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<p>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 <i>R</i> = 0.94 compared to experts), increasing safety and potentially reducing cost. A blinded study (<i>n</i> = 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; <i>P</i> 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.</p>

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AI-guided precision parenteral nutrition for neonatal intensive care units

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月25日

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  • Thanaphong Phongpreecha,
  • Marc Ghanem,
  • Jonathan D. Reiss,
  • Tomiko T. Oskotsky,
  • Samson J. Mataraso,
  • Davide De Francesco,
  • S. Momsen Reincke,
  • Camilo Espinosa,
  • Philip Chung,
  • Taryn Ng,
  • Jean M. Costello,
  • Jennifer A. Sequoia,
  • Sheila Razdan,
  • Feng Xie,
  • Eloise Berson,
  • Yeasul Kim,
  • David Seong,
  • May Y. Szeto,
  • Faith Myers,
  • Hannah Gu,
  • John Feister,
  • Courtney P. Verscaj,
  • Laura A. Rose,
  • Lucas W. Y. Sin,
  • Boris Oskotsky,
  • Jacquelyn Roger,
  • Chi-hung Shu,
  • Sayane Shome,
  • Liu K. Yang,
  • Yuqi Tan,
  • Steven Levitte,
  • Ronald J. Wong,
  • Brice Gaudillière,
  • Martin S. Angst,
  • Thomas J. Montine,
  • John A. Kerner,
  • Roberta L. Keller,
  • Gary M. Shaw,
  • Karl G. Sylvester,
  • Janene Fuerch,
  • Valerie Chock,
  • Shabnam Gaskari,
  • David K. Stevenson,
  • Marina Sirota,
  • Lawrence S. Prince,
  • Nima Aghaeepour

摘要

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.