Risk Models for Adverse Events in Microsurgery for Intracranial Unruptured Aneurysms

Authors

Victor E. Staartjes, Machine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zürich, Switzerland.
Alexios A. Adamides, Department of Neurosurgery, The Royal Melbourne Hospital, Melbourne, Victoria, Australia.
Pasquale Anania, Department of Neurosurgery, IRCCS Ospedale Policlinico San Martino, Genoa, Italy.
Mustafa K. Baskaya, Department of Neurosurgery, University of Wisconsin, Madison, Wisconsin, USA.
Vladimir Beneš, Department of Neurosurgery, Central Military Hospital, Charles University, Prague, Czech Republic.
Amir R. Dehdashti, Department of Neurosurgery, North Shore University Hospital, Northwell Health, Manhasset, New York, USA.
Antonio Di Ieva, Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Macquarie University, Sydney, Australia.
Paolo Ferroli, Department of Neurosurgery, Istituto Neurologico Carlo Besta, Milan, Italy.
Asgeir S. Jakola, Department of Neurosurgery, Sahlgrenska University Hospital, Institute of Neuroscience and Physiology, Sahlgrenska Academy, Gothenburg, Sweden.
Giuseppe Lanzino, Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota, USA.
Michael T. Lawton, Department of Neurosurgery, Barrow Neurological Institute, Phoenix, Arizona, USA.
Ondra Petr, Department of Neurosurgery, University Hospital Innsbruck, Innsbruck, Austria.
Thomas Petutschnigg, Department of Neurosurgery and Stroke Research Center Bern, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Giampietro Pinna, Department of Neurosurgery, University Hospital Verona, Verona, Italy.
Dino Podlesek, Department of Neurosurgery, University Hospital Dresden, Dresden, Germany.
Ivan Radovanovic, Department of Neurosurgery, Toronto Western Hospital, Toronto, Ontario, Canada.
Veit Rohde, Department of Neurosurgery, University Hospital Göttingen, Göttingen, Germany.
Nico Stroh-Holly, Department of Neurosurgery, Kepler University Hospital, Linz, Austria.
Carmelo Lucio Sturiale, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy.
Anthony C. Wang, Department of Neurosurgery, University of California Los Angeles, Los Angeles, California, USA.
Luca Regli, Machine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zürich, Switzerland.
Giuseppe Esposito, Machine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zürich, Switzerland.

Document Type

Article

Abstract

BACKGROUND AND OBJECTIVES: Preventive treatment of unruptured intracranial aneurysms (UIAs) requires assessment of treatment risks vs expected benefit. Although established scores exist to estimate rupture and growth risk, currently, no externally validated tools exist to estimate the risks of microsurgical treatment of UIAs. Clinical prediction models based on machine learning enable generation of personalized risk estimates for each individual patient based on their specific patient and aneurysm characteristics. METHODS: Using data from 20 international centers from the prediction of adverse events after microsurgery for intracranial unruptured aneurysms study on patients treated microsurgically for UIAs, we developed and externally validated clinical prediction models for 3 outcomes measured at hospital discharge: poor neurological outcome (modified Rankin Score ≥3), new sensorimotor neurological deficits, and all-cause adverse events (Clavien-Dindo Grade ≥1). RESULTS: A total of 3705 patients were included. Data from 13 centers (2881, 78%) were used for model development. Fully trained models were evaluated on 824 patients (22%) from 7 additional centers. Average age was 56 ± 12 years, and 1049 (28%) were male. At discharge, poor neurological outcome was seen in 514 patients (14%). New sensorimotor deficits were observed in 534 patients (14%), and 894 patients (24%) experienced adverse events until discharge. At external validation, prediction of poor neurological outcome was achieved with good calibration and an area under the curve (AUC) = 0.70 (95% CI: 0.63-0.75). Similarly, new neurological deficits were predicted with good calibration and with an AUC = 0.69 (95% CI: 0.63-0.74). Prediction of all-cause adverse events only achieved an AUC = 0.59 (95% CI: 0.55-0.64) with fair calibration. The prediction model was integrated into a web application accessible at https://neurosurgery.shinyapps.io/PRAEMIUM/. CONCLUSION: The developed models for prediction of poor neurological outcome and new sensorimotor neurological deficits at discharge exhibit good calibration and fair discrimination based on a multinational external validation, indicating that the predicted probabilities correspond well to real-world risks and may thus be clinically useful in more objectively estimating the risk of microsurgical treatment.

Publication Date

12-5-2025

Publication Title

Neurosurgery

E-ISSN

1524-4040

PubMed ID

41358665

Digital Object Identifier (DOI)

10.1227/neu.0000000000003867

Recommended Citation

Staartjes, Victor E.; Adamides, Alexios A.; Anania, Pasquale; Baskaya, Mustafa K.; Beneš, Vladimir; Dehdashti, Amir R.; Di Ieva, Antonio; Ferroli, Paolo; Jakola, Asgeir S.; Lanzino, Giuseppe; Lawton, Michael T.; Petr, Ondra; Petutschnigg, Thomas; Pinna, Giampietro; Podlesek, Dino; Radovanovic, Ivan; Rohde, Veit; Stroh-Holly, Nico; Sturiale, Carmelo Lucio; Wang, Anthony C.; Regli, Luca; and Esposito, Giuseppe, "Risk Models for Adverse Events in Microsurgery for Intracranial Unruptured Aneurysms" (2025). Neurosurgery. 2287.
https://scholar.barrowneuro.org/neurosurgery/2287