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

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.