Journal article
Development and external validation of a clinical prediction model for functional impairment after intracranial tumor surgery
- Staartjes, Victor E. 2Amsterdam UMC, Vrije Universiteit Amsterdam, Neurosurgery, Amsterdam Movement Sciences, Amsterdam, The Netherlands;
- Broggi, Morgan 3Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan;
- Zattra, Costanza Maria 3Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan;
- Vasella, Flavio 1Department of Neurosurgery and Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland;
- Velz, Julia 1Department of Neurosurgery and Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland;
- Schiavolin, Silvia 4Neurology, Public Health and Disability Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy;
- Serra, Carlo 1Department of Neurosurgery and Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland;
- Bartek, Jiri 7Department of Neurosurgery, Rigshospitalet, Copenhagen, Denmark;
- Fletcher-Sandersjöö, Alexander 6Department of Clinical Neuroscience and Medicine, Karolinska Institutet, Stockholm, Sweden;
- Förander, Petter 6Department of Clinical Neuroscience and Medicine, Karolinska Institutet, Stockholm, Sweden;
- Kalasauskas, Darius 8Department of Neurosurgery, University Medical Center, Johannes Gutenberg University Mainz, Germany;
- Renovanz, Mirjam 8Department of Neurosurgery, University Medical Center, Johannes Gutenberg University Mainz, Germany;
- Ringel, Florian 8Department of Neurosurgery, University Medical Center, Johannes Gutenberg University Mainz, Germany;
- Brawanski, Konstantin R. 9Department of Neurosurgery, Medical University of Innsbruck, Austria;
- Kerschbaumer, Johannes 9Department of Neurosurgery, Medical University of Innsbruck, Austria;
- Freyschlag, Christian F. 9Department of Neurosurgery, Medical University of Innsbruck, Austria;
- Jakola, Asgeir S. 11Institute of Neuroscience and Physiology, Sahlgrenska Academy, Gothenburg, Sweden;
- Sjåvik, Kristin 12Department of Neurosurgery, University Hospital of North Norway, Tromsö;
- Solheim, Ole 13Department of Neurosurgery, St. Olav’s University Hospital, Trondheim, Norway;
- Schatlo, Bawarjan 14Department of Neurosurgery, Georg August University, University Medical Center, Göttingen, Germany;
- Sachkova, Alexandra 14Department of Neurosurgery, Georg August University, University Medical Center, Göttingen, Germany;
- Bock, Hans Christoph 14Department of Neurosurgery, Georg August University, University Medical Center, Göttingen, Germany;
- Hussein, Abdelhalim 14Department of Neurosurgery, Georg August University, University Medical Center, Göttingen, Germany;
- Rohde, Veit 14Department of Neurosurgery, Georg August University, University Medical Center, Göttingen, Germany;
- Broekman, Marike L. D. 16Department of Neurosurgery, Leiden University Medical Center, Leiden;
- Nogarede, Claudine O. 16Department of Neurosurgery, Leiden University Medical Center, Leiden;
- Lemmens, Cynthia M. C. 17Department of Neurology, Haaglanden Medical Center, The Hague, The Netherlands; and
- Kernbach, Julius M. 18Department of Neurosurgery, Faculty of Medicine, RWTH Aachen University, Aachen, Germany
- Neuloh, Georg 18Department of Neurosurgery, Faculty of Medicine, RWTH Aachen University, Aachen, Germany
- Bozinov, Oliver 1Department of Neurosurgery and Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland;
- Krayenbühl, Niklaus 1Department of Neurosurgery and Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland;
- Sarnthein, Johannes 1Department of Neurosurgery and Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland;
- Ferroli, Paolo 3Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan;
- Regli, Luca 1Department of Neurosurgery and Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland;
- Stienen, Martin N.
Published in:
- Journal of Neurosurgery. - Journal of Neurosurgery Publishing Group (JNSPG). - 2020, p. 1-8
English
OBJECTIVE Decision-making for intracranial tumor surgery requires balancing the oncological benefit against the risk for resection-related impairment. Risk estimates are commonly based on subjective experience and generalized numbers from the literature, but even experienced surgeons overestimate functional outcome after surgery. Today, there is no reliable and objective way to preoperatively predict an individual patient’s risk of experiencing any functional impairment. METHODS The authors developed a prediction model for functional impairment at 3 to 6 months after microsurgical resection, defined as a decrease in Karnofsky Performance Status of ≥ 10 points. Two prospective registries in Switzerland and Italy were used for development. External validation was performed in 7 cohorts from Sweden, Norway, Germany, Austria, and the Netherlands. Age, sex, prior surgery, tumor histology and maximum diameter, expected major brain vessel or cranial nerve manipulation, resection in eloquent areas and the posterior fossa, and surgical approach were recorded. Discrimination and calibration metrics were evaluated. RESULTS In the development (2437 patients, 48.2% male; mean age ± SD: 55 ± 15 years) and external validation (2427 patients, 42.4% male; mean age ± SD: 58 ± 13 years) cohorts, functional impairment rates were 21.5% and 28.5%, respectively. In the development cohort, area under the curve (AUC) values of 0.72 (95% CI 0.69–0.74) were observed. In the pooled external validation cohort, the AUC was 0.72 (95% CI 0.69–0.74), confirming generalizability. Calibration plots indicated fair calibration in both cohorts. The tool has been incorporated into a web-based application available at https://neurosurgery.shinyapps.io/impairment/ . CONCLUSIONS Functional impairment after intracranial tumor surgery remains extraordinarily difficult to predict, although machine learning can help quantify risk. This externally validated prediction tool can serve as the basis for case-by-case discussions and risk-to-benefit estimation of surgical treatment in the individual patient.
- Language
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- English
- Open access status
- closed
- Identifiers
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- DOI 10.3171/2020.4.jns20643
- ISSN 0022-3085
- Persistent URL
- https://sonar.ch/global/documents/164505
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