What's Happening?
Researchers at the Icahn School of Medicine at Mount Sinai have developed an artificial intelligence (AI) tool named NutriSightT to predict which critically ill patients on ventilators are at risk of underfeeding. The study, published in Nature Communications,
highlights the importance of the first week on a ventilator for providing proper nutrition, as patients' needs can change rapidly. The AI tool analyzes routine ICU data, including vital signs, lab results, medications, and feeding information, to predict underfeeding risk in advance. The model updates predictions every four hours, allowing clinicians to intervene early and adjust care. The study found that 41% to 53% of patients were underfed by day three, and 25% to 35% remained underfed by day seven. The tool aims to support personalized feeding plans and guide nutrition teams, ultimately improving patient outcomes.
Why It's Important?
The development of NutriSightT represents a significant advancement in personalized healthcare for critically ill patients. By enabling early identification of underfeeding risks, the tool can help clinicians provide timely nutrition interventions, potentially improving recovery and outcomes. This approach aligns with the broader trend of using AI to enhance patient care by providing data-driven insights. The ability to tailor nutrition plans to individual needs could lead to more effective treatment strategies and reduce the risk of complications associated with underfeeding. The research underscores the potential of AI in transforming healthcare practices, particularly in intensive care settings where patient conditions can change rapidly.
What's Next?
The research team plans to conduct prospective multi-site trials to test the effectiveness of acting on the AI tool's predictions in improving patient outcomes. They also aim to integrate NutriSightT into electronic health records and expand its application to broader individualized nutrition targets. These steps are crucial for validating the tool's utility in real-world settings and ensuring its seamless adoption in clinical practice. The success of these trials could pave the way for wider implementation of AI-driven tools in healthcare, enhancing the precision and efficiency of patient care.
