Early Risk Factors for the Duration of Severe Acute Respiratory Syndrome Coronavirus 2 Viral Positivity in Patients With Coronavirus Disease 2019

Abstract Background Pneumonia coronavirus disease 2019 (COVID-19) has became a pandemic. However, information on early risk factors for the duration of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral positivity is not yet available. Methods In this prospective study, a cohort of 137 patients with confirmed SARS-CoV-2 were enrolled. Clinical information and laboratory data were retrieved from electronic medical records. Viral positivity duration was calculated by the interval from the day of confirmed SARS-CoV-2 positive results to the day SARS-CoV-2 testing showed negative results in these 137 patients with COVID-19. Early risk factors for the duration of SARS-CoV-2 viral positivity were evaluated. Results The median SARS-CoV-2 viral positivity duration is 12 days (range, 4 to ~45) for this cohort. Cox regression results showed a significantly shorter viral positivity duration was related to younger age (hazard ratio [HR], .658; P = .017); disease not being severe (HR, .653; P = .076); higher lymphocyte (HR, 1.464; P = .033), eosinophil (HR, 1.514; P = .020), and CD8+ T-cell (HR, 1.745; P = .033) counts; and lower IL-6 (HR, .664; P = .036) and IL-10 (HR, .631; P = .021). Multivariate analysis with covariable-adjusted results showed that the CD8+ T-cell count (HR, 2.376; P= .114) was a predominant risk factor for the duration of SARS-CoV-2 viral positivity. Conclusions Our findings show early laboratory parameters such as CD8+ T-cell count to be risk factors for the duration of SARS-CoV-2 viral positivity, which has significance in the control and prevention of the disease.

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Sepsis-associated Encephalopathy In ICU Admissions: Prevalence, Early Risk of Death, and its Early Prevent and Control

10.21203/rs.3.rs-58687/v1 ◽

2020 ◽

Author(s):

Dao-Ming Tong ◽

Shao-Dan Wang ◽

Yuan-Wei Wang ◽

Ying Wang ◽

Yuan-Yuan Gu ◽

...

Keyword(s):

Risk Factors ◽

Critically Ill ◽

Cox Regression ◽

Abdominal Cavity ◽

Early Prevention ◽

Cox Regression Analysis ◽

Risk Of Death ◽

Early Risk ◽

Early Risk Factors ◽

And Control

Abstract Background: Sepsis-associated encephalopathy (SAE) is a common encephalopathy in ICU. We are to definite whether SAE present an high prevalence rate and early risk factors for death in ICU 48 hours, while to cognize its important of early prevention/ control.Methods: We retrospectively enrolled patients with acute critically ill from ICU (January, 2015 to January, 2017). All patients were selected from onset to ICU ≤3 hours. The prevalence and some early risk factors of death in SAE was estimated by using a continuous head and thorax /abdominal cavity CT scans. Results: 748 critically ill adults were analyzed. The prevalence of sepsis within initial 48 hours was 40.4% (302/748). The median time from infection to sepsis was 9 hours ( range, 1-48 ). The SAE (93.4%, 282/302) was diagnosed in sepsis patients, and most of them (96.8%) presented multiple organ dysfunction syndromes (MODS). While the fatality of SAE was in 32.0% at initial 48 hours and 69.1% at initial 14 days. Cox regression analysis, unused antibiotic within initial 3 hours (OR, 0.39; 95% CI, 0.22-0.89), severe inflammatory storm (OR, 0.70; 95% CI, 0.58- 0.94), lower GCS (OR, 2.7; 95% CI, 1.5-3.6), and MODS (OR, 0.37; 95% CI, 0.26-0.96) were early risk factors for death in SAE. Early risk factors for predicting SAE were related to severe inflammatory storm (OR, 3.10; 95% CI, 2.28-4.33), MODS (OR, 3.57; 95% CI, 2.73- 4.67), and unused antibiotics within initial 3 hours (OR, 0.25; 95% CI, 0.11-0.56).Conclusions: SAE in ICU is an high prevalent acute brain dysfunction and most with MODS. The early bad prognosis in SAE was related to the failure of early prevention and control.

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The pathway to special education: The role of early risk factors in disability identification

PsycEXTRA Dataset ◽

10.1037/e569872014-001 ◽

2014 ◽

Author(s):

Whitney A. Leboeuf ◽

Benjamin Brumley ◽

John W. Fantuzzo ◽

Cody A. Hostutler

Keyword(s):

Risk Factors ◽

Special Education ◽

Early Risk ◽

Early Risk Factors ◽

Disability Identification

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Lymphocyte subset analysis to evaluate the prognosis of HIV-negative patients with pneumocystis pneumonia

BMC Infectious Diseases ◽

10.1186/s12879-021-06124-5 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Fan Jin ◽

Jing Xie ◽

Huan-ling Wang

Keyword(s):

T Cell ◽

Cell Count ◽

Lymphocyte Subsets ◽

Nk Cell ◽

Cd8 T Cell ◽

Pneumocystis Pneumonia ◽

Lymphocyte Subset ◽

Cd4 T Cell ◽

Cell Counts ◽

Hiv Negative

Abstract Objectives We analysed the peripheral blood lymphocyte subsets of human immunodeficiency virus (HIV)-negative patients infected with pneumocystis pneumonia (PCP) to determine the relationships between the levels of different types of lymphocytes and the prognosis of patients. Methods We retrospectively reviewed HIV-negative patients with PCP diagnosed in our department. All the eligible patients underwent lymphocyte subset analysis on admission. Results A total of 88 HIV-negative PCP patients were enrolled in the study. In univariate analyses, low CD4+ T cell count, low CD8+ T cell count, and low natural killer cell (NK cell) count were associated with higher in-hospital mortality. CD8+ T cell count ≤300/μL was found to be an independent risk factor for poor prognosis in multivariate logistical regression analysis (p = 0.015, OR = 11.526, 95% CI = 1.597–83.158). Although low CD4+ T cell and NK cell counts were not independent risk factors, the mortality rates of PCP patients decreased as the CD4+ T cell and NK cell counts increased. Conclusion The immune process of Pneumocystis jirovecii infection is complex but important. We propose that lymphocyte subsets could give clinicians a better understanding of patient immune status, helping with the early identification of potentially lethal infections and treatment decision making, such as adjusting the immunosuppressive regimen and choosing an appropriate patient monitoring level.

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Early Risk Factors for Obesity in the First 1000 Days—Relationship with Body Fat and BMI at 2 Years

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18158179 ◽

2021 ◽

Vol 18 (15) ◽

pp. 8179

Author(s):

Mercedes Díaz-Rodríguez ◽

Celia Pérez-Muñoz ◽

Jesús Carretero-Bravo ◽

Catalina Ruíz-Ruíz ◽

Manuel Serrano-Santamaría ◽

...

Keyword(s):

Risk Factors ◽

Weight Gain ◽

Body Fat ◽

Early Pregnancy ◽

Body Fat Percentage ◽

Z Score ◽

Fat Percentage ◽

Early Risk ◽

Early Risk Factors ◽

Increased Risk

(1) Background: Obesity is defined as an excessive accumulation of body fat. Several early developmental factors have been identified which are associated with an increased risk of childhood obesity and increased adiposity in childhood. The primary objective of the present study is to analyse the effect of various early risk factors on Body Mass Index (BMI) and body fat percentage at 2 years of age. (2) Methods: A prospective cohort study design was used, with the sample consisting of 109 mother-child pairs from whom data were collected between early pregnancy and 2 years old. Adiposity was determined based on skinfold measurements using the Brooks and Siri formulae. Mean comparison tests (Student’s t-test and ANOVAs) and multiple linear regression models were used to analyse the relationship between early programming factors and dependent variables. (3) Results: Maternal excess weight during early pregnancy (β = 0.203, p = 0.026), gestational smoking (β = 0.192, p = 0.036), and accelerated weight gain in the first 2 years (β = − 0.269, p = 0.004) were significantly associated with high body fat percentage. Pre-pregnancy BMI and accelerated weight gain in the first 2 years were associated with high BMI z-score (β = 0.174, p = 0.047 and β = 0.417, p = 0.000 respectively). The cumulative effect of these variables resulted in high values compared to the baseline zero-factor group, with significant differences in BMI z-score (F = 8.640, p = 0.000) and body fat percentage (F = 5.402, p = 0.002) when three factors were present. (4) Conclusions: The presence of several early risk factors related to obesity in infancy was significantly associated with higher BMI z-score and body fat percentage at 2 years of age. The presence of more than one of these variables was also associated with higher adiposity at 2 years of age. Early prevention strategies should address as many of these factors as possible.

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