciv/3.1/

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