4
|
Phongpreecha T, Ghanem M, Reiss JD, Oskotsky TT, Mataraso SJ, De Francesco D, Reincke SM, Espinosa C, Chung P, Ng T, Costello JM, Sequoia JA, Razdan S, Xie F, Berson E, Kim Y, Seong D, Szeto MY, Myers F, Gu H, Feister J, Verscaj CP, Rose LA, Sin LWY, Oskotsky B, Roger J, Shu CH, Shome S, Yang LK, Tan Y, Levitte S, Wong RJ, Gaudillière B, Angst MS, Montine TJ, Kerner JA, Keller RL, Shaw GM, Sylvester KG, Fuerch J, Chock V, Gaskari S, Stevenson DK, Sirota M, Prince LS, Aghaeepour N. AI-guided precision parenteral nutrition for neonatal intensive care units. Nat Med 2025:10.1038/s41591-025-03601-1. [PMID: 40133525 DOI: 10.1038/s41591-025-03601-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2024] [Accepted: 02/17/2025] [Indexed: 03/27/2025]
Abstract
One in ten neonates are admitted to neonatal intensive care units, highlighting the need for precise interventions. However, the application of artificial intelligence (AI) in guiding neonatal care remains underexplored. Total parenteral nutrition (TPN) is a life-saving treatment for preterm neonates; however, implementation of the therapy in its current form is subjective, error-prone and resource-consuming. Here, we developed TPN2.0-a data-driven approach that optimizes and standardizes TPN using information collected routinely in electronic health records. We assembled a decade of TPN compositions (79,790 orders; 5,913 patients) at Stanford to train TPN2.0. In addition to internal validation, we also validated our model in an external cohort (63,273 orders; 3,417 patients) from a second hospital. Our algorithm identified 15 TPN formulas that can enable a precision-medicine approach (Pearson's R = 0.94 compared to experts), increasing safety and potentially reducing cost. A blinded study (n = 192) revealed that physicians rated TPN2.0 higher than current best practice. In patients with high disagreement between the actual prescriptions and TPN2.0, standard prescriptions were associated with increased morbidities (for example, odds ratio = 3.33; P value = 0.0007 for necrotizing enterocolitis), while TPN2.0 recommendations were linked to reduced risk. Finally, we demonstrated that TPN2.0 employing a transformer architecture enabled guideline-adhering, physician-in-the-loop recommendations that allow collaboration between the care team and AI.
Collapse
Affiliation(s)
- Thanaphong Phongpreecha
- Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Marc Ghanem
- Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Jonathan D Reiss
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Tomiko T Oskotsky
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
- Department of Pediatrics, University of California, San Francisco, CA, USA
| | - Samson J Mataraso
- Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Davide De Francesco
- Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - S Momsen Reincke
- Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Camilo Espinosa
- Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Philip Chung
- Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, CA, USA
| | - Taryn Ng
- Department of Pharmacy, Lucile Packard Children's Hospital, Palo Alto, CA, USA
| | - Jean M Costello
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
| | | | - Sheila Razdan
- Department of Pediatrics, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Keck School of Medicine of USC, Division of Neonatal and Infant Critical Care Unit, Children's Hospital Los Angeles, Los Angeles, CA, USA
| | - Feng Xie
- Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Eloise Berson
- Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Yeasul Kim
- Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - David Seong
- Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - May Y Szeto
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Faith Myers
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Hannah Gu
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - John Feister
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | | | - Laura A Rose
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Lucas W Y Sin
- Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, CA, USA
| | - Boris Oskotsky
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
| | - Jacquelyn Roger
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
| | - Chi-Hung Shu
- Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Sayane Shome
- Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Liu K Yang
- Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Yuqi Tan
- Department of Pathology, Stanford University, Stanford, CA, USA
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA
| | - Steven Levitte
- Division of Pediatric Gastroenterology, Hepatology and Nutrition, Stanford University, Stanford, CA, USA
| | - Ronald J Wong
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Brice Gaudillière
- Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, CA, USA
| | - Martin S Angst
- Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, CA, USA
| | | | - John A Kerner
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Roberta L Keller
- Department of Pediatrics, University of California, San Francisco, CA, USA
| | - Gary M Shaw
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Karl G Sylvester
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Janene Fuerch
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Valerie Chock
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Shabnam Gaskari
- Department of Pharmacy, Lucile Packard Children's Hospital, Palo Alto, CA, USA
| | | | - Marina Sirota
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
- Department of Pediatrics, University of California, San Francisco, CA, USA
| | | | - Nima Aghaeepour
- Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, CA, USA.
- Department of Pediatrics, Stanford University, Stanford, CA, USA.
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
| |
Collapse
|