Accuracy and precision of non-invasive cardiac output monitoring by electrical cardiometry: a systematic review and meta-analysis
Bibliographic record
Abstract
Cardiac output monitoring is used in critically ill and high-risk surgical patients. Intermittent pulmonary artery thermodilution and transpulmonary thermodilution, considered the gold standard, are invasive and linked to complications. Therefore, many non-invasive cardiac output devices have been developed and studied. One of those is electrical cardiometry. The results of validation studies are conflicting, which emphasize the need for definitive validation of accuracy and precision. We performed a database search of PubMed, Embase, Web of Science and the Cochrane Library of Clinical Trials to identify studies comparing cardiac output measurement by electrical cardiometry and a reference method. Pooled bias, limits of agreement (LoA) and mean percentage error (MPE) were calculated using a random-effects model. A pooled MPE of less than 30% was considered clinically acceptable. A total of 13 studies in adults (620 patients) and 11 studies in pediatrics (603 patients) were included. For adults, pooled bias was 0.03 L min −1 [95% CI − 0.23; 0.29], LoA − 2.78 to 2.84 L min −1 and MPE 48.0%. For pediatrics, pooled bias was − 0.02 L min −1 [95% CI − 0.09; 0.05], LoA − 1.22 to 1.18 L min −1 and MPE 42.0%. Inter-study heterogeneity was high for both adults (I 2 = 93%, p < 0.0001) and pediatrics (I 2 = 86%, p < 0.0001). Despite the low bias for both adults and pediatrics, the MPE was not clinically acceptable. Electrical cardiometry cannot replace thermodilution and transthoracic echocardiography for the measurement of absolute cardiac output values. Future research should explore it’s clinical use and indications.
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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 | 0.020 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.040 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".