AI Tool Predicts Underfeeding Risk in ICU Patients on Ventilators, Enabling Early Intervention

AI Could Help Predict Nutrition Risks in ICU Patients, Study Finds | Newswise

Newswise -- New York, NY [December 22, 2025] -- A new study by researchers at the Icahn School of Medicine at Mount Sinai suggests that artificial intelligence (AI) could help predict which critically ill patients on ventilators are at risk of underfeeding, potentially enabling clinicians to adjust nutrition early and improve patient care. Details of the study were published in the December 17 online issue of Nature Communications [https://doi.org/10.1038/s41467-025-66200-1]. The first week on a ventilator is especially important for providing proper nutrition, since patients' needs often shift quickly during this period, say the investigators. "Too many patients on ventilators in the intensive care unit (ICU) don't get the nutrition they need during the critical first week," says co-senior corresponding author Ankit Sakhuja, MBBS, MS, Associate Professor of Artificial Intelligence and Human Health, and Medicine (Data-Driven and Digital Medicine). "Their needs are changing rapidly, and it's easy for them to fall behind. We wanted to explore a simple, timely way to identify who is most at risk of being underfed so that clinicians could intervene earlier, adjust care, and make sure each patient receives the right support when it matters most." The research team built an AI tool, called NutriSightT, which analyzed routine ICU data such as vital signs, lab results, medications, and feeding information to predict, hours in advance, which patients may be underfed on days 3-7 of ventilation. Using large deidentified ICU datasets from Europe and the United States, the model was trained and validated to update predictions every four hours as patient conditions change. The study identified several key insights that could potentially help guide patient care: * Underfeeding is common early in ICU care. About 41 percent to 53 percent of patients were underfed by day three, and 25-35 percent remained underfed by day seven. * The model is dynamic and interpretable, showing which routine factors -- such as blood pressure, sodium levels, or sedation -- influence underfeeding risk. * The research could support personalized feeding plans, guide nutrition teams, and inform clinical trials to determine the most effective nutrition strategies for individual patients. The investigators emphasize that NutriSighT would not be intended to replace clinicians. Instead, it could serve as an early-warning system to help guide timely nutrition interventions. The research team's next steps include prospective multi-site trials to test whether acting on these predictions improves patient outcomes, careful integration into electronic health records, and expansion to broader individualized nutrition targets. "The significance of our study's findings is that, for the first time, it may be possible to identify which patients are at risk of underfeeding early in their ICU stay and tailor care to their individual needs," says co-senior author Girish N. Nadkarni, MD, MPH, Chair of the Windreich Department of Artificial Intelligence and Human Health, Director of the Hasso Plattner Institute for Digital Health, and Irene and Dr. Arthur M. Fishberg Professor of Medicine at the Icahn School of Medicine at Mount Sinai, and Chief AI Officer of the Mount Sinai Health System. "It represents an important step towards giving clinicians better information to make decisions about nutrition. Ultimately, the goal is to provide the right amount of nutrition to the right patient at the right time, which could help improve recovery and outcomes in critically ill patients and lay the groundwork for more personalized care strategies." The paper is titled "NutriSighT: Interpretable Transformer Model for Dynamic Prediction of Underfeeding Enteral Nutrition in Mechanically Ventilated Patients." The study's authors, as listed in the journal, are Mateen Jangda, Jayshil Patel, Akhil Vaid, Jaskirat Gill, Paul McCarthy, Jacob Desman, Rohit Gupta, Dhruv Patel, Nidhi Kavi, Shruti Bakare, Eyal Klang, Robert Freeman, Anthony Manasia, John Oropello, Lili Chan, Mayte Suarez-Farinas, Alexander W. Charney, Roopa Kohli-Seth, Girish N. Nadkarni, and Ankit Sakhuja. This study was supported by the National Institutes of Health (NIH) grant K08DK131286. See the journal paper for details on conflicts of interest: Nature Communications. For more Mount Sinai artificial intelligence news, visit: https://icahn.mssm.edu/about/artificial-intelligence. About Mount Sinai's Windreich Department of AI and Human Health Led by Girish N. Nadkarni, MD, MPH -- an international authority on the safe, effective, and ethical use of AI in health care -- Mount Sinai's Windreich Department of AI and Human Health is the first of its kind at a U.S. medical school, pioneering transformative advancements at the intersection of artificial intelligence and human health. The Department is committed to leveraging AI in a responsible, effective, ethical, and safe manner to transform research, clinical care, education, and operations. By bringing together world-class AI expertise, cutting-edge infrastructure, and unparalleled computational power, the department is advancing breakthroughs in multi-scale, multimodal data integration while streamlining pathways for rapid testing and translation into practice. The Department benefits from dynamic collaborations across Mount Sinai, including with the Hasso Plattner Institute for Digital Health at Mount Sinai -- a partnership between the Hasso Plattner Institute for Digital Engineering in Potsdam, Germany, and the Mount Sinai Health System -- which complements its mission by advancing data-driven approaches to improve patient care and health outcomes. At the heart of this innovation is the renowned Icahn School of Medicine at Mount Sinai, which serves as a central hub for learning and collaboration. This unique integration enables dynamic partnerships across institutes, academic departments, hospitals, and outpatient centers, driving progress in disease prevention, improving treatments for complex illnesses, and elevating quality of life on a global scale. In 2024, the Department's innovative NutriScan AI application, developed by the Mount Sinai Health System Clinical Data Science team in partnership with Department faculty, earned Mount Sinai Health System the prestigious Hearst Health Prize. NutriScan is designed to facilitate faster identification and treatment of malnutrition in hospitalized patients. This machine learning tool improves malnutrition diagnosis rates and resource utilization, demonstrating the impactful application of AI in health care. For more information on Mount Sinai's Windreich Department of AI and Human Health, visit: ai.mssm.edu About the Hasso Plattner Institute at Mount Sinai At the Hasso Plattner Institute for Digital Health at Mount Sinai, the tools of data science, biomedical and digital engineering, and medical expertise are used to improve and extend lives. The Institute represents a collaboration between the Hasso Plattner Institute for Digital Engineering in Potsdam, Germany, and the Mount Sinai Health System. Under the leadership of Girish Nadkarni, MD, MPH, who directs the Institute, and Professor Lothar Wieler, a globally recognized expert in public health and digital transformation, they jointly oversee the partnership, driving innovations that positively impact patient lives while transforming how people think about personal health and health systems. The Hasso Plattner Institute for Digital Health at Mount Sinai receives generous support from the Hasso Plattner Foundation. Current research programs and machine learning efforts focus on improving the ability to diagnose and treat patients. About the Icahn School of Medicine at Mount Sinai The Icahn School of Medicine at Mount Sinai is internationally renowned for its outstanding research, educational, and clinical care programs. It is the sole academic partner for the seven member hospitals* of the Mount Sinai Health System, one of the largest academic health systems in the United States, providing care to New York City's large and diverse patient population. The Icahn School of Medicine at Mount Sinai offers highly competitive MD, PhD, MD-PhD, and master's degree programs, with enrollment of more than 1,200 students. It has the largest graduate medical education program in the country, with more than 2,600 clinical residents and fellows training throughout the Health System. Its Graduate School of Biomedical Sciences offers 13 degree-granting programs, conducts innovative basic and translational research, and trains more than 560 postdoctoral research fellows. Ranked 11th nationwide in National Institutes of Health (NIH) funding, the Icahn School of Medicine at Mount Sinai is among the 99th percentile in research dollars per investigator according to the Association of American Medical Colleges. More than 4,500 scientists, educators, and clinicians work within and across dozens of academic departments and multidisciplinary institutes with an emphasis on translational research and therapeutics. Through Mount Sinai Innovation Partners (MSIP), the Health System facilitates the real-world application and commercialization of medical breakthroughs made at Mount Sinai.

New AI Tool May Help ICU Patients Get the Nutrition They Need

TUESDAY, Dec. 23, 2025 (HealthDay News) -- Critically ill patients who need breathing machines often miss getting enough nutrition, especially during the first few days in intensive care. Now, a new study suggests artificial intelligence (AI) could help doctors spot that problem sooner. Researchers at the Icahn School of Medicine at Mount Sinai in New York City reported that an AI tool was able to predict which patients on ventilators were likely to be underfed during their first week in the ICU. The findings were published Dec. 17 in the journal Nature Communications. "Too many patients on ventilators in the intensive care unit (ICU) don't get the nutrition they need during the critical first week," said study co-senior author Dr. Ankit Sakhuja, an associate professor of artificial intelligence and human health, and medicine at Mount Sinai. "Their needs are changing rapidly, and it's easy for them to fall behind," Sakhuja added in a news release. The research team developed an AI program called NutriSighT, which analyzes routine ICU information such as vital signs, lab tests, medications and feeding data, to estimate nutrition risk. The system updates its predictions every four hours, allowing doctors to see changes as a patient's condition shifts. Using ICU data from the United States and Europe, the researchers found that underfeeding was fairly common. Between 41% and 53% of patients were underfed by day three on a ventilator. By day seven, about 25% to 35% were still not getting enough nutrition. The AI model also showed which factors played a role in underfeeding risk: Blood pressure, sodium levels and sedation. Researchers say this transparency could help care teams adjust feeding plans earlier. "The significance of our study's findings is that, for the first time, it may be possible to identify which patients are at risk of underfeeding early in their ICU stay and tailor care to their individual needs," said co-senior author Dr. Girish Nadkarni, chief AI officer of the Mount Sinai Health System. "Ultimately, the goal is to provide the right amount of nutrition to the right patient at the right time, which could help improve recovery and outcomes in critically ill patients and lay the groundwork for more personalized care strategies." Nadkarni said. The researchers stressed that the tool is not meant to replace doctors or dietitians. Instead, it could act as an early warning system to help guide care decisions. Next, the team plans to test whether using the AI tool in real time improves patient recovery, and to explore how it could be integrated into electronic health records. More information The National Institutes of Health has more on nutrition risks in intensive care units. SOURCE: Mount Sinai Health System, news release, Dec. 22, 2025