Ohio State data to spotlight Linshom Medical respiratory sensor at anesthesia meeting

Research from The Ohio State University Wexner Medical Center has been selected for an oral abstract presentation at the 2026 ANESTHESIOLOGY annual meeting in San Diego. The NIH-funded study found Linshom Medical’s respiratory sensor detected respiratory depression episodes 11 minutes sooner than standard monitoring, a result that could support earlier intervention in recovery units and beyond.

Why it matters: - Earlier detection of respiratory depression can give care teams more time to intervene before patients deteriorate. - The findings could influence postoperative monitoring in high-risk settings such as the post-anesthesia care unit. - Linshom Medical is moving the research into a full interventional clinical trial in 2026, which could further test the device in real-world care.

What happened: - The Ohio State University Wexner Medical Center’s research was selected as one of 24 oral abstract presentations from 1,473 submissions for the 2026 ANESTHESIOLOGY annual meeting. - The data will be presented in the “Best Abstracts” session on Oct. 19, 2026, in San Diego. - Richard Urman, MD, MBA, principal investigator and Jay J. Jacoby Endowed Professor and chair of anesthesiology at Ohio State, will present the findings. - The study was NIH funded and titled “Advance Prediction of Respiratory Depression Events Using a Prospective Observational Study of a Continuous Predictive Respiratory Monitor.”

The details: - The study found the Linshom Medical sensor detected respiratory depression episodes 11 minutes sooner than standard of care. - Linshom Medical’s sensor detected 132 respiratory depression episodes, or 83.3%. - Standard of care detected 59 episodes, or 44.7%. - The study evaluated the device in the post-anesthesia care unit, where patients already had continuous pulse oximetry and dedicated nursing observation. - Richard Hughen, CEO of Linshom Medical, said the study represented four years of work for Linshom Medical and two years of work for Ohio State. - Hughen said the study tested the device in a setting with intensive monitoring and still found stronger detection than standard care. - Hughen said respiratory depression events are not short sleep apnea episodes and involve patients stopping breathing for 30 seconds or longer. - Linshom Medical says its device is AI-enabled, wearable, FDA cleared and designed to provide an operating-room-quality respiratory profile at the bedside. - The device continuously tracks respiratory rate, relative tidal volume and seconds since last breath in real time. - Linshom Medical says the device is intended to provide predictive, rather than responsive, respiratory monitoring. - The research was supported by the National Heart, Lung, and Blood Institute under award numbers 1R44HL164222-01 and 4R44HL164222-02. - The content of the research report is the responsibility of the authors and does not necessarily represent the official views of NIH. - Linshom Medical said more information is available at the company’s website.

Between the lines: - Selection for a “Best Abstracts” session signals the study stood out in a crowded submission pool. - The results suggest a wearable predictive monitor may add value even in environments with close bedside surveillance. - The move from observational research to an interventional trial suggests Linshom Medical and Ohio State want to test whether earlier alerts improve patient outcomes, not just detection rates.

What’s next: - Linshom Medical plans to extend the study into a full interventional clinical trial at Ohio State Wexner Medical Center in 2026. - The broader ANESTHESIOLOGY 2026 annual meeting runs Oct. 16–20, 2026, at the San Diego Convention Center. - More data from the trial could help determine whether earlier respiratory alerts translate into fewer complications and safer recovery care.

The bottom line: - Ohio State’s study gives Linshom Medical a high-profile stage and an important data point: its sensor spotted respiratory depression much earlier than standard monitoring.