Management of SARS-CoV-2 pneumonia in intensive care unit: An observational retrospective study comparing two bundles.
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ABSTRACT: Purpose
To compare the effects of two therapeutic bundles of management in SARS-CoV2 ICU patients.Materials and methods
Our retrospective, observational study was performed in a university ICU from March to June 2020 (first wave) and from September 2020 to January 2021 (second wave). In first wave, patients received bundle 1 including early invasive ventilation, hydroxychloroquine, cefotaxime and azithromycin. In second wave, bundle 2 included non-invasive oxygenation support and dexamethasone. The main outcome was in-hospital mortality. Secondary outcomes included ICU and hospital length of stay, ICU supportive therapies, viral clearance and antimicrobial resistance emergence.Results
129 patients with SARS-CoV-2 pneumonia were admitted to our ICU. Thirty-five were treated according to bundle 1 and 76 to bundle 2. In-hospital mortality was similar in the two groups (23%, p = 1). The hospital (p = 0.003) and ICU (p = 0.01) length of stay and ventilator-free days at 28 days (p = 0.03) were significantly reduced in bundle 2. Increasing age, vasopressor use and PaO2/FiO2 ratio < 125 were associated with in-hospital mortality.Conclusion
Within the limitations of our study, changes in therapeutic bundles for SARS-Cov-2 ICU patients might have no effect on in-hospital mortality but were associated with less exposure to mechanical ventilation and reduced hospital length of stay.
SUBMITTER: Lopez A
PROVIDER: S-EPMC8238648 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background: Phenotypes have been identified within heterogeneous disease, such as acute respiratory distress syndrome and sepsis, which are associated with important prognostic and therapeutic implications. The present study sought to assess whether phenotypes can be derived from intensive care patients with coronavirus disease 2019 (COVID-19), to assess the correlation with prognosis, and to develop a parsimonious model for phenotype identification. Methods: Adult patients with COVID-19 from Tongji hospital between January 2020 and March 2020 were included. The consensus k means clustering and latent class analysis (LCA) were applied to identify phenotypes using 26 clinical variables. We then employed machine learning algorithms to select a maximum of five important classifier variables, which were further used to establish a nested logistic regression model for phenotype identification. Results: Both consensus k means clustering and LCA showed that a two-phenotype model was the best fit for the present cohort (N = 504). A total of 182 patients (36.1%) were classified as hyperactive phenotype, who exhibited a higher 28-day mortality and higher rates of organ dysfunction than did those in hypoactive phenotype. The top five variables used to assign phenotypes were neutrophil-to-lymphocyte ratio (NLR), ratio of pulse oxygen saturation to the fractional concentration of oxygen in inspired air (Spo2/Fio2) ratio, lactate dehydrogenase (LDH), tumor necrosis factor α (TNF-α), and urea nitrogen. From the nested logistic models, three-variable (NLR, Spo2/Fio2 ratio, and LDH) and four-variable (three-variable plus TNF-α) models were adjudicated to be the best performing, with the area under the curve of 0.95 [95% confidence interval (CI) = 0.94-0.97] and 0.97 (95% CI = 0.96-0.98), respectively. Conclusion: We identified two phenotypes within COVID-19, with different host responses and outcomes. The phenotypes can be accurately identified with parsimonious classifier models using three or four variables.
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