Non-Invasive Cardiac Output Monitoring in Neonates Using Bioreactance: A Comparison with Echocardiography
Bibliographic record
Abstract
OBJECTIVES: Non-invasive cardiac output monitoring is a potentially useful clinical tool in the neonatal setting. Our aim was to evaluate a new method of non-invasive continuous cardiac output (CO) measurement (NICOM™) based on the principle of bioreactance in neonates. METHODS: In this prospective observational study, 10 neonates underwent 97 paired NICOM and echocardiography (echo) assessments of left ventricular output (LVO). For each neonate, NICOM measurements of left ventricular stroke volume (SV) and LVO over a 2- to 4-hour period were correlated with blinded, simultaneous, discrete echo measurements of SV and LVO. The precision and accuracy of the NICOM monitor relative to echo during periods of steady state were assessed. RESULTS: The infants' median birth weight was 2.72 kg (IQR 1.56-3.23 kg, range 1.44-4.00) and their median gestation was 37 weeks (IQR 31-40 weeks, range 31-41). Median NICOM SV and LVO readings were consistently lower than echo (2.6 ml [IQR 1.4-3.2, range 0.6-5.3] vs. 3.5 ml [IQR 2.1-4.4, range 1.1-6.8], and 400 ml/min [IQR 233-476] vs. 559 ml/min [IQR 386-652], p < 0.001). The NICOM LVO readings were lower than the echo readings by a mean of 153 ± 56 ml/kg. NICOM consistently under-read LVO by 31 ± 8%, and this systematic difference was constant across the range of LVOs obtained. There was a strong correlation between NICOM and echo measurements of LVO (r = 0.95, p < 0.001). CONCLUSION: Non-invasive cardiac output monitoring is feasible in neonates. Further validation studies in neonatal animal experimental models and human neonates need to be conducted before routine clinical use.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".