Improving guideline‐concordant thromboprophylaxis prescribing for children admitted to hospital with COVID‐19
Bibliographic record
Abstract
BACKGROUND: The incidence of venous thrombo-embolism (VTE) in hospitalized children has increased by 130%-200% over the last two decades. Given this increase, many centers utilize electronic clinical decision support (CDS) to prognosticate VTE risk and recommend prophylaxis. SARS-CoV-2 infection (COVID-19) is a risk factor for VTE; however, CDS developed before the COVID-19 pandemic may not accurately prognosticate VTE risk in children with COVID-19. This study's objective was to identify areas to improve thromboprophylaxis recommendations for children with COVID-19. METHODS: Inpatients with a positive COVID-19 test at admission were identified at a quaternary-care pediatric center between March 1, 2020 and January 20, 2022. The results of the institution's automated CDS thromboprophylaxis recommendations were compared to institutional COVID-19 thromboprophylaxis guidelines and to the actual thromboprophylaxis received. CDS optimization was performed to improve adherence to COVID-19 thromboprophylaxis recommendations. RESULTS: Of the 329 patients included in this study, 106 (28.2%) were prescribed pharmaco-prophylaxis, 167 (50.8%) were identified by the institutional COVID-19 guidelines as requiring pharmaco-prophylaxis, and 45 (13.2%) were identified by the CDS as needing pharmaco-prophylaxis. On univariate analysis, only age 12 years or more was associated with recipient of appropriate prophylaxis (OR 1.78, 95% CI: 1.13-2.82, p = .013). Five patients developed VTEs; three had symptoms at presentation, two were identified as high risk for VTE by both the automated and best practice assessments but were not prescribed pharmaco-prophylaxis. CONCLUSION: Automated thromboprophylaxis recommendations developed prior to the COVID-19 pandemic may not identify all COVID-19 patients needing pharmaco-prophylaxis. Existing CDS tools need to be updated to reflect COVID-19-specific risk factors for VTEs.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".