Development and Validation of an Algorithm for Thrombosis with Thrombocytopenia Syndrome (TTS) at Unusual Sites - Hobbi, Shayan; Saunders-Hastings, Patrick; Zhou, Cindy Ke; Beers, Jeffrey; Srikrishnan, Ananth; Hettinger, Aaron; Shenoy, Aarthi; Burrell, Timothy; Moll, Keran; Lloyd, Patricia C; Anderson, Steven A; Shoaibi, Azadeh; et al. (International journal of general medicine, 2023)
Thrombosis with thrombocytopenia syndrome (TTS) has been reported following receipt of adenoviral vector-based COVID-19 vaccines. However, no validation studies evaluating the accuracy of International Classification of Diseases-10-Clinical Modification (ICD-10-CM)-based algorithm for unusual site TTS are available in the published literature. The purpose of this study was to assess the performance of clinical coding to 1) leverage literature review and clinical input to develop an ICD-10-CM-based algorithm to identify unusual site TTS as a composite outcome and 2) validate the algorithm against the Brighton Collaboration's interim case definition using laboratory, pathology, and imaging reports in an academic health network electronic health record (EHR) within the US Food and Drug Administration (FDA) Biologics Effectiveness and Safety (BEST) Initiative. Validation of up to 50 cases per thrombosis site was conducted, with positive predictive values (PPV) and 95% confidence intervals (95% CI) calculated using pathology or imaging results as the gold standard. The algorithm identified 278 unusual site TTS cases, of which 117 (42.1%) were selected for validation. In both the algorithm-identified and validation cohorts, over 60% of patients were 56 years or older. The positive predictive value (PPV) for unusual site TTS was 76.1% (95% CI 67.2-83.2%) and at least 80% for all but one individual thrombosis diagnosis code. PPV for thrombocytopenia was 98.3% (95% CI 92.1-99.5%). This study represents the first report of a validated ICD-10-CM-based algorithm for unusual site TTS. A validation effort found that the algorithm performed at an intermediate-to-high PPV, suggesting that the algorithm can be used in observational studies including active surveillance of COVID-19 vaccines and other medical products.