(089) Greater Changes in Hematocrit Predict More Frequent Major Adverse Cardiac Events in Men Receiving Testosterone Therapy - A Retrospective Cohort Claims Database Analysis
Bibliographic record
Abstract
Abstract Introduction While recent studies have shown that Testosterone therapy (TTh)-induced secondary polycythemia, Hematocrit (Hct) > 52%, is an independent risk factor for major adverse cardiac events (MACE) within the first year of treatment, it is not clear whether erythrocytosis – i.e., degree of change in Hct from baseline Hct after starting TTh confers additional risk for MACE. Objective The objective of this study is to determine the incidence of MACE associated with the degree of Hct change from baseline in adult men receiving testosterone therapy for hypogonadism. Methods To assess whether an increase in Hct after initiating TTh is associated with an increased risk of MACE within 3 and 24 months of initiating TTh, we queried the TriNetX Research network database for men over the age of 18 with Hct values obtained within six months before starting TTh, and who had follow-up Hct measurements within three and 24 months after beginning TTh from 2010 through 2021. Men with and without a subsequent increase in Hct after initiating TTh were propensity matched. MACE was defined as myocardial infarction, stroke, or death. Results After matching, 10,291 men who experienced an any increase in Hct after initiating TTh and 6,320 controls who did not were included. Compared to controls who did not have an increase in Hct, the majority of men who had an increase in subsequent Hct had a statistically significant increased risk of major adverse cardiac events with greater change being associated with increase risk of MACE. Conclusions We demonstrate that increases in Hct from baseline are associated with increased risk of MACE, compared to men whose Hct remains stable while receiving TTh. Disclosure Any of the authors act as a consultant, employee or shareholder of an industry for: Boston Scientific, Coloplast, Endo Pharmaceuticals, Antares Pharma.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".