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Record W2981490757 · doi:10.1093/eurheartj/ehz746.0947

P6351A point-of-care risk score predicts 30-day readmission in patients hospitalized with heart failure (HF): derivation and validation of the LENT index

2019· article· en· W2981490757 on OpenAlexaffabout
Harriette G.C. Van Spall, S F Lee, Tauben Averbuch, Urun Erbas Oz, Dennis T. Ko, Stuart J. Connolly

Bibliographic record

VenueEuropean Heart Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsInstitute for Clinical Evaluative SciencesMcMaster UniversityPopulation Health Research Institute
Fundersnot available
KeywordsMedicineEmergency departmentHeart failureInternal medicineEmergency medicineProportional hazards modelFramingham Risk ScoreNatriuretic peptideCardiologyDisease

Abstract

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Abstract Background Risk prediction models in heart failure (HF) are typically complex, derived retrospectively from administrative databases, and modest in their ability to discriminate between high, medium, and low risk categories. The complexity of these models makes them difficult to use at the point of care. Purpose To determine if a simple risk index using Length of hospital stay (L), number of Emergency department visits in the preceding 6 months (E), and either admission or discharge N-Terminal (NT) prohormone of Brain Natriuretic Peptide (pro-BNP) at the point of care can predict 30-day readmissions in patients hospitalized for HF. Methods This is a sub-study of the Patient-Centered Care Transitions in HF (PACT-HF) stepped-wedge cluster randomized trial. We included 772 patients hospitalized for HF at 10 Canadian hospitals. We used log-binomial regression models with Length of stay, Emergency department visits in the preceding 6 months, and either admission or discharge N-Terminal prohormone of Brain Natriuretic Peptide (NT-pro-BNP) as the predictor variables and 30-day all-cause readmission as the outcome. We derived the LENT risk score from the β-coefficients of the regression model (Fig. 1). All the models were adjusted for post-discharge services. We assessed model discrimination with C-statistics and model calibration with the net reclassification index (NRI). We used the bootstrapping approach with 100 runs for internal validation. Results The LENT index had a possible score ranging from 1 to 13 (Fig 1). Increments in the LENT risk score were associated with an increased risk of 30-day readmission; a 1-point increase in the LENT index using the admission and discharge NT-pro-BNP predicted a 23% and 19% increase in 30-day readmission risk, respectively. The internal validation produced similar results. Compared to a null model, the LE index had an NRI of 0.35 [95% CI 0.18, 0.53], and admission and discharge NT-pro-BNP further improved calibration of the LE index (NRI 0.15 [95% CI 0, 0.32] and 0.20 [95% CI 0.03, 0.37], respectively). The LENT index offered modest discrimination for 30-day readmission (C-statistic 0.64 [95% CI 0.59, 0.69]), similar to more complex risk models. Figure 1. The LENT index scoring system Conclusion A simple risk index based on Length of stay, Emergent visits, and NT-pro-BNP at the point of care can reliably predict 30-day readmissions. The LENT index offers ease of use over traditional risk prediction models. Acknowledgement/Funding Canadian Institutes of Health Research, Ontario MOHLTC, Roche Diagnostics

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)0.008
metaresearch head score (Gemma)0.018
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 score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.235
Teacher spread0.224 · 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
Published2019
Admission routes2
Has abstractyes

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