Late thrombotic complications after <scp>SARS‐CoV</scp>‐2 infection in hemodialysis patients
Bibliographic record
Abstract
INTRODUCTION: There is an increased risk of thrombotic complications in patients with COVID-19. Hemodialysis patients are already at an increased risk for thromboembolic events such as stroke and pulmonary embolism. The aim of our study was to determine the incidence of late thrombotic complications (deep vein thrombosis, pulmonary embolism, stroke, new-onset vascular access thrombosis) in maintenance hemodialysis patients after recovery from COVID-19. METHODS: We performed a retrospective cohort study of 200 prevalent hemodialysis patients in our center at the start of the pandemic. We excluded incident patients after the cohort entry date and those who required hemodialysis for acute kidney injury, and excluded patients with less than 1 month follow-up due to kidney transplantation or death from non-thrombotic causes. FINDINGS: One-hundred and eighty five prevalent hemodialysis patients finally met the inclusion criteria; 37 patients (17.6%) had SARS-CoV-2 infection, out of which 10 (27%) died during the acute phase of disease without evidence of thrombotic events. There was an increased risk of thrombotic events in COVID-19 survivors compared to the non-infected cohort (18.5% vs. 1.9%, p = 0.002) after a median follow-up of 7 months. Multivariate regression analysis showed that COVID-19 infection increased risk for late thrombotic events adjusted for age, sex, hypertension, diabetes, antithrombotic treatment, and previous thrombotic events (Odds Ratio (OR) 26.4, 95% confidence interval 2.5-280.6, p = 0.01). Clinical and laboratory markers did not predict thrombotic events. CONCLUSIONS: There is an increased risk of late thrombotic complications in hemodialysis patients after infection with COVID-19. Further studies should evaluate the benefit of prolonged prophylactic anticoagulation in hemodialysis patients after recovery from 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".