Outcome Study Among Hospitalized Symptomatic COVID-19 Patients Who Required Chronic vs. Acute Hemodialysis (HD)

Background: Mortality among patients admitted for COVID19 infections who develop acute kidney injury (AKI) and require dialysis support has been reported to be >=60-70%. Data are lacking regarding how chronic HD patients with an impaired immune system may: 1) mount an inflammatory response to COVID19 infection and 2) survive the infection compared with those who require acute HD for AKI. We evaluated ferritin levels, hospital length of stay (LOS), and mortality rates among chronic HD patients and those who required acute HD during hospitalization for COVID19-related symptoms. Method(s): Inclusion: All patients hospitalized for COVID19-related symptoms who received HD at OV-UCLA Medical Center from January to December 2020. Data collection: Baseline characteristics: age, gender, body mass index (BMI), comorbidities (diabetes mellitus, hypertension, underlying chronic obstructive lung disease or asthma, history of malignancy, and heart failure). Medications: use of any inhibitor of the renin angiotensin aldosterone system. Lab studies: albumin, ferritin, basic chemistry. Hospital LOS and death from any cause. Data analysis: Ferritin levels, hospital LOS, and mortality among chronic and acute HD patients. Result(s): See Figure Conclusion(s): Among our hospitalized symptomatic COVID19 patients requiring HD support: 1. Ferritin levels were comparable between chronic HD and previously nondialysis patients who required acute HD support. 2. Chronic HD patients had shorter hospital LOS compared with those requiring acute HD (6.2 +/- 5.7d vs. 35.3 +/- 31.3d). The 2 groups had similar serum sodium, albumin, and ferritin levels. 3. Mortality was <5% (compared with 1.5% for the US population).among chronic HD patients and 58% among those who developed AKI requring acute HD. 4. Incidentally, BMI among current COVID19 cohort was 30.6 +/- 8.8 compared with 26.4 +/- 5.8 Kg/m2 from our 70 non- COVID dialysis patients included in a different study (p-value <0.001). (Figure Presented).

Crescentic IgA Nephropathy (cIgAN) in a Patient With COVID-19 Infection

Introduction: COVID19 infection has been linked to various glomerulonephropathies (GN) including collapsing focal segmental glomerulosclerosis, pauciimmune crescentic glomerulonephritis, and possibly minimal change disease and IgA nephropathy. Case Description: A 57-year-old obese man with hypertension, hyperlipidemia, prediabetes, chronic obstructive pulmonary disease, illicit drug use, status post Pfizer COVID vaccine (1st dose 4m prior, 2nd dose 3w later), and COVID19 infection 7w prior, presented with an acute onset purpuric rash that began from bilateral hands and feet and progressed to arms and legs. Patient denied joint pain or abdominal discomfort. Initial studies: Serum creatinine (Cr) 4.17 mg/dL (2.37 mg/dL 2w prior, baseline 0.93 mg/dL 4m prior). Urinalysis: > 50 red blood cells/high power field;Urine protein/Cr 4g/g, albumin/Cr >3g/g;Negative: HIV, ANCA, ANA, antiGBM, complements. Chest CT: Bilateral multifocal consolidative opacities concerning for aspiration, multifocal bacterial or viral pneumonia, or atypical presentation of COVID19 pneumonia. Skin biopsy: Leukocytoclastic vasculitis;No immunoreactants detected. Patient suffered from rapid respiratory deterioration, multiple hypotensive episodes, and acute kidney injury requiring mechanical ventilation and dialysis support. Kidney biopsy: IgA dominant immune complex mediated glomerulonephritis with focal/remote fibrous crescents;acute tubular injury. Treatment: Intravenous methylprednisolone 250 mg x 3d, followed by oral prednisone course. Patient recovered adequate function after 6w and was able to discontinue dialysis. Discussion(s): COVID19 infection-related inflammatory response may precipitate GN in susceptible individuals. Crescentic IgAN is known to be associated with acute inflammatory conditions involving lungs, gastrointestinal tract, and skin. The timeline for the development of cIgAN herein raises suspicion for COVID19 infection/pneumonia as the inciting event. (Figure Presented).

Effect of increased number of COVID-19 tests using supervised machine learning models

Machine learning is widely being used in medical field for disease diagnostics and research.The area of machine learning is mainly classified into 3 parts: supervised, unsupervised and reinforcement learning.Supervised machine learning (ML) algorithms are used in this paper for modeling and showing the impact of increased testing on the number of daily confirmed cases of COVID-19. The algorithms used to carry out this study are decision tree regression and random forest regression. Machine learning for modeling has proven to be significant for forecasting and hence decision making over the future course of actions. In this paper, Gaussian process regression has been used for modeling as well as forecasting the daily confirmed cases in South Korea. The results obtained show that if the number of tests conducted is increased to the population of South Korea, approximately equal to 51, 286, 183, the peak in the daily cases is obtained earlier and hence the overall number of daily cases is less compared to current cases.