ArticleBMC musculoskeletal disorders2023
Predicting patient-reported outcomes following lumbar spine surgery: development and external validation of multivariable prediction models.
Article in BMC musculoskeletal disorders, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Machine learning and neural network algorithms for prediction of C5 palsy after posterior surgery for ossification of posterior longitudinal ligament.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 · 2026Article
- Length of stay after lumbar decompressive surgery: prediction model, clinical reasons for prolonged stay, and association of prolonged stay with outcomes.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 · 2026Article
- Risk prediction in spine surgery: a scoping review of traditional models, artificial intelligence, and the challenge of clinical translation.Spine deformity · 2026Review
- Who Is the Surgeon Now: Human Hands or Machine Minds? Artificial Intelligence in Orthopedics from Diagnosis to Follow-Up-A Structured Narrative Review.Journal of clinical medicine · 2026Review
- Predicting high health care use in patients with spinal disorder in secondary care: model development and validation.Pain reports · 2026Article
- Beyond raw comparisons: Adjusted analysis reveals only minor inter-hospital differences in ACDF outcomes in Norway.Brain & spine · 2026Article
- External validation of a prediction model for disability and pain after lumbar disc herniation surgery: a prospective international registry-based cohort study.Acta orthopaedica · 2025Article
- The Core Outcome Measures Index (COMI) comes of age: 18 years of efficient and effective patient-reported outcome measurement in the field of spinal disorders. Part 1. Development of the COMI, interpretation of scores, and use in research.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 · 2025Review
- Machine learning predicts spinal cord stimulation surgery outcomes and reveals novel neural markers for chronic pain.Scientific reports · 2025Article
- Scoping Review of Machine Learning and Patient-Reported Outcomes in Spine Surgery.Bioengineering (Basel, Switzerland) · 2025Review
- Validity and accuracy of a machine learning predictive model in the exploitation of patient-related outcomes in spine surgery.Frontiers in surgery · 2025Article
- Construct validation of the Japanese Core Outcome Measures Index and the impact of diseases on patient-reported outcome measures in preoperative patients with lumbar spinal stenosis and disk herniation: a single-center observational study.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 · 2024Observational
- Artificial Intelligence for Clinically Meaningful Outcome Prediction in Orthopedic Research: Current Applications and Limitations.Current reviews in musculoskeletal medicine · 2024Review
- Advancing spine care through AI and machine learning: overview and applications.EFORT open reviews · 2024Review
- Machine Learning Models for Predicting Disability and Pain Following Lumbar Disc Herniation Surgery.JAMA network open · 2024Article
- Predicting patient reported outcome measures: a scoping review for the artificial intelligence-guided patient preference predictor.Frontiers in artificial intelligence · 2024Article
- Revolutionizing Spinal Care: Current Applications and Future Directions of Artificial Intelligence and Machine Learning.Journal of clinical medicine · 2023Review
- Appropriateness of specialized care referrals for LBP: a cross-sectional analysis.Frontiers in medicine · 2023Article
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4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aimed to develop and externally validate prediction models of spinal surgery outcomes based on a retrospective review of a prospective clinical database, uniquely comparing multivariate regression and random forest (machine learning) approaches, and identifying the most important predictors.
methodsOutcomes were change in back and leg pain intensity and Core Outcome Measures Index (COMI) from baseline to the last available postoperative follow-up (3-24 months), defined as minimal clinically important change (MCID) and continuous change score. Eligible patients underwent lumbar spine surgery for degenerative pathology between 2011 and 2021. Data were split by surgery date into development (N = 2691) and validation (N = 1616) sets for temporal external validation. Multivariate logistic and linear regression, and random forest classification and regression models, were fit to the development data and validated on the external data.
resultsAll models demonstrated good calibration in the validation data. Discrimination ability (area under the curve) for MCID ranged from 0.63 (COMI) to 0.72 (back pain) in regression, and from 0.62 (COMI) to 0.68 (back pain) in random forests. The explained variation in continuous change scores spanned 16%-28% in linear, and 15%-25% in random forests regression. The most important predictors included age, baseline scores on the respective outcome measures, type of degenerative pathology, previous spinal surgeries, smoking status, morbidity, and duration of hospital stay.
conclusionsThe developed models appear robust and generalisable across different outcomes and modelling approaches but produced only borderline acceptable discrimination ability, suggesting the need to assess further prognostic factors. External validation showed no advantage of the random forest approach.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.