Observational studyEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2019
Development of predictive models for all individual questions of SRS-22R after adult spinal deformity surgery: a step toward individualized medicine.
Observational study 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, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 57 citations in OpenAlex.
- Utility of machine learning algorithms in degenerative cervical and lumbar spine disease: a systematic review.Neurosurgical review · 2022Pooled it
- State-of-the-art review in AI and digital technologies for spine deformity.Spine deformity · 2026Review
- Predicting proximal junctional failure in adult spinal deformity patients using machine learning models based on spinal alignment parameters.Scientific reports · 2025Article
- Artificial intelligence in orthopedics: fundamentals, current applications, and future perspectives.Military Medical Research · 2025Review
- AI and machine learning in paediatric spine deformity surgery.Bone & joint open · 2025Article
- Article
- Scoping Review of Machine Learning and Patient-Reported Outcomes in Spine Surgery.Bioengineering (Basel, Switzerland) · 2025Review
- Predicting pediatric patient rehabilitation outcomes after spinal deformity surgery with artificial intelligence.Communications medicine · 2025Article
- Personalizing adult spinal deformity surgery through multimodal artificial intelligence.Acta orthopaedica et traumatologica turcica · 2024Review
- Predicting patient reported outcome measures: a scoping review for the artificial intelligence-guided patient preference predictor.Frontiers in artificial intelligence · 2024Article
- Artificial Intelligence in Scoliosis: Current Applications and Future Directions.Journal of clinical medicine · 2023Review
- Emerging Technologies within Spine Surgery.Life (Basel, Switzerland) · 2023Review
- Future Data Points to Implement in Adult Spinal Deformity Assessment for Artificial Intelligence Modeling Prediction: The Importance of the Biological Dimension.International journal of spine surgery · 2023Article
- Potential Applications of Artificial Intelligence and Machine Learning in Spine Surgery Across the Continuum of Care.International journal of spine surgery · 2023Article
- Inventory of Patient-Reported Outcome Measures Used in the Non-Operative Care of Scoliosis: A Scoping Review.Children (Basel, Switzerland) · 2023Article
- Surgeons' risk perception in ASD surgery: The value of objective risk assessment on decision making and patient counselling.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 · 2022Article
- Adult Spinal Deformity Surgery and Frailty: A Systematic Review.Global spine journal · 2022Article
- Artificial Intelligence in Adult Spinal Deformity.Acta neurochirurgica. Supplement · 2022Article
- The Influence of Baseline Clinical Status and Surgical Strategy on Early Good to Excellent Result in Spinal Lumbar Arthrodesis: A Machine Learning Approach.Journal of personalized medicine · 2021Article
- Validation of the ACS-NSQIP Risk Calculator: A Machine-Learning Risk Tool for Predicting Complications and Mortality Following Adult Spinal Deformity Corrective Surgery.International journal of spine surgery · 2021Article
Corrections and comments
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Authors and funding
20 authors at 10 institutions in 4 countries.
Funding
Abstract
purposeHealth-related quality of life (HRQL) instruments are essential in value-driven health care, but patients often have more specific, personal priorities when seeking surgical care. The Scoliosis Research Society-22R (SRS-22R), an HRQL instrument for spinal deformity, provides summary scores spanning several health domains, but these may be difficult for patients to utilize in planning their specific care goals. Our objective was to create preoperative predictive models for responses to individual SRS-22R questions at 1 and 2 years after adult spinal deformity (ASD) surgery to facilitate precision surgical care.
methodsTwo prospective observational cohorts were queried for ASD patients with SRS-22R data at baseline and 1 and 2 years after surgery. In total, 150 covariates were used in training machine learning models, including demographics, surgical data and perioperative complications. Validation was accomplished via an 80%/20% data split for training and testing, respectively. Goodness of fit was measured using area under receiver operating characteristic (AUROC) curves.
resultsIn total, 561 patients met inclusion criteria. The AUROC ranged from 56.5 to 86.9%, reflecting successful fits for most questions. SRS-22R questions regarding pain, disability and social and labor function were the most accurately predicted. Models were less sensitive to questions regarding general satisfaction, depression/anxiety and appearance.
conclusionsTo the best of our knowledge, this is the first study to explicitly model the prediction of individual answers to the SRS-22R questionnaire at 1 and 2 years after deformity surgery. The ability to predict individual question responses may prove useful in preoperative counseling in the age of individualized medicine. These slides can be retrieved under Electronic Supplementary Material.
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Registered trials
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.