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      A multi-center retrospective cohort study defines the spectrum of kidney pathology in Coronavirus 2019 Disease (COVID-19)

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      1 , 1 , 2 , 1 , 3 , 1 , 4 , 1 , 1 , 5 , 6 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 7 , 8 , 9 , 10 , 10 , 11 , 12 , 7 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 21 , 30 , 31 , 32 , 33 , 13 , 34 , 35 , 11 , 36 , 37 , 38 , 1 ,
      Kidney International
      International Society of Nephrology. Published by Elsevier Inc.
      COVID-19, coronavirus, SARS-CoV-2, kidney biopsy, renal pathology, acute kidney injury

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          Abstract

          Kidney failure is common in patients with Coronavirus Disease-19 (COVID-19) resulting in increased morbidity and mortality. In an international collaboration, 284 kidney biopsies were evaluated to improve understanding of kidney disease in COVID-19. Diagnoses were compared to five years of 63,575 native biopsies prior to the pandemic and 13,955 allograft biopsies to identify diseases increased in patients with COVID-19. Genotyping for APOL1 G1 and G2 alleles was performed in 107 African American and Hispanic patients. Immunohistochemistry for SARS-CoV-2 was utilized to assess direct viral infection in 273 cases along with clinical information at the time of biopsy. The leading indication for native biopsy was acute kidney injury (45.4%), followed by proteinuria with or without concurrent acute kidney injury (42.6%). There were more African American patients (44.6%) than patients of other ethnicities. The most common diagnosis in native biopsies was collapsing glomerulopathy (25.8%) which associated with high-risk APOL1 genotypes in 91.7% of cases. Compared to the five-year biopsy database, the frequency of myoglobin cast nephropathy and proliferative glomerulonephritis with monoclonal IgG deposits was also increased in patients with COVID-19 (3.3% and 1.7%, respectively), while there was a reduced frequency of chronic conditions (including diabetes mellitus, IgA nephropathy, and arterionephrosclerosis) as the primary diagnosis. In transplants, the leading indication was acute kidney injury (86.4%), for which rejection was the predominant diagnosis (61.4%). Direct SARS-CoV-2 viral infection was not identified. Thus, our multi-center large case series identified kidney diseases that disproportionately affect patients with COVID-19, demonstrated a high frequency of APOL1 high-risk genotypes within this group, with no evidence of direct viral infection within the kidney.

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          Hospitalization Rates and Characteristics of Patients Hospitalized with Laboratory-Confirmed Coronavirus Disease 2019 — COVID-NET, 14 States, March 1–30, 2020

          Since SARS-CoV-2, the novel coronavirus that causes coronavirus disease 2019 (COVID-19), was first detected in December 2019 ( 1 ), approximately 1.3 million cases have been reported worldwide ( 2 ), including approximately 330,000 in the United States ( 3 ). To conduct population-based surveillance for laboratory-confirmed COVID-19–associated hospitalizations in the United States, the COVID-19–Associated Hospitalization Surveillance Network (COVID-NET) was created using the existing infrastructure of the Influenza Hospitalization Surveillance Network (FluSurv-NET) ( 4 ) and the Respiratory Syncytial Virus Hospitalization Surveillance Network (RSV-NET). This report presents age-stratified COVID-19–associated hospitalization rates for patients admitted during March 1–28, 2020, and clinical data on patients admitted during March 1–30, 2020, the first month of U.S. surveillance. Among 1,482 patients hospitalized with COVID-19, 74.5% were aged ≥50 years, and 54.4% were male. The hospitalization rate among patients identified through COVID-NET during this 4-week period was 4.6 per 100,000 population. Rates were highest (13.8) among adults aged ≥65 years. Among 178 (12%) adult patients with data on underlying conditions as of March 30, 2020, 89.3% had one or more underlying conditions; the most common were hypertension (49.7%), obesity (48.3%), chronic lung disease (34.6%), diabetes mellitus (28.3%), and cardiovascular disease (27.8%). These findings suggest that older adults have elevated rates of COVID-19–associated hospitalization and the majority of persons hospitalized with COVID-19 have underlying medical conditions. These findings underscore the importance of preventive measures (e.g., social distancing, respiratory hygiene, and wearing face coverings in public settings where social distancing measures are difficult to maintain) † to protect older adults and persons with underlying medical conditions, as well as the general public. In addition, older adults and persons with serious underlying medical conditions should avoid contact with persons who are ill and immediately contact their health care provider(s) if they have symptoms consistent with COVID-19 (https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html) ( 5 ). Ongoing monitoring of hospitalization rates, clinical characteristics, and outcomes of hospitalized patients will be important to better understand the evolving epidemiology of COVID-19 in the United States and the clinical spectrum of disease, and to help guide planning and prioritization of health care system resources. COVID-NET conducts population-based surveillance for laboratory-confirmed COVID-19–associated hospitalizations among persons of all ages in 99 counties in 14 states (California, Colorado, Connecticut, Georgia, Iowa, Maryland, Michigan, Minnesota, New Mexico, New York, Ohio, Oregon, Tennessee, and Utah), distributed across all 10 U.S Department of Health and Human Services regions. § The catchment area represents approximately 10% of the U.S. population. Patients must be residents of a designated COVID-NET catchment area and hospitalized within 14 days of a positive SARS-CoV-2 test to meet the surveillance case definition. Testing is requested at the discretion of treating health care providers. Laboratory-confirmed SARS-CoV-2 is defined as a positive result by any test that has received Emergency Use Authorization for SARS-CoV-2 testing. ¶ COVID-NET surveillance officers in each state identify cases through active review of notifiable disease and laboratory databases and hospital admission and infection control practitioner logs. Weekly age-stratified hospitalization rates are estimated using the number of catchment area residents hospitalized with laboratory-confirmed COVID-19 as the numerator and National Center for Health Statistics vintage 2018 bridged-race postcensal population estimates for the denominator.** As of April 3, 2020, COVID-NET hospitalization rates are being published each week at https://gis.cdc.gov/grasp/covidnet/COVID19_3.html. For each case, trained surveillance officers conduct medical chart abstractions using a standard case report form to collect data on patient characteristics, underlying medical conditions, clinical course, and outcomes. Chart reviews are finalized once patients have a discharge disposition. COVID-NET surveillance was initiated on March 23, 2020, with retrospective case identification of patients admitted during March 1–22, 2020, and prospective case identification during March 23–30, 2020. Clinical data on underlying conditions and symptoms at admission are presented through March 30; hospitalization rates are updated weekly and, therefore, are presented through March 28 (epidemiologic week 13). The COVID-19–associated hospitalization rate among patients identified through COVID-NET for the 4-week period ending March 28, 2020, was 4.6 per 100,000 population (Figure 1). Hospitalization rates increased with age, with a rate of 0.3 in persons aged 0–4 years, 0.1 in those aged 5–17 years, 2.5 in those aged 18–49 years, 7.4 in those aged 50–64 years, and 13.8 in those aged ≥65 years. Rates were highest among persons aged ≥65 years, ranging from 12.2 in those aged 65–74 years to 17.2 in those aged ≥85 years. More than half (805; 54.4%) of hospitalizations occurred among men; COVID-19-associated hospitalization rates were higher among males than among females (5.1 versus 4.1 per 100,000 population). Among the 1,482 laboratory-confirmed COVID-19–associated hospitalizations reported through COVID-NET, six (0.4%) each were patients aged 0–4 years and 5–17 years, 366 (24.7%) were aged 18–49 years, 461 (31.1%) were aged 50–64 years, and 643 (43.4%) were aged ≥65 years. Among patients with race/ethnicity data (580), 261 (45.0%) were non-Hispanic white (white), 192 (33.1%) were non-Hispanic black (black), 47 (8.1%) were Hispanic, 32 (5.5%) were Asian, two (0.3%) were American Indian/Alaskan Native, and 46 (7.9%) were of other or unknown race. Rates varied widely by COVID-NET surveillance site (Figure 2). FIGURE 1 Laboratory-confirmed coronavirus disease 2019 (COVID-19)–associated hospitalization rates,* by age group — COVID-NET, 14 states, † March 1–28, 2020 Abbreviation: COVID-NET = Coronavirus Disease 2019–Associated Hospitalization Surveillance Network. * Number of patients hospitalized with COVID-19 per 100,000 population. † Counties included in COVID-NET surveillance: California (Alameda, Contra Costa, and San Francisco counties); Colorado (Adams, Arapahoe, Denver, Douglas, and Jefferson counties); Connecticut (New Haven and Middlesex counties); Georgia (Clayton, Cobb, DeKalb, Douglas, Fulton, Gwinnett, Newton, and Rockdale counties); Iowa (one county represented); Maryland (Allegany, Anne Arundel, Baltimore, Baltimore City, Calvert, Caroline, Carroll, Cecil, Charles, Dorchester, Frederick, Garrett, Harford, Howard, Kent, Montgomery, Prince George’s, Queen Anne’s, St. Mary’s, Somerset, Talbot, Washington, Wicomico, and Worcester counties); Michigan (Clinton, Eaton, Genesee, Ingham, and Washtenaw counties); Minnesota (Anoka, Carver, Dakota, Hennepin, Ramsey, Scott, and Washington counties); New Mexico (Bernalillo, Chaves, Dona Ana, Grant, Luna, San Juan, and Santa Fe counties); New York (Albany, Columbia, Genesee, Greene, Livingston, Monroe, Montgomery, Ontario, Orleans, Rensselaer, Saratoga, Schenectady, Schoharie, Wayne, and Yates counties); Ohio (Delaware, Fairfield, Franklin, Hocking, Licking, Madison, Morrow, Perry, Pickaway and Union counties); Oregon (Clackamas, Multnomah, and Washington counties); Tennessee (Cheatham, Davidson, Dickson, Robertson, Rutherford, Sumner, Williamson, and Wilson counties); and Utah (Salt Lake County). The figure is a bar chart showing laboratory-confirmed COVID-19–associated hospitalization rates, by age group, in 14 states during March 1–28, 2020 according to the Coronavirus Disease 2019–Associated Hospitalization Surveillance Network. FIGURE 2 Laboratory-confirmed coronavirus disease 2019 (COVID-19)–associated hospitalization rates,* by surveillance site † — COVID-NET, 14 states, March 1–28, 2020 Abbreviation: COVID-NET = Coronavirus Disease 2019–Associated Hospitalization Surveillance Network. * Number of patients hospitalized with COVID-19 per 100,000 population. † Counties included in COVID-NET surveillance: California (Alameda, Contra Costa, and San Francisco counties); Colorado (Adams, Arapahoe, Denver, Douglas, and Jefferson counties); Connecticut (New Haven and Middlesex counties); Georgia (Clayton, Cobb, DeKalb, Douglas, Fulton, Gwinnett, Newton, and Rockdale counties); Iowa (one county represented); Maryland (Allegany, Anne Arundel, Baltimore, Baltimore City, Calvert, Caroline, Carroll, Cecil, Charles, Dorchester, Frederick, Garrett, Harford, Howard, Kent, Montgomery, Prince George’s, Queen Anne’s, St. Mary’s, Somerset, Talbot, Washington, Wicomico, and Worcester counties); Michigan (Clinton, Eaton, Genesee, Ingham, and Washtenaw counties); Minnesota (Anoka, Carver, Dakota, Hennepin, Ramsey, Scott, and Washington counties); New Mexico (Bernalillo, Chaves, Dona Ana, Grant, Luna, San Juan, and Santa Fe counties); New York (Albany, Columbia, Genesee, Greene, Livingston, Monroe, Montgomery, Ontario, Orleans, Rensselaer, Saratoga, Schenectady, Schoharie, Wayne, and Yates counties); Ohio (Delaware, Fairfield, Franklin, Hocking, Licking, Madison, Morrow, Perry, Pickaway and Union counties); Oregon (Clackamas, Multnomah, and Washington counties); Tennessee (Cheatham, Davidson, Dickson, Robertson, Rutherford, Sumner, Williamson, and Wilson counties); and Utah (Salt Lake County). The figure is a bar chart showing laboratory-confirmed COVID-19–associated hospitalization rates, by surveillance site, in 14 states during March 1–28, 2020 according to the Coronavirus Disease 2019–Associated Hospitalization Surveillance Network. During March 1–30, underlying medical conditions and symptoms at admission were reported through COVID-NET for approximately 180 (12.1%) hospitalized adults (Table); 89.3% had one or more underlying conditions. The most commonly reported were hypertension (49.7%), obesity (48.3%), chronic lung disease (34.6%), diabetes mellitus (28.3%), and cardiovascular disease (27.8%). Among patients aged 18–49 years, obesity was the most prevalent underlying condition, followed by chronic lung disease (primarily asthma) and diabetes mellitus. Among patients aged 50–64 years, obesity was most prevalent, followed by hypertension and diabetes mellitus; and among those aged ≥65 years, hypertension was most prevalent, followed by cardiovascular disease and diabetes mellitus. Among 33 females aged 15–49 years hospitalized with COVID-19, three (9.1%) were pregnant. Among 167 patients with available data, the median interval from symptom onset to admission was 7 days (interquartile range [IQR] = 3–9 days). The most common signs and symptoms at admission included cough (86.1%), fever or chills (85.0%), and shortness of breath (80.0%). Gastrointestinal symptoms were also common; 26.7% had diarrhea, and 24.4% had nausea or vomiting. TABLE Underlying conditions and symptoms among adults aged ≥18 years with coronavirus disease 2019 (COVID-19)–associated hospitalizations — COVID-NET, 14 states,* March 1–30, 2020† Underlying condition Age group (yrs), no./total no. (%) Overall 18–49 50–64 ≥65 years Any underlying condition 159/178 (89.3) 41/48 (85.4) 51/59 (86.4) 67/71 (94.4) Hypertension 79/159 (49.7) 7/40 (17.5) 27/57 (47.4) 45/62 (72.6) Obesity§ 73/151 (48.3) 23/39 (59.0) 25/51 (49.0) 25/61 (41.0) Chronic metabolic disease¶ 60/166 (36.1) 10/46 (21.7) 21/56 (37.5) 29/64 (45.3)    Diabetes mellitus 47/166 (28.3) 9/46 (19.6) 18/56 (32.1) 20/64 (31.3) Chronic lung disease 55/159 (34.6) 16/44 (36.4) 15/53 (28.3) 24/62 (38.7)    Asthma 27/159 (17.0) 12/44 (27.3) 7/53 (13.2) 8/62 (12.9)    Chronic obstructive pulmonary disease 17/159 (10.7) 0/44 (0.0) 3/53 (5.7) 14/62 (22.6) Cardiovascular disease** 45/162 (27.8) 2/43 (4.7) 11/56 (19.6) 32/63 (50.8)    Coronary artery disease 23/162 (14.2) 0/43 (0.0) 7/56 (12.5) 16/63 (25.4)    Congestive heart failure 11/162 (6.8) 2/43 (4.7) 3/56 (5.4) 6/63 (9.5) Neurologic disease 22/157 (14.0) 4/42 (9.5) 4/55 (7.3) 14/60 (23.3) Renal disease 20/153 (13.1) 3/41 (7.3) 2/53 (3.8) 15/59 (25.4) Immunosuppressive condition 15/156 (9.6) 5/43 (11.6) 4/54 (7.4) 6/59 (10.2) Gastrointestinal/Liver disease 10/152 (6.6) 4/42 (9.5) 0/54 (0.0) 6/56 (10.7) Blood disorder 9/156 (5.8) 1/43 (2.3) 1/55 (1.8) 7/58 (12.1) Rheumatologic/Autoimmune disease 3/154 (1.9) 1/42 (2.4) 0/54 (0.0) 2/58 (3.4) Pregnancy†† 3/33 (9.1) 3/33 (9.1) N/A N/A Symptom §§ Cough 155/180 (86.1) 43/47 (91.5) 54/60 (90.0) 58/73 (79.5) Fever/Chills 153/180 (85.0) 38/47 (80.9) 53/60 (88.3) 62/73 (84.9) Shortness of breath 144/180 (80.0) 40/47 (85.1) 50/60 (83.3) 54/73 (74.0) Myalgia 62/180 (34.4) 20/47 (42.6) 23/60 (38.3) 19/73 (26.0) Diarrhea 48/180 (26.7) 10/47 (21.3) 17/60 (28.3) 21/73 (28.8) Nausea/Vomiting 44/180 (24.4) 12/47 (25.5) 17/60 (28.3) 15/73 (20.5) Sore throat 32/180 (17.8) 8/47 (17.0) 13/60 (21.7) 11/73 (15.1) Headache 29/180 (16.1) 10/47 (21.3) 12/60 (20.0) 7/73 (9.6) Nasal congestion/Rhinorrhea 29/180 (16.1) 8/47 (17.0) 13/60 (21.7) 8/73 (11.0) Chest pain 27/180 (15.0) 9/47 (19.1) 13/60 (21.7) 5/73 (6.8) Abdominal pain 15/180 (8.3) 6/47 (12.8) 6/60 (10.0) 3/73 (4.1) Wheezing 12/180 (6.7) 3/47 (6.4) 2/60 (3.3) 7/73 (9.6) Altered mental status/Confusion 11/180 (6.1) 3/47 (6.4) 2/60 (3.3) 6/73 (8.2) Abbreviations: COVID-NET = Coronavirus Disease 2019–Associated Hospitalization Surveillance Network; N/A = not applicable. * Counties included in COVID-NET surveillance: California (Alameda, Contra Costa, and San Francisco counties); Colorado (Adams, Arapahoe, Denver, Douglas, and Jefferson counties); Connecticut (New Haven and Middlesex counties); Georgia (Clayton, Cobb, DeKalb, Douglas, Fulton, Gwinnett, Newton, and Rockdale counties); Iowa (one county represented); Maryland (Allegany, Anne Arundel, Baltimore, Baltimore City, Calvert, Caroline, Carroll, Cecil, Charles, Dorchester, Frederick, Garrett, Harford, Howard, Kent, Montgomery, Prince George’s, Queen Anne’s, St. Mary’s, Somerset, Talbot, Washington, Wicomico, and Worcester counties); Michigan (Clinton, Eaton, Genesee, Ingham, and Washtenaw counties); Minnesota (Anoka, Carver, Dakota, Hennepin, Ramsey, Scott, and Washington counties); New Mexico (Bernalillo, Chaves, Dona Ana, Grant, Luna, San Juan, and Santa Fe counties); New York (Albany, Columbia, Genesee, Greene, Livingston, Monroe, Montgomery, Ontario, Orleans, Rensselaer, Saratoga, Schenectady, Schoharie, Wayne, and Yates counties); Ohio (Delaware, Fairfield, Franklin, Hocking, Licking, Madison, Morrow, Perry, Pickaway and Union counties); Oregon (Clackamas, Multnomah, and Washington counties); Tennessee (Cheatham, Davidson, Dickson, Robertson, Rutherford, Sumner, Williamson, and Wilson counties); and Utah (Salt Lake County). † COVID-NET included data for one child aged 5–17 years with underlying medical conditions and symptoms at admission; data for this child are not included in this table. This child was reported to have chronic lung disease (asthma). Symptoms included fever, cough, gastrointestinal symptoms, shortness of breath, chest pain, and a sore throat on admission. § Obesity is defined as calculated body mass index (BMI) ≥30 kg/m2, and if BMI is missing, by International Classification of Diseases discharge diagnosis codes. Among 73 patients with obesity, 51 (69.9%) had obesity defined as BMI 30–<40 kg/m2, and 22 (30.1%) had severe obesity defined as BMI ≥40 kg/m2. ¶ Among the 60 patients with chronic metabolic disease, 45 had diabetes mellitus only, 13 had thyroid dysfunction only, and two had diabetes mellitus and thyroid dysfunction. ** Cardiovascular disease excludes hypertension. †† Restricted to women aged 15–49 years. §§ Symptoms were collected through review of admission history and physical exam notes in the medical record and might be determined by subjective or objective findings. In addition to the symptoms in the table, the following less commonly reported symptoms were also noted for adults with information on symptoms (180): hemoptysis/bloody sputum (2.2%), rash (1.1%), conjunctivitis (0.6%), and seizure (0.6%). Discussion During March 1–28, 2020, the overall laboratory-confirmed COVID-19–associated hospitalization rate was 4.6 per 100,000 population; rates increased with age, with the highest rates among adults aged ≥65 years. Approximately 90% of hospitalized patients identified through COVID-NET had one or more underlying conditions, the most common being obesity, hypertension, chronic lung disease, diabetes mellitus, and cardiovascular disease. Using the existing infrastructure of two respiratory virus surveillance platforms, COVID-NET was implemented to produce robust, weekly, age-stratified hospitalization rates using standardized data collection methods. These data are being used, along with data from other surveillance platforms (https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview.html), to monitor COVID-19 disease activity and severity in the United States. During the first month of surveillance, COVID-NET hospitalization rates ranged from 0.1 per 100,000 population in persons aged 5–17 years to 17.2 per 100,000 population in adults aged ≥85 years, whereas cumulative influenza hospitalization rates during the first 4 weeks of each influenza season (epidemiologic weeks 40–43) over the past 5 seasons have ranged from 0.1 in persons aged 5–17 years to 2.2–5.4 in adults aged ≥85 years ( 6 ). COVID-NET rates during this first 4-week period of surveillance are preliminary and should be interpreted with caution; given the rapidly evolving nature of the COVID-19 pandemic, rates are expected to increase as additional cases are identified and as SARS-CoV-2 testing capacity in the United States increases. In the COVID-NET catchment population, approximately 49% of residents are male and 51% of residents are female, whereas 54% of COVID-19-associated hospitalizations occurred in males and 46% occurred in females. These data suggest that males may be disproportionately affected by COVID-19 compared with females. Similarly, in the COVID-NET catchment population, approximately 59% of residents are white, 18% are black, and 14% are Hispanic; however, among 580 hospitalized COVID-19 patients with race/ethnicity data, approximately 45% were white, 33% were black, and 8% were Hispanic, suggesting that black populations might be disproportionately affected by COVID-19. These findings, including the potential impact of both sex and race on COVID-19-associated hospitalization rates, need to be confirmed with additional data. Most of the hospitalized patients had underlying conditions, some of which are recognized to be associated with severe COVID-19 disease, including chronic lung disease, cardiovascular disease, diabetes mellitus ( 5 ). COVID-NET does not collect data on nonhospitalized patients; thus, it was not possible to compare the prevalence of underlying conditions in hospitalized versus nonhospitalized patients. Many of the documented underlying conditions among hospitalized COVID-19 patients are highly prevalent in the United States. According to data from the National Health and Nutrition Examination Survey, hypertension prevalence among U.S. adults is 29% overall, ranging from 7.5%–63% across age groups ( 7 ), and age-adjusted obesity prevalence is 42% (range across age groups = 40%–43%) ( 8 ). Among hospitalized COVID-19 patients, hypertension prevalence was 50% (range across age groups = 18%–73%), and obesity prevalence was 48% (range across age groups = 41%–59%). In addition, the prevalences of several underlying conditions identified through COVID-NET were similar to those for hospitalized influenza patients identified through FluSurv-NET during influenza seasons 2014–15 through 2018–19: 41%–51% of patients had cardiovascular disease (excluding hypertension), 39%–45% had chronic metabolic disease, 33%–40% had obesity, and 29%–31% had chronic lung disease ( 6 ). Data on hypertension are not collected by FluSurv-NET. Among women aged 15–49 years hospitalized with COVID-19 and identified through COVID-NET, 9% were pregnant, which is similar to an estimated 9.9% of the general population of women aged 15–44 years who are pregnant at any given time based on 2010 data. †† Similar to other reports from the United States ( 9 ) and China ( 1 ), these findings indicate that a high proportion of U.S. patients hospitalized with COVID-19 are older and have underlying medical conditions. The findings in this report are subject to at least three limitations. First, hospitalization rates by age and COVID-NET site are preliminary and might change as additional cases are identified from this surveillance period. Second, whereas minimum case data to produce weekly age-stratified hospitalization rates are usually available within 7 days of case identification, availability of detailed clinical data are delayed because of the need for medical chart abstractions. As of March 30, chart abstractions had been conducted for approximately 200 COVID-19 patients; the frequency and distribution of underlying conditions during this time might change as additional data become available. Clinical course and outcomes will be presented once the number of cases with complete medical chart abstractions are sufficient; many patients are still hospitalized at the time of this report. Finally, testing for SARS-CoV-2 among patients identified through COVID-NET is performed at the discretion of treating health care providers, and testing practices and capabilities might vary widely across providers and facilities. As a result, underascertainment of cases in COVID-NET is likely. Additional data on testing practices related to SARS-CoV-2 will be collected in the future to account for underascertainment using described methods ( 10 ). Early data from COVID-NET suggest that COVID-19–associated hospitalizations in the United States are highest among older adults, and nearly 90% of persons hospitalized have one or more underlying medical conditions. These findings underscore the importance of preventive measures (e.g., social distancing, respiratory hygiene, and wearing face coverings in public settings where social distancing measures are difficult to maintain) to protect older adults and persons with underlying medical conditions. Ongoing monitoring of hospitalization rates, clinical characteristics, and outcomes of hospitalized patients will be important to better understand the evolving epidemiology of COVID-19 in the United States and the clinical spectrum of disease, and to help guide planning and prioritization of health care system resources. Summary What is already known about this topic? Population-based rates of laboratory-confirmed coronavirus disease 2019 (COVID-19)–associated hospitalizations are lacking in the United States. What is added by this report? COVID-NET was implemented to produce robust, weekly, age-stratified COVID-19–associated hospitalization rates. Hospitalization rates increase with age and are highest among older adults; the majority of hospitalized patients have underlying conditions. What are the implications for public health practice? Strategies to prevent COVID-19, including social distancing, respiratory hygiene, and face coverings in public settings where social distancing measures are difficult to maintain, are particularly important to protect older adults and those with underlying conditions. Ongoing monitoring of hospitalization rates is critical to understanding the evolving epidemiology of COVID-19 in the United States and to guide planning and prioritization of health care resources.
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            Renal histopathological analysis of 26 postmortem findings of patients with COVID-19 in China

            Although the respiratory and immune systems are the major targets of Coronavirus Disease 2019 (COVID-19), acute kidney injury and proteinuria have also been observed. Currently, detailed pathologic examination of kidney damage in critically ill patients with COVID-19 has been lacking. To help define this we analyzed kidney abnormalities in 26 autopsies of patients with COVID-19 by light microscopy, ultrastructural observation and immunostaining. Patients were on average 69 years (19 male and 7 female) with respiratory failure associated with multiple organ dysfunction syndrome as the cause of death. Nine of the 26 showed clinical signs of kidney injury that included increased serum creatinine and/or new-onset proteinuria. By light microscopy, diffuse proximal tubule injury with the loss of brush border, non-isometric vacuolar degeneration, and even frank necrosis was observed. Occasional hemosiderin granules and pigmented casts were identified. There were prominent erythrocyte aggregates obstructing the lumen of capillaries without platelet or fibrinoid material. Evidence of vasculitis, interstitial inflammation or hemorrhage was absent. Electron microscopic examination showed clusters of coronavirus particles with distinctive spikes in the tubular epithelium and podocytes. Furthermore, the receptor of SARS-CoV-2, ACE2 was found to be upregulated in patients with COVID-19, and immunostaining with SARS-CoV nucleoprotein antibody was positive in tubules. In addition to the direct virulence of SARS-CoV-2, factors contributing to acute kidney injury included systemic hypoxia, abnormal coagulation, and possible drug or hyperventilation-relevant rhabdomyolysis. Thus, our studies provide direct evidence of the invasion of SARSCoV-2 into kidney tissue. These findings will greatly add to the current understanding of SARS-CoV-2 infection.
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              ACUTE KIDNEY INJURY IN PATIENTS HOSPITALIZED WITH COVID-19

              The rate of acute kidney injury (AKI) associated with patients hospitalized with Covid-19, and associated outcomes are not well understood. This study describes the presentation, risk factors and outcomes of AKI in patients hospitalized with Covid-19. We reviewed the health records for all patients hospitalized with Covid-19 between March 1, and April 5, 2020, at 13 academic and community hospitals in metropolitan New York. Patients younger than 18 years of age, with end stage kidney disease or with a kidney transplant were excluded. AKI was defined according to KDIGO criteria. Of 5,449 patients admitted with Covid-19, AKI developed in 1,993 (36.6%). The peak stages of AKI were stage 1 in 46.5%, stage 2 in 22.4% and stage 3 in 31.1%. Of these, 14.3% required renal replacement therapy (RRT). AKI was primarily seen in Covid-19 patients with respiratory failure, with 89.7% of patients on mechanical ventilation developing AKI compared to 21.7% of non-ventilated patients. 276/285 (96.8%) of patients requiring RRT were on ventilators. Of patients who required ventilation and developed AKI, 52.2% had the onset of AKI within 24 hours of intubation. Risk factors for AKI included older age, diabetes mellitus, cardiovascular disease, black race, hypertension and need for ventilation and vasopressor medications. Among patients with AKI, 694 died (35%), 519 (26%) were discharged and 780 (39%) were still hospitalized. AKI occurs frequently among patients with Covid-19 disease. It occurs early and in temporal association with respiratory failure and is associated with a poor prognosis.
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                Author and article information

                Journal
                Kidney Int
                Kidney Int
                Kidney International
                International Society of Nephrology. Published by Elsevier Inc.
                0085-2538
                1523-1755
                3 August 2021
                3 August 2021
                Affiliations
                [1 ]Arkana Laboratories, 10810 Executive Center Drive #100, Little Rock AR USA 72211
                [2 ]University of Michigan, 500 S State Street, Ann Arbor, MI USA 48109
                [3 ]University of Illinois at Chicago College of Medicine / Advocate Christ Medical Center, Department of Internal Medicine, 1853 W Polk St, Oak Lawn IL USA 60612
                [4 ]University of Arkansas for Medical Sciences, Nephrology Division, 4301 W Markham St, Little Rock, AR USA 72205
                [5 ]AmeriPath Laboratories, Pathology, 225 N.E. 97 th St #600, Oklahoma City OK USA 73114
                [6 ]Manipal Hospital – Bangalore, Department of Pathology, 98 HAL Old Airport Rd, Bangalore, Karnataka India 560017
                [7 ]Rush University Medical Center, Nephrology Division, 1620 W. Harrison St, Chicago IL USA 60612
                [8 ]University of Pennsylvania Perelman School of Medicine, Department of Pathology, 3400 Civic Center Blvd, Philadelphia PA USA 19104
                [9 ]University of California Los Angeles Health System, Department of Pathology and Laboratory Medicine, 140833 Le Conte Ave, Los Angeles, CA USA 90095
                [10 ]UCSF Medical Center, Department of Pathology, 505 Panassus Avenue, CA USA 92103
                [11 ]Medical College of Wisconsin, 9200 W. Wisconsin Avenue, WDL Building L73, Milkaukee, WI USA 53226
                [12 ]University of Utah School of Medicine, 50 N Medical Drive, Salt Lake City UT 84132
                [13 ]University of Zurich, Department of Pathology and Molecular Biology, University Hospital Zurich, Schmelzberstrasse 8091, Zurich, Switzerland
                [14 ]Brigham and Women's Hospital, Renal Division, 75 Francis Street, Boston, MA USA 02115
                [15 ]Albuquerque Nephrology Associates, 4333 Pan American Fwy NE, Albuquerque, NM USA 87107
                [16 ]Renal Associates of West Michigan, 330 E Beltline Ave NE, Suite 100, Grand Rapids, MI USA 49509
                [17 ]Tulane University School of Medicine, Tulane University Hypertension and Renal Center of Excellence, 6823 St. Charles Avenue, New Orleans, LA USA 70118
                [18 ]Tulane School of Medicine, 1430 Tulane Ave, New Orleans, LA USA 70112
                [19 ]Nephrology and Hypertension Associated LTD, 1790 Barron Street, Oxford, MS USA 38655
                [20 ]Michigan Kidney Consultants, 44200 Woodward Ave, Suite 209, Pontiac, MI USA 48341
                [21 ]Shoals Kidney & Hypertension Center, 422 East Dr Hicks Boulevard, Suite A, Florence, AL USA 35630
                [22 ]Iowa Kidney Physicians PC, 1215 Pleasant Street, Suite 100, Des Moines, IA USA 50309
                [23 ]Midwest Nephrology Associates, 70 Jungermann Circle, Suite 405, St. Peters, MO USA 63376
                [24 ]Richmond Nephrology Associates, 7001 West Broad Street, Suite A, Richmond, VA USA 23294
                [25 ]Nephrology Associates of NE Florida, 2 Shircliff Way DePaul Bldg Suite 700, Jacksonville, FL USA 32204
                [26 ]Northwest Indiana Nephrology, 6061 Broadway, Merrillville, IN USA 46410
                [27 ]Georgia Nephrology, 595 Hurricane Shoals Road NW, Suite 100, Lawrenceville, GA USA 30046
                [28 ]The Kidney Clinic, 2386 Clower Street, Suite C105, Snellville, GA USA 30078
                [29 ]Nephrology & Hypertension Associates LTD, 1542 Medical Park Circle, Tupelo, MS USA 38801
                [30 ]Nephrology Associates of Central Florida, 2501 N Orange Avenue #53, Orlando, FL USA 32804
                [31 ]Nephrology Associates of Tidewater Ltd., Norfolk, VA USA 23510
                [32 ]Cleveland Clinic, 9500 Euclid Ave L25, Cleveland, OH USA 44195
                [33 ]Henry Ford Hospital, 2799 W Grand Blvd, Detroit, MI USA 48202
                [34 ]Kantonal Hospital of Graubunden, Loestrasse 170, CH-7000, Chur, Switzerland
                [35 ]Mt Auburn Nephrology, 8260 Pine Road, Cincinnati OH USA 45236
                [36 ]Mohamad El Kassem MD (private practice), Nephrology, Coral Springs, FL USA
                [37 ]Ochsner Health System, Deparment of Nephrology, 1514 Jefferson Hwy, New Orleans LA USA 70121
                [38 ]Ochsner Clinical School, The University of Queensland (Australia), Department of Nephrology, St. Lucia, QLD, AUS
                Author notes
                [] Corresponding author: Tiffany Caza, MD/PhD, 10810 Executive Center Drive #100, Little Rock, AR 72211 Phone: (501)-492-2695
                Article
                S0085-2538(21)00734-1
                10.1016/j.kint.2021.07.015
                8328528
                34352311
                668e79bf-3d24-4a12-b1ab-409bc5376efc
                © 2021 International Society of Nephrology. Published by Elsevier Inc.

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                History
                : 13 January 2021
                : 14 July 2021
                : 23 July 2021
                Categories
                Clinical Investigation

                Nephrology
                covid-19,coronavirus,sars-cov-2,kidney biopsy,renal pathology,acute kidney injury
                Nephrology
                covid-19, coronavirus, sars-cov-2, kidney biopsy, renal pathology, acute kidney injury

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