Development and external validation of a clinical prediction model for functional impairment after intracranial tumor surgery
| Autor: | Bawarjan Schatlo, Alexander Fletcher-Sandersjöö, Claudine O. Nogarede, Costanza M Zattra, Kristin Sjåvik, Alexandra Sachkova, Johannes Kerschbaumer, Oliver Bozinov, Martin N. Stienen, Niklaus Krayenbühl, Georg Neuloh, Carlo Serra, Christian F. Freyschlag, Veit Rohde, Mirjam Renovanz, Hans Christoph Bock, Johannes Sarnthein, Paolo Ferroli, Flavio Vasella, Konstantin Brawanski, Luca Regli, Marike L. D. Broekman, Cynthia M. C. Lemmens, Jiri Bartek, Florian Ringel, Victor E. Staartjes, Ole Solheim, Morgan Broggi, Darius Kalasauskas, Julius M Kernbach, Abdelhalim Hussein, Silvia Schiavolin, Febns, Asgeir Store Jakola, Julia Velz, Petter Förander |
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| Rok vydání: | 2021 |
| Předmět: |
Adult
Male Microsurgery medicine.medical_specialty Functional impairment Adolescent Intracranial tumor Nerve manipulation outcome prediction Young Adult 03 medical and health sciences Postoperative Complications 0302 clinical medicine Predictive Value of Tests Humans Medicine Generalizability theory neurosurgery Prospective Studies Registries Karnofsky Performance Status Aged Retrospective Studies Aged 80 and over Brain Neoplasms business.industry External validation Area under the curve Reproducibility of Results General Medicine Middle Aged Surgery predictive analytics machine learning functional impairment 030220 oncology & carcinogenesis oncology Cohort Female Neurosurgery business 030217 neurology & neurosurgery |
| Zdroj: | Journal of Neurosurgery Journal of Neurosurgery, 134(6), 1743-1750. AMER ASSOC NEUROLOGICAL SURGEONS |
| ISSN: | 1933-0693 0022-3085 |
| DOI: | 10.3171/2020.4.jns20643 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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