External Validation of Twelve Existing Survival Prediction Models for Patients with Spinal Metastases.

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Survival prediction models for patients with spinal metastases may inform patients and clinicians in shared decision-making. To externally validate all existing survival prediction models for patients with spinal metastases DESIGN: Prospective cohort study using retrospective data PATIENT SAMPLE: 953 patients OUTCOME MEASURES: Survival in months, area under the curve (AUC), and calibration intercept and slope METHOD: This study included patients with spinal metastases referred to a single tertiary referral center between 2016-2021. Twelve models for predicting 3, 6, and 12-month survival were externally validated Bollen, Mizumoto, Modified Bauer, New England Spinal Metastasis Score, Original Bauer, Oswestry Spinal Risk Index (OSRI), PathFx, Revised Katagiri, Revised Tokuhashi, Skeletal Oncology Research Group Machine Learning Algorithm (SORG-MLA), Tomita, and Van der Linden. Discrimination was assessed using (AUC) and calibration using the intercept and slope. Calibration was considered appropriate if calibration measures were close to their ideal values with narrow confidence intervals. In total, 953 patients were included. Survival was 76.4% at 3 months (728/953), 62.2% at 6 months (593/953), and 50.3% at 12 months (479/953). Revised Katagiri yielded AUCs of 0.79 (95%CI 0.76;0.82) to 0.81 (95%CI 0.79;0.84), Bollen yielded AUCs of 0.76 (95%CI 0.73;0.80) to 0.77 (95%CI 0.75;0.80), and OSRI yielded AUCs of 0.75 (95%CI 0.72;0.78) to 0.77 (95%CI 0.74;0.79). The other nine prediction models yielded AUCs ranging from 0.59 (95%CI 0.55;0.63) to 0.76 (95%CI 0.74;0.79). None of the twelve models yielded appropriate calibration. Twelve survival prediction models for patients with spinal metastases yielded poor to fair discrimination and poor calibration. Survival prediction models may inform decision-making in patients with spinal metastases, provided that recalibration using recent patient data is performed. This study was funded by the AOSpine under the Discovery & Innovation award (AOS-DIA-22-012-TUM). A total amount of CHF 40,000 ($45,000) was received.