Core outcomes for assessing surgical learning curves in high-grade glioma surgery: a European Delphi study.

Introduction: Surgical resection plays an important role in the current treatment of high-grade glioma (HGG). As neurosurgical techniques advance, evaluating the surgical learning curve is important to optimize patient outcomes, ensure safety, and support robust clinical research. However, there is currently no consensus on which outcome measures should be used to evaluate the surgical learning curve. Research question: What are the most appropriate outcome measures to assess the surgical learning curve in HGG surgery? Material and methods: A three-round Delphi study comprising two online questionnaires and an expert consensus meeting was conducted. Outcomes were scored on relevance and feasibility on a 9-point Likert scale. Respondents included European neurosurgeons, neuro-oncologists, radiation oncologists, medical oncologists, and specialized nurses. The consensus meeting involved 13 experts, including two high-grade glioma patients. Results: The first and second questionnaires consisted of 24 and 27 (of which 22 were reassessed) outcomes and received 50 and 64 responses, respectively. Following the two questionnaire rounds, eight outcomes were initially accepted; these were subsequently reviewed during the expert consensus meeting, which led to the establishment of a final set of five outcomes, namely: percentage tumor resected, residual tumor remnant, permanent post-operative new neurological symptoms/deterioration, adverse events Clavien-Dindo ≥2 < 72 h after surgery and onco-functional outcome scale. Discussion and conclusion: This study provides the first consensus-based set of outcome measures for assessing the learning curve in HGG surgery, providing a foundation for standardized assessment of learning curves in HGG surgery. It is now necessary to validate this set in research practice.