ABSTRACT
SARS-CoV-2 infection diagnosis is mainly based on nasopharyngeal swab real-time reverse-transcriptase-polymerase reaction positivity in the presence of clinical and radiological suspicion of viral infection. In case of negative test, bronchoscopy with bronchoalveolar lavage (BAL) is the most used technique to rule out SARS-CoV-2 infection. The role of BAL in COVID-19 diagnosis is debated since its diagnostic yield is highly influenced by several factors. Nevertheless, the analysis of BAL fluid provides many important information about the COVID-19 alveolar environment. The analysis of alveolar populations, as well as cytokines and chemokine production, may contribute to better understand the pathogenetic mechanisms involved in COVID-19, providing information on patients' prognosis. Moreover, many efforts have been spent on transcriptome analysis of host response to SARS-CoV-2 infection. In this narrative review we explored the role of BAL in diagnosis and prognosis of patients with COVID-19, focusing on its diagnostic yield, on its role in the study of alveolar cytology, cytokines and chemokines production, microbiological coinfection and how they can influence the prognosis of COVID-19 patients. Copyright © 2022 EDIZIONI MINERVA MEDICA.
ABSTRACT
BACKGROUND AND AIMS: AKI is the most frequent complication after respiratory failure in COVID-19. AKI increases mortality risk, length of hospital stay and healthcare costs, with possible progression towards CKD. Study aims: (1) evaluation of AKI incidence in 1020 COVID-19 hospitalized patients;(2) comparison of AKI incidence in COVID-19 versus pre-pandemic period;(3) establishment of out-patient follow-up for monitoring kidney, lung, motor and immune function;(4) creation of a biobank for biomarker discovery studies. METHOD: AKI incidence was calculated matching laboratory and administrative data of 26 214 hospitalized patients in 2018-2019 and in 1020 COVID-19 patients in 2020-2021: KDIGO algorithms were applied for AKI grading. After 12 months from discharge, 232 COVID AKI patients and relative controls matched for age and gender were evaluated for kidney (eGFR, biomarkers of tubular damage NGAL, CCl- 14, DKK-3), lung (DLCO, CT scan) and neuro-motor (SPPB, 2-min walking test, post-traumatic stress test-IES) function. RESULTS: Before the pandemic, in-hospital AKI incidence was 18% (10% KDIGO 1, 5% KDIGO 2, 3% KDIGO 3): median age of AKI patients was 69. In-hospital mortality was 3.5% in non-AKI group versus 15% in AKI group in accordance with KDIGO stages. In COVID patients, AKI incidence increased to 37% (20% KDIGO 1.11% KDIGO 2, 6% KDIGO 3): median age of patients was 54. In-hospital mortality was 31% in the AKI group;AKI is an independent risk factor for death. After 12 months from hospital discharge, COVID AKI patients showed a persistent reduction of respiratory function (severe DLCO impairment < 60%) related to the extent of CT scan abnormalities. AKI patients also presented the motor function impairment and a worse post-traumatic stress response. GFR reduction was 1.8 mL/min in non-AKI patients versus 9.7 mL/min in AKI COVID patients not related to age. Urinary DKK-3 and CCL-14 were also higher in the AKI group. Last, IgG response after SARS-CoV-2 vaccination was significantly lower in the AKI group. CONCLUSION: AKI incidence was significantly increased during COVID-19 in respect to the pre-pandemic period, with an association with higher mortality in class 2-3 KDIGO. In the post-COVID follow-up, AKI was associated with lung and neuromotor function impairment, a defective antibody response and a sudden GFR decline concomitant to the persistence of tubular injury biomarkers. These results suggest the importance of nephrological and multidisciplinary follow-up of frail patients who developed AKI during hospitalization for COVID-19. (Table Presented).
ABSTRACT
BACKGROUND: In the present study we aimed to create a model able to predict the short-term need of hospital beds for COVID-19 patients, during SARS-CoV-2 outbreak. METHODS: We retrospectively revised data about all COVID-19 patients hospitalized at a University Hospital in Northern Italy, between March 1 and April 29, 2020. Several polynomial models (from first to fourth order) were fitted to estimate the relationship between the time and the number of occupied hospital beds during the entire period and after the local peak of the outbreak and to provide the prediction of short-term hospital beds demand. Model selection was based on the adjusted R-2 (aR(2)) Index and likelihood ratio test (LRT). RESULTS: We included 836 hospitalizations (800 COVID-19 patients). The median length of hospital in-stay was 12 days. According to the aR(2), the fourth order models best fitted the data considering the entire time period. When only the data after the peak was selected, no statistical improvement was found adding terms of order 3 and 4 and lower order polynomial models were considered for the forecasting of the hospital beds demand. Both approaches had a decreasing trend in the number of occupied beds along with time;however, the quadratic one showed a faster reduction in the predicted number of beds required by patients affected by COVID-19. CONCLUSIONS: We propose a model to predict the hospital bed requirement during the descending phase of COVID-19 outbreak, the validation of which might contribute to decision makers policy in the next weeks of pandemic.