Point-of-Care Hemostatic Testing in Cardiac Surgery
Bibliographic record
Abstract
BACKGROUND: Cardiac surgery is frequently complicated by coagulopathic bleeding that is difficult to optimally manage using standard hemostatic testing. We hypothesized that point-of-care hemostatic testing within the context of an integrated transfusion algorithm would improve the management of coagulopathy in cardiac surgery and thereby reduce blood transfusions. METHODS: We conducted a pragmatic multicenter stepped-wedge cluster randomized controlled trial of a point-of-care-based transfusion algorithm in consecutive patients undergoing cardiac surgery with cardiopulmonary bypass at 12 hospitals from October 6, 2014, to May 1, 2015. Following a 1-month data collection at all participating hospitals, a transfusion algorithm incorporating point-of-care hemostatic testing was sequentially implemented at 2 hospitals at a time in 1-month intervals, with the implementation order randomly assigned. No other aspects of care were modified. The primary outcome was red blood cell transfusion from surgery to postoperative day 7. Other outcomes included transfusion of other blood products, major bleeding, and major complications. The analysis adjusted for secular time trends, within-hospital clustering, and patient-level risk factors. All outcomes and analyses were prespecified before study initiation. RESULTS: Among the 7402 patients studied, 3555 underwent surgery during the control phase and 3847 during the intervention phase. Overall, 3329 (45.0%) received red blood cells, 1863 (25.2%) received platelets, 1645 (22.2%) received plasma, and 394 (5.3%) received cryoprecipitate. Major bleeding occurred in 1773 (24.1%) patients, and major complications occurred in 740 (10.2%) patients. The trial intervention reduced rates of red blood cell transfusion (adjusted relative risk, 0.91; 95% confidence interval, 0.85-0.98; P=0.02; number needed to treat, 24.7), platelet transfusion (relative risk, 0.77; 95% confidence interval, 0.68-0.87; P<0.001; number needed to treat, 16.7), and major bleeding (relative risk, 0.83; 95% confidence interval, 0.72-0.94; P=0.004; number needed to treat, 22.6), but had no effect on other blood product transfusions or major complications. CONCLUSIONS: Implementation of point-of-care hemostatic testing within the context of an integrated transfusion algorithm reduces red blood cell transfusions, platelet transfusions, and major bleeding following cardiac surgery. Our findings support the broader adoption of point-of-care hemostatic testing into clinical practice. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT02200419.
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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 | 0.002 |
| 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 | 0.000 | 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".