Maxime Cannesson
University of California-Los Angeles, CA·Medicine & Clinical Research
Surgery itself rarely kills patients today, but deaths after surgery remain a leading cause of death in the United States. Major complications occur at similar rates across hospitals, about one in four patients after major surgery.
- Fields
Medicine & Clinical Research
Biomedical Engineering
Computer Science
- Topics
perioperative medicine
anesthesiology
postoperative complications
- Grants
- 1 known grant, through Aug 2027
What they work on
- Develop machine learning models that predict major complications after surgery by combining electronic health record data, high-fidelity physiological waveform features, and genomic data.
- Identify and quantify patterns that separate surgical patients who go on to develop major postoperative complications from those at very low risk, using annotated health records augmented with waveform and genomic data.
- Design a prototype clinical decision support tool through simulated real-time bedside management, measuring diagnostic accuracy, time to correct diagnosis, and time to intervention.
- Test the feasibility and acceptability of using the tool at discharge from the post-anesthesia care unit to assign remote monitoring, in a three-phase pilot study at UCLA.
More about the research
Yet the chance of dying after such a complication can be up to 2.5 times higher at some hospitals than others. This gap is called failure to rescue: a complication that is not caught and treated in time. Spotting at-risk patients early could help close the gap.
Why it matters
Patients undergoing major surgery could benefit if hospitals catch complications earlier. A tool that flags high-risk patients as they leave the recovery unit could direct remote monitoring to those who need it. This may help hospitals with high post-surgery death rates reduce failure to rescue and narrow the gap between hospitals.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Sunny Lou
Washington University in St Louis, MO·Medicine & Clinical Research·Early-career
Embed S-PATH, a personalized AI model that predicts a patient's risk of needing a surgical blood transfusion, into the electronic health record as a clinical decision support tool for presurgical blood orders.
Alexander Friedman
Columbia University, NY·Medicine & Clinical Research
Measure how closely care for severe hypertension, obstetric hemorrhage, sepsis and abnormal maternal vital signs follows safety bundle recommendations, using EHR data from eight consortium hospitals.
Romain Pirracchio
University of California-San Francisco, CA·Medicine & Clinical Research
Train machine learning models on electronic health record data from three health systems to estimate each patient's individual treatment effect from antibiotics, fluid resuscitation and vasopressors in suspected community-onset lung sepsis.
The school
University of California-Los Angeles: 84 labs on PI Pages, $1.9B a year on research, 803 research doctorates a year.
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