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Health Resource Implications of Heart Failure Hospitalizations in Younger Patients Compared With Older Patients

2017· letter· en· W2736997071 on OpenAlexaffabout
Finlay A. McAlister, Erik Youngson, Padma Kaul

Bibliographic record

VenueCirculation · 2017
Typeletter
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsCanadian VIGOUR Centre
Fundersnot available
KeywordsMedicineHeart failureUnit (ring theory)Internal medicineFamily medicineGerontologyPsychology

Abstract

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Although heart failure (HF) is less common in individuals <50, recent studies have demonstrated a substantial increase in the frequency of young HF over the past 2 decades. 1,2 This has been attributed to the increasing prevalence of obesity, type 2 diabetes mellitus, and hypertension, and better treatments for congenital heart disease, coronary artery disease, dyslipidemia, or hypertension. 2 Recent studies reported decreasing mortality rates in older patients with HF over the past 2 decades, but no appreciable changes in the standardized mortality rate for HF patients <50 years since the turn of the millennium. 1,2However, little is known about hospital resource use by younger versus older patients with HF, and this has major implications for future resource planning.In this retrospective cohort study we examined outcomes for all patients >20 years hospitalized with a primary diagnosis of heart failure in Canada between April 2004 and December 2013.Details on the databases used, International Classification of Diseases, 10th Revision case definitions for HF and all comorbidities, and analytic methods have been published already. 3 This study was approved by the University of Alberta Health Research Ethics Board with waiver of informed consent because we were using deidentified data.In this secondary analysis, we compared 3 outcomes between patients ≤50 years versus those >50 years at the time of their index hospitalization: index hospitalization mortality and, in those that survived to be discharged, length of stay and 30day readmission rates.Adjusted analyses were done using generalized linear mixed models and including baseline covariates recommended by the Centers for Medicare & Medicaid Services (www.cms.gov) for each of the outcomes, as well as hospital type, attending physician specialty, calendar year, number of hospitalizations in the prior 6 months, day of discharge (for the readmission analysis), and 2 random-effects variables to account for clustering effects of province and of hospital.Of the 241 533 patients admitted with a primary diagnosis of HF (mean, 77.4 years, 50.0%male), 7373 (3.1%) were ≤50 (Table ).Younger patients exhibited substantially lower mortality during the index hospitalization (3.3% versus 10.4%, P<0.0001), which was maintained (adjusted odds ratio, 0.36; 95% confidence interval, 0.31-0.41)after adjustment.Of those who survived to discharge (n=217 039), younger patients also had lower 30-day readmissions for any cause (14.1% versus 18.3%, P<0.0001; adjusted odds ratio, 0.82; 95% confidence interval, 0.77-0.88)or for HF (4.7% versus 6.8%, P<0.0001; adjusted odds ratio, 0.74; 95% confidence interval, 0.66-0.83).Although younger patients had shorter length of stay (7.5 days versus 7.8 days, P<0.0001), the difference was not substantial and the association flipped after adjustment for baseline variables: adjusted mean, 8.4 days versus 8.2 days (P=0.002).Although prior studies of young patients with HF have focused on their lower mortality risk, their lower comorbidity burdens, and their increased likelihood of

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.047
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.017
GPT teacher head0.270
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreCommentary

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".

Quick stats

Citations0
Published2017
Admission routes2
Has abstractyes

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