Researchers in the Montefiore Einstein Department of Anesthesiology created a retrospective study analyzing respiratory failure after surgery in more than 100,000 adult patients at Montefiore, resulting in the creation of a prediction model to determine which patients were at a high risk of postoperative respiratory failure. The creation of the machine learning model – the RESPIRE model – was led by Dr. Michael Kiyatkin, Anesthesiologist, Intensivist, and Director of Quality Improvement for Critical Care Anesthesiology. Published in the Journal of Clinical Anesthesia, the research shows that the RESPIRE model is more accurate than two leading prediction tools. Determining which patients are at a high risk of respiratory failure after surgery is an important step in perioperative care that will allow our providers to take the necessary precautions to prevent this outcome and ensure successful postoperative recoveries for our patients.

Read the full publication in the Journal of Clinical Anesthesia.