Abstract and Introduction
Objective: There is increasing emphasis on patient-reported outcomes (PROs) to quantitatively evaluate quality outcomes from degenerative spine surgery. However, accurate prediction of PROs is challenging due to heterogeneity in outcome measures, patient characteristics, treatment characteristics, and methodological characteristics. The purpose of this study was to evaluate the current landscape of independently validated predictive models for PROs in elective degenerative spinal surgery with respect to study design and model generation, training, accuracy, reliability, variance, and utility.
Methods: The authors analyzed the current predictive models in PROs by performing a search of the PubMed and Ovid databases using PRISMA guidelines and a PICOS (participants, intervention, comparison, outcomes, study design) model. They assessed the common outcomes and variables used across models as well as the study design and internal validation methods.
Results: A total of 7 articles met the inclusion criteria, including a total of 17 validated predictive models of PROs after adult degenerative spine surgery. National registry databases were used in 4 of the studies. Validation cohorts were used in 2 studies for model verification and 5 studies used other methods, including random sample bootstrapping techniques. Reported c-index values ranged from 0.47 to 0.79. Two studies report the area under the curve (0.71–0.83) and one reports a misclassification rate (9.9%). Several positive predictors, including high baseline pain intensity and disability, demonstrated high likelihood of favorable PROs.
Conclusions: A limited but effective cohort of validated predictive models of spine surgical outcomes had proven good predictability for PROs. Instruments with predictive accuracy can enhance shared decision-making, improve rehabilitation, and inform best practices in the setting of heterogeneous patient characteristics and surgical factors.
THE shift toward value-based interventions to combat the rising costs of healthcare requires the adoption of quantitative quality measures. In spine surgery, patient-reported outcomes (PROs) have come to the forefront as quantitative measures to evaluate pain, functional ability, and quality of life following spine surgery.[21,34] While several spinal interventions have been shown to improve PROs through controlled clinical studies,[26,27] individual outcomes can be heterogeneous. The ability to predict individual outcomes can facilitate clinical decision-making in patient selection and managing patient expectations.
Ideal PRO tools are associated with high accuracy, reproducibility, and ease of use for wide applications in the clinical setting. Recently, there have been several efforts across multiple groups to develop predictive models for PROs in spine surgery. Incorporation of large patient registries that sample a more varied population improves generalizability and validity of recent models. With the increased popularity of predictive models, a critical view of how they are derived and validated is needed as they will soon be incorporated in clinical workflow and decision-making. The present study represents the first of its kind to evaluate the landscape of predictive models in PROs for function and pain following elective spine surgery (cervical and lumbar). In this systematic review, we evaluated currently available predictive models with regard to accuracy, how they were created and validated, c-indices, and misclassification rates. With increased utilization and reliance on PROs, predictive models represent useful tools to guide future incorporation and measured applicability of their benefit to clinical practice.
Neurosurg Focus. 2019;45(5):e10 © 2019 American Association of Neurological Surgeons