scholarly journals Discriminant Models for the Prediction of Viral Shedding Time and Disease Progression in COVID-19

2020 ◽  
Author(s):  
Wen Yang Li ◽  
Yuhao Guo ◽  
Xiaowei Zheng ◽  
Hongwen Zhao ◽  
Jian Kang ◽  
...  
Keyword(s):  
Disease Progression ◽  
Disease Severity ◽  
Clinical Features ◽  
Antiviral Treatment ◽  
Therapeutic Regimen ◽  
Predictive Values ◽  
Viral Shedding ◽  
Imaging Characteristics ◽  
Short Term Exposure ◽  
Discriminant Models

Abstract Background COVID-19 infection can cause life-threatening respiratory disease. This study aimed to fully characterize the clinical features associated with postponed viral shedding and disease progression, then develop and validate two prognostic discriminant models. Methods This study included 125 hospitalized patients with COVID-19. 44 parameters were recorded, including age, gender, underlying comorbidities, epidemic features, laboratory indexes, imaging characteristics and therapeutic regimen, et al. F-test and χ2 test were used for feature selection. All models were developed with 4-fold cross-validation, and the final performances of each model were compared by the Area Under Receiving Operating Curve (AUROC). After optimizing the parameters via L2 regularization, prognostic discriminant models were built to predict postponed viral shedding and disease progression of COVID-19 infection. The test set was then used to detect the predictive values via assessing models sensitivity and specificity. Results 69 patients had a postponed viral shedding time (> 14 days), and 28 of 125 patients progressed into severe cases. Eleven and six demographic, clinical features and therapeutic regimen were significantly associated with postponed viral shedding and disease progressing, respectively (p < 0.05). The optimal discriminant models are: y1 (postponed viral shedding) = -0.244 + 0.2829x1 (the interval from the onset of symptoms to antiviral treatment) + 0.2306x4 (age) + 0.234x28 (Urea) − 0.2847x34 (Dual-antiviral therapy) + 0.3084x38 (Treatment with antibiotics) + 0.3025x21 (Treatment with Methylprednisolone); y2 (disease progression) = -0.348–0.099x2 (interval from Jan 1st, 2020 to individualized onset of symptoms) + 0.0945x4 (age) + 0.1176x5 (imaging characteristics) + 0.0398x8 (short- term exposure to Wuhan) − 0.1646x19 (lymphocyte counts) + 0.0914x20 (neutrophil counts) + 0.1254x21 (neutrphil/lymphocyte ratio) + 0.1397x22 (C-Reactive Protein) + 0.0814x23 (Procalcitonin) + 0.1294x24 (Lactic dehydrogenase) + 0.1099x29 (Creatine kinase). The output ≥ 0 predicted postponed viral shedding or disease progressing to severe/critical state. These two models yielded the maximum AUROC, and faired best in terms of prognostic performance (sensitivity of 73.3%, 75%, and specificity of 78.6%, 75% for prediction of postponed viral shedding and disease severity, respectively). Conclusion The two discriminant models could effectively predict the postponed viral shedding and disease severity, and be used as early-warning tools for COVID-19.

Impact of influenza virus during pregnancy: from disease severity to vaccine efficacy

2020 ◽  
Vol 15 (7) ◽  
pp. 441-453
Author(s):  
Ana Vazquez-Pagan ◽  
Rebekah Honce ◽  
Stacey Schultz-Cherry
Keyword(s):  
Influenza Virus ◽  
Virus Infection ◽  
Immune Responses ◽  
Pregnant Women ◽  
Disease Severity ◽  
Influenza A ◽  
Antiviral Treatment ◽  
Influenza Virus Infection ◽  
Severe Influenza ◽  
The Impact

Pregnant women are among the individuals at the highest risk for severe influenza virus infection. Infection of the mother during pregnancy increases the probability of adverse fetal outcomes such as small for gestational age, preterm birth and fetal death. Animal models of syngeneic and allogeneic mating can recapitulate the increased disease severity observed in pregnant women and are used to define the mechanism(s) of that increased severity. This review focuses on influenza A virus pathogenesis, the unique immunological landscape during pregnancy, the impact of maternal influenza virus infection on the fetus and the immune responses at the maternal–fetal interface. Finally, we summarize the importance of immunization and antiviral treatment in this population and highlight issues that warrant further investigation.

scholarly journals Correlation of Pandemic (H1N1) 2009 Viral Load with Disease Severity and Prolonged Viral Shedding in Children

2010 ◽  
Vol 16 (8) ◽  
pp. 1265-1272 ◽  
Author(s):  
Chung-Chen Li ◽  
Lin Wang ◽  
Hock-Liew Eng ◽  
Huey-Ling You ◽  
Ling-Sai Chang ◽  
...  
Keyword(s):  
Viral Load ◽  
Disease Severity ◽  
Pandemic H1n1 ◽  
Viral Shedding ◽  
Pandemic H1n1 2009

scholarly journals IDENTIFICATION OF FACTORS ASSOCIATED WITH DISEASE PROGRESSION IN THE CONTRALATERAL HIP OF PATIENTS WITH SYMPTOMATIC FEMOROACETABULAR IMPINGEMENT

2021 ◽  
Vol 9 (7_suppl3) ◽  
pp. 2325967121S0014
Author(s):  
Adam Khan ◽  
Craig R. Louer ◽  
Wahid Abu-Amer ◽  
Gail Pashos ◽  
Cecilia Pascual Garrido ◽  
...  
Keyword(s):  
Disease Progression ◽  
Femoroacetabular Impingement ◽  
External Rotation ◽  
Univariate Analysis ◽  
Alpha Angle ◽  
Hip Osteoarthritis ◽  
Symptom Development ◽  
Ucla Activity Score ◽  
Center Edge ◽  
Imaging Characteristics

Introduction: Femoroacetabular Impingement (FAI) is one of the most common causes of hip osteoarthritis. Nevertheless, the factors contributing to symptom development and FAI disease progression are poorly understood. Hypothesis/Purpose: The purpose of this study was to (1) investigate rates of initial and subsequent symptom development in the contralateral hip of patients with FAI, and (2) identify predictors of disease progression (symptom development) in the contralateral hip. Methods: This prospective study included a minimum 5 year follow-up of the contralateral hip in 179 patients undergoing FAI surgery. Symptoms (moderate pain) were monitored over the study course. Univariate analysis compared patient and FAI imaging characteristics of patients developing symptoms to those who remained asymptomatic. Results: 146 patients (146 hips, 81.5%) were included (min 5 year, mean 6.7 years). Thirty-nine (26%) presented with symptoms in the contralateral hip while 34 (23%) developed symptoms. Head-neck offset ratio (HNOR) on AP pelvis radiographs was significantly lower among hips that developed symptoms (0.164 vs. 0.153 p=0.025). Maximum alpha angle (p=0.413), lateral center edge angle (p=0.704), and crossover sign (p=0.115) were not predictive of symptoms. Patients with a UCLA activity score greater than 9 were less likely to develop symptoms (14% vs. 46%, p=0.081), but this was not statistically significant. The total arc of rotation in extension (35.740 vs 45.140, p=0.012) and 900 of flexion (40.00 vs 50.800, p=0.009) as well as external rotation at 900 of flexion (28.940 vs 36.590, p=0.020) were decreased in hips developing symptoms. Internal Rotation in flexion was not significantly decreased in symptomatic patients (11.060 vs 14.20, p=0.113). Conclusions: We identified unique radiographic and physical exam findings that are associated with symptom development in patients with FAI. Specifically, decreased hip rotation arc and decreased HNOR were strongly associated with disease progression and may represent important factors for future risk modeling in FAI patients.

scholarly journals Clinical characteristics and outcomes of COVID-19 long-term nucleic acid positive patients

2021 ◽  
pp. 1-10
Author(s):  
Xuan Zhu ◽  
Xinxin Zhu ◽  
Min Wang ◽  
Fang Yang ◽  
Zhibing Sun ◽  
...  
Keyword(s):  
Nucleic Acid ◽  
Clinical Characteristics ◽  
Clinical Symptoms ◽  
Control Group ◽  
Viral Shedding ◽  
Imaging Characteristics ◽  
And Gender ◽  
Acid Test ◽  
Pulmonary Ct

OBJECTIVE: This study aimed to investigate the clinical characteristics and outcomes of coronavirus disease-19 (COVID-19) long-term nucleic acid positive patients (hereinafter referred to as CLTAPs). METHODS: Patients were recruited from the Xiaogan Central Hospital between 16 January 2020 and 28 March 2020. Among the 562 cases of patients with laboratory-identified COVID-19 infection by real-time polymerase chain reaction (qtPCR), 19 cases of COVID-19 patients with more than 41 days from the first to the last time of nucleic acid test were selected as the study group, and 76 cases of age- and gender-matched COVID-19 patients were selected as the control group (hereinafter referred to as C-CLTAPs). Demographic characteristics, clinical symptoms, laboratory examination and computed tomography (CT) imaging characteristics were retrospectively analyzed. RESULTS: On admission, among the 562 cases of patients with COVID-19, there were 398 cases of ordinary COVID-19 patients, 99 cases of severe COVID-19 patients and 99 cases of critical COVID-19 patients. CLTAPs had milder clinical symptoms and longer viral shedding time in comparison to C-CLTAPs. Compared to C-CLTAPs, CLTAPs had a lower infection index at admission. CLTAPs used less oxygen therapy and a higher proportion of hydroxychloroquine treatment in comparison to C-CLTAPs. In comparison to C-CLTAPs, CLTAPs showed slower pulmonary CT progression and faster pulmonary CT absorption. CONCLUSION: In this study, out of the 562 cases, we found 19 CLTAPs. The clinical differences between CLTAPs and C-CLTAPs were compared and analyzed. We hope that these finding can provide a theoretical basis for the treatment of CLTAPs.

scholarly journals Machine learning using clinical data at baseline predicts the efficacy of vedolizumab at week 22 in patients with ulcerative colitis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jun Miyoshi ◽  
Tsubasa Maeda ◽  
Katsuyoshi Matsuoka ◽  
Daisuke Saito ◽  
Sawako Miyoshi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ulcerative Colitis ◽  
Prediction Model ◽  
Clinical Features ◽  
Unmet Need ◽  
Patient Treatment ◽  
Clinical Activity ◽  
Prediction Tool ◽  
Real World Data ◽  
Predictive Values

AbstractPredicting the response of patients with ulcerative colitis (UC) to a biologic such as vedolizumab (VDZ) before administration is an unmet need for optimizing individual patient treatment. We hypothesized that the machine-learning approach with daily clinical information can be a new, promising strategy for developing a drug-efficacy prediction tool. Random forest with grid search and cross-validation was employed in Cohort 1 to determine the contribution of clinical features at baseline (week 0) to steroid-free clinical remission (SFCR) with VDZ at week 22. Among 49 clinical features including sex, age, height, body weight, BMI, disease duration/phenotype, treatment history, clinical activity, endoscopic activity, and blood test items, the top eight features (partial Mayo score, MCH, BMI, BUN, concomitant use of AZA, lymphocyte fraction, height, and CRP) were selected for logistic regression to develop a prediction model for SFCR at week 22. In the validation using the external Cohort 2, the positive and negative predictive values of the prediction model were 54.5% and 92.3%, respectively. The prediction tool appeared useful for identifying patients with UC who would not achieve SFCR at week 22 during VDZ therapy. This study provides a proof-of-concept that machine learning using real-world data could permit personalized treatment for UC.

Serum glial fibrillary acidic protein correlates with multiple sclerosis disease severity

2018 ◽  
Vol 26 (2) ◽  
pp. 210-219 ◽  
Author(s):  
Heidi Högel ◽  
Eero Rissanen ◽  
Christian Barro ◽  
Markus Matilainen ◽  
Marjo Nylund ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Nervous System ◽  
Glial Fibrillary Acidic Protein ◽  
Disease Progression ◽  
Disease Severity ◽  
Single Molecule ◽  
Progressive Disease ◽  
Disease Duration ◽  
Acidic Protein ◽  
Csf Levels

Background: Cerebrospinal fluid (CSF) levels of two soluble biomarkers, glial fibrillary acidic protein (GFAP) and neurofilament light chain (NfL), have been shown to associate with multiple sclerosis (MS) disease progression. Now, both biomarkers can be detected reliably in serum, and importantly, their serum levels correlate well with their CSF levels. Objective: To evaluate the usability of serum GFAP measurement as a biomarker of progressive disease and disease severity in MS. Methods: Clinical course, Expanded Disability Status Scale (EDSS), disease duration, patient age and magnetic resonance imaging (MRI) parameters were reviewed in 79 MS patients in this cross-sectional hospital-based study. Serum samples were collected for measurement of GFAP and NfL concentrations using single molecule array (Simoa) assay. A cohort of healthy controls was evaluated for comparison. Results: Higher serum concentrations of both GFAP and NfL were associated with higher EDSS, older age, longer disease duration, progressive disease course and MRI pathology. Conclusion: Earlier studies have demonstrated that GFAP, unlike NfL, is not increased in association with acute focal inflammation-related nervous system damage. Our work suggests that GFAP serum level associates with disease progression in MS and could potentially serve as an easily measurable biomarker of central nervous system (CNS) pathology related to disease progression in MS.

scholarly journals Not Only High Number and Specific Comorbidities but Also Age Are Closely Related to Progression and Poor Prognosis in Patients With COVID-19

2022 ◽  
Vol 8 ◽  
Author(s):  
Dafeng Liu ◽  
Yongli Zheng ◽  
Jun Kang ◽  
Dongmei Wang ◽  
Lang Bai ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Disease Progression ◽  
Disease Severity ◽  
Poor Prognosis ◽  
Chronic Obstructive ◽  
Clinical Trial Registry ◽  
Alcoholic Fatty Liver ◽  
Clinical Type ◽  
Conversion Time ◽  
Negative Conversion

Background: Some patients with comorbidities and rapid disease progression have a poor prognosis.Aim: We aimed to investigate the characteristics of comorbidities and their relationship with disease progression and outcomes of COVID-19 patients.Methods: A total of 718 COVID-19 patients were divided into five clinical type groups and eight age-interval groups. The characteristics of comorbidities were compared between the different clinical type groups and between the different age-interval groups, and their relationships with disease progression and outcomes of COVID-19 patients were assessed.Results: Approximately 91.23% (655/718) of COVID-19 patients were younger than 60 years old. Approximately 64.76% (465/718) had one or more comorbidities, and common comorbidities included non-alcoholic fatty liver disease (NAFLD), hyperlipidaemia, hypertension, diabetes mellitus (DM), chronic hepatitis B (CHB), hyperuricaemia, and gout. COVID-19 patients with comorbidities were older, especially those with chronic obstructive pulmonary disease (COPD) and cardiovascular disease (CVD). Hypertension, DM, COPD, chronic kidney disease (CKD) and CVD were mainly found in severe COVID-19 patients. According to spearman correlation analysis the number of comorbidities was correlated positively with disease severity, the number of comorbidities and NAFLD were correlated positively with virus negative conversion time, hypertension, CKD and CVD were primarily associated with those who died, and the above-mentioned correlation existed independently of age. Risk factors included age, the number of comorbidities and hyperlipidaemia for disease severity, the number of comorbidities, hyperlipidaemia, NAFLD and COPD for the virus negative conversion time, and the number of comorbidities and CKD for prognosis. Number of comorbidities and age played a predictive role in disease progression and outcomes.Conclusion: Not only high number and specific comorbidities but also age are closely related to progression and poor prognosis in patients with COVID-19. These findings provide a reference for clinicians to focus on not only the number and specific comorbidities but also age in COVID-19 patients to predict disease progression and prognosis.Clinical Trial Registry: Chinese Clinical Trial Register ChiCTR2000034563.

scholarly journals 4-Ethylguaiacol modulates neuroinflammation and Th1/Th17 differentiation to ameliorate disease severity in experimental autoimmune encephalomyelitis

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Wen-Tsan Weng ◽  
Ping-Chang Kuo ◽  
Dennis A. Brown ◽  
Barbara A. Scofield ◽  
Destin Furnas ◽  
...  
Keyword(s):  
Autoimmune Disease ◽  
Experimental Autoimmune Encephalomyelitis ◽  
Immune Responses ◽  
Disease Progression ◽  
Disease Severity ◽  
Autoimmune Encephalomyelitis ◽  
Relapsing Remitting ◽  
Th17 Differentiation ◽  
Experimental Autoimmune

Abstract Background Multiple sclerosis (MS) is a progressive autoimmune disease characterized by the accumulation of pathogenic inflammatory immune cells in the central nervous system (CNS) that subsequently causes focal inflammation, demyelination, axonal injury, and neuronal damage. Experimental autoimmune encephalomyelitis (EAE) is a well-established murine model that mimics the key features of MS. Presently, the dietary consumption of foods rich in phenols has been reported to offer numerous health benefits, including anti-inflammatory activity. One such compound, 4-ethylguaiacol (4-EG), found in various foods, is known to attenuate inflammatory immune responses. However, whether 4-EG exerts anti-inflammatory effects on modulating the CNS inflammatory immune responses remains unknown. Thus, in this study, we assessed the therapeutic effect of 4-EG in EAE using both chronic and relapsing-remitting animal models and investigated the immunomodulatory effects of 4-EG on neuroinflammation and Th1/Th17 differentiation in EAE. Methods Chronic C57BL/6 EAE and relapsing-remitting SJL/J EAE were induced followed by 4-EG treatment. The effects of 4-EG on disease progression, peripheral Th1/Th17 differentiation, CNS Th1/Th17 infiltration, microglia (MG) activation, and blood-brain barrier (BBB) disruption in EAE were evaluated. In addition, the expression of MMP9, MMP3, HO-1, and Nrf2 was assessed in the CNS of C57BL/6 EAE mice. Results Our results showed that 4-EG not only ameliorated disease severity in C57BL/6 chronic EAE but also mitigated disease progression in SJL/J relapsing-remitting EAE. Further investigations of the cellular and molecular mechanisms revealed that 4-EG suppressed MG activation, mitigated BBB disruption, repressed MMP3/MMP9 production, and inhibited Th1 and Th17 infiltration in the CNS of EAE. Furthermore, 4-EG suppressed Th1 and Th17 differentiation in the periphery of EAE and in vitro Th1 and Th17 cultures. Finally, we found 4-EG induced HO-1 expression in the CNS of EAE in vivo as well as in MG, BV2 cells, and macrophages in vitro. Conclusions Our work demonstrates that 4-EG confers protection against autoimmune disease EAE through modulating neuroinflammation and inhibiting Th1 and Th17 differentiation, suggesting 4-EG, a natural compound, could be potentially developed as a therapeutic agent for the treatment of MS/EAE.

scholarly journals Critically Ill vs. Non-Critically Ill Patients With COVID-19 Pneumonia: Clinical Features, Laboratory Findings, and Prediction

2021 ◽  
Vol 11 ◽  
Author(s):  
Wandong Hong ◽  
Qin Chen ◽  
Songzan Qian ◽  
Zarrin Basharat ◽  
Vincent Zimmer ◽  
...  
Keyword(s):  
Critical Illness ◽  
Disease Severity ◽  
Critically Ill ◽  
Clinical Features ◽  
Critically Ill Patients ◽  
Interleukin 6 ◽  
Discharge Rate ◽  
Laboratory Data ◽  
Laboratory Findings ◽  
Laboratory Parameters

ObjectivesThe objective of this study was to investigate the clinical features and laboratory findings of patients with and without critical COVID-19 pneumonia and identify predictors for the critical form of the disease.MethodsDemographic, clinical, and laboratory data of 63 COVID-19 pneumonia patients were retrospectively reviewed. Laboratory parameters were also collected within 3–5 days, 7–9 days, and 11–14 days of hospitalization. Outcomes were followed up until March 12, 2020.ResultsTwenty-two patients developed critically ill pneumonia; one of them died. Upon admission, older patients with critical illness were more likely to report cough and dyspnoea with higher respiration rates and had a greater possibility of abnormal laboratory parameters than patients without critical illness. When compared with the non-critically ill patients, patients with serious illness had a lower discharge rate and longer hospital stays, with a trend towards higher mortality. The interleukin-6 level in patients upon hospital admission was important in predicting disease severity and was associated with the length of hospitalization.ConclusionsMany differences in clinical features and laboratory findings were observed between patients exhibiting non-critically ill and critically ill COVID-19 pneumonia. Non-critically ill COVID-19 pneumonia also needs aggressive treatments. Interleukin-6 was a superior predictor of disease severity.

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