Trends in Noncardiovascular Comorbidities Among Patients Hospitalized for Heart Failure
Why this work is in the frame
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Bibliographic record
Abstract
Background: The increase in medical complexity among patients hospitalized with heart failure (HF) may be reflected by an increase in concomitant noncardiovascular comorbidities. Among patients hospitalized with HF, the temporal trends in the prevalence of noncardiovascular comorbidities have not been well described. Methods and Results: We used data from 207 984 patients in the Get With The Guidelines–Heart Failure registry (from 2005 to 2014) to evaluate the prevalence and trends of noncardiovascular comorbidities (chronic obstructive pulmonary disorder/asthma, anemia, diabetes mellitus, obesity [body mass index ≥30 kg/m 2 ], and renal impairment) among patients hospitalized with HF. Medicare beneficiaries aged ≥65 years were used to assess 30-day mortality. The prevalence of 0, 1, 2, and ≥3 noncardiovascular comorbidities was 18%, 30%, 27%, 25%, respectively. From 2005 to 2014, there was a decline in patients with 0 noncardiovascular comorbidities (22%–16%; P <0.0001) and an increase in patients with ≥3 noncardiovascular comorbidities (18%–29%; P <0.0001). Among Medicare beneficiaries, there was an increased 30-day adjusted mortality risk among patients with 1 noncardiovascular comorbidity (hazard ratio, 1.16; 95% confidence interval, 1.09–1.24; P <0.0001), 2 noncardiovascular comorbidities (hazard ratio, 1.34; 95% confidence interval, 1.25–1.44; P <0.0001), and ≥3 noncardiovascular comorbidities (hazard ratio, 1.63; 95% confidence interval, 1.51–1.75; P <0.0001). Similar trends were seen for in-hospital mortality. Conclusions: Patients admitted in hospital for HF have an increasing number of noncardiovascular comorbidities over time, which are associated with worse outcomes. Strategies addressing the growing burden of noncardiovascular comorbidities may represent an avenue to improve outcomes and should be included in the delivery of in-hospital HF care.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it