Study design and oversight
A comprehensive written protocol was developed, and plans for interim data reviews and a study oversight committee were established with representation from NESTcc, PEDSnet, Johnson & Johnson, and Lahey Medical Center, which reviewed and approved the final study protocol prior to any data analysis. The institutional review boards of Lahey Medical Center and Children’s Hospital of Philadelphia reviewed and cleared the study protocol prior to the review of any study data.
Study environment, data source and data element extraction
Epic Systems (Verona, Wisconsin, USA) has been used as the EHR system at all Lahey Health hospitals and clinics to support all clinical activities. Data elements were extracted from Lahey’s clinical data warehouse derived from the Epic EHR, containing demographic, clinical, laboratory, and claims data for all patient encounters in any Lahey facility. The universal device identifier (UDI) system12 was not implemented within the Lahey EHR until after the study period, and therefore device implants were identified through manufacturer model numbers recorded in the EHR surgical log. Of note, all patients undergoing spine surgery at Lahey are routinely followed with in-person or telehealth visits at 30 days and 12 months postoperatively, thereby minimizing the risk of the loss to follow-up in the first year after spine surgery at the medical center.
Patient eligibility, device exposures, and endpoint definitions
Patients 18 years or older, undergoing spinal arthrodesis surgery between April 1, 2015, and December 31, 2018, at a Lahey Health System hospital were included in the study (see online supplemental figure S1). Patients were excluded from the analysis if their index surgical procedure included the cervical spine or if the patient had any spine surgery performed in the 12 months prior to their qualifying surgery.
Among the patients eligible for inclusion, treatment with the device of interest (the CB), as well as prespecified alternative devices (the DePuy Synthes OPAL and the Medtronic CAPSTONE PEEK) was identified by matching surgical log device implant records to device model numbers available from the manufactures.
The primary safety outcome was the proportion of patients undergoing spine reoperation for any cause at 1 year. Secondary outcomes included mortality, hospitalization for surgical site infection, any hospitalization within 1 year, and requirement for blood transfusion during index hospitalization. The safety endpoints and clinical covariate definitions used for this study are provided in online supplemental tables S1 and S2 of the Supplemental content.
Covariate and data validation
Each demographic, clinical, and procedural covariate and outcome was validated through manual chart review of a random 5% sample of patients included in each cohort. Discrepancies found in manual chart review were used to refine covariate filter definitions, and the random 5% manual chart review was repeated until there was 100% agreement between the extraction filters and domain expert chart review for all covariates and outcomes.
Risk adjustment methods
Multivariable adjusted logistic regression models were developed to estimate the probability of being treated with the CB. The model included risk factors for the adverse outcomes of interest, as well as factors considered to influence the selection of IVBI. A total of 12 demographic, clinical and procedural variables were included in the final propensity score model (variable definitions available in online supplemental table S6 of the Supplemental content) including age, gender, preoperative body mass index, history of coronary artery disease, heart failure, diabetes, active smoking and history of any prior spine surgery. Procedural covariates included surgeon specific annual spine surgical volume, emergency surgery status, American Society of Anesthesiology physical status classification, and number of spinal levels stabilized during index surgery.
The propensity-matched comparison group was selected on the basis of a non-parsimonious propensity model and matched in a 1:1 manner with CB cases matched within a caliper width of 0.6 of the SD of the logit of the propensity score.13 Missing data were handled using univariate rules, assuming absence of a condition for dichotomous variables, and using the median value for continuous variables. The relative imbalance between the CB and comparator groups was assessed using absolute standardized mean difference in covariate means and proportions, with values greater than 0.10 considered suboptimally balanced.14
Because the number of patients successfully matched could be low and lead to an underpowered analysis, we also performed inverse probability of treatment weighting (IPTW) analysis. IPTW included all patients in both the treatment and control groups, weighted based on the probability of treatment with the device of interest, and therefore minimizes information loss through case exclusion. Weights were trimmed at 5% and 95% to avoid excessive influence of patients with extremes of propensity scores.15
A ‘Falsification Hypothesis’ analysis was also prespecified in order to assess the possibility of significant residual confounding after propensity matching or weighting. For this analysis, patients were evaluated for the development of late postoperative renal dysfunction after postoperative day 30, defined as an increase in serum creatinine by at least 50%. Late kidney dysfunction was not thought to be plausibly related to the implantation of a particular IVBI, and was therefore considered an appropriate endpoint for use in Falsification Hypothesis testing.
For the propensity match analysis, a significant difference was considered present if the confidence intervals (CI) between two independent proportions, as measured by the Wilson method, did not cross zero16 when using an alpha of 0.05. For IPTW, adjusted odds ratio (ORadj) were considered significant for increased risk of adverse events if the ORadj was greater than 1.0, with a 95% CI excluding 1.0. All analyses were performed within the DELTA application, relying on R-based statistical packages.7–9