Association between echocardiographic features and inflammatory biomarkers with clinical outcomes in COVID-19 patients in Saudi Arabia
Bibliographic record
Abstract
Background Respiratory infections are one of the most common comorbidities identified in hospitalized patients. The coronavirus disease 2019 (COVID-19) pandemic greatly impacted healthcare systems, including acute cardiac services. Aim This study aimed to describe the echocardiographic findings of patients with COVID-19 infections and their correlations with inflammatory biomarkers, disease severity, and clinical outcomes. Methods This observational study was conducted between June 2021 and July 2022. The analysis included all patients diagnosed with COVID-19 who had transthoracic echocardiographic (TTE) scans within 72 h of admission. Results The enrolled patients had a mean age of 55.6 ± 14.7 years, and 66.1% were male. Of the 490 enrolled patients, 203 (41.4%) were admitted to the intensive care unit (ICU). Pre-ICU TTE findings showed significantly higher incidence right ventricular dysfunction (28 [13.8%] vs. 23 [8.0%]; P = 0.04) and left ventricular (LV) regional wall motion abnormalities (55 [27.1%] vs. 29 [10.1%]; p < 0.001) in ICU patients compared to non-ICU patients. In-hospital mortality was 11 (2.2%), all deaths of ICU patients. The most sensitive predictors of ICU admission (p < 0.05): cardiac troponin I level (area under the curve [AUC] = 0.733), followed by hs-CRP (AUC = 0.620), creatine kinase-MB (AUC = 0.617), D-dimer (AUC = 0.599), and lactate dehydrogenase (AUC = 0.567). Binary logistic regression showed that reduced LV ejection fraction (LVEF), elevated pulmonary artery systolic pressure, and dilated right ventricle were echocardiographic predictors of poor outcomes (p < 0.05). Conclusion Echocardiography is a valuable tool in assessing admitted patients with COVID-19. Lower LVEF, pulmonary hypertension, higher D-dimer, C-reactive protein, and B-type natriuretic peptide levels were predictors of poor outcomes.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".