Relationship Between BMI and Disease Severity in COVID-19 Patients

Background Obesity and increased body mass index (BMI) are associated with coronavirus disease 2019 (COVID-19)-related complications and severity. They can exacerbate the cytokine storm and lead to severe symptoms or death in obese patients. Objectives This cross-sectional descriptive study included patients with COVID-19 admitted to the Razi Hospital in Ahvaz, Iran, from January 2019 to December 2020. Methods We evaluated the effect of BMI of patients admitted to the general ward and invasive unit care (ICU) on the length of hospitalization. Results We included a total of 466 patients (male: 281 or 60.3% vs. female: 185 or 39.7%) with a mean age of 59.49 ± 14.5 years in the study. Also, 47 (10.1%) patients were admitted to the ICU, and 418 (89.7%) patients to the general ward. A higher BMI was associated with longer hospitalization (P < 0.001). Patients with BMI in the range of 18.5 - 24.9 experienced a longer hospitalization (10-20 days) (P < 0.001). BMI had no significant effect on ICU hospitalization (P = 0.36). Also, there was no significant difference between the two groups regarding the length of hospitalization (P = 0.49). Furthermore, non-diabetic patients were less likely to be admitted to the ICU (73.3% vs. 26.7%) (P < 0.001). The number of discharged patients was higher in patients admitted to the general ward compared to those admitted to the ICU (93.8% vs. 63.8%) (P < 0.001). Conclusions According to our results, a higher BMI was a risk factor for COVID-19, especially in the early stage of infection.

Relationship Between BMI and Disease Severity in COVID-19 Patients.

Background: Obesity and increased body mass index (BMI) are associated with coronavirus disease 2019 (COVID-19)-related complications and severity. They can exacerbate the cytokine storm and lead to severe symptoms or death in obese patients. Objectives: This cross-sectional descriptive study included patients with COVID-19 admitted to the Razi Hospital in Ahvaz, Iran, from January 2019 to December 2020. Methods: We evaluated the effect of BMI of patients admitted to the general ward and invasive unit care (ICU) on the length of hospitalization. Results: We included a total of 466 patients (male: 281 or 60.3% vs. female: 185 or 39.7%) with a mean age of 59.49 ± 14.5 years in the study. Also, 47 (10.1%) patients were admitted to the ICU, and 418 (89.7%) patients to the general ward. A higher BMI was associated with longer hospitalization (P < 0.001). Patients with BMI in the range of 18.5 - 24.9 experienced a longer hospitalization (10-20 days) (P < 0.001). BMI had no significant effect on ICU hospitalization (P = 0.36). Also, there was no significant difference between the two groups regarding the length of hospitalization (P = 0.49). Furthermore, non-diabetic patients were less likely to be admitted to the ICU (73.3% vs. 26.7%) (P < 0.001). The number of discharged patients was higher in patients admitted to the general ward compared to those admitted to the ICU (93.8% vs. 63.8%) (P < 0.001). Conclusions: According to our results, a higher BMI was a risk factor for COVID-19, especially in the early stage of infection.

The diagnostic accuracy of Artificial Intelligence-Assisted CT imaging in COVID-19 disease: A systematic review and meta-analysis.

Artificial intelligence (AI) systems have become critical in support of decision-making. This systematic review summarizes all the data currently available on the AI-assisted CT-Scan prediction accuracy for COVID-19. The ISI Web of Science, Cochrane Library, PubMed, Scopus, CINAHL, Science Direct, PROSPERO, and EMBASE were systematically searched. We used the revised Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool to assess all included studies' quality and potential bias. A hierarchical receiver-operating characteristic summary (HSROC) curve and a summary receiver operating characteristic (SROC) curve have been implemented. The area under the curve (AUC) was computed to determine the diagnostic accuracy. Finally, 36 studies (a total of 39,246 image data) were selected for inclusion into the final meta-analysis. The pooled sensitivity for AI was 0.90 (95% CI, 0.90-0.91), specificity was 0.91 (95% CI, 0.90-0.92) and the AUC was 0.96 (95% CI, 0.91-0.98). For deep learning (DL) method, the pooled sensitivity was 0.90 (95% CI, 0.90-0.91), specificity was 0.88 (95% CI, 0.87-0.88) and the AUC was 0.96 (95% CI, 0.93-0.97). In case of machine learning (ML), the pooled sensitivity was 0.90 (95% CI, 0.90-0.91), specificity was 0.95 (95% CI, 0.94-0.95) and the AUC was 0.97 (95% CI, 0.96-0.99). AI in COVID-19 patients is useful in identifying symptoms of lung involvement. More prospective real-time trials are required to confirm AI's role for high and quick COVID-19 diagnosis due to the possible selection bias and retrospective existence of currently available studies.