PROPOSE. Development and validation of a prediction model for shared decision making for patients with lumbar spinal stenosis

Decompression for lumbar spinal stenosis (LSS) is the most frequently performed spine surgery in Denmark. According to the Danish spine registry DaneSpine, at 1 year after surgery, about 75% of patients experiences considerable pain relief and around 66% improvement in quality of life. However, 25%...

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Veröffentlicht in:North American Spine Society journal (NASSJ) 2024-03, Vol.17, p.100309-100309, Article 100309
Hauptverfasser: Pedersen, Casper Friis, Andersen, Mikkel Østerheden, Carreon, Leah Yacat, Skov, Simon Toftgaard, Doering, Peter, Eiskjær, Søren
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Sprache:eng
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Zusammenfassung:Decompression for lumbar spinal stenosis (LSS) is the most frequently performed spine surgery in Denmark. According to the Danish spine registry DaneSpine, at 1 year after surgery, about 75% of patients experiences considerable pain relief and around 66% improvement in quality of life. However, 25% do not improve very much. We have developed a predictive decision support tool, PROPOSE. It is intended to be used in the clinical conversation between healthcare providers and LSS patients as a shared decision-making aid presenting pros and cons of surgical intervention. This study presents the development and evaluation of PROPOSE in a clinical setting. For model development, 6.357 LSS patients enrolled in DaneSpine were identified. For model validation, predictor response and predicted outcome was collected via PROPOSE from 228 patients. Observed outcome at 1 year was retrieved from DaneSpine. All participants were treated at 3 Danish spine centers. The outcome measures presented are improvement in walking distance, the Oswestry Disability Index, EQ-5D-3L and leg/back pain on the Visual Analog Scale. Outcome variables were dichotomized into success (1) and failure (0). With the exception of walking distance, a success was defined as reaching minimal clinically important difference at 1-year follow-up. Models were trained using Multivariate Adaptive Regression Splines. Performance was assessed by inspecting confusion matrix, ROC curves and comparing GCV (generalized cross-validation) errors. Final performance of the models was evaluated on independent test data. The walking distance model demonstrated excellent performance with an AUC of 0.88 and a Brier score of 0.14. The VAS leg pain model had the lowest discriminatory performance with an AUC of 0.67 and a Brier score of 0.22. PROPOSE works in a real-world clinical setting as a proof of concept and demonstrates acceptable performance. It may have the potential of aiding shared decision making.
ISSN:2666-5484
2666-5484
DOI:10.1016/j.xnsj.2024.100309