Epidemiological Trends of Heart Failure Subtypes, Characteristics, and Outcomes Within Inpatient Hospitalizations
Bibliographic record
Abstract
Background: This secondary analysis of a cross-sectional observational study aimed to evaluate the impact of heart failure (HF) classification on inpatient outcomes and demographic associations. Methods: Data from the 2019 National Inpatient Sample (NIS) included 259,025 patients older than 18 years with a primary International Classification of Diseases, 10th Revision (ICD-10) diagnosis of HF. Results: Weighted results for this study showed that HF subtypes were stratified as diastolic (35.63%, n = 92,300), systolic (30.09%, n = 77,931), combined systolic-diastolic (18.74%, n = 48,529), other (11.81%, n = 30,593), end-stage (1.56%, n = 4,030), right (1.18%, n = 3,063), and biventricular (1.00%, n = 2,579). Acuity was categorized as acute on chronic HF (72.68%, n = 188,106), acute HF (10.79%, n = 27,948), chronic HF (1.84%, n = 4,778), and indeterminate (15%, n = 38,193). Demographically, older adults (≥ 75 years), African Americans, and males were found to be more frequently admitted, with age being the most significant factor. Younger patients (< 75 years) were more often diagnosed with non-diastolic HF, while minority groups had higher incidences of systolic and combined HF. Females were more likely to have diastolic HF compared to males. Right, biventricular, and end-stage HF were associated with increased inpatient costs, longer hospital stays, and higher mortality rates. Detailed HF classification reveals significant variations in inpatient outcomes and demographic associations. Conclusions: Advanced HF subtypes incur higher costs, longer hospital stays, and increased mortality, underscoring the need for improved classification and earlier intervention across diverse populations. Further research is needed to refine HF diagnosis and coding to better understand and manage these conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".