Intrapulmonary and Intracardiac Shunts in Adult COVID-19 Versus Non-COVID Acute Respiratory Distress Syndrome ICU Patients Using Echocardiography and Contrast Bubble Studies (COVID-Shunt Study): A Prospective, Observational Cohort Study
Bibliographic record
Abstract
OBJECTIVES: Studies have suggested intrapulmonary shunts may contribute to hypoxemia in COVID-19 acute respiratory distress syndrome (ARDS) with worse associated outcomes. We evaluated the presence of right-to-left (R-L) shunts in COVID-19 and non-COVID ARDS patients using a comprehensive hypoxemia workup for shunt etiology and associations with mortality. DESIGN: Prospective, observational cohort study. SETTING: Four tertiary hospitals in Edmonton, Alberta, Canada. PATIENTS: Adult critically ill, mechanically ventilated, ICU patients admitted with COVID-19 or non-COVID (November 16, 2020, to September 1, 2021). INTERVENTIONS: Agitated-saline bubble studies with transthoracic echocardiography/transcranial Doppler ± transesophageal echocardiography assessed for R-L shunts presence. MEASUREMENTS AND MAIN RESULTS: Primary outcomes were shunt frequency and association with hospital mortality. Logistic regression analysis was used for adjustment. The study enrolled 226 patients (182 COVID-19 vs 42 non-COVID). Median age was 58 years (interquartile range [IQR], 47-67 yr) and Acute Physiology and Chronic Health Evaluation II scores of 30 (IQR, 21-36). In COVID-19 patients, the frequency of R-L shunt was 31 of 182 COVID patients (17.0%) versus 10 of 44 non-COVID patients (22.7%), with no difference detected in shunt rates (risk difference [RD], -5.7%; 95% CI, -18.4 to 7.0; p = 0.38). In the COVID-19 group, hospital mortality was higher for those with R-L shunt compared with those without (54.8% vs 35.8%; RD, 19.0%; 95% CI, 0.1-37.9; p = 0.05). This did not persist at 90-day mortality nor after adjustment with regression. CONCLUSIONS: There was no evidence of increased R-L shunt rates in COVID-19 compared with non-COVID controls. R-L shunt was associated with increased in-hospital mortality for COVID-19 patients, but this did not persist at 90-day mortality or after adjusting using logistic regression.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".