Abstract 230: Prediabetes In Young Adults And Its Association With Type 1 Myocardial Infarction-related Admissions And Outcomes: A Population-based Analysis In The United States
Bibliographic record
Abstract
Background: Prediabetes (pDM) has recently drawn attention for being associated with poor outcomes after acute myocardial infarction (MI). We aimed to analyze the incidence and odds of type 1 MI admissions, and outcomes using a nationally representative sample. Methods: We queried the National Inpatient Sample (2018) to identify T1MI-related hospitalizations (T1RH) in young (18-44 years) adults with vs without pDM using ICD-10 codes. T1RHs with DM were excluded. Demographics, comorbidities and outcomes including major cardiovascular and cerebrovascular adverse events (MACCE) were compared between two cohorts. Results: Overall prevalence of pDM in young adults hospitalized in 2018 was 0.4% (31460/7851019). T1RH was found to be significantly higher in the pDM vs. non-pDM cohort (2.15%, 675/31460 vs. 0.3%, 21655/7820953) among all non-diabetic admissions in young adults. T1RH with pDM often had males (78.5 vs 72.8%), blacks (26.7 vs 21%), Hispanics (18.3 vs 11.5%), Asian/Pacific Islanders (6.9 vs 3.1%), patients from higher-income quartile (19.1 vs 15.8%), urban-teaching (81.5 vs 72.2%), Midwest (23.7 vs 21.9%) and West (23 vs 16.4%) region hospitals, and patients with higher rates of hyperlipidemia (68.1 vs 47.3%), obesity (48.9 vs 25.7%), fluid-electrolyte imbalance (18.5 vs 15.3%). The univariate (OR 7.9, 95CI 6.54-9.53) and adjusted multivariate analysis (OR 1.71, 95CI 1.38-2.12) revealed significantly higher odds of T1MI in the pDM vs non-pDM cohort (p<0.001) [Table 1] . However, T1RH’s outcome for MACCE (adjusted) did not differ between two cohorts (P=0.074). Furthermore, T1RH with pDM had higher transfers to short-term facilities (6.7 vs 5.3%, p<0.001). Conclusion: Young patients with prediabetes had significantly higher T1MI hospitalizations without any impact on subsequent MACCEs. This highlights the need for aggressive management of CVD risk factors in the young by primary care physicians to curtail acute cardiac events and healthcare costs.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".