Comparison of arterial pressure cardiac output monitoring with transpulmonary thermodilution in septic patients.
Bibliographic record
Abstract
BACKGROUND: The measurement of cardiac output in critically ill patients is complicated by rapid pathophysiological changes. The aim of this study was to compare the recently developed Arterial Pressure Cardiac Output algorithm (APCO) with transpulmonary thermodilution (TDCO). Clinical and hemodynamic parameters were tested for their impact on the measurements. MATERIAL/METHODS: Twenty septic patients were examined. Cardiac output measurements were performed simultaneously on 3 consecutive days. The data were evaluated using regression analysis and the Bland Altman approach. RESULTS: Bland Altman analysis presented a bias of 0.72 L/min and limits of agreement of 2.16 to 3.61 L/min for TDCO vs. APCO. Statistically significant covariables in the regression analysis were systemic vascular resistance (p<0.001), mean arterial pressure (p<0.001), cardiac function index (p=0.01), global end-diastolic index (p=0.02) and stroke volume index (p=0.005). Multiple linear regression analysis showed the residual percentage error decreased from 49.1% to 21.5%. CONCLUSIONS: The APCO algorithm provides a broad range of hemodynamic measurements with a minimally invasive approach and simple access to the patient's hemodynamic state. However, an underestimation at high cardiac output and an overestimation at low cardiac output relative to transpulmonary thermodilution were observed in septic patients. Therefore, the APCO algorithm in its current state cannot be substituted for transpulmonary thermodilution.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".