The Degree of the Predischarge Pulmonary Congestion in Patients Hospitalized for Worsening Heart Failure Predicts Readmission and Mortality
Bibliographic record
Abstract
BACKGROUND: Prediction of readmission and death after hospitalization for heart failure (HF) is an unmet need. AIM: We evaluated the ability of clinical parameters, NT-proBNP level and noninvasive lung impedance (LI), to predict time to readmission (TTR) and time to death (TTD). METHODS AND RESULTS: The present study is a post hoc analysis of the IMPEDANCE-HF extended trial comprising 290 patients with LVEF ≤45% and New York Heart Association functional class II-IV, randomized 1:1 to LI-guided or conventional therapy. Of all patients, 206 were admitted 766 times for HF during a follow-up of 57 ± 39 months. The normal LI (NLI), representing the "dry" lung status, was calculated for each patient at study entry. The current degree of pulmonary congestion (PC) compared with its dry status was represented by ΔLIR = ([measured LI/NLI] - 1) × 100%. Twenty-six parameters recorded during HF admission were used to predict TTR and TTD. To determine the parameter which mainly impacted TTR and TTD, variables were standardized, and effect size (ES) was calculated. Multivariate analysis by the Andersen-Gill model demonstrated that ΔLIRadmission (ES = 0.72), ΔLIRdischarge (ES = -3.14), group assignment (ES = 0.2), maximal troponin during HF admission (ES = 0.19), LVEF related to admission (ES = -0.22) and arterial hypertension (ES = 0.12) are independent predictors of TTR (p < 0.01, χ2 = 1,206). Analysis of ES showed that residual PC assessed by ∆LIRdischarge was the most prominent predictor of TTR. One percent improvement in predischarge PC, assessed by ∆LIRdischarge, was associated with a likelihood of TTR increase by 14% (hazard ratio [HR] 1.14, 95% confidence interval [CI] 1.13-1.15, p < 0.01) and TTD increase by 8% (HR 1.08, 95% CI 1.07-1.09, p < 0.01). CONCLUSION: The degree of predischarge PC assessed by ∆LIR is the most dominant predictor of TTR and TTD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".