Clinical spectrum of COVID-19 and plasma angiotensin II levels

Angiotensin II levels in COVID-19 are controversial. We studied 12 hospitalized patients, including their baseline levels of peripheral lymphocyte subsets (via flow cytometry) and plasma angiotensin II (via radioimmunoassay). Controls comprised radioimmunoassay's 124 healthy subjects. Angiotensin II levels (pg/ml) were elevated among patients versus controls (Mean +or- standard deviation: 98.8 +or- 146.9 versus 23.7 +or- 15.6, p < 0.0001;Median, interquartile range: 27, 20 to 116 versus 22, 14 to 28). Half the patients had lymphocytopenia (< 1000 cells/mm3), and the CD3+/CD4+ counts were negatively associated with body mass index, viral load, hospital stay and non-home discharge. Angiotensin II imbalance appears to be a biomarker for COVID-19 morbidity and merits further investigation.

Markers of blood-brain barrier disruption increase early and persistently in COVID-19 patients with neurological manifestations.

Background: Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 infection is associated with disorders affecting the peripheral and the central nervous system. A high number of patients develop post-COVID-19 syndrome with the persistence of a large spectrum of symptoms, including neurological, beyond 4 weeks after infection. Several potential mechanisms in the acute phase have been hypothesized, including damage of the blood-brain-barrier (BBB). We tested weather markers of BBB damage in association with markers of brain injury and systemic inflammation may help in identifying a blood signature for disease severity and neurological complications. Methods: Blood biomarkers of BBB disruption (MMP-9, GFAP), neuronal damage (NFL) and systemic inflammation (PPIA, IL-10, TNFα) were measured in two COVID-19 patient cohorts with high disease severity (ICUCovid; n=79) and with neurological complications (NeuroCovid; n=78), and in two control groups free from COVID-19 history, healthy subjects (n=20) and patients with amyotrophic lateral sclerosis (ALS; n=51). Samples from COVID-19 patients were collected during the first and the second wave of COVID-19 pandemic in Lombardy, Italy. Evaluations were done at acute and chronic phases of the COVID-19 infection. Results: Blood biomarkers of BBB disruption and neuronal damage are high in COVID-19 patients with levels similar to or higher than ALS. NeuroCovid patients display lower levels of the cytokine storm inducer PPIA but higher levels of MMP-9 than ICUCovid patients. There was evidence of different temporal dynamics in ICUCovid compared to NeuroCovid patients with PPIA and IL-10 showing the highest levels in ICUCovid patients at acute phase. On the contrary, MMP-9 was higher at acute phase in NeuroCovid patients, with a severity dependency in the long-term. We also found a clear severity dependency of NFL and GFAP levels, with deceased patients showing the highest levels. Discussion: The overall picture points to an increased risk for neurological complications in association with high levels of biomarkers of BBB disruption. Our observations may provide hints for therapeutic approaches mitigating BBB disruption to reduce the neurological damage in the acute phase and potential dysfunction in the long-term.

Influence of Co-morbidities During SARS-CoV-2 Infection in an Indian Population.

Background: Since the outbreak of COVID-19 pandemic the interindividual variability in the course of the disease has been reported, indicating a wide range of factors influencing it. Factors which were the most often associated with increased COVID-19 severity include higher age, obesity and diabetes. The influence of cytokine storm is complex, reflecting the complexity of the immunological processes triggered by SARS-CoV-2 infection. A modern challenge such as a worldwide pandemic requires modern solutions, which in this case is harnessing the machine learning for the purpose of analysing the differences in the clinical properties of the populations affected by the disease, followed by grading its significance, consequently leading to creation of tool applicable for assessing the individual risk of SARS-CoV-2 infection. Methods: Biochemical and morphological parameters values of 5,000 patients (Curisin Healthcare (India) were gathered and used for calculation of eGFR, SII index and N/L ratio. Spearman's rank correlation coefficient formula was used for assessment of correlations between each of the features in the population and the presence of the SARS-CoV-2 infection. Feature importance was evaluated by fitting a Random Forest machine learning model to the data and examining their predictive value. Its accuracy was measured as the F1 Score. Results: The parameters which showed the highest correlation coefficient were age, random serum glucose, serum urea, gender and serum cholesterol, whereas the highest inverse correlation coefficient was assessed for alanine transaminase, red blood cells count and serum creatinine. The accuracy of created model for differentiating positive from negative SARS-CoV-2 cases was 97%. Features of highest importance were age, alanine transaminase, random serum glucose and red blood cells count. Conclusion: The current analysis indicates a number of parameters available for a routine screening in clinical setting. It also presents a tool created on the basis of these parameters, useful for assessing the individual risk of developing COVID-19 in patients. The limitation of the study is the demographic specificity of the studied population, which might restrict its general applicability.

Artificial intelligence approach toward analysis of COVID-19 development—Personalized and epidemiological model

Although ML has been examined for a variety of epidemiological and clinical concerns, as well as for COVID-19 survival prediction, there is a notable lack of research dealing with ML utilization in predicting disease severity changes during the course of the disease. This chapter encompasses two approaches in predicting COVID-19 spread—personalized model for predicting disease development in infected individual patients and an epidemiological model for predicting disease spread in population. Personalized model uses XGboost for the classification of infected individuals into four different groups based on the values of blood biomarkers analyzed by Gradient boosting regressor and chosen as biomarkers with the highest effect on the classification of COVID-19 patients. The epidemiological model includes two proposed methods—differential equation-based SEIRD model and an LSTM deep learning model. Proposed models can be used as tools useful in the research and control of infectious illnesses and in reducing the burden on the health system. © 2022 Elsevier Inc. All rights reserved.

Sleep alterations following COVID-19 are associated with both neuroinflammation and psychological disorders, although at different times.

Introduction: By the end of 2019, severe acute respiratory syndrome coronavirus 2 rapidly spread all over the world impacting mental health and sleep habits. Insomnia, impaired sleep quality, and circadian rhythm alterations were all observed during the pandemic, especially among healthcare workers and in patients with acute and post-acute COVID-19. Sleep disruption may induce a pro-inflammatory state associated with an impairment of immune system function. Objective: We investigated the relationship between sleep alterations, psychological disorders, and inflammatory blood biomarkers in patients with post-acute COVID-19. Methods: We enrolled 47 subjects diagnosed with COVID-19 pneumonia at Santa Maria della Misericordia University Hospital (Udine, Italy) between March and May 2020. Selected patients were evaluated at 2 months (T1) and 10 months (T2) after discharge. Each time, we collected clinical interviews, neurological examinations, and self-administered questionnaires to assess sleep and life quality, anxiety, depression, and post-traumatic stress disorder. Blood biomarkers of endothelial activation, neuroinflammation, and inflammatory cytokines were also measured at each follow-up. Collected variables were analyzed using comparisons between groups and linear regression models. Results: Prevalence of insomnia increased from 10.6% up to 27.3% after COVID-19. Poor sleep quality was found in 41.5% of patients at both study visits. At T1 follow-up, poor sleepers showed higher levels of neurofilament light chain, vascular cell adhesion molecule 1, and interleukin 10; no significant associations were found between sleep quality and psychological disorders. At T2 follow-up, lower sleep quality was associated with higher levels of vascular cell adhesion molecule 1 and interleukin 8, but also with higher scores for anxiety, depression, and post-traumatic stress disorder. Conclusion: Our results suggest an association of poor sleep quality with both psychological disorders and neuroinflammation, although at different times, in previously hospitalized patients with moderate-to-critical COVID-19.

Prognosing the risk of COVID-19 death through a machine learning-based routine blood panel: A retrospective study in Brazil.

BACKGROUND: Despite an extensive network of primary care availability, Brazil has suffered profoundly during the COVID-19 pandemic, experiencing the greatest sanitary collapse in its history. Thus, it is important to understand phenotype risk factors for SARS-CoV-2 infection severity in the Brazilian population in order to provide novel insights into the pathogenesis of the disease. OBJECTIVE: This study proposes to predict the risk of COVID-19 death through machine learning, using blood biomarkers data from patients admitted to two large hospitals in Brazil. METHODS: We retrospectively collected blood biomarkers data in a 24-h time window from 6,979 patients with COVID-19 confirmed by positive RT-PCR admitted to two large hospitals in Brazil, of whom 291 (4.2%) died and 6,688 (95.8%) were discharged. We then developed a large-scale exploration of risk models to predict the probability of COVID-19 severity, finally choosing the best performing model regarding the average AUROC. To improve generalizability, for each model five different testing scenarios were conducted, including two external validations. RESULTS: We developed a machine learning-based panel composed of parameters extracted from the complete blood count (lymphocytes, MCV, platelets and RDW), in addition to C-Reactive Protein, which yielded an average AUROC of 0.91 ± 0.01 to predict death by COVID-19 confirmed by positive RT-PCR within a 24-h window. CONCLUSION: Our study suggests that routine laboratory variables could be useful to identify COVID-19 patients under higher risk of death using machine learning. Further studies are needed for validating the model in other populations and contexts, since the natural history of SARS-CoV-2 infection and its consequences on the hematopoietic system and other organs is still quite recent.

A Hybrid Feature Selection Approach to Screen a Novel Set of Blood Biomarkers for Early COVID-19 Mortality Prediction.

The increase in coronavirus disease 2019 (COVID-19) infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has placed pressure on healthcare services worldwide. Therefore, it is crucial to identify critical factors for the assessment of the severity of COVID-19 infection and the optimization of an individual treatment strategy. In this regard, the present study leverages a dataset of blood samples from 485 COVID-19 individuals in the region of Wuhan, China to identify essential blood biomarkers that predict the mortality of COVID-19 individuals. For this purpose, a hybrid of filter, statistical, and heuristic-based feature selection approach was used to select the best subset of informative features. As a result, minimum redundancy maximum relevance (mRMR), a two-tailed unpaired t-test, and whale optimization algorithm (WOA) were eventually selected as the three most informative blood biomarkers: International normalized ratio (INR), platelet large cell ratio (P-LCR), and D-dimer. In addition, various machine learning (ML) algorithms (random forest (RF), support vector machine (SVM), extreme gradient boosting (EGB), naïve Bayes (NB), logistic regression (LR), and k-nearest neighbor (KNN)) were trained. The performance of the trained models was compared to determine the model that assist in predicting the mortality of COVID-19 individuals with higher accuracy, F1 score, and area under the curve (AUC) values. In this paper, the best performing RF-based model built using the three most informative blood parameters predicts the mortality of COVID-19 individuals with an accuracy of 0.96 ± 0.062, F1 score of 0.96 ± 0.099, and AUC value of 0.98 ± 0.024, respectively on the independent test data. Furthermore, the performance of our proposed RF-based model in terms of accuracy, F1 score, and AUC was significantly better than the known blood biomarkers-based ML models built using the Pre_Surv_COVID_19 data. Therefore, the present study provides a novel hybrid approach to screen the most informative blood biomarkers to develop an RF-based model, which accurately and reliably predicts in-hospital mortality of confirmed COVID-19 individuals, during surge periods. An application based on our proposed model was implemented and deployed at Heroku.

Original Article Combined CT score, blood mononuclear cell count, LDH, and plasma D-dimer for viral pneumonia diagnosis: a retrospective study

Objective: Due to a continued increase in viral pneumonia incidence and resulting high mortality, fast and accurate diagnosis is important for effective management. This investigation examined the significance of blood biomarkers and the CT score in the early diagnosis of viral pneumonia. Methods: Patients who were hospitalized due to radiologically-confirmed pneumonia and underwent virus antigen rapid test were enrolled. Their clinical information was compared. Blood mononuclear cell count, LDH, and plasma D-dimer were obtained. To evaluate the utility of biomarker levels in differentiating viral pneumonia from other pneumonia, ROC curves were developed to analyze the AUC. The optimal cut-off thresholds, specificity, sensitivity, and predictive values were assessed using the Youden index. The added value of the multi-marker approach was delineated using IDI and Reclassification analyses using NRI;IDI and NRI values were examined with 95% CI. Results: Overall, 1163 inpatients were recruited between January 2017 and January 2021. They were sub-divided into the viral pneumonia (n = 563) and non-viral pneumonia (n = 600) categories. We found that the CT score, blood mononuclear cell count, LDH, and plasma D-dimer were markedly elevated in viral pneumonia patients. At an LDH threshold of 693.595 U/L, an AUC of ROC was 0.805 in differentiating viral pneumonia. The combination of CT score and blood biomarkers had an ROC AUC value of 0.908. Conclusions: Combining elevated biomarkers with CT assessments outperformed the CT score alone in identifying viral pneumonia. It is crucial to better characterize the significance of biomarkers in combination with CT assessments in the diagnosis of viral pneumonia.

Artificial intelligence approach towards assessment of condition of COVID-19 patients - Identification of predictive biomarkers associated with severity of clinical condition and disease progression.

BACKGROUND AND OBJECTIVES: Although ML has been studied for different epidemiological and clinical issues as well as for survival prediction of COVID-19, there is a noticeable shortage of literature dealing with ML usage in prediction of disease severity changes through the course of the disease. In that way, predicting disease progression from mild towards moderate, severe and critical condition, would help not only to respond in a timely manner to prevent lethal results, but also to minimize the number of patients in hospitals where this is not necessary. METHODS: We present a methodology for the classification of patients into 4 distinct categories of the clinical condition of COVID-19 disease. Classification of patients is based on the values of blood biomarkers that were assessed by Gradient boosting regressor and which were selected as biomarkers that have the greatest influence in the classification of patients with COVID-19. RESULTS: The results show that among several tested algorithms, XGBoost classifier achieved best results with an average accuracy of 94% and an average F1-score of 94.3%. We have also extracted 10 best features from blood analysis that are strongly associated with patient condition and based on those features we can predict the severity of the clinical condition. CONCLUSIONS: The main advantage of our system is that it is a decision tree-based algorithm which is easier to interpret, instead of the use of black box models, which are not appealing in medical practice.

Blood biomarkers to detect new-onset atrial fibrillation and cardioembolism in ischemic stroke patients.

BACKGROUND: Accumulating data suggest blood biomarkers could inform stroke etiology. OBJECTIVE: The purpose of this study was to investigate the performance of multiple blood biomarkers in elucidating stroke etiology with a focus on new-onset atrial fibrillation (AF) and cardioembolism. METHODS: Between January and December 2017, information on clinical and laboratory parameters and stroke characteristics was prospectively collected from ischemic stroke patients recruited from the National University Hospital, Singapore. Multiple blood biomarkers (N-terminal pro-brain natriuretic peptide [NT-proBNP], d-dimer, S100ß, neuron-specific enolase, vitamin D, cortisol, interleukin-6, insulin, uric acid, and albumin) were measured in plasma. These variables were compared with stroke etiology and the risk of new-onset AF and cardioembolism using multivariable regression methods. RESULTS: Of the 515 ischemic stroke patients (mean age 61 years; 71% men), 44 (8.5%) were diagnosed with new-onset AF, and 75 (14.5%) had cardioembolism. The combination of 2 laboratory parameters (total cholesterol ≤169 mg/dL; triglycerides ≤44.5 mg/dL) and 3 biomarkers (NT-proBNP ≥294 pg/mL; S100ß ≥64 pg/mL; cortisol ≥471 nmol/l) identified patients with new-onset AF (negative predictive value [NPV] 90%; positive predictive value [PPV] 73%; area under curve [AUC] 85%). The combination of 2 laboratory parameters (total cholesterol ≤169 mg/dL; triglycerides ≤44.5 mg/dL) and 2 biomarkers (NT-proBNP ≥507 pg/mL; S100ß ≥65 pg/mL) identified those with cardioembolism (NPV 86%; PPV 78%; AUC 87%). Adding clinical predictors did not improve the performance of these models. CONCLUSION: Blood biomarkers could identify patients with increased likelihood of cardioembolism and direct the search for occult AF.

Cross-Sectional Exploration of Plasma Biomarkers of Alzheimer's Disease in Down Syndrome: Early Data from the Longitudinal Investigation for Enhancing Down Syndrome Research (LIFE-DSR) Study.

With improved healthcare, the Down syndrome (DS) population is both growing and aging rapidly. However, with longevity comes a very high risk of Alzheimer's disease (AD). The LIFE-DSR study (NCT04149197) is a longitudinal natural history study recruiting 270 adults with DS over the age of 25. The study is designed to characterize trajectories of change in DS-associated AD (DS-AD). The current study reports its cross-sectional analysis of the first 90 subjects enrolled. Plasma biomarkers phosphorylated tau protein (p-tau), neurofilament light chain (NfL), amyloid ß peptides (Aß1-40, Aß1-42), and glial fibrillary acidic protein (GFAP) were undertaken with previously published methods. The clinical data from the baseline visit include demographics as well as the cognitive measures under the Severe Impairment Battery (SIB) and Down Syndrome Mental Status Examination (DS-MSE). Biomarker distributions are described with strong statistical associations observed with participant age. The biomarker data contributes to understanding DS-AD across the spectrum of disease. Collectively, the biomarker data show evidence of DS-AD progression beginning at approximately 40 years of age. Exploring these data across the full LIFE-DSR longitudinal study population will be an important resource in understanding the onset, progression, and clinical profiles of DS-AD pathophysiology.

First and Second Waves of Coronavirus Disease 2019 in Madrid, Spain: Clinical Characteristics and Hematological Risk Factors Associated With Critical/Fatal Illness.

OBJECTIVES: This study aims to determine similarities and differences in clinical characteristics between the patients from two waves of severe acute respiratory syndrome coronavirus-2 infection at the time of hospital admission, as well as to identify risk biomarkers of coronavirus disease 2019 severity. DESIGN: Retrospective observational study. SETTING: A single tertiary-care center in Madrid. PATIENTS: Coronavirus disease 2019 adult patients admitted to hospital from March 4, 2020, to March 25, 2020 (first infection wave), and during July 18, 2020, and August 20, 2020 (second infection wave). INTERVENTIONS: Treatment with a hospital-approved drug cocktail during hospitalization. MEASUREMENTS AND MAIN RESULTS: Demographic, clinical, and laboratory data were compared between the patients with moderate and critical/fatal illness across both infection waves. The median age of patients with critical/fatal coronavirus disease 2019 was 67.5 years (interquartile range, 56.75-78.25 yr; 64.5% male) in the first wave and 59.0 years (interquartile range, 48.25-80.50 yr; 70.8% male) in the second wave. Hypertension and dyslipidemia were major comorbidities in both waves. Body mass index over 25 and presence of bilateral pneumonia were common findings. Univariate logistic regression analyses revealed an association of a number of blood parameters with the subsequent illness progression and severity in both waves. However, some remarkable differences were detected between both waves that prevented an accurate extrapolation of prediction models from the first wave into the second wave. Interleukin-6 and d-dimer concentrations at the time of hospital admission were remarkably higher in patients who developed a critical/fatal condition only during the first wave (p < 0.001), although both parameters significantly increased with disease worsening in follow-up studies from both waves. Multivariate analyses from wave 1 rendered a predictive signature for critical/fatal illness upon hospital admission that comprised six blood biomarkers: neutrophil-to-lymphocyte ratio (≥ 5; odds ratio, 2.684 [95% CI, 1.143-6.308]), C-reactive protein (≥ 15.2 mg/dL; odds ratio, 2.412 [95% CI, 1.006-5.786]), lactate dehydrogenase (≥ 411.96 U/L; odds ratio, 2.875 [95% CI, 1.229-6.726]), interleukin-6 (≥ 78.8 pg/mL; odds ratio, 5.737 [95% CI, 2.432-13.535]), urea (≥ 40 mg/dL; odds ratio, 1.701 [95% CI, 0.737-3.928]), and d-dimer (≥ 713 ng/mL; odds ratio, 1.903 [95% CI, 0.832-4.356]). The predictive accuracy of the signature was 84% and the area under the receiver operating characteristic curve was 0.886. When the signature was validated with data from wave 2, the accuracy was 81% and the area under the receiver operating characteristic curve value was 0.874, albeit most biomarkers lost their independent significance. Follow-up studies reassured the importance of monitoring the biomarkers included in the signature, since dramatic increases in the levels of such biomarkers occurred in critical/fatal patients over disease progression. CONCLUSIONS: Most parameters analyzed behaved similarly in the two waves of coronavirus disease 2019. However, univariate logistic regression conducted in both waves revealed differences in some parameters associated with poor prognosis in wave 1 that were not found in wave 2, which may reflect a different disease stage of patients on arrival to hospital. The six-biomarker predictive signature reported here constitutes a helpful tool to classify patient's prognosis on arrival to hospital.

Blood Biomarkers for Detection of Brain Injury in COVID-19 Patients.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus attacks multiple organs of coronavirus disease 2019 (COVID-19) patients, including the brain. There are worldwide descriptions of neurological deficits in COVID-19 patients. Central nervous system (CNS) symptoms can be present early in the course of the disease. As many as 55% of hospitalized COVID-19 patients have been reported to have neurological disturbances three months after infection by SARS-CoV-2. The mutability of the SARS-COV-2 virus and its potential to directly affect the CNS highlight the urgency of developing technology to diagnose, manage, and treat brain injury in COVID-19 patients. The pathobiology of CNS infection by SARS-CoV-2 and the associated neurological sequelae of this infection remain poorly understood. In this review, we outline the rationale for the use of blood biomarkers (BBs) for diagnosis of brain injury in COVID-19 patients, the research needed to incorporate their use into clinical practice, and the improvements in patient management and outcomes that can result. BBs of brain injury could potentially provide tools for detection of brain injury in COVID-19 patients. Elevations of BBs have been reported in cerebrospinal fluid (CSF) and blood of COVID-19 patients. BB proteins have been analyzed in CSF to detect CNS involvement in patients with infectious diseases, including human immunodeficiency virus and tuberculous meningitis. BBs are approved by the U.S. Food and Drug Administration for diagnosis of mild versus moderate traumatic brain injury and have identified brain injury after stroke, cardiac arrest, hypoxia, and epilepsy. BBs, integrated with other diagnostic tools, could enhance understanding of viral mechanisms of brain injury, predict severity of neurological deficits, guide triage of patients and assignment to appropriate medical pathways, and assess efficacy of therapeutic interventions in COVID-19 patients.