Increased risk of dialysis circuit clotting in hemodialysis patients with <scp>COVID</scp>‐19 is associated with elevated <scp>FVIII</scp>, fibrinogen and <scp>D</scp>‐dimers
Bibliographic record
Abstract
Abstract Introduction Severe COVID‐19 infections increase the risk of thrombotic events and Intensive Care Units reported increased extracorporeal circuit clotting (ECC) in COVID‐19 patients with acute kidney injury. We wished to determine whether hemodialysis (HD) patients with COVID‐19 also have increased risk of circuit clotting. Methods We reviewed coagulation studies and HD records, 4 weeks before and after COVID‐19 polymerase chain reaction detection in HD patients between April 2020 and June 2021. Findings Sixty‐eight (33.5%) of 203 HD patients with COVID‐19, 65% male, mean age 64.9 ± 15.3 years, experienced some circuit clotting, and no clotting recorded prior to positive test results. In those who experienced ECC, prothrombin, activated partial thromboplastin or thrombin times were not different, whereas median factor VIII (273 [168–419] vs. 166 [139–225] IU/dl, p < 0.001), D‐dimers (2654 [1381–6019] vs. 1351 [786–2334] ng/ml, p < 0.05), and fibrinogen (5.6 ± 1.4 vs. 4.9 ± 1.4 g/L, p < 0.05) were greater. Antithrombin (94 [83–112] vs. 89 [84–103] IU/dl), protein C (102 [80–130] vs. 86 [76–106] IU/dl), protein S (65 [61–75] vs. 65 [52–79] IU/dl) and platelet counts (193 [138–243] vs. 174 [138–229] × 109/L) did not differ. On multivariable logistic analysis, circuit clotting was associated with log factor VIII (odds ratio [OR] 14.8 (95% confidence limits [95% CL] 1.12–19.6), p = 0.041), fibrinogen (OR 1.57 [95% CL 1.14–21.7], p = 0.006) and log D dimer (OR 4.8 [95% CL 1.16–12.5], p = 0.028). Discussion Extracorporeal circuit clotting was increased within 4 weeks of testing positive for COVID‐19. Clotting was associated with increased factor VIII, fibrinogen and D‐dimer, suggesting that the risk of circuit clotting was related to the inflammatory response to COVID‐19.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".