FUSE-ML: development and external validation of a clinical prediction model for mid-term outcomes after lumbar spinal fusion for degenerative disease.
Victor Egon StaartjesVittorio StumpoLuca RicciardiNicolai MaldanerHubert A J EversdijkMoira VieliOlga Ciobanu-CarausAntonino RacoMassimo MiscusiAndrea PernaLuca ProiettiGiorgio LofreseMichele DughieroFrancesco CultreraNicola NicassioSeong Bae AnYoon HaAymeric AmelotIrene AlcobendasJose M Viñuela-PrietoMaria L Gandía-GonzálezPierre-Pascal GirodSara LenerNikolaus KöglAnto AbramovicNico Akhavan SafaChristoph J LauxMazda FarshadDave O'RiordanMarkus LoiblAnne F MannionAlba ScerratiGranit MolliqajEnrico TessitoreMarc L SchröderW Peter VandertopMartin N StienenLuca RegliCarlo SerraPublished in: European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society (2022)
Outcomes after lumbar spinal fusion for degenerative disease remain difficult to predict. Although assistive clinical prediction models can help in quantifying potential benefits of surgery and the externally validated FUSE-ML tool may aid in individualized risk-benefit estimation, truly impacting clinical practice in the era of "personalized medicine" necessitates more robust tools in this patient population.