Calculation of blood loss in cardiac surgery: How should we monitor?
Bibliographic record
Abstract
Background The bleeding in cardiac surgery remains a significant clinical problem. There is no “gold standard” method to quantify blood loss. Traditional measurement of drainage volume often underestimates or overestimates, as it does not consider the type of fluid. We hypothesized that blood loss could be more accurately calculated using the Hb/kg index in terms of haemoglobin (Hb) mass loss per kilogram of the patient’s body mass. This study aimed to Objective To develop a novel approach for calculating actual blood loss using the Hb/kg index. Methods This single-center prospective study included 195 patients who underwent cardiac surgery between October 2023 and November 2024. The Hb/kg index was calculated based on intraoperative Hb loss, Hb loss via chest tubes, packed red blood cell transfusions and patient weight. Eighty-six additional clinical predictors were analyzed using conventional statistics and machine learning algorithms. Predictors with statistically significant Spearman correlations were included for further analysis. Results Lasso regression achieved the best overall performance in predicting Hb/kg index. It yielded the lowest mean squared error (0.08 ± 0.04), mean absolute percentage error (0.18 ± 0.10), with the highest correlation (0.92 ± 0.06) and R² score (0.82 ± 0.13). BMI showed a significant negative relationship (−0.018, p < 0.001). Postoperative Hb and haematocrit values had negative correlation (−0.69, p < 0.001 and −0.07, p < 0.015), while initial Hb was positively correlated (0.85, p < 0.001). Conclusions This method provides a more reliable and clinically relevant tool to calculate actual blood loss and allows for a more precise assessment and treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".