A scoping review of complication prediction models in spinal surgery: An analysis of model development, validation and impact.

Background: Predictive analytics are being used increasingly in the field of spinal surgery with the development of models to predict post-surgical complications. Predictive models should be valid, generalizable, and clinically useful. The purpose of this review was to identify existing post-surgical complication prediction models for spinal surgery and to determine if these models are being adequately investigated with internal/external validation, model updating and model impact studies. Methods: This was a scoping review of studies pertaining to models for the prediction of post-surgical complication after spinal surgery published over 10 years (2010-2020). Qualitative data was extracted from the studies to include study classification, adherence to Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines and risk of bias (ROB) assessment using the Prediction model study Risk Of Bias Assessment Tool (PROBAST). Model evaluation was determined using area under the curve (AUC) when available. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement was used as a basis for the search methodology in four different databases. Results: Thirty studies were included in the scoping review and 80% (24/30) included model development with or without internal validation. Twenty percent (6/30) were exclusively external validation studies and only one study included an impact analysis in addition to model development and internal validation. Two studies referenced the TRIPOD guidelines and there was a high ROB in 100% of the studies using the PROBAST tool. Conclusions: The majority of post-surgical complication prediction models in spinal surgery have not undergone standardized model development and internal validation or adequate external validation and impact evaluation. As such there is uncertainty as to their validity, generalizability, and clinical utility. Future efforts should be made to use existing tools to ensure standardization in development and rigorous evaluation of prediction models in spinal surgery.