Outcomes of patients with end-stage kidney disease hospitalized with COVID-19.

Given the high risk of infection-related mortality, patients with end-stage kidney disease (ESKD) may be at increased risk with COVID-19. To assess this, we compared outcomes of patients with and without ESKD, hospitalized with COVID-19. This was a retrospective study of patients admitted with COVID-19 from 13 New York. hospitals from March 1, 2020, to April 27, 2020, and followed through May 27, 2020. We measured primary outcome (in-hospital death), and secondary outcomes (mechanical ventilation and length of stay), Of 10,482 patients with COVID-19, 419 had ESKD. Patients with ESKD were older, had a greater percentage self-identified as Black, and more comorbid conditions. Patients with ESKD had a higher rate of in-hospital death than those without (31.7% vs 25.4%, odds ratio 1.38, 95% confidence interval 1.12 - 1.70). This increase rate remained after adjusting for demographic and comorbid conditions (adjusted odds ratio 1.37, 1.09 - 1.73). The odds of length of stay of seven or more days was higher in the group with compared to the group without ESKD in both the crude and adjusted analysis (1.62, 1.27 - 2.06; vs 1.57, 1.22 - 2.02, respectively). There was no difference in the odds of mechanical ventilation between the groups. Independent risk factors for in-hospital death for patients with ESKD were increased age, being on a ventilator, lymphopenia, blood urea nitrogen and serum ferritin. Black race was associated with a lower risk of death. Thus, among patients hospitalized with COVID-19, those with ESKD had a higher rate of in-hospital death compared to those without ESKD

Usefulness of Elevated Troponin to Predict Death in Patients with COVID-19 and Myocardial Injury.

Elevations in troponin levels have been shown to predict mortality in patients with coronavirus disease 2019 (COVID-19). The role of inflammation in myocardial injury remains unclear. We sought to determine the association of elevated troponin with mortality in a large, ethnically diverse population of patients hospitalized with COVID-19, and to determine the association of elevated inflammatory markers with increased troponin levels. We reviewed all patients admitted at our health system with COVID-19 from March 1 to April 27, 2020, who had a troponin assessment within 48 hours of admission. We used logistic regression to calculate odds ratios (ORs) for mortality during hospitalization, controlling for demographics, comorbidities, and markers of inflammation. Of 11159 patients hospitalized with COVID-19, 6247 had a troponin assessment within 48 hours. Of these, 4426 (71%) patients had normal, 919 (15%) had mildly elevated, and 902 (14%) had severely elevated troponin. Acute phase and inflammatory markers were significantly elevated in patients with mildly and severely elevated troponin compared to normal troponin. Patients with elevated troponin had significantly increased odds of death for mildly elevated compared to normal troponin (adjusted OR, 2.06; 95% CI, 1.68-2.53; P \u3c .001) and for severely elevated compared to normal troponin (OR, 4.51; 95% CI, 3.66-5.54; P \u3c .001) independently of elevation in inflammatory markers. In conclusion, patients hospitalized with COVID-19 and elevated troponin had markedly increased mortality compared to patients with normal troponin levels. This risk was independent of cardiovascular comorbidities and elevated markers of inflammation

Atrial fibrillation is an independent predictor for in-hospital mortality in patients admitted with SARS-CoV-2 infection.

Background Atrial fibrillation (AF) is the most encountered arrhythmia and has been associated with worse in-hospital outcomes. Objective This study was to determine the incidence of AF in patients hospitalized with coronavirus disease 2019 (COVID-19) as well as its impact on in-hospital mortality. Methods Patients hospitalized with a positive COVID-19 polymerase chain reaction test between March 1 and April 27, 2020, were identified from the common medical record system of 13 Northwell Health hospitals. Natural language processing search algorithms were used to identify and classify AF. Patients were classified as having AF or not. AF was further classified as new-onset AF vs history of AF. Results AF occurred in 1687 of 9564 patients (17.6%). Of those, 1109 patients (65.7%) had new-onset AF. Propensity score matching of 1238 pairs of patients with AF and without AF showed higher in-hospital mortality in the AF group (54.3% vs 37.2%; P \u3c .0001). Within the AF group, propensity score matching of 500 pairs showed higher in-hospital mortality in patients with new-onset AF as compared with those with a history of AF (55.2% vs 46.8%; P = .009). The risk ratio of in-hospital mortality for new-onset AF in patients with sinus rhythm was 1.56 (95% confidence interval 1.42-1.71; P \u3c .0001). The presence of cardiac disease was not associated with a higher risk of in-hospital mortality in patients with AF (P = .1). Conclusion In patients hospitalized with COVID-19, 17.6% experienced AF. AF, particularly new-onset, was an independent predictor of in-hospital mortality

Comparative Survival Analysis of Immunomodulatory Therapy for Coronavirus Disease 2019 Cytokine Storm.

BACKGROUND:Cytokine storm is a marker of coronavirus disease 2019 (COVID-19) illness severity and increased mortality. Immunomodulatory treatments have been repurposed to improve mortality outcomes. RESEARCH QUESTION:Do immunomodulatory therapies improve survival in patients with COVID-19 cytokine storm (CCS)? STUDY DESIGN AND METHODS:We conducted a retrospective analysis of electronic health records across the Northwell Health system. COVID-19 patients hospitalized between March 1, 2020, and April 24, 2020, were included. CCS was defined by inflammatory markers: ferritin, \u3e 700 ng/mL; C-reactive protein (CRP), \u3e 30 mg/dL; or lactate dehydrogenase (LDH), \u3e 300 U/L. Patients were subdivided into six groups: no immunomodulatory treatment (standard of care) and five groups that received either corticosteroids, anti-IL-6 antibody (tocilizumab), or anti-IL-1 therapy (anakinra) alone or in combination with corticosteroids. The primary outcome was hospital mortality. RESULTS:Five thousand seven hundred seventy-six patients met the inclusion criteria. The most common comorbidities were hypertension (44%-59%), diabetes (32%-46%), and cardiovascular disease (5%-14%). Patients most frequently met criteria with high LDH (76.2%) alone or in combination, followed by ferritin (63.2%) and CRP (8.4%). More than 80% of patients showed an elevated D-dimer. Patients treated with corticosteroids and tocilizumab combination showed lower mortality compared with patients receiving standard-of-care (SoC) treatment (hazard ratio [HR], 0.44; 95% CI, 0.35-0.55; P \u3c .0001) and with patients treated with corticosteroids alone (HR, 0.66; 95% CI, 0.53-0.83; P = .004) or in combination with anakinra (HR, 0.64; 95% CI, 0.50-0.81; P = .003). Corticosteroids when administered alone (HR, 0.66; 95% CI, 0.57-0.76; P \u3c .0001) or in combination with tocilizumab (HR, 0.43; 95% CI, 0.35-0.55; P \u3c .0001) or anakinra (HR, 0.68; 95% CI, 0.57-0.81; P \u3c .0001) improved hospital survival compared with SoC treatment. INTERPRETATION:The combination of corticosteroids with tocilizumab showed superior survival outcome when compared with SoC treatment and treatment with corticosteroids alone or in combination with anakinra. Furthermore, corticosteroid use either alone or in combination with tocilizumab or anakinra was associated with reduced hospital mortality for patients with CCS compared with patients receiving SoC treatment

Machine learning to assist clinical decision-making during the COVID-19 pandemic.

Background:The number of cases from the coronavirus disease 2019 (COVID-19) global pandemic has overwhelmed existing medical facilities and forced clinicians, patients, and families to make pivotal decisions with limited time and information. Main body:While machine learning (ML) methods have been previously used to augment clinical decisions, there is now a demand for Emergency ML. Throughout the patient care pathway, there are opportunities for ML-supported decisions based on collected vitals, laboratory results, medication orders, and comorbidities. With rapidly growing datasets, there also remain important considerations when developing and validating ML models. Conclusion:This perspective highlights the utility of evidence-based prediction tools in a number of clinical settings, and how similar models can be deployed during the COVID-19 pandemic to guide hospital frontlines and healthcare administrators to make informed decisions about patient care and managing hospital volume

Gastrointestinal Bleeding in Hospitalized COVID-19 Patients: A Propensity Score Matched Cohort Study.

Background and aims Gastrointestinal (GI) bleeding has been observed among patients hospitalized with COVID-19. Recently anticoagulation has shown to decrease mortality, but it is unclear if this contributes to increased GI bleeding. The aims of this study are: 1) to examine if there are risk factors for GI bleeding in COVID-19 patients 2) to study whether there is a mortality difference between hospitalized patients with COVID-19 with and without GI bleeding. Methods This is a propensity score matched case-control study from a large health system in the New York metropolitan area between March 1st and April 27th. COVID-19 patients with GI bleeding were matched 1:1 to COVID-19 patients without bleeding using a propensity score that took into account comorbidities, demographics, GI bleeding risk factors, and length of stay. Results Of 11, 158 hospitalized with COVID-19, 314 patients were identified with GI bleeding. The point prevalence of GI bleeding was 3%. There were no identifiable risk factors for GI bleeding. Use of anticoagulation medication or antiplatelet agents were not associated with increased risk of GI bleeding in COVID-19 patients. For patients that developed a GI bleed during the hospitalization, there was an increased mortality risk in the GI bleeding group (OR 1.58, p=0.02). Conclusion Use of anticoagulation or antiplatelet agents were not risk factors for GI bleeding in a large cohort of hospitalized COVID-19 patients. Those with GI bleeding during the hospitalization had increased mortality

Body Mass Index as a Risk Factor for Clinical Outcomes in Patients Hospitalized with COVID-19 in New York.

OBJECTIVE:To examine the association between body mass indexes (BMI) and clinical outcomes among patients with COVID-19 infection. METHODS:We included 10,861 patients with COVID-19 infection admitted to the Northwell Health system hospitals during the period of March 1 to April 27, 2020. BMI was classified as: underweight, normal, overweight, obesity class I, II, and III. Primary outcomes are invasive mechanical ventilation (IMV) and death. RESULTS:There were 243 (2.2%) underweight, 2,507 (23.1%) normal weight, 4,021 (37.0%) overweight, 2,345 (21.6%) obesity class I, 990 (9.1%) obesity class II, and 755 (7.0%) obesity class III patients. Patients who are overweight (OR=1.27[95% CI, 1.11-1.46]), obesity class I (OR=1.48 [95% CI, 1.27-1.72]), obesity class II (OR=1.89[95% CI, 1.56-2.28]), and obesity class III (OR=2.31 [95% CI, 1.88-2.85]) had increased risk of requiring IMV. Underweight and obesity classes II and III were statistically associated with death (OR=1.44 [95% CI, 1.08-1.92]; OR=1.25 [95% CI 1.03-1.52]; OR=1.61 [95% CI 1.30-2.00], respectively). Among patients who were on IMV, BMI was not associated with inpatient deaths. CONCLUSION:Patients who are underweight or with obesity are at a risk for mechanical ventilation and death, suggesting pulmonary complications (indicated by IMV) is a significant contributor for poor outcomes in COVID-19 infection