Use of Novel Non-Invasive Techniques and Biomarkers to Guide Outpatient Management of Fluid Overload and Reduce Hospital Readmission: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Aims Fluid congestion is a leading cause of hospital admission, readmission, and mortality in heart failure (HF). We performed a systematic review and meta-analysis to determine the effectiveness of an advanced fluid management programme (AFMP). The AFMP was defined as an intervention providing tailored diuretic therapy guided by intravascular volume assessment, in hospitalized patients or after discharge. The AFMP group was compared with patients who received standard care treatment. The aim of this systematic review and meta-analysis was to determine the effectiveness of an AFMP in improving patient outcomes. Methods and results A systematic review of randomized controlled trials, case–control studies, and crossover studies using the terms ‘heart failure’, ‘fluid management’, and ‘readmission’ was conducted in PubMed, CINAHL, and Scopus up until November 2020. Studies reporting the association of an AFMP on readmission and/or mortality were included in our meta-analyses. Risk of bias was assessed in non-randomized studies using the Newcastle–Ottawa Scale. From 232 retrieved studies, 12 were included in the data synthesis. The 6040 patients in the included studies had a mean age of 72 ± 4 years and mean left ventricular ejection fraction of 39 ± 8%, there were slightly more men (n = 3022) than women, and the follow-up period was a mean of 4.8 ± 3.1 months. Readmission data were available in 5362 patients; of these, 1629 were readmitted. Mortality data were available in 5787 patients; of these, 584 died. HF patients who had an AFMP in hospital and/or after discharge had lower odds of all-cause readmission (odds ratio—OR 0.64 [95% confidence interval—CI 0.44, 0.92], P = 0.02) with moderate heterogeneity (I2 = 46.5) and lower odds of all-cause mortality (OR 0.82 [95% CI 0.69, 0.98], P = 0.03) with low heterogeneity (I2 = 0). The use of an AFMP was equally effective in reducing readmission and mortality regardless of age and follow-up duration. Effective pre-discharge diuresis was associated with significantly lower readmission odds (OR 0.43 [95% CI 0.26, 0.71], P = 0.001) compared with a fluid management plan as part of post-discharge follow-up. Conclusions An effective AFMP is associated with improving readmission and mortality in HF. Our results encourage attainment of optimal volume status at discharge and prescription of optimal diuretic dose. Ongoing support to maintain euvolaemia and effective collaboration between healthcare teams, along with effective patient education and engagement, may help to reduce adverse outcomes in HF patients.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.033 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".