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  • Author or Editor: Silvia Schiavolin x
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Paolo Ferroli, Morgan Broggi, Silvia Schiavolin, Francesco Acerbi, Valentina Bettamio, Dario Caldiroli, Alberto Cusin, Emanuele La Corte, Matilde Leonardi, Alberto Raggi, Marco Schiariti, Sergio Visintini, Angelo Franzini and Giovanni Broggi

OBJECT

The Milan Complexity Scale—a new practical grading scale designed to estimate the risk of neurological clinical worsening after performing surgery for tumor removal—is presented.

METHODS

A retrospective study was conducted on all elective consecutive surgical procedures for tumor resection between January 2012 and December 2014 at the Second Division of Neurosurgery at Fondazione IRCCS Istituto Neurologico Carlo Besta of Milan. A prospective database dedicated to reporting complications and all clinical and radiological data was retrospectively reviewed. The Karnofsky Performance Scale (KPS) was used to classify each patient’s health status. Complications were divided into major and minor and recorded based on etiology and required treatment. A logistic regression model was used to identify possible predictors of clinical worsening after surgery in terms of changes between the preoperative and discharge KPS scores. Statistically significant predictors were rated based on their odds ratios in order to build an ad hoc complexity scale. For each patient, a corresponding total score was calculated, and ANOVA was performed to compare the mean total scores between the improved/unchanged and worsened patients. Relative risk (RR) and chi-square statistics were employed to provide the risk of worsening after surgery for each total score.

RESULTS

The case series was composed of 746 patients (53.2% female; mean age 51.3 ± 17.1). The most common tumors were meningiomas (28.6%) and glioblastomas (24.1%). The mortality rate was 0.94%, the major complication rate was 9.1%, and the minor complication rate was 32.6%. Of 746 patients, 523 (70.1%) patients improved or remained unchanged, and 223 (29.9%) patients worsened. The following factors were found to be statistically significant predictors of the change in KPS scores: tumor size larger than 4 cm, cranial nerve manipulation, major brain vessel manipulation, posterior fossa location, and eloquent area involvement (Nagelkerke R2 = 0.286). A grading scale was obtained with scores ranging between 0 and 8. Worsened patients showed mean total scores that were significantly higher than the improved/unchanged scores (3.24 ± 1.55 vs 1.47 ± 1.58; p < 0.001). Finally, a grid was developed to show the risk of worsening after surgery for each total score: scores higher than 3 are suggestive of worse clinical outcome.

CONCLUSIONS

Through the evaluation of the 5 aforementioned parameters—the Big Five—the Milan Complexity Scale enables neurosurgeons to estimate the risk of a negative clinical course after brain tumor surgery and share these data with the patient. Furthermore, the Milan Complexity Scale could be used for research and educational purposes and better health system management.

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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.