ABSTRACT
BACKGROUND AND AIMS Acute kidney injury (AKI) is a common complication among patients hospitalized with COVID-19. The incidence of AKI is estimated to be around 5%–80%, according to the series, but data on renal function evolution is limited. Our main objective was to describe the incidence of AKI in patients with SARS-CoV-2 infection;secondarily, we analysed the severity of AKI and medium-term renal function evolution in these patients. METHOD A retrospective observational study that included patients hospitalized a single hospital, diagnosed with SARS-CoV-2 infection, who developed AKI (March-May 2020). We register clinical and demographic characteristics, creatinine upon admission and prior to discharge, as well as creatinine and CKD-EPI glomerular filtration rate (eGFR) after at least 3 months after discharge. CKD was defined according to KDIGO stages based on the eGFR (G3-G5). The KDIGO classification was used to define and classify AKI. Recovery of kidney function was defined as difference in at discharge or post-hospitalization creatinine < 0.3 mg/dL with respect basal creatinine. The clinical follow-up ranged from admission to death or end of study. RESULTS Of 258 patients hospitalized with SARS-CoV-2 infection, AKI occurred in 73 (28.3%). 63% (n = 46) were men;the mean of age was 69 years (57–76). DRA severity: 35 (48%) KDIGO-1, 15 (21%) KDIGO-2 and 23 (31%) KDIGO-3. The mean stay was associated with the severity of AKI: 7 days (3–11) for KDIGO-1, 11 days for KDIGO-2 (5–22) and 12 days (8–35) for KDIGO-3 (P = .02). The stage of CKD established differences in the severity of AKI: 66.6% (n = 6) of the patients with CKD G4–G5 presented AKI-KDIGO 3 versus only 25.0% (n = 4) in the CKD-G3 patients (P = .02). Admission to the ICU was more frequent in KDIGO 2–3 versus KDIGO-1 [39% (n = 15) versus 9% (n = 3);P < .01]. Of the 48 patients discharged, 30 (62.5%) had recovered their baseline renal function upon discharge. Only 2 are still on RRT after 8 months (2.7% of all patients). Of the 25 patients died (34% of patients with AKI) with a median time of 3 days from DRA diagnosis (1–8). Renal function of 35 patients was monitored, which correspond to 19 (54%) KDIGO-1, 8 (23%) KDIGO-2, 8 (23%) KDIGO-3 stages. In these patients, analytical control starting 3 months after hospitalization revealed FG 66 (SD 30;56–76) mL/min/1.73 m2. We have not found differences in renal function between pre- and post-hospitalization in related test. A total of 77% (n = 37) of discharged patients recovered their baseline renal function in the post-hospitalization control. CONCLUSION The incidence of AKI in the context of COVID-19 in our series was 28.3%, with an associated mortality of 34.2%. Most of the patients presented with AKI KDIGO 1 (47.9%). The severity of AKI is associated with a longer hospital stay, admission to the ICU and the requirement for RRT. The advanced stages of CKD pre-admission showed more severity of AKI. The maintenance in TRS in our series has been 2.7%. Patients who were discharged for recovery/improvement of COVID-19 had normalized kidney function during subsequent follow-up, regardless of the severity of the AKI developed on admission for COVID-19.
ABSTRACT
Passive immunotherapy has been evaluated as a therapeutic alternative for patients with COVID-19 disease. Equine polyclonal immunotherapy for COVID-19 (EPIC) showed adequate safety and potential efficacy in a clinical trial setting and obtained emergency use authorisation in Argentina. We studied its utility in a real world setting with a larger population. Methods: We conducted a retrospective cohort study at "Hospital de Campana Escuela-Hogar" in Corrientes, Argentina, to assess safety and effectiveness of EPIC in hospitalized adults with severe COVID-19 pneumonia. Primary endpoints were 28-days all cause mortality and safety. Mortality and improvement in modified WHO clinical scale at 14 and 21 days were secondary endpoints. Potential confounder adjustment was made by logistic regression weighted by the inverse of the probability of receiving the treatment (IPTW) and doubly robust approach. Results: Clinical records of 395 exposed (EPIC) and 446 non-exposed (Controls) patients admitted between November 2020 and April 2021 were analyzed. Median age was 58 years, 56.8% males. Mortality at 28 days was 15.7% ( EPIC) vs 21.5% (Control). After IPTW adjustment the OR was 0.66 (95 % CI: 0.46 - 0.96) p= 0.03. The effect was more evident in the subgroup who received two EPIC doses (complete treatment, n=379), OR 0.58 (95% CI 0.39 to 0.85) p=0.005. Overall and serious adverse events were not significantly different between groups.
Subject(s)
COVID-19 , PneumoniaABSTRACT
Cross-validation is a well-known and widely used bandwidth selection method in nonparametric regression estimation. However, this technique has two remarkable drawbacks: (i) the large variability of the selected bandwidths, and (ii) the inability to provide results in a reasonable time for very large sample sizes. To overcome these problems, bagging cross-validation bandwidths are analyzed in this paper. This approach consists in computing the cross-validation bandwidths for a finite number of subsamples and then rescaling the averaged smoothing parameters to the original sample size. Under a random-design regression model, asymptotic expressions up to a second-order for the bias and variance of the leave-one-out cross-validation bandwidth for the Nadaraya--Watson estimator are obtained. Subsequently, the asymptotic bias and variance and the limit distribution are derived for the bagged cross-validation selector. Suitable choices of the number of subsamples and the subsample size lead to an $n^{-1/2}$ rate for the convergence in distribution of the bagging cross-validation selector, outperforming the rate $n^{-3/10}$ of leave-one-out cross-validation. Several simulations and an illustration on a real dataset related to the COVID-19 pandemic show the behavior of our proposal and its better performance, in terms of statistical efficiency and computing time, when compared to leave-one-out cross-validation.
Subject(s)
COVID-19ABSTRACT
On 19th March, the World Health Organisation declared a pandemic. Through this global spread, many nations have witnessed exponential growth of confirmed cases brought under control by severe mass quarantine or lockdown measures. However, some have, through a different timeline of actions, prevented this exponential growth. Currently as some continue to tackle growth, others attempt to safely lift restrictions whilst avoiding a resurgence. This study seeks to quantify the impact of government actions in mitigating viral transmission of SARS-CoV-2 by a novel soft computing approach that makes concurrent use of a neural network model, to predict the daily slope increase of cumulative infected, and an optimiser, with a parametrisation of the government restriction time series, to understanding the best set of mitigating actions. Data for two territories, Italy and Taiwan, have been gathered to model government restrictions in traveling, testing and enforcement of social distance measures as well as people connectivity and adherence to government actions. It is found that a larger and earlier testing campaign with tighter entry restrictions benefit both regions, resulting in significantly less confirmed cases. Interestingly, this scenario couples with an earlier but milder implementation of nationwide restrictions for Italy, thus supporting Taiwan's lack of nationwide lockdown. The results, found with a purely data-driven approach, are in line with the main findings of mathematical epidemiological models, proving that the proposed approach has value and that the data alone contains valuable knowledge to inform decision makers.