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  • Author or Editor: Konstantin R. Brawanski x
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Victor E. Staartjes, Morgan Broggi, Costanza Maria Zattra, Flavio Vasella, Julia Velz, Silvia Schiavolin, Carlo Serra, Jiri Bartek Jr., Alexander Fletcher-Sandersjöö, Petter Förander, Darius Kalasauskas, Mirjam Renovanz, Florian Ringel, Konstantin R. Brawanski, Johannes Kerschbaumer, Christian F. Freyschlag, Asgeir S. 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 and FEBNS

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.