Development and validation of a predictive model algorithm to identify anaphylaxis in adults with type 2 diabetes in U.S. administrative claims data. Issue 7 (5th May 2021) - Beachler, Daniel C.; Taylor, Devon H.; Anthony, Mary S.; Yin, Ruihua; Li, Ling; Saltus, Catherine W.; Li, Lin; Shaunik, Alka; Walsh, Kathleen E.; Rothman, Kenneth J.; Johannes, Catherine B.; Aroda, Vanita R.; et al.
Abstract: Purpose: To use medical record adjudication and predictive modeling methods to develop and validate an algorithm to identify anaphylaxis among adults with type 2 diabetes (T2D) in administrative claims. Methods: A conventional screening algorithm that prioritized sensitivity to identify potential anaphylaxis cases was developed and consisted of diagnosis codes for anaphylaxis or relevant signs and symptoms. This algorithm was applied to adults with T2D in the HealthCore Integrated Research Database (HIRD) from 2016 to 2018. Clinical experts adjudicated anaphylaxis case status from redacted medical records. We used confirmed case status as an outcome for predictive models developed using lasso regression with 10‐fold cross‐validation to identify predictors and estimate the probability of confirmed anaphylaxis. Results: Clinical adjudicators reviewed medical records with sufficient information from 272 adults identified by the anaphylaxis screening algorithm, which had an estimated Positive Predictive Value (PPV) of 65% (95% confidence interval [CI]: 60%–71%). The predictive model algorithm had a c‐statistic of 0.95. The model's probability threshold of 0.60 excluded 89% (84/94) of false positives identified by the screening algorithm, with a PPV of 94% (95% CI: 91%–98%). The model excluded very few true positives (15 of 178), and identified 92% (95% CI: 87%–96%) of the cases selected by the screening algorithm. Conclusions: Predictive modeling techniques yielded an accurate algorithm with high PPV and sensitivity for identifying anaphylaxis in administrative claims. This algorithm could be considered in future safety studies using similar claims data to reduce potential outcome misclassification.