Abstract 13697: Prevalence and Disability Associated With Vitamin D Deficiency Amongst Patients With Cardiovascular and Cerebrovascular Disorders
Bibliographic record
Abstract
Introduction: Previous studies had provided evidence that vitamin D deficiency is a strong negative predictor for survival and recovery after severe vascular events but national estimate on disability related burden is not clear. Hypothesis: We evaluate the prevalence of vitamin D deficiency (VDD) amongst patients with cardiovascular disease (CVD) and cerebrovascular disorders (CeVD) and to find out whether or not CVD and CeVD in the presence of VDD increases the disability. Methods: We performed a retrospective analysis of the Nationwide Inpatient Sample data (years 2016-2017) in adults (≥18 years) hospitalizations. We identified patients with secondary diagnosis of VDD and primary diagnosis of CVD (AFib, CHF, IHD, acute MI, and angina) and CeVD (AIS, TIA, ICeH and SAH) using ICD-10-CM codes. We performed a chi-square test and multivariable survey logistic regression to analyze disability of patients with CVD and CeVD in presence of VDD. Disability/loss of function was investigated by APRDRGs severity using 3M Health Information Systems software. (Score 1-4 indicates minor to extreme loss of function) Results: Among 58,259,589 US hospitalizations, 3.44%, 2.15%, 0.06%, 1.28%, 11.49%, 1.71%, 0.38%, 0.23% and 0.08% had primary admission of IHD, acute MI, angina, AFib, CHF, AIS, TIA, ICeH and SAH, respectively and 1.82% had VDD. Prevalence of hospitalizations due to CHF (14.66% vs 11.43%), AIS (1.87% vs 1.71%) and TIA (0.4% vs 0.38%) was higher; and IHD (2.62% vs 3.45%), acute MI (1.58% vs 2.16), angina (0.05% vs 0.06%), AFib (1.14% vs 1.28%), ICeH (0.17% vs 0.23%) and SAH (0.05% vs 0.08%) was lower among VDD patients in compare to non-VDD. (p<0.0001) In regression analysis, VDD was associated with higher odds of severe or extreme disability amongst patients hospitalized with AIS (OR:1.1; 95%CI:1.06-1.14), ICeH (1.22; 1.08-1.39), TIA (1.36; 1.25-1.47), IHD (1.37; 1.33-1.41), acute MI (1.44; 1.38-1.49), Afib (1.10; 1.06-1.15), and CHF (1.03; 1.02-1.05) in comparison to without VDD. Conclusions: CVD and CeVD in presence of VDD increase the disability amongst US hospitalizations. Future studies should be planned to evaluate the discharge outcomes as well as the effect of identification and in-hospital management of VDD on improvement of the outcomes.
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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 | 0.000 |
| 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) | 0.003 | 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".