Objective

Adverse event (AE) reporting is crucial for determining safety of trials. Adverse events are captured manually by clinical research associates (CRAs) and research nurses (RNs), and prior studies show underreporting. It is necessary to understand AE reporting training, processes, and institution-level differences to improve AE capture.

Methods

A 26-item questionnaire regarding AE reporting training, identification, tracking, and challenges was distributed to all Children's Oncology Group (COG) CRAs and RNs from February 15 to March 11, 2019, regardless of if they report AEs based on limitations of COG rosters. Results were tabulated. Institutions were grouped by self-reported full-time equivalents and compared using χ2 tests.

Results

Of 1315 CRAs and 2703 RNs surveyed, 509 (12.7%) responded. Of those, 369 (64.9%) representing 71.8% of COG institutions report AEs. Only data from respondents who report AEs were collected and analyzed. There was a range in AE training; COG training modules were most common (79.7%). There was wide variability in AE ascertainment; only 51.2% use standardized approaches at their site. There was no standard AE tracking method; larger sites more commonly use spreadsheets (P = 0.002) and smaller sites more commonly use paper (P = 0.028). The greatest AE reporting challenges were differences between protocols (70%) and between AE definitions and documentation (53%). Half of the respondents endorsed 6 of 13 proposed tools for improving reporting including online AE reporting modules (75.3%), tip sheets for interpreting Common Terminology Criteria for Adverse Events definitions (67.5%), and standardized AE tracking forms (66.9%). Only half of the respondents reported that all colleagues at their site followed the same AE reporting practices, and there was no dominant AE tracking approach across the respondents.

Discussion

There is wide variability in AE reporting training and practices. Numerous challenges exist, including differences between trials, challenges in interpreting AE definitions, and engaging clinicians.

Conclusions

Respondents are eager for additional central resources. These results provide a roadmap for areas of potential improvement.

Background

Pediatric oncology patients are at an increased risk of invasive bacterial infection due to immunosuppression. The risk of such infection in the absence of severe neutropenia (absolute neutrophil count ≥ 500/μL) is not well established and a validated prediction model for blood stream infection (BSI) risk offers clinical usefulness.

Methods

A 6-site retrospective external validation was conducted using a previously published risk prediction model for BSI in febrile pediatric oncology patients without severe neutropenia: the Esbenshade/Vanderbilt (EsVan) model. A reduced model (EsVan2) excluding 2 less clinically reliable variables also was created using the initial EsVan model derivative cohort, and was validated using all 5 external validation cohorts. One data set was used only in sensitivity analyses due to missing some variables.

Results

From the 5 primary data sets, there were a total of 1197 febrile episodes and 76 episodes of bacteremia. The overall C statistic for predicting bacteremia was 0.695, with a calibration slope of 0.50 for the original model and a calibration slope of 1.0 when recalibration was applied to the model. The model performed better in predicting high-risk bacteremia (gram-negative or Staphylococcus aureus infection) versus BSI alone, with a C statistic of 0.801 and a calibration slope of 0.65. The EsVan2 model outperformed the EsVan model across data sets with a C statistic of 0.733 for predicting BSI and a C statistic of 0.841 for high-risk BSI.

Conclusions

The results of this external validation demonstrated that the EsVan and EsVan2 models are able to predict BSI across multiple performance sites and, once validated and implemented prospectively, could assist in decision making in clinical practice. Cancer 2017;123:3781-3790. © 2017 American Cancer Society.