FUSE-ML: development and external validation of a clinical prediction model for mid-term outcomes after lumbar spinal fusion for degenerative disease.
Eur Spine J
; 31(10): 2629-2638, 2022 10.
Article
en En
| MEDLINE
| ID: mdl-35188587
ABSTRACT
BACKGROUND:
Indications and outcomes in lumbar spinal fusion for degenerative disease are notoriously heterogenous. Selected subsets of patients show remarkable benefit. However, their objective identification is often difficult. Decision-making may be improved with reliable prediction of long-term outcomes for each individual patient, improving patient selection and avoiding ineffective procedures.METHODS:
Clinical prediction models for long-term functional impairment [Oswestry Disability Index (ODI) or Core Outcome Measures Index (COMI)], back pain, and leg pain after lumbar fusion for degenerative disease were developed. Achievement of the minimum clinically important difference at 12 months postoperatively was defined as a reduction from baseline of at least 15 points for ODI, 2.2 points for COMI, or 2 points for pain severity.RESULTS:
Models were developed and integrated into a web-app ( https//neurosurgery.shinyapps.io/fuseml/ ) based on a multinational cohort [N = 817; 42.7% male; mean (SD) age 61.19 (12.36) years]. At external validation [N = 298; 35.6% male; mean (SD) age 59.73 (12.64) years], areas under the curves for functional impairment [0.67, 95% confidence interval (CI) 0.59-0.74], back pain (0.72, 95%CI 0.64-0.79), and leg pain (0.64, 95%CI 0.54-0.73) demonstrated moderate ability to identify patients who are likely to benefit from surgery. Models demonstrated fair calibration of the predicted probabilities.CONCLUSIONS:
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.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Fusión Vertebral
Tipo de estudio:
Diagnostic_studies
/
Etiology_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Eur Spine J
Asunto de la revista:
ORTOPEDIA
Año:
2022
Tipo del documento:
Article
País de afiliación:
Suiza