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.

FUSE-ML: development and external validation of a clinical prediction model for mid-term outcomes after lumbar spinal fusion for degenerative disease / Staartjes, Victor E.; Stumpo, Vittorio; Ricciardi, Luca; Maldaner, Nicolai; Eversdijk, Hubert A. J.; Vieli, Moira; Ciobanu-Caraus, Olga; Raco, Antonino; Miscusi, Massimo; Perna, Andrea; Proietti, Luca; Lofrese, Giorgio; Dughiero, Michele; Cultrera, Francesco; Nicassio, Nicola; An, Seong Bae; Yoon, Ha; Amelot, Aymeric; Alcobendas, Irene; Vinuela-Prieto, Jose M.; Gandia-Gonzalez, Maria L.; Girod, Pierre-Pascal; Lener, Sara; Koegl, Nikolaus; Abramovic, Anto; Safa, Nico Akhavan; Laux, Christoph J.; Farshad, Mazda; O'Riordan, Dave; Loibl, Markus; Mannion, Anne F.; Scerrati, Alba; Molliqaj, Granit; Tessitore, Enrico; Schroeder, Marc L.; Vandertop, W. Peter; Stienen, Martin N.; Regli, Luca; Serra, Carlo. - In: EUROPEAN SPINE JOURNAL. - ISSN 0940-6719. - (2022). [10.1007/S00586-022-07135-9]

FUSE-ML: development and external validation of a clinical prediction model for mid-term outcomes after lumbar spinal fusion for degenerative disease

Ricciardi, Luca;Raco, Antonino;Miscusi, Massimo;Ha, Yoon;
2022

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.
2022
Clinical prediction model; Machine learning; Neurosurgery; Outcome prediction; Predictive analytics; Spinal fusion
01 Pubblicazione su rivista::01a Articolo in rivista
FUSE-ML: development and external validation of a clinical prediction model for mid-term outcomes after lumbar spinal fusion for degenerative disease / Staartjes, Victor E.; Stumpo, Vittorio; Ricciardi, Luca; Maldaner, Nicolai; Eversdijk, Hubert A. J.; Vieli, Moira; Ciobanu-Caraus, Olga; Raco, Antonino; Miscusi, Massimo; Perna, Andrea; Proietti, Luca; Lofrese, Giorgio; Dughiero, Michele; Cultrera, Francesco; Nicassio, Nicola; An, Seong Bae; Yoon, Ha; Amelot, Aymeric; Alcobendas, Irene; Vinuela-Prieto, Jose M.; Gandia-Gonzalez, Maria L.; Girod, Pierre-Pascal; Lener, Sara; Koegl, Nikolaus; Abramovic, Anto; Safa, Nico Akhavan; Laux, Christoph J.; Farshad, Mazda; O'Riordan, Dave; Loibl, Markus; Mannion, Anne F.; Scerrati, Alba; Molliqaj, Granit; Tessitore, Enrico; Schroeder, Marc L.; Vandertop, W. Peter; Stienen, Martin N.; Regli, Luca; Serra, Carlo. - In: EUROPEAN SPINE JOURNAL. - ISSN 0940-6719. - (2022). [10.1007/S00586-022-07135-9]
File allegati a questo prodotto
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1705834
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 7
  • ???jsp.display-item.citation.isi??? 8
social impact