Through technological innovations, patient cohorts can be examined from multiple views with high-dimensional, multiscale biomedical data to classify clinical phenotypes and predict outcomes. Here, we aim to present our approach for analyzing multimodal data using unsupervised and supervised sparse linear methods in a COVID-19 patient cohort. This prospective cohort study of 149 adult patients was conducted in a tertiary care academic center. First, we used sparse canonical correlation analysis (CCA) to identify and quantify relationships across different data modalities, including viral genome sequencing, imaging, clinical data, and laboratory results. Then, we used cooperative learning to predict the clinical outcome of COVID-19 patients: Intensive care unit admission. We show that serum biomarkers representing severe disease and acute phase response correlate with original and wavelet radiomics features in the LLL frequency channel (cor(Xu₁, Zv₁) = 0.596, p value < 0.001). Among radiomics features, histogram-based first-order features reporting the skewness, kurtosis, and uniformity have the lowest negative, whereas entropy-related features have the highest positive coefficients. Moreover, unsupervised analysis of clinical data and laboratory results gives insights into distinct clinical phenotypes. Leveraging the availability of global viral genome databases, we demonstrate that the Word2Vec natural language processing model can be used for viral genome encoding. It not only separates major SARS-CoV-2 variants but also allows the preservation of phylogenetic relationships among them. Our quadruple model using Word2Vec encoding achieves better prediction results in the supervised task. The model yields area under the curve (AUC) and accuracy values of 0.87 and 0.77, respectively. Our study illustrates that sparse CCA analysis and cooperative learning are powerful techniques for handling high-dimensional, multimodal data to investigate multivariate associations in unsupervised and supervised tasks.

Sarcopenia and frailty are critical factors linked with poor clinical outcomes among elderly individuals. This study aims to investigate the association between nutritional assessment tests and frailty with muscle thickness measured by ultrasound and their relationship with weaning among critically ill elderly patients. Patients who were over 65 years old and required invasive ventilation were assessed for nutritional status and clinical frailty scale upon admission to the intensive care unit. Additionally, the thickness of their rectus femoris and vastus intermedius muscles were measured by ultrasound within 48 hours of intubation. Correlation analysis was conducted to examine the relationship between screening tests, frailty, and ultrasound results. The association between these parameters and weaning success was also evaluated. Between May and August 2022, 32 consecutive patients were enrolled in the study. The mean age was 79.3 ± 7.9, and 18 (56.3%) of them were female. Median APACHE-II- and first-day SOFA scores were 22.5 (16.2-29.7) and 7 (5-10.75), respectively. There was a moderate negative correlation between the thickness of the rectus femoris and frailty (r= -0.41, p= 0.036), and there was a moderate positive correlation between the rectus femoris and geriatric nutritional risk index (r= 0.45, p= 0.017). Of them, 18 (56.3%) patients were classified as weaning failure in which the mean frailty score was higher (7.6 ± 0.9 vs 6.5 ± 1.7, p= 0.035), sepsis (18 vs 7, p<0.001) and use of vasopressor (17 vs 6, p= 0.004) more common, and in-hospital mortality were higher (18 vs 5, p<0.001). Bedside ultrasound could be beneficial for detecting nutritional high-risk patients. Frailty was associated with muscle thickness, and it was also associated with weaning failure.

Scientists from all over the world have been intensively working to discover different aspects of Coronavirus disease 2019 (COVID-19) since the first cluster of cases was reported in China. Herein, we aimed to investigate unclear issues related to transmission and pathogenesis of disease as well as accuracy of diagnostic tests and treatment modalities. A literature search on PubMed, Ovid, and EMBASE databases was conducted, and articles pertinent to identified search terms were extracted. A snow-ball search strategy was followed in order to retrieve additional relevant articles. It was reported that viral spread may occur during the asymptomatic phase of infection, and viral load was suggested to be a useful marker to assess disease severity. In contrast to immune response against viral infections, cytotoxic T lymphocytes decline in SARS-CoV-2 infection, which can be partially explained by direct invasion of T lymphocytes or apoptosis activated by SARS-CoV-2. Dysregulation of the urokinase pathway, cleavage of the SARS-CoV-2 Spike protein by FXa and FIIa, and consumption coagulopathy were the proposed mechanisms of the coagulation dysfunction in COVID-19. False-negative rates of reverse transcriptase polymerase chain reaction varied between 3% and 41% across studies. The probability of the positive test was proposed to decrease with the number of days past from symptom onset. Safety issues related to infection spread limit the use of high flow nasal oxygen (HFNO) and continuous positive airway pressure (CPAP) in hypoxic patients. Further studies are required to elucidate the challenging issues, thus enhancing the management of COVID-19 patients.

Background: Coronavirus disease 2019 (COVID-19) is one of the biggest pandemic causing acute respiratory failure (ARF) in the last century. Seasonal influenza carries high mortality, as well. The aim of this study was to compare features and outcomes of critically-ill COVID-19 and influenza patients with ARF.Methods: Patients with COVID-19 and influenza admitted to intensive care unit with ARF were retrospectively analyzed.Results: Fifty-four COVID-19 and 55 influenza patients with ARF were studied. Patients with COVID-19 had 32% of hospital mortality, while those with influenza had 47% (P=0.09). Patients with influenza had higher Eastern Cooperative Oncology Group, Clinical Frailty Scale, Acute Physiology and Chronic Health Evaluation II and admission Sequential Organ Failure Assessment (SOFA) scores than COVID-19 patients (P<0.01). Secondary bacterial infection, admission acute kidney injury, procalcitonin level above 0.2 ng/ml were the independent factors distinguishing influenza from COVID-19 while prone positioning differentiated COVID-19 from influenza. Invasive mechanical ventilation (odds ratio [OR], 42.16; 95% confidence interval [CI], 9.45–187.97), admission SOFA score more than 4 (OR, 5.92; 95% CI, 1.85–18.92), malignancy (OR, 4.95; 95% CI, 1.13–21.60), and age more than 65 years (OR, 3.31; 95% CI, 0.99–11.03) were found to be independent risk factors for hospital mortality.Conclusions: There were few differences in clinical features of critically-ill COVID-19 and influenza patients. Influenza cases had worse performance status and disease severity. There was no significant difference in hospital mortality rates between COVID-19 and influenza patients.