Comprehensive analysis of immunogenic cell death-related gene and construction of prediction model based on WGCNA and multiple machine learning in severe COVID-19.

The death of coronavirus disease 2019 (COVID-19) is primarily due to from critically ill patients, especially from ARDS complications caused by SARS-CoV-2. Therefore, it is essential to contribute an in-depth understanding of the pathogenesis of the disease and to identify biomarkers for predicting critically ill patients at the molecular level. Immunogenic cell death (ICD), as a specific variant of regulatory cell death driven by stress, can induce adaptive immune responses against cell death antigens in the host. Studies have confirmed that both innate and adaptive immune pathways are involved in the pathogenesis of SARS-CoV-2 infection. However, the role of ICD in the pathogenesis of severe COVID-19 has rarely been explored. In this study, we systematically evaluated the role of ICD-related genes in COVID-19. We conducted consensus clustering, immune infiltration analysis, and functional enrichment analysis based on ICD differentially expressed genes. The results showed that immune infiltration characteristics were altered in severe and non-severe COVID-19. In addition, we used multiple machine learning methods to screen for five risk genes (KLF5, NSUN7, APH1B, GRB10 and CD4), which are used to predict COVID-19 severity. Finally, we constructed a nomogram to predict the risk of severe COVID-19 based on the classification and recognition model, and validated the model with external data sets. This study provides a valuable direction for the exploration of the pathogenesis and progress of COVID-19, and helps in the early identification of severe cases of COVID-19 to reduce mortality.

Sociocultural adjustment and social support as predictors for international medical sciences students' anxiety during COVID-19.

BACKGROUND: The prevalence of anxiety is high among international medical sciences students and it increased even more during the COVID-19 pandemic due to different restrictions and social isolation. Successful sociocultural adjustment and social support could be important factors in overcoming those challenges, however, there is a lack of studies which would investigate the role of those factors among inter- national medical students. This study aimed to assess the role of sociocultural adjustment and social support as predictors for international medical students' anxiety during COVID-19. METHODS: Two measurements were conducted via self-reported questionnaires which consisted of three scales - SCAS, MSPSS and GAD-7. In total, 82 international medical students participated in both measurements in this longitudinal study. RESULTS: The findings indicated that 37% of international students had symptoms of moderate or severe anxiety during their first year of studies at university. In the second year, during the COVID-19 pandemic and an official lockdown, 35% of international students had symptoms of moderate or severe anxiety. In addition, this study showed that gender and sociocultural adjustment did not play a role as predictors of students' anxiety during the second year of studies. However, this study revealed that social support provided by family during the first year of studies, as well as having friends or family members who had been ill with COVID-19 predicted higher levels of anxiety at second measurement, while sociocultural adjustment was an even stronger predictor of anxiety in the second year of studies of international medical students. CONCLUSIONS: This knowledge can help to better understand how international medical students felt during the COVID-19 pandemic and what role the above- mentioned factors played in the students' anxiety. As the anxiety level is quite high among international medical students, universities and mental health service providers should take it into consideration and help them to overcome those challenges.

Identification of Age-Related Characteristic Genes Involved in Severe COVID-19 Infection Among Elderly Patients Using Machine Learning and Immune Cell Infiltration Analysis.

Elderly patients infected with severe acute respiratory syndrome coronavirus 2 are at higher risk of severe clinical manifestation, extended hospitalization, and increased mortality. Those patients are more likely to experience persistent symptoms and exacerbate the condition of basic diseases with long COVID-19 syndrome. However, the molecular mechanisms underlying severe COVID-19 in the elderly patients remain unclear. Our study aims to investigate the function of the interaction between disease-characteristic genes and immune cell infiltration in patients with severe COVID-19 infection. COVID-19 datasets (GSE164805 and GSE180594) and aging dataset (GSE69832) were obtained from the Gene Expression Omnibus database. The combined different expression genes (DEGs) were subjected to Gene Ontology (GO) functional enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Diseases Ontology functional enrichment analysis, Gene Set Enrichment Analysis, machine learning, and immune cell infiltration analysis. GO and KEGG enrichment analyses revealed that the eight DEGs (IL23A, PTGER4, PLCB1, IL1B, CXCR1, C1QB, MX2, ALOX12) were mainly involved in inflammatory mediator regulation of TRP channels, coronavirus disease-COVID-19, and cytokine activity signaling pathways. Three-degree algorithm (LASSO, SVM-RFE, KNN) and correlation analysis showed that the five DEGs up-regulated the immune cells of macrophages M0/M1, memory B cells, gamma delta T cell, dendritic cell resting, and master cell resisting. Our study identified five hallmark genes that can serve as disease-characteristic genes and target immune cells infiltrated in severe COVID-19 patients among the elderly population, which may contribute to the study of pathogenesis and the evaluation of diagnosis and prognosis in aging patients infected with severe COVID-19.

Incidence, management and prognosis of new-onset sarcoidosis post COVID-19 infection.

BACKGROUND AND AIM: SARS-CoV-2 infection has been linked to hyperinflammation in multiple organs with a potential mechanistic link with resulting autoimmunity. There have been reports of many inflammatory complications following COVID-19, including sarcoidosis. A literature review on new-onset sarcoidosis following COVID-19 is lacking. We evaluated potential associations between COVID-19 and development of new-onset sarcoidosis. METHODS: Articles discussing biopsy-proven sarcoidosis after confirmed COVID-19 infection, published 1956 until April 2023, were included. All article types were deemed eligible except opinion and review articles. RESULTS: A pooled total of 15 patients with new-onset diagnosis of sarcoidosis after COVID-19 infection were included, 45.5% female, mean age 46.1 years (standard deviation 14.7) at onset of sarcoidosis. Patients were from: Europe (n=11); North America (n=2); South America (n=1); Asia (n=1). The mean time between COVID-19 infection and diagnosis of sarcoidosis was 56.3 days, although this ranged from 10 to 140 days. Organ systems predominantly affected by sarcoidosis were: pulmonary (n=11); cutaneous (n=3); cardiac (n=2); ocular (n=1); systemic (n=1) (with overlapping features in certain patients). Sarcoidosis was treated as follows: glucocorticoids (n=8); azathioprine (n=1); cardiac re-synchronisation therapy (n=1); heart transplant (n=1). All patients were reported to have survived, with one requiring intensive care admission. CONCLUSIONS: Our result suggests there is a potential link between COVID-19 and new-onset sarcoidosis. The potential mechanism for this is through cytokine mediated immune modulation in COVID-19 infection. Obtaining a tissue sample remains key in confirming the diagnosis of sarcoidosis and this may be delayed during active COVID-19 infection.

Clinical characteristics and prognosis of patients with COVID-19 on mechanical ventilation undergoing continuous renal replacement therapy.

BACKGROUND: The coronavirus disease (COVID-19) pandemic has significantly strained global healthcare, particularly in the management of patients requiring mechanical ventilation (MV) and continuous renal replacement therapy (CRRT). This study investigated the characteristics and prognoses of these patients. METHODS: This multicenter retrospective cohort study gathered data from patients with COVID-19 across 26 medical centers. Logistic analysis was used to identify the factors associated with CRRT implementation. RESULTS: Of the 640 patients with COVID-19 who required MV, 123 (19.2%) underwent CRRT. Compared to the non-CRRT group, the CRRT group was older and exhibited higher sequential organ failure assessment scores. The incidence of hypertension, diabetes, cardiovascular disease, chronic neurological disease, and chronic kidney disease was also higher in the CRRT group. Moreover, the CRRT group had higher intensive care unit (ICU) (75.6% vs. 26.9%, p < 0.001) and in-hospital (79.7% vs. 29.6%, p < 0.001) mortality rates. CRRT implementation was identified as an independent risk factor for both ICU mortality (hazard ratio [HR]:1.833, 95% confidence interval [CI]:1.342-2.505, p < 0.001) and in-hospital mortality (HR: 2.228, 95% CI: 1.648-3.014, p < 0.001). Refractory respiratory failure (n = 99, 19.1%) was the most common cause of death in the non-CRRT death group, and shock with multi-organ failure (n = 50, 40.7%) was the most common cause of death in the CRRT death group. Shock with multi-organ failure and cardiac death were significantly more common in the CRRT death group, compared to non-CRRT death group. CONCLUSION: This study indicates that CRRT is associated with higher ICU and in-hospital mortality rates in patients with COVID-19 who require MV. Notably, the primary cause of death in the CRRT group was shock with multi-organ failure, emphasizing the severe clinical course for these patients, while refractory respiratory failure was most common in non-CRRT patients.

Predicting Acute Cardiovascular Complications in COVID-19: Insights from a Specialized Cardiac Referral Department.

BACKGROUND COVID-19 increases the risk of acute cardiovascular diseases (CVDs), including acute coronary syndrome (ACS), acute pulmonary embolism (APE), and acute myocarditis (AMyo). The actual impact of CVDs on mortality of patients with COVID-19 remains unknown. This study aimed to determine whether CVDs influence the course of COVID-19 pneumonia and if they can be easily detected by using common tests and examinations. MATERIAL AND METHODS Data of 249 consecutive patients with COVID-19 hospitalized in a dedicated cardiology department were analyzed. On admission, clinical status, biomarkers, computed tomography, and bedside echocardiography were performed. RESULTS D-dimer level predicted APE (AUC=0.850 95% CI [0.765; 0.935], P<0.001) with sensitivity of 69.4% and specificity of 96.2% for a level of 4968.0 ng/mL, and NT-proBNP predicted AMyo (AUC=0.692 95% CI [0.502; 0.883], P=0.004) and showed sensitivity of 54.5%, with specificity of 86.5% for the cut-off point of 8970 pg/mL. Troponin T levels were not useful for diagnostic differentiation between CVDs. An extent of lung involvement predicted mortality (OR=1.03 95% CI [1.01;1.04] for 1% increase, P<0.001). After adjusting for lung involvement, ACS increased mortality, compared with COVID-19 pneumonia only (OR=5.27 95% CI [1.76; 16.38] P=0.003), while APE and AMyo did not affect risk for death. CONCLUSIONS D-dimer and NT-proBNP, but not troponin T, are useful in differentiating CVDs in patients with COVID-19. ACS with COVID-19 increased in-hospital mortality independently from extent of lung involvement, while coexisting APE or AMyo did not.

Prevalence, patterns, and predictors of SARS-CoV-2 RNA and culturable virus in tears of case-ascertained household cohort.

PURPOSE: To investigate the prevalence, patterns, and predictors of SARS-CoV-2 RNA and culturable virus in tears of case-ascertained household cohort. DESIGN: Prospective, longitudinal case-ascertained household cohort identified through convenience sampling. METHODS: This analysis was restricted to individuals who were non-hospitalized, symptomatic, and tested positive for SARS-CoV-2 by nasal RT-PCR. Tears and anterior nasal biospecimens were serially collected throughout the acute period. Tears specimens were collected by the study staff using Schirmer test strips, and nasal specimens were self-collected. For both, SARS-CoV-2 RNA was quantified using qRT-PCR and culturable virus was detected using presence of cytopathic effect (CPE) in tissue culture; positive CPE was confirmed by a qRT-PCR step. A series of cross-sectional unadjusted analyses were performed investigating the relationship between different sociodemographic determinants and biological factors associated with tears RNA positivity. RESULTS: Among the 83 SARS-CoV-2 infected participants, ten (12%) had at least one RNA positive tears specimen. Amongst these ten, five (50%) had concurrent presence of culturable virus, at a median of 7 days post symptom onset (IQR: 4-7 days) (absolute range: 4-8 days). CONCLUSIONS: In this longitudinal cohort, we found evidence of culturable virus in the tears of a small proportion of non-hospitalized SARS-CoV-2 infected individuals. Current public health infection precautions do not account for transmission via tears, so these findings may improve our understanding of potential sources of SARS-CoV-2 transmission and contribute to developing future guidelines.

Lung volume measurement using chest CT in COVID-19 patients: a cohort study in Japan.

OBJECTIVE: This study aimed to investigate the utility of CT quantification of lung volume for predicting critical outcomes in COVID-19 patients. METHODS: This retrospective cohort study included 1200 hospitalised patients with COVID-19 from 4 hospitals. Lung fields were extracted using artificial intelligence-based segmentation, and the percentage of the predicted (%pred) total lung volume (TLC (%pred)) was calculated. The incidence of critical outcomes and posthospitalisation complications was compared between patients with low and high CT lung volumes classified based on the median percentage of predicted TLCct (n=600 for each). Prognostic factors for residual lung volume loss were investigated in 208 patients with COVID-19 via a follow-up CT after 3 months. RESULTS: The incidence of critical outcomes was higher in the low TLCct (%pred) group than in the high TLCct (%pred) group (14.2% vs 3.3%, p<0.0001). Multivariable analysis of previously reported factors (age, sex, body mass index and comorbidities) demonstrated that CT-derived lung volume was significantly associated with critical outcomes. The low TLCct (%pred) group exhibited a higher incidence of bacterial infection, heart failure, thromboembolism, liver dysfunction and renal dysfunction than the high TLCct (%pred) group. TLCct (%pred) at 3 months was similarly divided into two groups at the median (71.8%). Among patients with follow-up CT scans, lung volumes showed a recovery trend from the time of admission to 3 months but remained lower in critical cases at 3 months. CONCLUSION: Lower CT lung volume was associated with critical outcomes, posthospitalisation complications and slower improvement of clinical conditions in COVID-19 patients.

Perceived Work Demands and Emergency Department Crowding as Predictors of Objective Stress among Emergency Physicians: A Shift-Level Approach.

OBJECTIVE: To understand shift-level determinants of emergency physician (EP) burnout, relationships were tested between EP shift demands, stress, and fatigue. METHOD: EP (N = 16) were assessed over 114 shifts that occurred before and during the COVID-19 pandemic. Salivary cortisol (an indicator of stress) and self-reported fatigue were collected prior to and following each shift. An objective crowding score (NEDOCS) per shift was calculated. Shift demands were assessed at the end of each shift. RESULTS: Multilevel models revealed that shift demands, NEDOCS, and the pandemic were related to higher levels of end-of-shift cortisol, but not fatigue. Cortisol levels were higher for shifts with a higher number of demands, greater crowding, and during the pandemic. CONCLUSIONS: Shift demands predicted objective indicators of stress, but not self-reported fatigue. Interventions are needed to decrease stress and shift demands to reduce EP burnout.

Ventilator-associated pneumonia related to extended-spectrum beta-lactamase producing Enterobacterales during severe acute respiratory syndrome coronavirus 2 infection: risk factors and prognosis.

BACKGROUND: Patients infected with the severe acute respiratory syndrome coronavirus 2 (SARS-COV 2) and requiring mechanical ventilation suffer from a high incidence of ventilator associated pneumonia (VAP), mainly related to Enterobacterales. Data regarding extended-spectrum beta-lactamase producing Enterobacterales (ESBL-E) VAP are scarce. We aimed to investigate risk factors and outcomes of ESBL-E related VAP among critically ill coronavirus infectious disease-19 (COVID-19) patients who developed Enterobacterales related VAP. PATIENTS AND METHODS: We performed an ancillary analysis of a multicenter prospective international cohort study (COVID-ICU) that included 4929 COVID-19 critically ill patients. For the present analysis, only patients with complete data regarding resistance status of the first episode of Enterobacterales related VAP (ESBL-E and/or carbapenem-resistant Enterobacterales, CRE) and outcome were included. RESULTS: We included 591 patients with Enterobacterales related VAP. The main causative species were Enterobacter sp (n = 224), E. coli (n = 111) and K. pneumoniae (n = 104). One hundred and fifteen patients (19%), developed a first ESBL-E related VAP, mostly related to Enterobacter sp (n = 40), K. pneumoniae (n = 36), and E. coli (n = 31). Eight patients (1%) developed CRE related VAP. In a multivariable analysis, African origin (North Africa or Sub-Saharan Africa) (OR 1.7 [1.07-2.71], p = 0.02), time between intubation and VAP (OR 1.06 [1.02-1.09], p = 0.002), PaO2/FiO2 ratio on the day of VAP (OR 0.997 [0.994-0.999], p = 0.04) and trimethoprim-sulfamethoxazole exposure (OR 3.77 [1.15-12.4], p = 0.03) were associated with ESBL-E related VAP. Weaning from mechanical ventilation and mortality did not significantly differ between ESBL-E and non ESBL-E VAP. CONCLUSION: ESBL-related VAP in COVID-19 critically-ill patients was not infrequent. Several risk factors were identified, among which some are modifiable and deserve further investigation. There was no impact of resistance of the first Enterobacterales related episode of VAP on outcome.

Analysis of learning curves in predictive modeling using exponential curve fitting with an asymptotic approach.

The existence of large volumes of data has considerably alleviated concerns regarding the availability of sufficient data instances for machine learning experiments. Nevertheless, in certain contexts, addressing limited data availability may demand distinct strategies and efforts. Analyzing COVID-19 predictions at pandemic beginning emerged a question: how much data is needed to make reliable predictions? When does the volume of data provide a better understanding of the disease's evolution and, in turn, offer reliable forecasts? Given these questions, the objective of this study is to analyze learning curves obtained from predicting the incidence of COVID-19 in Brazilian States using ARIMA models with limited available data. To fulfill the objective, a retrospective exploration of COVID-19 incidence across the Brazilian States was performed. After the data acquisition and modeling, the model errors were assessed by employing a learning curve analysis. The asymptotic exponential curve fitting enabled the evaluation of the errors in different points, reflecting the increased available data over time. For a comprehensive understanding of the results at distinct stages of the time evolution, the average derivative of the curves and the equilibrium points were calculated, aimed to identify the convergence of the ARIMA models to a stable pattern. We observed differences in average derivatives and equilibrium values among the multiple samples. While both metrics ultimately confirmed the convergence to stability, the equilibrium points were more sensitive to changes in the models' accuracy and provided a better indication of the learning progress. The proposed method for constructing learning curves enabled consistent monitoring of prediction results, providing evidence-based understandings required for informed decision-making.

Wastewater based epidemiology as a surveillance tool during the current COVID-19 pandemic on a college campus (East Carolina University) and its accuracy in predicting SARS-CoV-2 outbreaks in dormitories.

The COVID-19 outbreak led governmental officials to close many businesses and schools, including colleges and universities. Thus, the ability to resume normal campus operation required adoption of safety measures to monitor and respond to COVID-19. The objective of this study was to determine the efficacy of wastewater-based epidemiology as a surveillance method in monitoring COVID-19 on a college campus. The use of wastewater monitoring as part of a surveillance program to control COVID-19 outbreaks at East Carolina University was evaluated. During the Spring and Fall 2021 semesters, wastewater samples (N = 830) were collected every Monday, Wednesday, and Friday from the sewer pipes exiting the dormitories on campus. Samples were analyzed for SARS-CoV-2 and viral quantification was determined using qRT-PCR. During the Spring 2021 semester, there was a significant difference in SARS-CoV-2 virus copies in wastewater when comparing dorms with the highest number student cases of COVID-19 and those with the lowest number of student cases, (p = 0.002). Additionally, during the Fall 2021 semester it was observed that when weekly virus concentrations exceeded 20 copies per ml, there were new confirmed COVID-19 cases 85% of the time during the following week. Increases in wastewater viral concentration spurred COVID-19 swab testing for students residing in dormitories, aiding university officials in effectively applying COVID testing policies. This study showed wastewater-based epidemiology can be a cost-effective surveillance tool to guide other surveilling methods (e.g., contact tracing, nasal/salvia testing, etc.) to identify and isolate afflicted individuals to reduce the spread of pathogens and potential outbreaks within a community.

The predictive power of data: machine learning analysis for Covid-19 mortality based on personal, clinical, preclinical, and laboratory variables in a case-control study.

BACKGROUND AND PURPOSE: The COVID-19 pandemic has presented unprecedented public health challenges worldwide. Understanding the factors contributing to COVID-19 mortality is critical for effective management and intervention strategies. This study aims to unlock the predictive power of data collected from personal, clinical, preclinical, and laboratory variables through machine learning (ML) analyses. METHODS: A retrospective study was conducted in 2022 in a large hospital in Abadan, Iran. Data were collected and categorized into demographic, clinical, comorbid, treatment, initial vital signs, symptoms, and laboratory test groups. The collected data were subjected to ML analysis to identify predictive factors associated with COVID-19 mortality. Five algorithms were used to analyze the data set and derive the latent predictive power of the variables by the shapely additive explanation values. RESULTS: Results highlight key factors associated with COVID-19 mortality, including age, comorbidities (hypertension, diabetes), specific treatments (antibiotics, remdesivir, favipiravir, vitamin zinc), and clinical indicators (heart rate, respiratory rate, temperature). Notably, specific symptoms (productive cough, dyspnea, delirium) and laboratory values (D-dimer, ESR) also play a critical role in predicting outcomes. This study highlights the importance of feature selection and the impact of data quantity and quality on model performance. CONCLUSION: This study highlights the potential of ML analysis to improve the accuracy of COVID-19 mortality prediction and emphasizes the need for a comprehensive approach that considers multiple feature categories. It highlights the critical role of data quality and quantity in improving model performance and contributes to our understanding of the multifaceted factors that influence COVID-19 outcomes.

Predicting the wicking rate of nitrocellulose membranes from recipe data: a case study using ANN at a membrane manufacturing in South Korea.

Lateral flow assays have been widely used for detecting coronavirus disease 2019 (COVID-19). A lateral flow assay consists of a Nitrocellulose (NC) membrane, which must have a specific lateral flow rate for the proteins to react. The wicking rate is conventionally used as a method to assess the lateral flow in membranes. We used multiple regression and artificial neural networks (ANN) to predict the wicking rate of NC membranes based on membrane recipe data. The developed ANN predicted the wicking rate with a mean square error of 0.059, whereas the multiple regression had a square error of 0.503. This research also highlighted the significant impact of the water content on the wicking rate through images obtained from scanning electron microscopy. The findings of this research can cut down the research and development costs of novel NC membranes with a specific wicking rate significantly, as the algorithm can predict the wicking rate based on the membrane recipe.

Factors influencing Thai university students' decisions to take COVID-19 vaccine booster doses: a cross-sectional survey.

BACKGROUND: We aimed to describe the acceptance of COVID-19 vaccine booster doses and factors influencing this among Thai university students. METHODS: A cross-sectional survey was conducted between July and September 2022. All university students in Thailand were eligible to participate. We explored the acceptance rate of COVID-19 vaccine booster doses and regular vaccines (if available) among university students. Associations between factors influencing the acceptance of vaccination were analyzed by multiple logistic regression analysis. RESULTS: A total of 322 participants were surveyed (78.9% female, age 18 to 49 years (mean = 22.6, standard deviation = 5.47)). Most participants (85.7%) were undergraduate students (Bachelor level), and a proportion (84.8%) had a background in health sciences studies. The proportions who accepted booster doses and regular vaccines were 52.8% and 69.3%, respectively. Vaccine accessibility was found to be significantly associated with the acceptance of booster doses (adjusted odds ratio (AOR) = 2.77, 95% confidence interval (CI) = 1.10-6.97), while the availability of scientific evidence (AOR = 3.44, 95% CI = 1.21-9.77) was significantly associated with the acceptance of regular vaccines. CONCLUSIONS: This study contributes to addressing the knowledge gap regarding acceptance of COVID-19 vaccine booster doses among university students in Thailand. Our findings revealed that vaccine accessibility and the availability of scientific evidence, as well as vaccination costs, influenced individuals' decisions around accepting vaccine booster doses. Further research should focus on the dynamics of vaccine acceptance to facilitate the development of targeted strategies and support vaccination policymaking in Thailand.

Use of serum KL-6 and chest radiographic severity grade to predict 28-day mortality in COVID-19 patients with pneumonia: a retrospective cohort study.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has had a global social and economic impact. An easy assessment procedure to handily identify the mortality risk of inpatients is urgently needed in clinical practice. Therefore, the aim of this study was to develop a simple nomogram model to categorize patients who might have a poor short-term outcome. METHODS: A retrospective cohort study of 189 COVID-19 patients was performed at Shanghai Ren Ji Hospital from December 12, 2022 to February 28, 2023. Chest radiography and biomarkers, including KL-6 were assessed. Risk factors of 28-day mortality were selected by a Cox regression model. A nomogram was developed based on selected variables by SMOTE strategy. The predictive performance of the derived nomogram was evaluated by calibration curve. RESULTS: In total, 173 patients were enrolled in this study. The 28-day mortality event occurred in 41 inpatients (23.7%). Serum KL-6 and radiological severity grade (RSG) were selected as the final risk factors. A nomogram model was developed based on KL-6 and RSG. The calibration curve suggested that the nomogram model might have potential clinical value. The AUCs for serum KL-6, RSG, and the combined score in the development group and validation group were 0.885 (95% CI: 0.804-0.952), 0.818 (95% CI: 0.711-0.899), 0.868 (95% CI: 0.776-0.942) and 0.932 (95% CI: 0.862-0.997), respectively. CONCLUSIONS: Our results suggested that the nomogram based on KL-6 and RSG might be a potential method for evaluating 28-day mortality in COVID-19 patients. A high combined score might indicate a poor outcome in COVID-19 patients with pneumonia.

Prognostic value of the neutrophil-to-lymphocyte ratio and C-reactive-protein-to-prealbumin ratio in hospitalized older patients with coronavirus disease 2019.

The neutrophil-to-lymphocyte ratio (NLR) and C-reactive protein-to-prealbumin ratio (CPAR) are novel markers of inflammation. The CPAR is an indicator of inflammation and malnutrition. We evaluated NLR and CPAR in combination as indicators of disease severity and prognosis in hospitalized older patients with coronavirus disease 2019 (COVID-19). A total of 222 hospitalized patients with COVID-19 (aged > 60 years) were divided into non-severe and severe groups. The severe group was subdivided into the surviving and deceased subgroups. We retrospectively assessed the predictive power of NLR and CPAR in combination (NLR + CPAR) to determine the prognosis of hospitalized older patients with COVID-19. The NLR and CPAR were significantly higher in the severe group than in the non-severe group (P < .001). Furthermore, the NLR and CPAR were higher in the deceased subgroup than in the surviving subgroup (P < .001). Pearson correlation analysis showed a highly significant positive correlation between NLR and CPAR (P < .001, r = 0.530). NLR + CPAR showed an area under the curve of 0.827 and sensitivity of 83.9% in the severe group; the area under the curve was larger (0.925) and sensitivity was higher (87.1%) in the deceased subgroup. The receiver operating characteristic curve of NLR + CPAR was significantly different from the receiver operating characteristic curves of either biomarker alone (P < .001). Kaplan-Meier analysis showed that patients in the severe group with elevated NLR + CPAR had a significantly lower 90-day survival rate than patients who lacked this finding (odds ratio 7.87, P < .001). NLR + CPAR may enable early diagnosis and assessment of disease severity in hospitalized older patients with COVID-19. This may also enable the identification of high-risk older patients with COVID-19 at the time of admission.

Risk stratification and prognosis prediction using cardiac biomarkers in COVID-19: a single-centre retrospective cohort study.

OBJECTIVE: There is a need for a robust tool to stratify the patient's risk with COVID-19. We assessed the prognostic values of cardiac biomarkers for COVID-19 patients. METHODS: This is a single-centre retrospective cohort study. Consecutive laboratory-confirmed COVID-19 patients admitted to the Kobe City Medical Center General Hospital from July 2020 to September 2021 were included. We obtained cardiac biomarker values from electronic health records and institutional blood banks. We stratified patients with cardiac biomarkers as high-sensitive troponin I (hsTnI), N-terminal pro-B-type natriuretic peptide (NT-proBNP), creatine kinase (CK) and CK myocardial band (CK-MB), using the clinically relevant thresholds. Prespecified primary outcome measure was all-cause death. RESULTS: A total of 917 patients were included. hsTnI, NT-proBNP, CK and CK-MB were associated with the significantly higher cumulative 30-day incidence of all-cause death (hsTnI: <5.0 ng/L group; 4.3%, 5.0 ng/L-99%ile upper reference limit (URL) group; 8.8% and ≥99% ile URL group; 25.2%, p<0.001. NT-proBNP: <125 pg/mL group; 5.3%, 125-900 pg/mL group; 10.5% and ≥900 pg/mL group; 31.9%, p<0.001. CK: