Venous Thromboembolism Among People With HIV: Design, Implementation, and Findings of a Centralized Adjudication System in Clinical Care Sites Across the United States
Bibliographic record
Abstract
BACKGROUND: People with HIV (PWH) are at increased risk for venous thromboembolism (VTE). We conducted this study to characterize VTE including provoking factors among PWH in the current treatment era. METHODS: We included PWH with VTE between 2010 and 2020 at 6 sites in the CFAR Network of Integrated Clinical Systems cohort. We ascertained for possible VTE using diagnosis, VTE-related imaging, and VTE-related procedure codes, followed by centralized adjudication of primary data by expert physician reviewers. We evaluated sensitivity and positive predictive value of VTE ascertainment approaches. VTEs were classified by type and anatomic location. Reviewers identified provoking factors such as hospitalizations, infections, and other potential predisposing factors such as smoking. RESULTS: We identified 557 PWH with adjudicated VTE: 239 (43%) had pulmonary embolism with or without deep venous thrombosis, and 318 (57%) had deep venous thrombosis alone. Ascertainment with clinical diagnoses alone missed 6% of VTEs identified with multiple ascertainment approaches. DVTs not associated with intravenous lines were most often in the proximal lower extremities. Among PWH with VTE, common provoking factors included recent hospitalization (n = 134, 42%), infection (n = 133, 42%), and immobilization/bed rest (n = 78, 25%). Only 57 (10%) PWH had no provoking factor identified. Smoking (46%), HIV viremia (27%), and injection drug use (22%) were also common. CONCLUSIONS: We conducted a robust adjudication process that demonstrated the benefits of multiple ascertainment approaches followed by adjudication. Provoked VTEs were more common than unprovoked events. Nontraditional and modifiable potential predisposing factors such as viremia and smoking were common.
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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 | 0.041 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".