Age-Stratified Sex Differences in COVID-19 Hospital Outcomes

Introduction: Sex differences in COVID-19 outcomes are well-known and have been ascribed to numerous factors including age-dependent sex hormones. We hypothesize that the protective effect of female sex in hospitalized COVID-19 patients attenuates with age. Method(s): We retrospectively analyzed patients who were hospitalized for COVID-19 infection at three hospitals of the Rush University System for Health (RUSH) (Chicago, IL) between March to December 2020. The primary endpoints were in-hospital mortality and major adverse cardiovascular events (MACE), defined as a composite of acute myocardial infarction, cardiac arrest, acute heart failure, and stroke. Stratified logistic regression was performed to estimate the odds ratios of these endpoints in male compared to female patients by age group (<45, 45-55, 55-65, 65-75, and >=75 years). Result(s): Of 1705 patients (age 58.1+/-16.9 years, 54.3% male, 24.6% White) who were hospitalized for COVID-19 infection, 179 (10.5%) patients experienced in-hospital mortality and 290 (17.0%) patients experienced MACE, respectively. The incidence of these outcomes progressively increased with age in both sexes. In patients <45 years of age, there was a trend towards increased risk for inhospital mortality (aOR 4.47;95% CI: 0.54 - 42.38) and MACE (aOR 2.43;95% CI: 0.97 - 6.10) in men compared to women. However, this trend attenuated with increasing age strata and there was a slight decrease in risk for in-hospital mortality (aOR 0.79;95% CI: 0.39 - 1.58) and MACE (aOR 0.70;95% CI: 0.38 - 1.28) among middle-aged (55-65 years of age) men compared to women. Conclusion(s): In this multi-hospital registry of COVID-19 patients, there was a reverse J-shaped trend in odds of in-hospital mortality and MACE in men compared to women. Female sex appeared to be an independent protective factor for adverse hospital outcomes among patients <55 years of age but not among older patients, suggesting a protective role of premenopausal sex hormones.

IDF21-0311 Pre-Admission, Diabetic Glycemic Control on 60-Day Mortality and Major Adverse Cardiovascular Events from COVID-19

Background: Patients with diabetes mellitus (DM) are at increased risk for intubation, death, and other complications from COVID-19. However, the importance of a patient’s glycemic control preceding the COVID-19 infection is less well understood. Method: From March to November 2020, data from adult patients with confirmed COVID-19 admitted to Rush University System for Health (RUSH) was studied. Patients with both a pre-existing history of diabetes mellitus (DM) and a hemoglobin A1c (HbA1c) measurement during their hospitalization were included. Based on their HbA1c, patients were then divided into 4 groups: adequate glycemic control (≤ 6.5), mild elevation (6.5 – 7.4), intermediate elevation (7.5 – 8.4), and severe elevation (≥ 8.5). Multivariable logistic regression, adjusted for age, body mass index, and pre-existing history of atrial fibrillation, coronary artery disease, hypertension, and chronic obstructive pulmonary disorder, was performed with glycemic control group as a predictor for 60-day mortality and severe COVID-19, which was a composite of 60-day mortality or requiring the intensive care unit, non-invasive positive pressure ventilation, or mechanical ventilation. Major adverse cardiac events (MACE) were defined as nonfatal myocardial injury, nonfatal stroke, or cardiovascular death. Results: Of the 1682 patients admitted, 774 had pre-existing DM, and 534 had HbA1c measurement during their hospitalization. The median HbA1c value was 8.0% (interquartile range 6.6% – 9.9%). In our entire cohort, 75 (14.0%) and 280 (52.4%) patients suffered 60-day mortality and severe COVID-19 infection, respectively. When adjusting for baseline characteristics and comorbidities, patients with mild (adjusted odds ratio [aOR] 2.39 [CI 1.04 – 5.83];p < 0.05) and intermediate (aOR 3.59 [CI 1.49 – 9.12];p < 0.01) HbA1c elevation were at increased risk of 60-day mortality compared to those with adequate glycemic control;no statistically significant difference was present in those with severe elevation (aOR 2.19 [CI 0.95 – 5.44];p = 0.08). Furthermore, only the mild HbA1c elevation group was at increased risk for severe COVID-19 infection (aOR 1.88 [CI 1.06 – 3.38];p < 0.05). Those with intermediate (aOR 1.77 [CI 0.94 – 3.33];p = 0.08) or severe (aOR 1.57 [CI 0.92 – 2.70];p = 0.10) HbA1c elevation were not at higher risk for severe COVID-19 infection. When comparing other 60-day outcomes, there was no difference between the glycemic groups in MACE, life-threatening arrhythmia, deep venous thrombosis, acute renal failure requiring renal replacement therapy, and pulmonary embolism (Table 1). Discussion: In our cohort, patients with DM with an HbA1c of 6.5 – 8.4 were at increased risk of 60-day mortality, while those with an HbA1c of 6.5 – 7.4 were at an increased risk of severe COVID-19 infection.

Clinical validation of automated QTc measurements from single lead ECG using a novel smartwatch

Introduction: A possible side-effect of various medical drugs is prolongation of the electric repolarization of the heart, measured as the corrected QT-interval (QTc). Patients treated with these drugs should be monitored frequently via an ECG to screen for early changes indicating possible life-threating arrythmias. Especially during the Covid-19 pandemic, remote patient monitoring gained importance. The Withings Scanwatch offers automated analysis of the QTc remotely, thereby obviating the need for in-person visits. We aimed to compare automated QTc-measurements using a single lead ECG (SL-ECG) of a novel smartwatch (Withings Scanwatch, SW-ECG) with manual-measured QTc from a nearly simultaneously recorded standard 12-lead ECG. Methods: We enrolled consecutive patients referred to a tertiary hospital for cardiac workup in a prospective, observational study. To obtain a SW-ECG, patients were instructed to keep their index finger on the stainless steel ring on the top case of the smartwatch continuously for 30 seconds The QT-interval was manually interpreted by two blinded, independent cardiologists through the tangent-method, using lead II or V5/ V6. Bazett's formula was used to calculate QTc. Results: We prospectively enrolled 317 patients (48% female, mean age 63.3 ± 17.2 years). The smartwatch was able to automatically measure QTc-intervals in 177 patients (56%). The diagnostic accuracy of SW-ECG for detection of a QTc-interval ≥ 460ms as quantified by the area under the curve (AUC) was 0.91 (95%CI 86.4-95.9). The Bland-Altman analysis resulted in a bias of 6.6ms (95% limit of agreement (LoA) - 58.6ms to 71.9ms) comparing automated QTc measurements via SW-ECG with manual QTc-measurement via 12-lead ECG (Figure 1). In 12 patients (6.9%) the difference between the two measurements was greater than the LoA. Premature ventricular complexes, noise or differences in heart rate were responsible in 8.3%, 83.0% and 8.3%, respectively, for observed outliers. Conclusion: In this clinical validation of a direct-to-consumer smartwatch we found fair to good agreement between automated-SW-ECG QTc-measurements and manual 12-lead-QTc measurements. The SW-ECG, however, was only able to automatically calculate QTc-intervals in one half of all assessed patients. Our work shows, that the automated algorithm of the SW-ECG needs to be improved to be useful in a clinical setting. (Figure Presented).

Vasopressor use and 60-day outcomes among covid-19 patients in the intensive care unit

Introduction: Vasopressor use has been associated with higher mortality rates in patients with COVID-19, the association between the maximum number of concurrent vasopressors with mortality has not yet been studied. Methods: A retrospective cohort study was conducted on patients admitted with COVID-19 to the intensive care unit (ICU) at Rush University System for Health in Illinois between March and October 2020. Multivariable logistic regression, adjusted for age, BMI, history of CAD and diabetes, was used to determine if an increasing number of vasopressors is associated with higher 60-day mortality. Results: A total of 637 patients met the inclusion criteria. Composite 60-day mortality was 28.6%. Of the 637 patients who met inclusion criteria, 338 (53.1%) required the support of at least one vasopressor. When compared to patients with no vasopressor requirement, those who required 1 (adjusted OR [aOR] 3.27, p<0.01), 2 (aOR 4.71, p<0.01), 3 (aOR 26.2, p<0.01), and 4 or 5 (aOR 106.38, p<0.01) vasopressor(s) were at increased risk of 60-day mortality (Figure 1). Additionally, the incidence of mechanical ventilation, venous thromboembolism, ventricular arrhythmia, and new renal replacement therapy increased with additional vasopressor requirement (p < 0.001 for each outcome;Table 1). There was no statistical difference in the incidence of MACE between the groups (p = 0.139). Conclusion: In this cohort, each additional vasopressor added was associated with escalating 60-day mortality. Identifying these high-risk patients can help determine prognostic outcomes and guide decision-making.

In-hospital predictors of 60-day readmission in COVID-19 patients

Introduction: Preventing hospital readmissions can improve a patient's quality of life and decrease healthcare costs. While prior work has focused on pre-existing comorbidities to predict COVID-19 readmissions, the prognostic role of in-hospital data and complications has been less studied. Methods: Data was collected on adult patients diagnosed with COVID-19 and admitted to a multicenter hospital system in Illinois between March and November 2020. Our cohort consisted of COVID-19 hospitalization survivors excluding those discharged to hospice care. Major adverse events (MAEs) were defined as venous thromboembolism (VTE), myocardial injury (troponin greater than upper limit of normal), stroke, new requirement for renal replacement therapy (RRT), life-threatening arrhythmia, or acute heart failure exacerbation. The primary outcome was readmission within 60 days of initial hospitalization. Results: From the 1406 survivors of the index hospitalization, 223 (15.9%) patients were readmitted within 60 days. Those readmitted were older and more likely to have underlying comorbidities including atrial fibrillation, coronary artery disease, and hypertension (Table 1). Length of stay between the readmission and non-readmission groups was trending towards statistical significance (10.52 days vs 8.95 days, p=0.053). Those with one or more MAE during their index hospitalization, when adjusted for age and body mass index, were at an increased risk of readmission (adjusted odds ratio [aOR] 1.90, p<0.01). Readmitted patients were more likely to have VTE during their index hospitalization than those not readmitted (7.2% vs 3.7%, p<0.05). The incidence of new RRT (4.9% vs 2.5%, p=0.083) and myocardial injury (3.6% vs 1.5%, p=0.067) between the groups was also trending towards statistical significance (Table 1). No statistical difference was present between the other individual MAEs;however, this is limited by small sample sizes of certain MAEs. Of the 322 patients with echocardiography during the index admission, 82 (25.5%) were readmitted. In this cohort, left ventricular ejection fraction (LVEF) that was reduced (LVEF <50%) or hyperdynamic (LVEF >65%) was not a statistically significant predictor of readmission (Figure 1). Lastly, discharge disposition was predictive of readmission as those being sent to acute rehab (OR 2.04, p<0.01), long-term acute care (OR 2.58, p<0.01), or skilled nursing facility (OR 2.67, p<0.001) were at higher risk compared to those who were discharged to home (Figure 1). Conclusion: In this cohort, the occurrence of any MAE during index COVID-19 hospitalization, particularly VTE, RRT, and myocardial injury, can be used to predict 60-day readmission. Furthermore, discharge disposition, but not LVEF, demonstrated prognostic value in our cohort. Identifying high risk patients prior to discharge helps health care providers focus resources on patients most likely to be readmitted.

Soluble urokinase-type plasminogen activator receptor (suPAR) is associated with risk for thromboembolic complications and mortality in patients with COVID-19

Background/Introduction: The high prevalence of thromboembolism in patients with COVID-19 causes significant morbidity and mortality. The soluble urokinase-type plasminogen activator receptor (suPAR), a known inflammatory and immune mediator in several renal and cardiovascular conditions, has recently been shown to correlate with acute kidney injury and severe respiratory failure in COVID-19. To date, no study has investigated the association between suPAR and thromboembolism in COVID-19. Purpose: To evaluate associations between suPAR, thromboembolic complications, and mortality in COVID-19. Methods: We conducted a retrospective cohort study of a random sample of 109 patients among those hospitalised at a tertiary medical centre comprising three hospitals between March and June 2020 for COVID-19 who had blood samples collected and stored on admission. Serum suPAR was measured using a commercially available enzyme immunoassay. Baseline (hospital admission) variables extracted from electronic medical records included age, sex, race/ethnicity, body mass index (BMI), history of cardiovascular disease (including deep venous thrombosis [DVT] and pulmonary embolism [PE]), serum creatinine, serum D-dimer, incident DVT/PE, and death during hospitalization. Patients were subsequently grouped by su- PAR quartiles. Associations between suPAR, thromboembolic complications (PE and/or DVT), and overall mortality were evaluated using multivariable logistic regression. Results: Among the 109 patients, mean age was 56 (standard deviation [SD], 16) years, 34 (39%) were women, mean BMI was 35 (SD, 8) kg/m2, 78 (71%) had coexisting cardiovascular disease, median creatinine level was 1.2 (interquartile range [IQR]: 0.8-2.3) mg/dl, median D-dimer level was 1.5 (IQR, 0.8-6.4) μg/ml, and median suPAR level was 10.1 (IQR: 4.1-14.4) pg/mL. Seven (6%) patients were found to have PE, 18 (17%) developed PE/DVT, and 22 (20%) died during the admission (Table). Per quartile higher suPAR level, there was higher risk for PE or DVT (OR=2.02, 95% CI 1.07-3.83, p=0.03). Compared to those in the lowest suPAR quartile, patients in the highest quartile had 11.1 times higher risk for PE/DVT (OR=11.1, 95% CI 1.51-81.8, p=0.02, Figure). SuPAR is also associated with overall mortality, with 2.25 times higher risk of death seen per quartile increase in suPAR level (OR= 2.25, 95% CI 1.24-4.06, p=0.007). Conclusion: Higher suPAR levels at the time of hospital admission is associated with higher risk for thromboembolic complications i.e., PE and DVT, as well as mortality in patients with COVID-19.

Cardiac arrest in patients hospitalized for COVID-19: A tertiary medical center retrospective cohort study

Background/Introduction: Patients with COVID-19 are at increased risk for mortality during hospitalization. Better definition of the incidence, predictors, and outcomes of cardiac arrest during hospitalization for COVID-19 may support early identification and intervention. Purpose: To estimate the incidence of in-hospital cardiac arrest in patients with COVID-19, describe the temporal trends in incidence of and survival after cardiac arrest, summarise characteristics of those who experienced a cardiac arrest, and compare the characteristics of survivors versus nonsurvivors of cardiac arrest. Methods: We conducted a retrospective cohort study of patients admitted for COVID-19 to a tertiary medical center comprising three hospitals between March and November 2020. Data entry is ongoing for more than 2000 patients admitted through 2021. Clinical variables extracted via review of electronic medical records included age, sex, race/ethnicity, body mass index, history of cardiovascular disease (ie., coronary artery disease, congestive heart failure, atrial fibrillation, or cerebrovascular event), other comorbidities included in the Charlson comorbidity index, date of admission, duration of hospitalization, all cardiac arrest events during hospitalization, presenting rhythm during first cardiac arrest, and death. Data were described using summary statistics. Multivariable logistic regression was used to evaluate associations. Results: Among 1666 patients, 107 (6.4%) experienced at least one inhospital cardiac arrest event during hospitalization for COVID-19, of which 25 (23%) survived to hospital discharge. From March to October 2020, there was a decrease in estimated cardiac arrest incidence in-hospital from 8.2% to 3%, whereas estimated survival to hospital discharge after an arrest remained similar at approximately 20% (Figure). Compared to those who did not, patients who experienced in-hospital cardiac arrest were older and more likely to have existing cardiovascular disease, as well as other comorbidities. Similar factors were associated with lower chance of survival after cardiac arrest (Table). Patients with pulseless ventricular tachycardia/ fibrillation (VT/VF) as presenting rhythm in cardiac arrest had better survival to hospital discharge compared to those with other rhythms (OR 3.3, p=0.02). Younger age (per 10 years, OR=0.7, p=0.03) and fewer comorbidities (per one fewer comorbidity, OR=1.5, p=0.05) were associated with better survival after cardiac arrest in multivariable logistic regression. Conclusion: There was a decline in estimated incidence of cardiac arrest during hospitalization for COVID-19 since beginning of pandemic, with survival to hospital discharge after cardiac arrest estimated to be stable at around 20%. Younger age and fewer comorbidities especially cardiovascular disease were associated with better survival after an in-hospital cardiac arrest. (Figure Presented).