Towards standardized measurement of adverse events in spine surgery: conceptual model and pilot evaluation

BACKGROUND: Independent of efficacy, information on safety of surgical procedures is essential for informed choices. We seek to develop standardized methodology for describing the safety of spinal operations and apply these methods to study lumbar surgery. We present a conceptual model for evaluating the safety of spine surgery and describe development of tools to measure principal components of this model: (1) specifying outcome by explicit criteria for adverse event definition, mode of ascertainment, cause, severity, or preventability, and (2) quantitatively measuring predictors such as patient factors, comorbidity, severity of degenerative spine disease, and invasiveness of spine surgery. METHODS: We created operational definitions for 176 adverse occurrences and established multiple mechanisms for reporting them. We developed new methods to quantify the severity of adverse occurrences, degeneration of lumbar spine, and invasiveness of spinal procedures. Using kappa statistics and intra-class correlation coefficients, we assessed agreement for the following: four reviewers independently coding etiology, preventability, and severity for 141 adverse occurrences, two observers coding lumbar spine degenerative changes in 10 selected cases, and two researchers coding invasiveness of surgery for 50 initial cases. RESULTS: During the first six months of prospective surveillance, rigorous daily medical record reviews identified 92.6% of the adverse occurrences we recorded, and voluntary reports by providers identified 38.5% (surgeons reported 18.3%, inpatient rounding team reported 23.1%, and conferences discussed 6.1%). Trained observers had fair agreement in classifying etiology of 141 adverse occurrences into 18 categories (kappa = 0.35), but agreement was substantial (kappa ≥ 0.61) for 4 specific categories: technical error, failure in communication, systems failure, and no error. Preventability assessment had moderate agreement (mean weighted kappa = 0.44). Adverse occurrence severity rating had fair agreement (mean weighted kappa = 0.33) when using a scale based on the JCAHO Sentinel Event Policy, but agreement was substantial for severity ratings on a new 11-point numerical severity scale (ICC = 0.74). There was excellent inter-rater agreement for a lumbar degenerative disease severity score (ICC = 0.98) and an index of surgery invasiveness (ICC = 0.99). CONCLUSION: Composite measures of disease severity and surgery invasiveness may allow development of risk-adjusted predictive models for adverse events in spine surgery. Standard measures of adverse events and risk adjustment may also facilitate post-marketing surveillance of spinal devices, effectiveness research, and quality improvement

Syndromics: A Bioinformatics Approach for Neurotrauma Research

Substantial scientific progress has been made in the past 50 years in delineating many of the biological mechanisms involved in the primary and secondary injuries following trauma to the spinal cord and brain. These advances have highlighted numerous potential therapeutic approaches that may help restore function after injury. Despite these advances, bench-to-bedside translation has remained elusive. Translational testing of novel therapies requires standardized measures of function for comparison across different laboratories, paradigms, and species. Although numerous functional assessments have been developed in animal models, it remains unclear how to best integrate this information to describe the complete translational “syndrome” produced by neurotrauma. The present paper describes a multivariate statistical framework for integrating diverse neurotrauma data and reviews the few papers to date that have taken an information-intensive approach for basic neurotrauma research. We argue that these papers can be described as the seminal works of a new field that we call “syndromics”, which aim to apply informatics tools to disease models to characterize the full set of mechanistic inter-relationships from multi-scale data. In the future, centralized databases of raw neurotrauma data will enable better syndromic approaches and aid future translational research, leading to more efficient testing regimens and more clinically relevant findings