Accuracy of aPTT monitoring in critically ill patients treated with unfractionated heparin.
Bibliographic record
Abstract
INTRODUCTION: The anticoagulant effect of unfractionated heparin (UFH) is usually monitored by means of the activated partial thromboplastin time (aPTT). In critically ill patients, however, increased levels of acute phase proteins may decrease the accuracy of the aPTT, leading to inadequate UFH dosing. In these circumstances, the anti-Xa assay is recommended for monitoring. OBJECTIVE: We aimed to analyse the accuracy of the aPTT for the monitoring of UFH dosing in critically ill patients. METHODS: In critically ill patients treated with therapeutic doses of UFH, we compared aPTT levels with simultaneously measured anti-Xa levels as the gold standard. Sensitivity and specificity of the aPTT were determined for different cut-off points, receiver operating characteristic (ROC) curves were constructed and their areas under the curve (AUCs) were calculated. RESULTS: A total of 171 paired blood samples from 58 patients were analysed. Concordant aPTT and anti-Xa values were observed in 108 (63.2%) data pairs. In 33 data pairs (19.3%) the aPTT was discordantly high and in 30 data pairs (17.5%) discordantly low. The sensitivity of the aPTT in detecting UFH underdosing and overdosing was 0.63 and 0.37, respectively. When considering alternative thresholds, ROC curves for underdosing and overdosing had AUCs of 0.71 and 0.81, respectively. CONCLUSION: In this small cohort of critically ill patients, the aPTT was accurate in 63.2% of the blood samples. Its sensitivity to detect UFH underdosing and overdosing was low (0.63 and 0.37, respectively). We conclude that in critically ill patients, the aPTT is not accurate enough to detect UFH underdosing and overdosing.
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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 | 0.005 | 0.023 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".