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Abstract 12085: Emergency Heart Failure Mortality Risk Grade (EHMRG) Score Quintiles Correlate With Short and Long-Term Healthcare Costs

2023· article· en· W4389953428 on OpenAlexaffabout
Harsh R. Parikh, Ava John‐Baptiste, Steven Chu, Maria Santiago, Jiming Fang, Peter C. Austin, Joan Porter, Heather J. Ross, Douglas S. Lee

Bibliographic record

VenueCirculation · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Systems and Practices
Canadian institutionsUniversity Health NetworkInstitute for Clinical Evaluative SciencesUniversity of Toronto
Fundersnot available
KeywordsMedicineCohortEmergency medicineActivity-based costingEmergency departmentHealth careHeart failurePopulationCohort studyRisk assessmentFramingham Risk ScoreAcute careDemographyInternal medicineEnvironmental healthDisease

Abstract

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Introduction: The use of the EHMRG model in the emergency department (ED) has been demonstrated to improve outcomes for patients presenting with acute heart failure (HF). It is unknown if the EHMRG model also correlates with healthcare costs, which has not been examined in other HF risk models. Hypothesis: We hypothesized that patients with higher EHMRG risk scores, i.e., sicker, would have higher costs of care in short-term (30-day) and longer-term (up to 2 year) time horizons. Methods: We examined direct costs of care from a health payer perspective using the original EHMRG derivation cohort of 11,857 patients hospitalized with acute HF in Ontario, Canada (from 2004) and linking to population-based case-costing databases. Costs were stratified by the quintiles of the EHMRG risk score (Q1 = lowest risk, Q5 = highest risk). Costs (in 2021 Canadian dollars) were categorized into hospital, physician, drug, home care, long term care (LTC), and other costs. Results: Patients in the lowest EHMRG risk quintile (Q1), compared to higher quintiles (Q2-Q5) had lower total cost of care in both the short-term ($8,113 vs $9,442, $10,557, $12,078 and $15,005, p <0.001) and long-term ($46,661 vs $55,904, $58,904, $63,286 and $64,229, p<0.001). This positive correlation between EHMRG risk quintile and cost of care was also observed for hospital costs ($26,388 [Q1] vs $35,659 [Q5], p<.0001) and LTC costs ($1,236 [Q1] vs $6,316 [Q5], p<.0001) at all time intervals, including at 2 years shown here. Physician costs and drug costs were positively correlated with EHMRG risk quintiles at 30 days (physician: $1,241 [Q1] vs $1,762 [Q5], p<0.0001; drug: $219 [Q1] vs $282 [Q5], p<.0001), and negatively correlated at 2 years (physician: $7,943 [Q1] vs $7,175 [Q5], p<.0001; drug: $4,539 [Q1] vs $4,377 [Q5], p<.0001). There was an increasing trend of costs irrespective of the formulation of the EHMRG risk score as 7-day risk quintile, 30-day risk quintile, or 30-day risk tertile. Conclusion: The EHMRG7 risk score was positively correlated with healthcare costs from 30 days to 2 years after initial ED presentation. This novel correlational study demonstrated that in the absence of an associated intervention, higher mortality risk in HF patients portends higher costs in short and long-term follow-up.

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)0.003
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.664
Threshold uncertainty score0.675

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.141
GPT teacher head0.455
Teacher spread0.313 · 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
GenreEmpirical

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".

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Citations0
Published2023
Admission routes2
Has abstractyes

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