Predicting pulmonary embolism in patients infected with COVID-19 based on D-dimer levels and days between diagnosis of the infection and D-dimer determination
Bibliographic record
Abstract
Ruling out pulmonary embolism (PE) can be challenging in a situation of elevated D-dimer values such as in a case of COVID-19 infection. Our objective was to evaluate the difference in D-dimer values of subjects infected with COVID-19 in those with PE and those without and to analyze the predictive value of D-dimer for PE in these subjects based on the day of D-dimer determination. This was an observational, retrospective study, conducted at a tertiary hospital. All subjects with PCR-confirmed COVID-19 infection requiring hospital admission at our institution between the months of March and April 2020 were included in the study. We compared D-dimer levels in subjects who went on to develop a PE and those who did not. We then created a model to predict the subsequent development of a PE with the current D-dimer levels of the subject. D-dimer levels changed over time from COVID-19 diagnosis, but were always higher in subjects who went on to develop a PE. Regarding the predictive model created, the area under the curve of the ROC analyses of the cross-validation predictions was 0.72. The risk of pulmonary embolism for the same D-dimer levels varied depending on the number of days elapsed since COVID-19 diagnosis and D-dimer determination. To conclude, D-dimer levels were elevated in subjects with a COVID-19 infection, especially in those with PE. D-dimer levels increased during the first 10 days after the diagnosis of the infection and can be used to predict the risk of PE in COVID-19 subjects.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".