Development and Validation of an Algorithm for Thrombosis with Thrombocytopenia Syndrome (TTS) at Unusual Sites
Bibliographic record
Abstract
Introduction: 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. Methods: 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. Results: 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%). Discussion: 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".