ObjectivesThe aim of the study was to analyze the incidence of COVID-19 with early renal injury, and to explore the value of multi-index combined detection in diagnosis of early renal injury in COVID-19. DesignThe study was an observational, descriptive study. SettingThis study was carried out in a tertiary hospital in Guangdong, China. Participants patients diagnosed with COVID- Primary and secondary outcome measuresThe primary outcome was to evaluate the incidence of early renal injury in COVID-19. In this study, the estimated glomerular filtration rate (eGFR), endogenous creatinine clearance (Ccr) and urine microalbumin / urinary creatinine ratio (UACR) were calculated to assess the incidence of early renal injury. Secondary outcomes were the diagnostic value of urine microalbumin (UMA), α 1-microglobulin (A1M), urine immunoglobulin-G (IGU), urine transferring (TRU) alone and in combination in diagnosis of COVID-19 with early renal injury. ResultsWhile all patients had no significant abnormalities in serum creatinine (Scr) and blood urea nitrogen (BUN), the abnormal rates of eGFR, Ccr, and UACR were 66.7%, 41.7%, and 41.7%, respectively. Urinary microprotein detection indicated that the area under curve (AUC) of multi-index combined to diagnose early renal injury in COVID-19 was 0.875, which was higher than UMA (0,813), A1M (0.813), IGU (0.750) and TRU (0.750) alone. Spearman analysis showed that the degree of early renal injury was significantly related to C-reactive protein (CRP) and neutrophil ratio (NER), suggesting that the more severe the infection, the more obvious the early renal injury. Hypokalemia and hyponatremia were common in patients with COVID-19, and there was a correlation with the degree of renal injury.All rights reserved. No reuse allowed without permission.author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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The aim of the study was to analyze the characteristics of renal function in patients diagnosed with COVID-19. Methods: In this retrospective, single-center study, we included all confirmed cases of COVID-19 in a tertiary hospital in Guangdong, China from January 20, 2020 to March 20, 2020. Blood and urine laboratory findings related to renal function were summarized, and the estimated glomerular filtration rate (eGFR) and endogenous creatinine clearance (Ccr) were also calculated to assess the renal function. Results: A total of 12 admitted hospital patients were diagnosed with COVID-19, included 3 severe cases, and 9 common cases. Serum creatinine (Scr) was not abnormally elevated in all of the patients, and blood urea nitrogen (BUN) was abnormally elevated in only 25.0% of the patients. However, compared with the recovery period, the patient's Scr and BUN increased significantly in peak of disease (p-scr = 0.002 & p-bun < 0.001). By observing the fluctuations in Scr and BUN from admission to recovery, it was found that the peak of Scr and BUN appeared within the first 14 day of the course of the disease. Urinary microprotein detection indicated that the abnormally elevated rates of urine microalbumin (UMA), α1-microglobulin (A1M), urine immunoglobulin-G (IGU), and urine transferring (TRU) standardized by urinary creatinine in peak of disease were 41.7, 41.7, 50.0, and 16.7%, respectively. The abnormal rates of the calculated eGFR and Ccr were 66.7 and 41.7%. Conclusion: Scr and BUN were generally increased during the course of COVID-19. Detection of urinary microproteins and application of multiple indicators assessment could be helpful for discovering abnormal renal function in patients with COVID-19. However, the evidence is limited due to the small sample size and observational nature. Additional studies, especially large prospective cohort studies, are required to confirm these findings.

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Purpose To construct a predicting model for urosepsis risk for patients with upper urinary tract calculi based on ultrasound and urinalysis. Materials and Methods A retrospective study was conducted in patients with upper urinary tract calculi admitted between January 2016 and January 2020. The patients were randomly grouped into the training and validation sets. The training set was used to identify the urosepsis risk factors and construct a risk prediction model based on ultrasound and urinalysis. The validation set was used to test the performance of the artificial neural network (ANN). Results Ultimately, 1716 patients (10.8% cases and 89.2% control) were included. Eight variables were selected for the model: sex, age, body temperature, diabetes history, urine leukocytes, urine nitrite, urine glucose, and degree of hydronephrosis. The area under the receiver operating curve in the validation and training sets was 0.945 (95% CI: 0.903-0.988) and 0.992 (95% CI: 0.988-0.997), respectively. Sensitivity, specificity, and Yuden index of the validation set (training set) were 80.4% (85.9%), 98.2% (99.0%), and 0.786 (0.849), respectively. Conclusions A preliminary screening model for urosepsis based on ultrasound and urinalysis was constructed using ANN. The model could provide risk assessments for urosepsis in patients with upper urinary tract calculi.