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Development and external validation of a clinical prediction model for functional impairment after intracranial tumor surgery.

Journal article
Authors Victor E Staartjes
Morgan Broggi
Costanza Maria Zattra
Flavio Vasella
Julia Velz
Silvia Schiavolin
Carlo Serra
Jiri Bartek
Alexander Fletcher-Sandersjöö
Petter Förander
Darius Kalasauskas
Mirjam Renovanz
Florian Ringel
Konstantin R Brawanski
Johannes Kerschbaumer
Christian F Freyschlag
Asgeir Store Jakola
Kristin Sjåvik
Ole Solheim
Bawarjan Schatlo
Alexandra Sachkova
Hans Christoph Bock
Abdelhalim Hussein
Veit Rohde
Marike L D Broekman
Claudine O Nogarede
Cynthia M C Lemmens
Julius M Kernbach
Georg Neuloh
Oliver Bozinov
Niklaus Krayenbühl
Johannes Sarnthein
Paolo Ferroli
Luca Regli
Martin N Stienen
Published in Journal of neurosurgery
Pages 1-8
ISSN 1933-0693
Publication year 2020
Published at Institute of Neuroscience and Physiology, Department of Clinical Neuroscience
Pages 1-8
Language en
Links dx.doi.org/10.3171/2020.4.JNS20643
www.ncbi.nlm.nih.gov/entrez/query.f...
Subject categories Neurosurgery

Abstract

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.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.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/.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.

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