System Maintenance Notice

Please note that maintenance work is scheduled between 4 AM and 5 AM - CET. During this time, you may experience reduced performance. Thanks for your patience.

Publications

Journal Article Ahead of print

PubMed: 42925034 DOI: 10.1227/neu.0000000000004203

Machine Learning Prediction of Treatment Failure After Middle Meningeal Artery Embolization for Subdural Hematoma.

Neurosurgery

Authors: Bluyé DeMessie, Muhammed Amir Essibayi, Hamza Adel Salim, Alireza Karandish, Ahmed Y Azzam, Deepak Khatri, Neil Haranhalli, Amanda Baker, Richard Zampolin, Allan L Brook, Seon-Kyu Lee, Nimer Adeeb, Dhairya A Lakhani, James Grist, Fulvio Zaccagna, Yan-Lin Li, Davide Simonato, Diego Alejandro Ortega, Nicole Cancelliere, Jose Danilo Diestro, Joseph Carnevale, Craig Schreiber, Atakan Orscelik, Zachary Abecassis, Spencer Raub, Georgios S Sioutas, Abdulrhman Alsalama, Clairice Pearce, Giancarlo Salsano, Rahim Abo Kasem, Svetlana Kvint, Andrew Falzon, Vance Cantrell, Brian Holliday, Jefferson O Abaricia, Pedram D Maleknia, Leonardo Cruz-Criollo, Samantha Schimmel, Basel Musmar, Matthew Alexander, Jorge Rios Zermeno, Prateeka Koul, Ahmed Aljuboori, Dominik F Vollherbst, Domagoj Gajski, Jared Cooper, Omar Alwakaa, Mohamad Ezzeldin, Christopher S Ogilvy, Fawaz Al-Mufti … and 49 other authors

Affiliations

  • Department of Neurological Surgery and Radiology, Montefiore-Einstein Cerebrovascular Research Lab, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York, USA.
  • Department of Neuroradiology, MD Anderson Cancer Center, Houston, Texas, USA.
  • Department of Neuroradiology, Rockefeller Neuroscience Institute, West Virginia University, Morgantown, West Virginia, USA.
  • Department of Neurosurgery, Ochsner-Louisiana State University, Shreveport, Louisiana, USA.
  • Oxford Centre for Clinical Magnetic Resonance Research (OCMR), Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
  • Medical Sciences Division, Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK.
  • Medical Sciences Division, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
  • Neurovascular Centre and RADIS Lab, St. Michael's Hospital, University of Toronto, Toronto Metropolitan University, Toronto, ON, Canada.
  • Department of Neurological Surgery, Weill Cornell Medicine/New York-Presbyterian Hospital, New York, New York, USA.
  • Department of Neurological Surgery and Radiology, Mayo Clinic, Rochester, Minnesota, USA.
  • Department of Neurological Surgery, University of Washington, Seattle, Washington, USA.
  • Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Department of Neurosurgery, University of Illinois Chicago, Chicago, Illinois, USA.
  • Unit of Neuroradiology, IRCCS Ospedale Policlinico San Martino, Genoa, Italy.
  • Department of Neurology and Neurosurgery, Medical University of South Carolina, Charleston, South Carolina, USA.
  • Department of Radiology and Neurosurgery, NYU Langone Health, New York, New York, USA.
  • Atkinson Morley Regional Neurosciences Centre, St George's University Hospital, NHS Trust, London, UK.
  • Department of Radiology, Interventional Neuroradiology, Lehigh Valley Health Network, Allentown, Pennsylvania, USA.
  • Department of Interventional Radiology and Neurosurgery, Oregon Health and Science University, Portland, Oregon, USA.
  • The University of Alabama at Birmingham, Birmingham, Alabama, USA.
  • Department of Neurology, University of Iowa Healthcare, Iowa City, Iowa, USA.
  • Department of Neurosurgery, University of Iowa Healthcare, Iowa City, Iowa, USA.
  • Department of Radiology, University of Iowa Healthcare, Iowa City, Iowa, USA.
  • Department of Neurosurgery and Brain Repair, University of South Florida, Tampa, Florida, USA.
  • Department of Neurological Surgery, Thomas Jefferson University, Philadelphia, Pennsylvania, USA.
  • Neurointerventional Surgery, Sutter Sacramento Medical Center, Sacramento, California, USA.
  • Department of Neurological Surgery and Radiology, Mayo Clinic, Jacksonville, Florida, USA.
  • Department of Neurosurgery, Geisinger, Wilkes-Barre, Pennsylvania, USA.
  • Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.
  • Department of Neurosurgery, UHC Sestre Milosrdnice, University of Applied Health Sciences, Zagreb, Croatia.
  • Department of Neurosurgery, Westchester Medical Center, New York Medical College, Valhalla, New York, USA.
  • Neurosurgical Service, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.
  • HCA Houston Healthcare Kingwood - Neuro Endovascular Surgery, Kingwood, Texas, USA.

Abstract

BACKGROUND AND OBJECTIVES

Middle meningeal artery embolization (MMAE) is effective for chronic subdural hematoma (SDH), but failure rates vary and risk prediction tools are lacking. This study developed and validated a machine learning (ML) model to predict MMAE treatment failure for SDH.

METHODS

We conducted an international, multi-institutional retrospective cohort study of patients who underwent MMAE for SDH between 2019 and 2025. The cohort was split into training and test sets, stratified by treatment failure, with all hematomas from each patient assigned to a single cohort to prevent data leakage. Multiple ML algorithms were evaluated using 5-fold cross-validation, with gradient boosting selected as the final model. The primary outcome was treatment failure, defined as rescue surgical evacuation or persistent/recurrent SDH requiring retreatment. Model performance was assessed using area under the curve-receiver operating characteristic. Patients were stratified into low-risk (<5%), medium-risk (5%-15%), and high-risk (>15%) groups using a priori clinically defined thresholds.

RESULTS

Among 692 treated hematomas in the test cohort, 64 (9.2%) experienced treatment failure. The gradient boosting model achieved an area under the curve-receiver operating characteristic of 0.856 (95% CI, 0.803-0.902) before calibration and 0.832 (95% CI, 0.766-0.889) after isotonic calibration, with excellent calibration (Brier score, 0.053). The most important predictors were age-platelet interaction, platelet-hemoglobin ratio, platelet count, SDH thickness, and age. Observed treatment failure was 1.6% (95% CI, 0.4%-5.7%) in the low-risk group (n = 124; 17.9%), 6.0% (95% CI, 4.3%-8.4%) in the medium-risk group (n = 516; 74.6%), and 59.6% (95% CI, 46.1%-71.8%) in the high-risk group (n = 52; 7.5%) (P < .001 for trend), a 37.2-fold difference between high- and low-risk groups.

CONCLUSION

This ML model stratified MMAE patients into risk groups with a 37.2-fold difference in treatment failure. High-risk patients had 59.6% failure, whereas low-risk patients had 1.6% failure. Risk stratification may improve patient selection and monitoring.

MeSH

Hematoma, Treatment Failure, Hematoma, Subdural, Arteries, Machine Learning, Therapeutics, Learning, Blood Platelets, Meningeal Arteries