Subclinical Congestion Evaluated by Point of Care Ultrasound (POCUS) at Discharge Predicts Readmission in Patients with Acute Heart Failure: Prognostic Cohort Study
Bibliographic record
Abstract
Background: Heart failure (HF) is a complex entity that increases the risk of adverse outcomes. Point of care ultrasound (POCUS) allows easy lung and systemic venous congestion identification. Using ultrasound to detect sub-clinical congestion at discharge may help predict readmissions and mortality. Outcomes: The primary outcome was to address 30-day rehospitalization, and as a secondary outcome we investigated readmission and mortality in patients with residual congestion assessed with POCUS. Methods: A prospective prognostic cohort study was conducted at a tertiary-level institution in Colombia. Patients with acute decompensated heart failure (ADHF) at discharge were evaluated using POCUS through lung ultrasound (LUS), portal vein pulsatility (PVP), and a composite assessment of residual congestion. Inclusion criteria were ADHF, over 18 years old, with a “warm-wet” clinical profile. POCUS was performed using an ultraportable device using LUS and PVP. Statistical analysis used logistic regression models to estimate the association between ultrasound congestion and outcomes. Results: A total of 100 patients were included. The population was mostly female, with a median age of 78 years; 59% were hypertensive, and 39% had type 2 diabetes. Median NT-ProBNP was 3878 pg/ml. At discharge, 55% of patient had an inferior vena cava (IVC) over 2 cm, 54% had interstitial syndrome, and 41% had PVP >30%. Regarding 30-day readmission, we found an odds ratio (OR) 7.22 (95% CI 2.7-19.3) for interstitial syndrome; for PVP >30%, an OR 24.61 (95% CI 7.7-78.1) and an OR 13.19 (95% CI 2.7-62.6) for composite of residual congestion. Conclusion: Patients with ADHF and sub-clinical congestion, evidenced in LUS and PVP, were more likely to have readmission within 30 days of discharge. These findings should be confirmed with clinical trials to assess the effectiveness of a POCUS-guided treatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".