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

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Harvard

Staartjes, VE, Broggi, M, Zattra, CM, Vasella, F, Velz, J, Schiavolin, S, Serra, C, Bartek, J, Fletcher-Sandersjöö, A, Förander, P, Kalasauskas, D, Renovanz, M, Ringel, F, Brawanski, KR, Kerschbaumer, J, Freyschlag, CF, Jakola, AS, Sjåvik, K, Solheim, O, Schatlo, B, Sachkova, A, Bock, HC, Hussein, A, Rohde, V, Broekman, MLD, Nogarede, CO, Lemmens, CMC, Kernbach, JM, Neuloh, G, Bozinov, O, Krayenbühl, N, Sarnthein, J, Ferroli, P, Regli, L, Stienen, MN & FEBNS 2021, 'Development and external validation of a clinical prediction model for functional impairment after intracranial tumor surgery', Journal of Neurosurgery, vol. 134, no. 6, pp. 1743-1750. https://doi.org/10.3171/2020.4.JNS20643

APA

Staartjes, V. E., Broggi, M., Zattra, C. M., Vasella, F., Velz, J., Schiavolin, S., Serra, C., Bartek, J., Fletcher-Sandersjöö, A., Förander, P., Kalasauskas, D., Renovanz, M., Ringel, F., Brawanski, K. R., Kerschbaumer, J., Freyschlag, C. F., Jakola, A. S., Sjåvik, K., Solheim, O., ... FEBNS (2021). Development and external validation of a clinical prediction model for functional impairment after intracranial tumor surgery. Journal of Neurosurgery, 134(6), 1743-1750. https://doi.org/10.3171/2020.4.JNS20643

CBE

Staartjes VE, Broggi M, Zattra CM, Vasella F, Velz J, Schiavolin S, Serra C, Bartek J, Fletcher-Sandersjöö A, Förander P, Kalasauskas D, Renovanz M, Ringel F, Brawanski KR, Kerschbaumer J, Freyschlag CF, Jakola AS, Sjåvik K, Solheim O, Schatlo B, Sachkova A, Bock HC, Hussein A, Rohde V, Broekman MLD, Nogarede CO, Lemmens CMC, Kernbach JM, Neuloh G, Bozinov O, Krayenbühl N, Sarnthein J, Ferroli P, Regli L, Stienen MN, FEBNS. 2021. Development and external validation of a clinical prediction model for functional impairment after intracranial tumor surgery. Journal of Neurosurgery. 134(6):1743-1750. https://doi.org/10.3171/2020.4.JNS20643

MLA

Vancouver

Author

Staartjes, Victor E ; Broggi, Morgan ; Zattra, Costanza Maria ; Vasella, Flavio ; Velz, Julia ; Schiavolin, Silvia ; Serra, Carlo ; Bartek, Jiri ; Fletcher-Sandersjöö, Alexander ; Förander, Petter ; Kalasauskas, Darius ; Renovanz, Mirjam ; Ringel, Florian ; Brawanski, Konstantin R ; Kerschbaumer, Johannes ; Freyschlag, Christian F ; Jakola, Asgeir S ; Sjåvik, Kristin ; Solheim, Ole ; Schatlo, Bawarjan ; Sachkova, Alexandra ; Bock, Hans Christoph ; Hussein, Abdelhalim ; Rohde, Veit ; Broekman, Marike L D ; Nogarede, Claudine O ; Lemmens, Cynthia M C ; Kernbach, Julius M ; Neuloh, Georg ; Bozinov, Oliver ; Krayenbühl, Niklaus ; Sarnthein, Johannes ; Ferroli, Paolo ; Regli, Luca ; Stienen, Martin N ; FEBNS. / Development and external validation of a clinical prediction model for functional impairment after intracranial tumor surgery. In: Journal of Neurosurgery. 2021 ; Vol. 134, No. 6. pp. 1743-1750.

Bibtex

@article{36848099efc64d17811a4bddf315ee3a,
title = "Development and external validation of a clinical prediction model for functional impairment after intracranial tumor surgery",
abstract = "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.",
keywords = "Functional impairment, Machine learning, Neurosurgery, Oncology, Outcome prediction, Predictive analytics",
author = "Staartjes, {Victor E} and Morgan Broggi and Zattra, {Costanza Maria} and Flavio Vasella and Julia Velz and Silvia Schiavolin and Carlo Serra and Jiri Bartek and Alexander Fletcher-Sandersj{\"o}{\"o} and Petter F{\"o}rander and Darius Kalasauskas and Mirjam Renovanz and Florian Ringel and Brawanski, {Konstantin R} and Johannes Kerschbaumer and Freyschlag, {Christian F} and Jakola, {Asgeir S} and Kristin Sj{\aa}vik and Ole Solheim and Bawarjan Schatlo and Alexandra Sachkova and Bock, {Hans Christoph} and Abdelhalim Hussein and Veit Rohde and Broekman, {Marike L D} and Nogarede, {Claudine O} and Lemmens, {Cynthia M C} and Kernbach, {Julius M} and Georg Neuloh and Oliver Bozinov and Niklaus Krayenb{\"u}hl and Johannes Sarnthein and Paolo Ferroli and Luca Regli and Stienen, {Martin N} and FEBNS",
year = "2021",
month = jun,
doi = "10.3171/2020.4.JNS20643",
language = "English",
volume = "134",
pages = "1743--1750",
journal = "Journal of Neurosurgery",
issn = "0022-3085",
publisher = "American Association of Neurological Surgeons",
number = "6",

}

RIS

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 - 2021/6

Y1 - 2021/6

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

U2 - 10.3171/2020.4.JNS20643

DO - 10.3171/2020.4.JNS20643

M3 - Journal article

C2 - 32534490

VL - 134

SP - 1743

EP - 1750

JO - Journal of Neurosurgery

JF - Journal of Neurosurgery

SN - 0022-3085

IS - 6

ER -

ID: 61631744