TY - JOUR
T1 - Development and external validation of a clinical prediction model for functional impairment after intracranial tumor surgery
AU - Staartjes, Victor E
AU - Broggi, Morgan
AU - Zattra, Costanza Maria
AU - Vasella, Flavio
AU - Velz, Julia
AU - Schiavolin, Silvia
AU - Serra, Carlo
AU - Bartek, Jiri
AU - Fletcher-Sandersjöö, Alexander
AU - Förander, Petter
AU - Kalasauskas, Darius
AU - Renovanz, Mirjam
AU - Ringel, Florian
AU - Brawanski, Konstantin R
AU - Kerschbaumer, Johannes
AU - Freyschlag, Christian F
AU - Jakola, Asgeir S
AU - Sjåvik, Kristin
AU - Solheim, Ole
AU - Schatlo, Bawarjan
AU - Sachkova, Alexandra
AU - Bock, Hans Christoph
AU - Hussein, Abdelhalim
AU - Rohde, Veit
AU - Broekman, Marike L D
AU - Nogarede, Claudine O
AU - Lemmens, Cynthia M C
AU - Kernbach, Julius M
AU - Neuloh, Georg
AU - Bozinov, Oliver
AU - Krayenbühl, Niklaus
AU - Sarnthein, Johannes
AU - Ferroli, Paolo
AU - Regli, Luca
AU - Stienen, Martin N
AU - FEBNS
PY - 2020/6/12
Y1 - 2020/6/12
N2 - 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.
AB - 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.
KW - Functional impairment
KW - Machine learning
KW - Neurosurgery
KW - Oncology
KW - Outcome prediction
KW - Predictive analytics
UR - http://www.scopus.com/inward/record.url?scp=85107313973&partnerID=8YFLogxK
U2 - 10.3171/2020.4.JNS20643
DO - 10.3171/2020.4.JNS20643
M3 - Journal article
C2 - 32534490
SN - 0022-3085
VL - 134
SP - 1743
EP - 1750
JO - Journal of Neurosurgery
JF - Journal of Neurosurgery
IS - 6
ER -