Whole-Body Impedance Cardiography-Derived Haemodynamic Parameters Associated With Outcomes in Clinically Stable Heart Failure Patients With Reduced Ejection Fraction
Bibliographic record
Abstract
Background Heart failure with reduced ejection fraction (HFrEF) is one of the most prevalent chronic cardiac conditions. Despite recent advances, overall morbidity and mortality remain high. Routine outpatient evaluation is predominantly based on subjective descriptions of symptoms. The utility of non-invasively obtained haemodynamic parameters in identifying high-risk patients with HFrEF has not been described. Methods Clinically stable (>3 months) patients with HFrEF from the tertiary HF clinic at St. Boniface Hospital, Canada were recruited. Resting and exercise-augmented (25 watts, up to 12 minutes on a mounted bike) haemodynamic parameters were obtained using a Non-Invasive Cardiac System (NICaS), a whole-body impedance cardiography-based technology. Electronic patient records were reviewed to identify outcomes. Results Overall, 63 patients (63.4±15.4 years; 11 [17.5%] female, mean body mass index 31.0±7.1 kg/m 2 ) were recruited. Resting stroke index (SI) correlated with post-exercise SI (r=0.56). At 12-month follow-up, 25 of 63 subjects (39.7%) experienced adverse outcomes (emergency department presentation or unplanned HF hospitalisation, new-onset arrhythmia, referral for cardiac implantable electronic device implantation/optimisation, referral for palliative care and all-cause death). Lower resting SI, either supine (35.5±9.5 vs 40.3±8.0 mL/m 2 ; p=0.02) or sitting (32.1±7.9 vs 37.6±7.9 mL/m 2 ; p=0.009) and exaggerated exercise-augmented Granov–Goor index, a surrogate marker of impaired left ventricular contractility, were the hemodynamic parameters most strongly associated with outcomes. Conclusions Non-invasively obtained resting SI and higher exercise-augmented Granov–Goor index identify a high-risk cohort among patients with clinically stable HFrEF. Such findings should be validated in a larger prospective study.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".