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

Journal of neurosurgery, vol. 134, pp. 1743–1750

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.

Authors 36

1. Victor E. Staartjes

   University of Zurich · University Hospital Zurich · Amsterdam University Medical Centers · Amsterdam Movement Sciences · Vrije Universiteit Amsterdam

   Affiliation as printed

   Amsterdam UMC, Vrije Universiteit Amsterdam, Neurosurgery, Amsterdam Movement Sciences, Amsterdam, The Netherlands;

   Department of Neurosurgery and Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland;

   Amsterdam UMC, Vrije Universiteit Amsterdam,

2. Morgan Broggi

   Fondazione IRCCS Istituto Neurologico Carlo Besta

   Affiliation as printed

   Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan;

3. Costanza Maria Zattra

   Fondazione IRCCS Istituto Neurologico Carlo Besta

   Affiliation as printed

   Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan;

4. Flavio Vasella

   University of Zurich · University Hospital Zurich

   Affiliation as printed

   Department of Neurosurgery and Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland;

5. Julia Velz

   University of Zurich · University Hospital Zurich

   Affiliation as printed

   Department of Neurosurgery and Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland;

6. Silvia Schiavolin

   Fondazione IRCCS Istituto Neurologico Carlo Besta

   Affiliation as printed

   Neurology, Public Health and Disability Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy;

7. Carlo Serra

   University of Zurich · University Hospital Zurich

   Affiliation as printed

   Department of Neurosurgery and Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland;

8. Jiri Jr Bartek

   Karolinska University Hospital · Rigshospitalet · Karolinska Institutet

   Affiliation as printed

   Department of Clinical Neuroscience and Medicine, Karolinska Institutet, Stockholm, Sweden;

   Department of Neurosurgery, Karolinska University Hospital, Stockholm;

   Department of Neurosurgery, Rigshospitalet, Copenhagen, Denmark;

9. Alexander Fletcher‐Sandersjöö

   Karolinska University Hospital · Karolinska Institutet

   Affiliation as printed

   Department of Clinical Neuroscience and Medicine, Karolinska Institutet, Stockholm, Sweden;

   Department of Neurosurgery, Karolinska University Hospital, Stockholm;

10. Petter Förander

    Karolinska University Hospital · Karolinska Institutet

    Affiliation as printed

    Department of Clinical Neuroscience and Medicine, Karolinska Institutet, Stockholm, Sweden;

    Department of Neurosurgery, Karolinska University Hospital, Stockholm;

11. Darius Kalasauskas

    Johannes Gutenberg University Mainz · University Medical Center of the Johannes Gutenberg University Mainz

    Affiliation as printed

    Department of Neurosurgery, University Medical Center, Johannes Gutenberg University Mainz, Germany;

12. Mirjam Renovanz

    Johannes Gutenberg University Mainz · University Medical Center of the Johannes Gutenberg University Mainz

    Affiliation as printed

    Department of Neurosurgery, University Medical Center, Johannes Gutenberg University Mainz, Germany;

13. Florian A. Ringel

    Johannes Gutenberg University Mainz · University Medical Center of the Johannes Gutenberg University Mainz

    Affiliation as printed

    Department of Neurosurgery, University Medical Center, Johannes Gutenberg University Mainz, Germany;

14. Konstantin Brawanski

    Innsbruck Medical University · Universität Innsbruck

    Affiliation as printed

    Department of Neurosurgery, Medical University of Innsbruck, Austria;

15. Johannes Kerschbaumer

    Innsbruck Medical University · Universität Innsbruck

    Affiliation as printed

    Department of Neurosurgery, Medical University of Innsbruck, Austria;

16. Christian Franz Freyschlag

    Innsbruck Medical University · Universität Innsbruck

    Affiliation as printed

    Department of Neurosurgery, Medical University of Innsbruck, Austria;

17. Asgeir Store Jakola

    Sahlgrenska University Hospital · University of Gothenburg

    Affiliation as printed

    Department of Neurosurgery, Sahlgrenska University Hospital, Gothenburg;

    Institute of Neuroscience and Physiology, Sahlgrenska Academy, Gothenburg, Sweden;

18. Kristin Sjåvik

    University Hospital of North Norway

    Affiliation as printed

    Department of Neurosurgery, University Hospital of North Norway, Tromsö;

19. Ole Skeidsvoll Solheim

    St Olav's University Hospital

    Affiliation as printed

    Department of Neurosurgery, St. Olav’s University Hospital, Trondheim, Norway;

    Department of Neurosurgery, St. Olav's University Hospital, Trondheim, Norway;

20. Bawarjan Schatlo

    Universitätsmedizin Göttingen · University of Göttingen

    Affiliation as printed

    Department of Neurosurgery, Georg August University, University Medical Center, Göttingen, Germany;

21. Alexandra Sachkova

    Universitätsmedizin Göttingen · University of Göttingen

    Affiliation as printed

    Department of Neurosurgery, Georg August University, University Medical Center, Göttingen, Germany;

22. Hans Christoph Bock

    Universitätsmedizin Göttingen · University of Göttingen

    Affiliation as printed

    Department of Neurosurgery, Georg August University, University Medical Center, Göttingen, Germany;

23. Abdelhalim Hussein

    Universitätsmedizin Göttingen · University of Göttingen

    Affiliation as printed

    Department of Neurosurgery, Georg August University, University Medical Center, Göttingen, Germany;

24. Veit Rohde

    Universitätsmedizin Göttingen · University of Göttingen

    Affiliation as printed

    Department of Neurosurgery, Georg August University, University Medical Center, Göttingen, Germany;

25. Marike L. D. Broekman Aachen Department of Neurosurgery, Leiden University Medical Center

    Leiden University Medical Center · Medisch Centrum Haaglanden · Stichting Haaglanden Medisch Centrum

    Affiliation as printed

    Department of Neurosurgery, Haaglanden Medical Center, The Hague;

    Department of Neurosurgery, Leiden University Medical Center, Leiden;

26. Claudine O. Nogarede Aachen Department of Neurosurgery, Leiden University Medical Center

    Leiden University Medical Center · Medisch Centrum Haaglanden · Stichting Haaglanden Medisch Centrum

    Affiliation as printed

    Department of Neurosurgery, Haaglanden Medical Center, The Hague;

    Department of Neurosurgery, Leiden University Medical Center, Leiden;

27. Cynthia M C Lemmens

    Medisch Centrum Haaglanden · Stichting Haaglanden Medisch Centrum

    Affiliation as printed

    Department of Neurology, Haaglanden Medical Center, The Hague, The Netherlands; and

28. Julius M. Kernbach Aachen Department of Neurosurgery Faculty of Medicine

    RWTH Aachen University

    Affiliation as printed

    Department of Neurosurgery, Faculty of Medicine, RWTH Aachen University, Aachen, Germany

29. Georg Neuloh Aachen Department of Neurosurgery Faculty of Medicine

    RWTH Aachen University

    Affiliation as printed

    Department of Neurosurgery, Faculty of Medicine, RWTH Aachen University, Aachen, Germany

30. Oliver Bozinov

    University of Zurich · University Hospital Zurich

    Affiliation as printed

    Department of Neurosurgery and Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland;

31. Niklaus Krayenbühl

    University of Zurich · University Hospital Zurich

    Affiliation as printed

    Department of Neurosurgery and Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland;

32. Johannes Sarnthein

    University of Zurich · University Hospital Zurich

    Affiliation as printed

    Department of Neurosurgery and Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland;

33. Paolo Ferroli

    Fondazione IRCCS Istituto Neurologico Carlo Besta

    Affiliation as printed

    Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan;

34. Luca Regli

    University of Zurich · University Hospital Zurich

    Affiliation as printed

    Department of Neurosurgery and Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland;

35. Martin Nikolaus Stienen

    University of Zurich · University Hospital Zurich

    Affiliation as printed

    Department of Neurosurgery and Machine Intelligence in Clinical Neuroscience (MICN) Laboratory , Clinical Neuroscience Center , University Hospital Zurich , University of Zurich , Switzerland;

    Department of Neurosurgery and Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland;

36. FEBNS

    University of Zurich · University Hospital Zurich

    Affiliation as printed

    Department of Neurosurgery and Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland;