COVID-19 Infection Is Associated With Increased In-Hospital Mortality and Complications in Patients With Acute Heart Failure: Insight From National Inpatient Sample (2020)

Introduction: Patients with acute heart failure (AHF) exacerbation are susceptible to complications in the setting of COVID-19 infection. Data regarding the clinical outcomes of COVID-19 in patients admitted with AHF is limited. Methods: We used the national inpatient sample database by utilizing ICD-10 codes to identify all hospitalizations with a diagnosis of AHF in 2020. We classified the sample into AHF with COVID-19 infection versus those without COVID-19. Primary outcome was in-hospital mortality. Secondary outcomes were acute myocardial infarction, need for pressors, mechanical cardiac support, cardiogenic shock, and cardiac arrest. Also, we evaluated for acute pulmonary embolism (PE), bacterial pneumonia, need for a ventilator, and acute kidney injury (AKI). Results: We identified a total of 694,920 of AHF hospitalizations, 660,463 (95.04%) patients without COVID-19 and 34,457 (4.96%) with COVID-19 infection. For baseline comorbidities, diabetes mellitus, chronic heart failure, ESRD, and coagulopathy were significantly higher among AHF patients with COVID-19 (P < .01). While CAD, prior MI, percutaneous coronary intervention, and coronary artery bypass graft, atrial fibrillation, chronic obstructive pulmonary disease, and peripheral vascular disease were higher among those without COVID-19. After adjustment for baseline comorbidities, in-hospital mortality (aOR 5.08 [4.81 to 5.36]), septic shock (aOR 2.54 [2.40 to 2.70]), PE (aOR 1.75 [1.57 to 1.94]), and AKI (aOR 1.33 [1.30 to 1.37]) were significantly higher among AHF with COVID-19 patients. The mean length of stay (5 vs 7 days, P < .01) and costs of hospitalization ($42,143 vs $60,251, P < .01) were higher among AHF patients with COVID-19 infection. Conclusion: COVID-19 infection in patients with AHF is associated with significantly higher in-hospital mortality, need for mechanical ventilation, septic shock, and AKI along with higher resource utilization. Predictors for mortality in AHF patients during the COVID-19 pandemic, COVID-19 infection, patients with end-stage heart failure, and atrial fibrillation. Studies on the impact of vaccination against COVID-19 in AHF patients are needed.

Challenges and solutions of echocardiography generalization for deep learning: a study in patients with constrictive pericarditis

Purpose

The inherent characteristics of transthoracic echocardiography (TTE) images such as low signal-to-noise ratio and acquisition variations can limit the direct use of TTE images in the development and generalization of deep learning models. As such, we propose an innovative automated framework to address the common challenges in the process of echocardiography deep learning model generalization on the challenging task of constrictive pericarditis (CP) and cardiac amyloidosis (CA) differentiation.

Approach

Patients with a confirmed diagnosis of CP or CA and normal cases from Mayo Clinic Rochester and Arizona were identified to extract baseline demographics and the apical 4 chamber view from TTE studies. We proposed an innovative preprocessing and image generalization framework to process the images for training the ResNet50, ResNeXt101, and EfficientNetB2 models. Ablation studies were conducted to justify the effect of each proposed processing step in the final classification performance.

Results

The models were initially trained and validated on 720 unique TTE studies from Mayo Rochester and further validated on 225 studies from Mayo Arizona. With our proposed generalization framework, EfficientNetB2 generalized the best with an average area under the curve (AUC) of 0.96 (± 0.01 ) and 0.83 (± 0.03 ) on the Rochester and Arizona test sets, respectively.

Conclusions

Leveraging the proposed generalization techniques, we successfully developed an echocardiography-based deep learning model that can accurately differentiate CP from CA and normal cases and applied the model to images from two sites. The proposed framework can be further extended for the development of echocardiography-based deep learning models.

The effect of influenza vaccine in reducing the severity of clinical outcomes in patients with COVID-19: a systematic review and meta-analysis

Recent evidence suggests that vaccination against influenza may reduce the clinical outcomes of COVID-19. This study looked at the link between influenza vaccination and the severity of COVID-19 infection. We searched five databases until August 2021. We included studies that reported the relationship between influenza vaccination and COVID-19 outcomes. We pooled the data as risk ratio (RR) or mean difference (MD), with 95% confidence intervals (CIs), the data pooled using fixed and random effects models according to the heterogeneity of results. Sixteen observational studies with 191,496 COVID-19 patients were included. In terms of mechanical ventilation, our analysis showed a significant favor for the influenza vaccinated group over the non-vaccinated group (RR = 0.72, 95% CI [0.54, 0.96], P = 0.03). However, the analysis indicated no statistically significant differences between vaccinated and non-vaccinated groups in the term of mortality rate (RR = 1.20, 95% CI [0.71, 2.04], P = 0.50), hospital admissions (RR = 1.04, 95% CI [0.84, 1.29], P = 0.75), intensive care admissions (RR = 0.84, 95% CI [0.44, 1.62], P = 0.60). There were no significant differences between those who had received the influenza vaccine and those who had not in COVID-19 clinical outcomes, except for mechanical ventilation which showed a significantly lower risk in the influenza vaccinated group compared to the non-vaccinated one. However, future research is encouraged as our data have limitations, and the influenza vaccine is regularly updated. Also, this does not exclude the importance of the influenza vaccine during the COVID-19 pandemic.

Electrocardiographic Abnormalities in Patients With Spinal Cord Injury With Deranged Lipid Profile

Introduction Spinal cord injury (SCI) can lead to severe disability and neurogenic shock, arrhythmias, autonomic dysfunction, pressure ulcers, etc., of the autonomic nervous system. Therefore, in these patients, cardiovascular problems should be investigated frequently. This study was conducted to evaluate the electrocardiographic (ECG) abnormalities in patients with spinal cord injury having inappropriate lipid profiles and their relationship with each other. Materials and methods This cross-sectional study was held in the Internal Medicine Department of Mayo Hospital, Lahore, for a one-year duration from May 2020 to May 2021. It included 58 patients with spinal cord injury, 35 of whom had paraplegia, and 23 had tetraplegia. Fasting blood samples were taken for lipid profile analysis. Twelve-lead ECGs three times a day for one month were taken and analyzed in the context of previously available ECGs. Results Out of 58, the lipid profiles were found abnormal in 47 patients, 18 of whom had a normal ECG. The lipid profile was normal in 12, of which only one patient had ECG abnormalities. Cholesterol levels were found normal in 39 patients and deranged in 19 patients; low-density lipoproteins in nine patients, triglycerides in 18 patients, and high-density lipoprotein values in one patient were abnormal. Conclusions Sinus bradycardia was the most common ECG abnormality found in SCI patients with deranged lipid profiles. Further studies are needed in the future to validate the findings of this study.