Development and internal validation of the Meningioma Functional Outcome Risk and Counseling Estimator 6 score, a...

Accurate prognostic assessment is crucial for guiding clinical decisions in meningioma patients. Traditional prognostic indexes have been limited by subjectivity, oversimplification, or lack of specific validation for meningiomas, leading to inaccuracies from misaligned scaling objectives. This study aimed to create a refined weighting system, integrating variables from prior indexes to better reflect prognostic significance in meningioma patients.

The authors retrospectively analyzed the data of 592 patients who underwent intracranial meningioma resection at a single institution (2009-2024). The primary endpoint was lack of functional independence at 6 weeks (modified Rankin Scale [mRS] score ≥ 3). Univariable screening and multivariable logistic regression identified independent predictors, which were translated into a simplified point-based system: Meningioma Functional Outcome Risk and Counseling Estimator (M-FORCE) 6 score. Internal validation was performed using 10,000 bootstrap resamples. Discrimination, calibration, and clinical utility (decision curve analysis) were assessed.

Skull base origin (OR 2.21), infratentorial location (OR 2.12), preoperative dependence (mRS score > 2, OR 2.27), tumor size ≥ 40 mm (OR 3.06), and comorbidity burden (Charlson Comorbidity Index [CCI] score > 2, OR 2.41; CCI score > 6, OR 2.46) independently predicted functional dependence at 6 weeks. These variables were incorporated into the M-FORCE 6 score. Patients were stratified into four risk groups: low (0-3 points, 6.2% risk), intermediate (4-6 points, 26.5% risk), high (7-9 points, 46.9% risk), and very high (10-13 points, 71.4% risk). The optimism-corrected areas under the curve of the multivariable model and the score were both 0.76, with excellent calibration and favorable net benefit on decision curve analysis.

The M-FORCE 6 score provides a transparent, meningioma-specific tool for preoperative risk stratification, integrating demographic, clinical, and radiological features into a simple and reproducible model. External validation is warranted to confirm its generalizability.