• Alexandra Grob, Jonas Rohr, Vittorio Stumpo, Moira Vieli, Olga Ciobanu-Caraus, Luca Ricciardi, Nicolai Maldaner, Antonino Raco, Massimo Miscusi, Andrea Perna, Luca Proietti, Giorgio Lofrese, Michele Dughiero, Francesco Cultrera, Marcello D'Andrea, Seong Bae An, Yoon Ha, Aymeric Amelot, Jorge Bedia Cadelo, Jose M Viñuela-Prieto, Maria L Gandía-González, Pierre-Pascal Girod, Sara Lener, Nikolaus Kögl, Anto Abramovic, Christoph J Laux, Mazda Farshad, Dave O'Riordan, Markus Loibl, Fabio Galbusera, Anne F Mannion, Alba Scerrati, Pasquale De Bonis, Granit Molliqaj, Enrico Tessitore, Marc L Schröder, Martin N Stienen, Luca Regli, Carlo Serra, and Victor E Staartjes.
• Machine Intelligence in Clinical Neuroscience and Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
• Eur Spine J. 2024 Jul 11.

BackgroundClinical prediction models (CPM), such as the SCOAP-CERTAIN tool, can be utilized to enhance decision-making for lumbar spinal fusion surgery by providing quantitative estimates of outcomes, aiding surgeons in assessing potential benefits and risks for each individual patient. External validation is crucial in CPM to assess generalizability beyond the initial dataset. This ensures performance in diverse populations, reliability and real-world applicability of the results. Therefore, we externally validated the tool for predictability of improvement in oswestry disability index (ODI), back and leg pain (BP, LP).MethodsProspective and retrospective data from multicenter registry was obtained. As outcome measure minimum clinically important change was chosen for ODI with ≥ 15-point and ≥ 2-point reduction for numeric rating scales (NRS) for BP and LP 12 months after lumbar fusion for degenerative disease. We externally validate this tool by calculating discrimination and calibration metrics such as intercept, slope, Brier Score, expected/observed ratio, Hosmer-Lemeshow (HL), AUC, sensitivity and specificity.ResultsWe included 1115 patients, average age 60.8 ± 12.5 years. For 12-month ODI, area-under-the-curve (AUC) was 0.70, the calibration intercept and slope were 1.01 and 0.84, respectively. For NRS BP, AUC was 0.72, with calibration intercept of 0.97 and slope of 0.87. For NRS LP, AUC was 0.70, with calibration intercept of 0.04 and slope of 0.72. Sensitivity ranged from 0.63 to 0.96, while specificity ranged from 0.15 to 0.68. Lack of fit was found for all three models based on HL testing.ConclusionsUtilizing data from a multinational registry, we externally validate the SCOAP-CERTAIN prediction tool. The model demonstrated fair discrimination and calibration of predicted probabilities, necessitating caution in applying it in clinical practice. We suggest that future CPMs focus on predicting longer-term prognosis for this patient population, emphasizing the significance of robust calibration and thorough reporting.© 2024. The Author(s).

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