Increased Risks of Major Cardiac Adverse Events in Stimulant Use Disorder as Compared With Other Substance Use Disorders: A Propensity-score Matching Cohort Study
Bibliographic record
Abstract
OBJECTIVES: Individuals with stimulant use disorders (StSUDs) present an elevated risk of cardiovascular complications compared with the general population. However, it remains unclear whether, within the subpopulation of individuals with substance use disorders (SUDs), those specifically affected by StSUDs face even higher cardiovascular complications. METHODS: We conducted a retrospective cohort study using the EVERSANA databank, spanning from January 2015 to December 2023. The EVERSANA data set comprises deidentified electronic health record data aggregated and standardized across the United States. Participants included patients diagnosed with SUDs, encompassing alcohol, cannabis, opioids, stimulants, tobacco, hallucinogens, sedative-hypnotics, or inhalants. We employed the International Classification of Disease 10th (ICD-10) version codes to define the presence of StSUD and SUD. Major adverse cardiac events (MACE) were assessed, and Cox proportional hazard ratios were adjusted using high-dimensional propensity score (hdPS) matching to account for potential confounders. RESULTS: Among 137,106 patients with SUD, 7706 (5.6%) had StSUD. The cohort was 50.2% female, 53.0% non-White, with a mean age of 49.1 years (SD±15). After adjustment, stimulant users exhibited significantly higher MACE rates (HR=1.37, 95% CI: 1.22-1.53, P <0.001), including an elevated risk of death (HR=1.23, 95% CI: 1.02-1.47, P =0.026). CONCLUSION: Individuals with StSUD face increased MACE compared with those with nonstimulant SUDs.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".