The Clinical Course and Prognostic Factors of COVID-19 in Patients with Chronic Kidney Disease, A Study in Six Centers

Introduction. Coronavirus disease 19 (COVID-19), has recently emerged as a great health challenge. The novel corona virus may affect the kidneys mainly as acute kidney injury (AKI). Also, the outcome of COVID-19 may be different in patients with underlying kidney disease. The aim of this study was to compare the outcome of COVID-19 in patients with and without underlying kidney disease. Methods. This was a retrospective study on 659 hospitalized COVID-19 patients in six centers of Iran. Patients were classified into kidney (chronic kidney disease (CKD), end-stage kidney disease (ESKD) or kidney transplantation) and non-kidney groups. The clinical conditions and laboratory data were extracted from the charts. Outcome was defined as death during hospitalization or within 30 days of discharge. Results. Among 659 COVID-19 patients (mean age: 60.7 ± 16.4, 56% male), 208 were in the kidney group (86 ESKD, 35 kidney transplants, and 87 CKD patients). AKI occurred in 41.8%. Incidence of AKI was 34.7% in non-kidney, 74.7% in CKD, and 51.4% in kidney transplant patients (P < .001). Totally 178 patients (27%) died and mortality rate was significantly higher in CKD patients (50.6 vs. 23.4%, P < .001). AKI was associated with increased mortality rate (OR = 2.588, CI: 1.707 to 3.925). Initial glomerular filtration rate (GFR) < 44.2 mL/min and elevated lactate dehydrogenase (LDH) and C-reactive protein (CRP) had significant association with mortality. Conclusion. We showed a higher mortality rate in COVID-19 patients with AKI and CKD. Low initial GFR and elevated LDH and CRP were associated with high mortality in COVID-19 patients. DOI: 10.52547/ijkd.6132

Deep Learning Analysis in Prediction of COVID-19 Infection Status Using Chest CT Scan Features.

Background and aims Non-contrast chest computed tomography (CT) scanning is one of the important tools for evaluating of lung lesions. The aim of this study was to use a deep learning approach for predicting the outcome of patients with COVID-19 into two groups of critical and non-critical according to their CT features. Methods This was carried out as a retrospective study from March to April 2020 in Baqiyatallah Hospital, Tehran, Iran. From total of 1078 patients with COVID-19 pneumonia who underwent chest CT, 169 were critical cases and 909 were non-critical. Deep learning neural networks were used to classify samples into critical or non-critical ones according to the chest CT results. Results The best accuracy of prediction was seen by the presence of diffuse opacities and lesion distribution (both=0.91, 95% CI: 0.83-0.99). The largest sensitivity was achieved using lesion distribution (0.74, 95% CI: 0.55-0.93), and the largest specificity was for presence of diffuse opacities (0.95, 95% CI: 0.9-1). The total model showed an accuracy of 0.89 (95% CI: 0.79-0.99), and the corresponding sensitivity and specificity were 0.71 (95% CI: 0.51-0.91) and 0.93 (95% CI: 0.87-0.96), respectively. Conclusions The results showed that CT scan can accurately classify and predict critical and non-critical COVID-19 cases.

Global Incidence and Mortality Rate of COVID-19; Special Focus on Iran, Italy and China.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a new coronavirus, was diagnosed in China in December 2019. Around the globe, a total of 71429 were infected up to February 17, 2020, with 98.9% of cases in China. On March 11, 2020, the World Health Organization (WHO) characterized the COVID-19 as 'pandemic'. Rapid positive worldwide incidence was the motivation behind this study to investigate the incidence and mortality globally. METHODS: We used the data published by the WHO until March 9, 2020. Non-parametric tests and change point analysis were used for inferences. RESULTS: Change point analysis for Iran and China and the world excluding China for the first 20 days revealed around 78, 195 and 2 further new cases per day, respectively. Italy had a big jump in incidence on the 36th day. Similarly, a sharp rise of positive cases was reported for the world on the 35th day. China successfully controlled the ascending reports of incidence on the 23rd day. Mortality in China and the world were almost similar for the first 20 days. There was an ascending incidence trend with two change points in Italy (30th and 36th days) and one change point in Iran on the 17th day. Mortality in the world jumped remarkably after day 42 with an estimation of almost more than 25 deaths per day. CONCLUSION: The incidence of COVID-19 varied by regions; however, after March 11, it became 'pandemic'. It was observed that after about 6 days with an emergence of sharp increase in incidences, there would be a mutation in mortality rate. On the other hand, the importance of 'on-time' quarantine programs in controlling this virus was confirmed.