scholarly journals Crosstalk between Heart Failure and Cognitive Impairment via hsa-miR-933/RELB/CCL21 Pathway

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Wenxiao Feng ◽  
Jie Yang ◽  
Wenchao Song ◽  
Yitao Xue
Keyword(s):  
Heart Failure ◽  
Logistic Regression ◽  
Cognitive Impairment ◽  
Regression Model ◽  
Messenger Rna ◽  
Logistic Regression Model ◽  
Regulatory Pathways ◽  
The Core ◽  
Ppi Networks ◽  
Underlying Mechanisms

Background. The association between heart failure (HF) and cognitive impairment has received increasing attention from scholars and researchers in recent years. However, no systematic studies have been carried out yet focused on the crosstalk between heart failure and cognitive impairment via miRNAs. Methods. GSE104150, GSE53473, GSE120584, and GSE116250 with RNA-seq data and clinical data were downloaded from the GSE database. All data were statistically analysed using R software to detect DE-miRNAs and DE-mRNAs associated with both HF and cognitive impairment. Protein-protein interaction (PPI) networks were mapped, and a logistic regression model for cognitive impairment prediction was developed. Furthermore, the TTRUST database and miRWalk were used to map miRNA-transcription factor (TF) and messenger RNA (mRNA) regulatory pathways. Finally, core TFs were enriched for analysis. Results. Differentially enriched DE-miRNAs and DE-mRNAs both present in HF and cognitive impairment were determined. A logistic regression model established based on DE-miRNAs was validated to have a strong performance in cognitive impairment prediction. The core miRNA-TF-mRNA pathway was formed by mapping the PPI networks associated with the two diseases. Further GSEA was performed with V-rel reticuloendotheliosis viral oncogene homolog B (RELB) as the core TF, and the retinol metabolism and gap junction pathways were analysed. Conclusions. This study was the first attempt to predict the crosstalk and examine underlying mechanisms between HF and cognitive impairment applying bioinformatics. The findings suggested a potential hsa-miR-933/RELB/CCL21 regulatory axis correlated with HF and neurological disorders (or cognitive impairment), according to PPI networks.

The impact of atrial fibrillation on hospitalization outcomes for patients with chronic lymphocytic leukemia: A large database analysis.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e19508-e19508
Author(s):  
Mohammad Ammad Ud Din ◽  
Samarthkumar Thakkar ◽  
Harsh P. Patel ◽  
Syed Ather Hussain ◽  
Aneeqa Zafar ◽  
...  
Keyword(s):  
Heart Failure ◽  
Atrial Fibrillation ◽  
Logistic Regression ◽  
Chronic Lymphocytic Leukemia ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Lymphocytic Leukemia ◽  
Total Cost ◽  
Patient Admissions ◽  
All Cause Mortality

e19508 Background: With the increased use of novel agents like Bruton tyrosine kinase inhibitors (BTKi) for the treatment of chronic lymphocytic leukemia (CLL), the incidence of atrial fibrillation (AF) is on the rise in these patients. However, the excess burden added by AF to the morbidity and mortality of CLL patients is unclear. Methods: Using the appropriate ICD-9 and ICD-10 codes, the National Inpatient Sample (NIS) database was accessed to gather data of hospitalized CLL patients with AF from 2008 to 2019. Propensity-score matching (PSM) and logistic regression model were performed to control for baseline patient factors like age, sex, income, and the relevant co-morbidities to match 7265 CLL patient admissions with AF and 7265 CLL patient admissions without AF. The primary outcome was all-cause mortality (ACM), while secondary outcomes included stroke, acute heart failure (AHF), and total cost of hospital stay. Results: The mean age of the cohorts was 82 years. Females made up 44% of both groups. The AF group had similar prevalence of systemic hypertension (62.38% vs 62.10%; p= 0.73), diabetes mellitus (5.09% vs 5.43%; p= 0.35), congestive heart failure (5.57% vs 5.36%; p= 0.58), valvular heart disease (1.17% vs 1.44%; p= 0.14), and pulmonary hypertension (0.21% vs 0.14%; p= 0.31) compared to the group without AF. PSM revealed CLL patients with AF had a higher rate of ACM (6.06% vs 4.47%; p= <0.0001), AHF (7.50% vs 3.85%; p= <0.001), and stroke (3.09% vs 1.65%; p= <0.0001). Admission in the AF group also had a higher median total cost of hospital stay ($9097 vs $7646). A logistic regression model was done to adjust for confounders which revealed similar results for the AF group with increased adjusted odd’s ratio (aOR) of ACM (aOR:1.39, 95% confidence interval (CI): 1.19-1.61; p= <0.001), AHF (aOR: 2.16, 95% CI: 1.85-2.52; p= <0.001), and stroke (aOR:1.94, 95% CI: 1.54-2.44; P= <0.001) (Table). Conclusions: Our data suggest that hospitalized CLL patients with AF are at a significantly increased risk of all-cause mortality, AHF, and stroke. Several limitations like the inability to establish the temporal relationship between CLL and AF and the lack of data regarding medications of individual patients are important to keep in mind while interpreting the results.[Table: see text]

scholarly journals Prevalence of, and risk factors for, cognitive impairment in lacunar stroke

2022 ◽  
pp. 174749302110649
Author(s):  
Laura Ohlmeier ◽  
Stefania Nannoni ◽  
Claudia Pallucca ◽  
Robin B Brown ◽  
Laurence Loubiere ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Logistic Regression ◽  
Cognitive Impairment ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Full Time ◽  
Lacunar Stroke ◽  
Stroke Patients ◽  
Increased Risk

Background: Small vessel disease (SVD) is associated with vascular cognitive impairment (VCI) but why VCI occurs in some, but not other patients, is uncertain. We determined the prevalence of, and risk factors for, VCI in a large cohort of patients with lacunar stroke. Methods: Participants with magnetic resonance imaging (MRI)-confirmed lacunar stroke were recruited in the multicenter DNA Lacunar 2 study and compared with healthy controls. A logistic regression model was used to determine which vascular risk factors and MRI parameters were independent predictors of VCI, assessed using the Brief Memory and Executive Test (BMET). Results: A total of 912 lacunar stroke patients and 425 controls were included, with mean ( SD) age of 64.6 (12.26) and 64.7 (12.29) years, respectively. VCI was detected in 38.8% lacunar patients and 13.4% controls. In a logistic regression model, diabetes mellitus (odds ratio (OR) = 1.98 (95% confidence interval (CI) = 1.40–2.80), p < 0.001) and higher body mass index (BMI) (OR = 1.03 (95% CI = 1.00–1.05), p = 0.029) were independently associated with increased risk of VCI, and years of full-time education with lower risk (OR = 0.92 (95% CI = 0.86–0.99), p = 0.018). When entering both lacune count and white matter hyperintensity (WMH) in the same logistic regression model, only WMH grade was significantly associated with VCI (OR = 1.46 (95% CI = 1.24–1.72), p < 0.001). Conclusion: VCI is common in lacunar stroke patients, affecting almost 40%. This prevalence suggests that it should be routinely screened for in clinical practice. Risk factors for VCI in patients with lacunar stroke include diabetes mellitus, depressive symptoms, higher BMI, and WMH severity, while education is protective.

scholarly journals Electronic medical record-based model to predict the risk of 90-day readmission for patients with heart failure

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Bo-yu Tan ◽  
Jun-yuan Gu ◽  
Hong-yan Wei ◽  
Li Chen ◽  
Su-lan Yan ◽  
...  
Keyword(s):  
Heart Failure ◽  
Logistic Regression ◽  
Regression Model ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Logistic Regression Model ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression ◽  
Distribution Width ◽  
Readmission Risk

Abstract Background Several heart failure (HF) risk models exist, however, most of them perform poorly when applied to real-world situations. This study aimed to develop a convenient and efficient risk model to identify patients with high readmission risk within 90 days of HF. Methods A multivariate logistic regression model was used to predict the risk of 90-day readmission. Data were extracted from electronic medical records from January 1, 2017 to December 31, 2017 and follow-up records of patients with HF within 3 months after discharge. Model performance was evaluated using a receiver operating characteristic curve. All statistical analysis was done using R version 3.5.0. Results A total of 350 patients met the inclusion criterion of being readmitted within in 90 days. All data sets were randomly divided into derivation and validation cohorts at a 7/3 ratio. The baseline data were fairly consistent among the derivation and validation cohorts. The variables most clearly related to readmission were logarithm of serum N-terminal pro b-type natriuretic peptide (NT-proBNP) level, red cell volume distribution width (RDW-CV), and Charlson comorbidity index (CCI). The model had good discriminatory ability (C-statistic = 0.73). Conclusions We developed and validated a multivariate logistic regression model to predict the 90-day readmission risk for Chinese patients with HF. The predictors included in the model are derived from electronic medical record (EMR) admission data, making it easier for physicians and pharmacists to identify high-risk patients and tailor more intensive precautionary strategies.

scholarly journals Work absence and multimorbidity in Portugal: results from the 1st National Health Examination Survey

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
J Matos ◽  
C Matias Dias ◽  
A Félix
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Chronic Diseases ◽  
National Health ◽  
Logistic Regression Model ◽  
Health Examination ◽  
Work Absence ◽  
Health Examination Survey ◽  
Absence From Work ◽  
The Impact

Abstract Background Studies on the impact of patients with multimorbidity in the absence of work indicate that the number and type of chronic diseases may increase absenteeism and that the risk of absence from work is higher in people with two or more chronic diseases. This study analyzed the association between multimorbidity and greater frequency and duration of work absence in the portuguese population between the ages of 25 and 65 during 2015. Methods This is an epidemiological, observational, cross-sectional study with an analytical component that has its source of information from the 1st National Health Examination Survey. The study analyzed univariate, bivariate and multivariate variables under study. A multivariate logistic regression model was constructed. Results The prevalence of absenteeism was 55,1%. Education showed an association with absence of work (p = 0,0157), as well as professional activity (p = 0,0086). It wasn't possible to verify association between the presence of chronic diseases (p = 0,9358) or the presence of multimorbidity (p = 0,4309) with absence of work. The prevalence of multimorbidity was 31,8%. There was association between age (p &lt; 0,0001), education (p &lt; 0,001) and yield (p = 0,0009) and multimorbidity. There is no increase in the number of days of absence from work due to the increase in the number of chronic diseases. In the optimized logistic regression model the only variables that demonstrated association with the variable labor absence were age (p = 0,0391) and education (0,0089). Conclusions The scientific evidence generated will contribute to the current discussion on the need for the health and social security system to develop policies to patients with multimorbidity. Key messages The prevalence of absenteeism and multimorbidity in Portugal was respectively 55,1% and 31,8%. In the optimized model age and education demonstrated association with the variable labor absence.

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