Validation of Sudden Cardiac Death Algorithm

• Activity Number: 381
• Type: Contributed
• Date/Time: Tuesday, August 2, 2016 : 10:30 AM to 11:15 AM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Zoe Bider-Canfield* and Shuhua Liang and T. Craig Cheetham
• Companies: Kaiser Permanente Southern California and Kaiser Permanente Southern California and Kaiser Permanente Southern California
• Keywords: Algorithm Validation ; Sudden Cardiac Death ; Positive Predictive Value ; Negative Predictive Value ; Sensitivity ; Specificity

Abstract

Purpose: Tracking drug safety using electronic medical records (EMR) requires the use of validated outcome measures. This study sought to validate an algorithm, previously developed to identify sudden cardiac deaths (SCD), using a new population and a different drug. Methods: All cardiovascular deaths (CVD) occurring within 10 days of drug initiation were identified from the EMR. The algorithm was used to classify SCD from the group of those with CVD. A random sample of all CVDs were chart adjudicated and classified as SCD and non-SCD. The positive predictive value (PPV), negative predictive value (NPV), sensitivity, specificity and kappa were calculated with chart adjudication as the gold standard. Results: A total of 49 charts were adjudicated. The algorithm performed well with 68% sensitivity, 89% specificity, 91% PPV, 62% NPV and a kappa statistic of 52%. Conclusion: The high specificity (89%) and PPV (91%) confirmed that the algorithm can be used to identify SCD among a cohort of CVDs using EMR data. Further work needs to be done to refine and improve the sensitivity and NPV of the algorithm.