Mitigation of Aerosols Generated During Exercise Testing With a Portable High-Efficiency Particulate Air Filter With Fume Hood.

BACKGROUND: The role of portable high-efficiency particulate air (HEPA) filters for supplemental aerosol mitigation during exercise testing is unknown and might be relevant during COVID-19 pandemic. RESEARCH QUESTION: What is the effect of portable HEPA filtering on aerosol concentration during exercise testing and its efficiency in reducing room clearance time in a clinical exercise testing laboratory? STUDY DESIGN AND METHODS: Subjects were six healthy volunteers aged 20 to 56 years. In the first experiment, exercise was performed in a small tent with controlled airflow with the use of a stationary cycle, portable HEPA filter with fume hood, and particle counter to document aerosol concentration. Subjects performed a four-stage maximal exercise test that lasted 12 min plus 5 min of pretest quiet breathing and 3 min of active recovery. First, they exercised without mitigation then with portable HEPA filter running. In a separate experiment, room aerosol clearance time was measured in a clinical exercise testing laboratory by filling it with artificially generated aerosols and measuring time to 99.9% aerosol clearance with heating, ventilation, and air conditioning (HVAC) only or HVAC plus portable HEPA filter running. RESULTS: In the exercise experiment, particle concentrations reached 1,722 ± 1,484/L vs 96 ± 124/L (P < .04) for all particles (>0.3 µm), 1,339 ± 1,281/L vs 76 ± 104/L (P < .05) for smaller particles (0.3 to 1.0 µm), and 333 ± 209/L vs 17 ± 19/L (P < .01) for larger particles (1.0 to 5.0 µm) at the end of the protocol in a comparison of mitigation vs portable HEPA filter. Use of a portable HEPA filter in a clinical exercise laboratory clearance experiment reduced aerosol clearance time 47% vs HVAC alone. INTERPRETATION: The portable HEPA filter reduced the concentration of aerosols generated during exercise testing by 96% ± 2% for all particle sizes and reduced aerosol room clearance time in clinical exercise testing laboratories. Portable HEPA filters therefore might be useful in clinical exercise testing laboratories to reduce the risk of COVID-19 transmission.

Characterization of Aerosol Generation During Various Intensities of Exercise.

BACKGROUND: Characterization of aerosol generation during exercise can inform the development of safety recommendations in the face of COVID-19. RESEARCH QUESTION: Does exercise at various intensities produce aerosols in significant quantities? STUDY DESIGN AND METHODS: In this experimental study, subjects were eight healthy volunteers (six men, two women) who were 20 to 63 years old. The 20-minute test protocol of 5 minutes rest, four 3-minute stages of exercise at 25%, 50%, 75%, and 100% of age-predicted heart rate reserve, and 3 minutes active recovery was performed in a clean, controlled environment. Aerosols were measured by four particle counters that were place to surround the subject. RESULTS: Age averaged 41 ± 14 years. Peak heart rate was 173 ± 17 beat/min (97% predicted); peak maximal oxygen uptake was 33.9 ± 7.5 mL/kg/min; and peak respiratory exchange ratio was 1.22 ± 0.10. Maximal ventilation averaged 120 ± 23 L/min, while cumulative ventilation reached 990 ± 192 L. Concentrations increased exponentially from start to 20 minutes (geometric mean ± geometric SD particles/liter): Fluke >0.3 µm = 66 ± 1.8 → 1605 ± 3.8; 0.3-1.0 µm = 35 ± 2.2 → 1095 ± 4.6; Fluke 1.0-5.0 µm = 21 ± 2.0 → 358 ± 2.3; P-Trak anterior = 637 ± 2.3 → 5148 ± 3.0; P-Trak side = 708 ± 2.7 → 6844 ± 2.7; P-Track back = 519 ± 3.1 → 5853 ± 2.8. All increases were significant at a probability value of <.05. Exercise at or above 50% of predicted heart rate reserve showed statistically significant increases in aerosol concentration. INTERPRETATION: Our data suggest exercise testing is an aerosol-generating procedure and, by extension, other activities that involve exercise intensities at or above 50% of predicted heart rate reserve. Results can guide recommendations for safety of exercise testing and other indoor exercise activities.

Risk Factors Underlying COVID-19 Lockdown-Induced Mental Distress.

Recent reports suggest that the COVID-19 lockdown resulted in changes in mental health, however, potential age-related changes and risk factors remain unknown. We measured COVID-19 lockdown-induced stress levels and the severity of depressive symptoms prior to and during the COVID-19 lockdown in different age groups and then searched for potential risk factors in a well-characterized general population-based sample. A total of 715 participants were tested for mental distress and related risk factors at two time-points, baseline testing prior to COVID-19 and follow-up testing during COVID-19, using a battery of validated psychological tests including the Perceived Stress Scale and the Patient Health Questionnaire. Longitudinal measurements revealed that the prevalence of moderate to high stress and the severity of depressive symptoms increased 1.4- and 5.5-fold, respectively, during the COVID-19 lockdown. This surge in mental distress was more severe in women, but was present in all age groups with the older age group exhibiting, cross-sectionally, the lowest levels of mental distress prior to and during the lockdown. Illness perception, personality characteristics such as a feeling of loneliness, and several lifestyle components were found to be associated with a significant increase in mental distress. The observed changes in mental health and the identified potential risk factors underlying these changes provide critical data justifying timely and public emergency-tailored preventive, diagnostic, and therapeutic mental health interventions, which should be integrated into future public health policies globally.

Transfusion support in COVID-19 patients: Impact on hospital blood component supply during the outbreak.

BACKGROUND: During the COVID-19 outbreak, most hospitals deferred elective surgical procedures to allow space for the overwhelming number of COVID-19 patient admissions, expecting a decrease in routine blood component requirements. However, because transfusion support needs of COVID-19 patients are not well known, its impact on hospital blood supply is uncertain. The objective of this study was to assess the effect of the COVID-19 pandemic on transfusion demand. STUDY DESIGN AND METHODS: Transfusion records during the peak of the COVID-19 pandemic (March 1-April 30, 2020) were reviewed in our center to assess changes in blood requirements. RESULTS: During this period 636 patients received a total of 2934 blood components, which reflects a 17.6% reduction in transfusion requirements with regard to the same period of 2019, and blood donations in Madrid dropped by 45%. The surgical blood demand decreased significantly during the outbreak (50.2%). Blood usage in the hematology and oncology departments remained unchanged, while the day ward demand halved, and intensive care unit transfusion needs increased by 116%. A total of 6.2% of all COVID inpatients required transfusion support. COVID-19 inpatients consumed 19% of all blood components, which counterbalanced the savings owed to the reduction in elective procedures. CONCLUSION: Although only a minority of COVID-19 inpatients required transfusion, the expected reduction in transfusion needs caused by the lack of elective surgical procedures is partially offset by the large number of admitted patients during the peak of the pandemic. This fact must be taken into account when planning hospital blood supply.

Left ventricular systolic dysfunction identification using artificial intelligence-augmented electrocardiogram in cardiac intensive care unit patients.

BACKGROUND: An artificial intelligence-augmented electrocardiogram (AI-ECG) can identify left ventricular systolic dysfunction (LVSD). We examined the accuracy of AI ECG for identification of LVSD (defined as LVEF ≤40% by transthoracic echocardiogram [TTE]) in cardiac intensive care unit (CICU) patients. METHOD: We included unique Mayo Clinic CICU patients admitted from 2007 to 2018 who underwent AI-ECG and TTE within 7 days, at least one of which was during hospitalization. Discrimination of the AI-ECG for LVSD was determined using receiver-operator characteristic curve (AUC) values. RESULTS: We included 5680 patients with a mean age of 68 ± 15 years (37% females). Acute coronary syndrome (ACS) was present in 55%. LVSD was present in 34% of patients (mean LVEF 48 ± 16%). The AI-ECG had an AUC of 0.83 (95% confidence interval 0.82-0.84) for discrimination of LVSD. Using the optimal cut-off, the AI-ECG had 73%, specificity 78%, negative predictive value 85% and overall accuracy 76% for LVSD. AUC values were higher for patients aged <70 years (0.85 versus 0.80), males (0.84 versus 0.79), patients without ACS (0.86 versus 0.80), and patients who did not undergo revascularization (0.84 versus 0.80). CONCLUSIONS: The AI-ECG algorithm had very good discrimination for LVSD in this critically-ill CICU cohort with a high prevalence of LVSD. Performance was better in younger male patients and those without ACS, highlighting those CICU patients in whom screening for LVSD using AI ECG may be more effective. The AI-ECG might potentially be useful for identification of LVSD in resource-limited settings when TTE is unavailable.

Artificial Intelligence ECG to Detect Left Ventricular Dysfunction in COVID-19: A Case Series.

Coronavirus disease 2019 (COVID-19) can result in deterioration of cardiac function, which is associated with high mortality. A simple point-of-care diagnostic test to screen for ventricular dysfunction would be clinically useful to guide management. We sought to review the clinical experience with an artificial intelligence electrocardiogram (AI ECG) to screen for ventricular dysfunction in patients with documented COVID-19. We examined all patients in the Mayo Clinic system who underwent clinically indicated electrocardiography and echocardiography within 2 weeks following a positive COVID-19 test and had permitted use of their data for research were included. Of the 27 patients who met the inclusion criteria, one had a history of normal ventricular function who developed COVID-19 myocarditis with rapid clinical decline. The initial AI ECG in this patient indicated normal ventricular function. Repeat AI ECG showed a probability of ejection fraction (EF) less than or equal to 40% of 90.2%, corroborated with an echocardiographic EF of 35%. One other patient had a pre-existing EF less than or equal to 40%, accurately detected by the algorithm before and after COVID-19 diagnosis, and another was found to have a low EF by AI ECG and echocardiography with the COVID-19 diagnosis. The area under the curve for detection of EF less than or equal to 40% was 0.95. This case series suggests that the AI ECG, previously shown to detect ventricular dysfunction in a large general population, may be useful as a screening tool for the detection of cardiac dysfunction in patients with COVID-19.