scholarly journals Troponin Elevation on Admission Along With Dynamic Changes and Their Association With Hemorrhagic Transformation After Thrombolysis

2021 ◽  
Vol 13 ◽  
Author(s):  
Zicheng Cheng ◽  
Zhenxiang Zhan ◽  
Xiaoyan Huang ◽  
Lingfan Xia ◽  
Tong Xu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Heart Disease ◽  
Regression Model ◽  
Cardiac Troponin ◽  
Logistic Regression Model ◽  
Hemorrhagic Transformation ◽  
Unfavorable Outcome ◽  
Cranial Computed Tomography ◽  
Dynamic Changes ◽  
Troponin Elevation

Background: Hemorrhagic transformation (HT) is a common complication of intravenous thrombolysis with alteplase. Cardiac troponin has been found to be associated with poor prognosis and cognitive impairment in acute ischemic stroke. But studies on the relationship between troponin and HT after thrombolysis are scarce.Methods: This study retrospectively analyzed thrombolytic patients from June 2015 to June 2021 in the Second Affiliated Hospital of Wenzhou Medical University. Cardiac troponin I were measured on admission and on following days to determine the presence of elevation and dynamic changes. HT within 24–36 h after treatment was identified by cranial computed tomography (CT). Besides, a score on the modified Rankin Scale (mRS) > 2 at discharge was defined as unfavorable outcome. Univariate analysis was used to explore the factors related to the troponin elevation on admission and troponin dynamic changes. Multivariate logistic regression model was used to investigated the association between troponin elevation on admission, troponin dynamic changes and HT after thrombolysis, respectively.Results: Troponin levels on admission were measured in 377 patients, and follow-up assay was performed in 292 patients (77.5%). 39 patients (10.3%) had troponin elevation on admission, and 66 patients (22.6%) had troponin dynamic changes comprising rising and falling pattern. The pre-existing heart disease, renal insufficiency and higher stroke severity are related to both troponin elevation on admission and the subsequent troponin dynamic changes. After adjusting the potential confounding factors, logistic regression model showed that patients with troponin elevation on admission had insignificant trend to develop HT (OR 2.23, 95%CI 0.96–5.21, p = 0.063), while patients with troponin dynamic changes had significantly higher risk of HT (OR 2.27, 95%CI 1.06–4.85, p = 0.034). Compared to the troponin elevation, a statistically stronger association was present between rising troponin dynamic changes and unfavorable outcome (OR 2.20, 95%CI 1.05–4.60, p = 0.037).Conclusion: Troponin dynamic changes are associated with HT after thrombolysis. Serial measurements are quite necessary in thrombolytic patients with risk factors associated with troponin dynamic changes (e.g., advanced age, pre-existing heart disease, higher NIHSS score, and troponin elevation on admission).

scholarly journals Exploring the diagnostic effectiveness for myocardial ischaemia based on CCTA myocardial texture features

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hengyu Zhao ◽  
Lijie Yuan ◽  
Zhishang Chen ◽  
Yuting Liao ◽  
Jiangzhou Lin
Keyword(s):  
Coronary Heart Disease ◽  
Logistic Regression ◽  
Heart Disease ◽  
Regression Model ◽  
Myocardial Ischaemia ◽  
Logistic Regression Model ◽  
Texture Features ◽  
Spearman Correlation ◽  
Multivariable Logistic Regression Model ◽  
Training Cohort

Abstract Background To explore the characteristics of myocardial textures on coronary computed tomography angiography (CCTA) images in patients with coronary atherosclerotic heart disease, a classification model was established, and the diagnostic effectiveness of CCTA for myocardial ischaemia patients was explored. Methods This was a retrospective analysis of the CCTA images of 155 patients with clinically diagnosed coronary heart disease from September 2019 to January 2020, 79 of whom were considered positive (myocardial ischaemia) and 76 negative (normal myocardial blood supply) according to their clinical diagnoses. By using the deep learning model-based CQK software, the myocardium was automatically segmented from the CCTA images and used to extract texture features. All patients were randomly divided into a training cohort and a test cohort at a 7:3 ratio. The Spearman correlation and least absolute shrinkage and selection operator (LASSO) method were used for feature selection. Based on the selected features of the training cohort, a multivariable logistic regression model was established. Finally, the test cohort was used to verify the regression model. Results A total of 387 features were extracted from the CCTA images of the 155 coronary heart disease patients. After performing dimensionality reduction with the Spearman correlation and LASSO, three texture features were selected. The accuracy, area under the curve, specificity, sensitivity, positive predictive value and negative predictive value of the constructed multivariable logistic regression model with the test cohort were 0.783, 0.875, 0.733, 0.875, 0.650 and 0.769, respectively. Conclusion CCTA imaging texture features of the myocardium have potential as biomarkers for diagnosing myocardial ischaemia.

Does micronutrients intake modulate the risk of coronary heart disease?

2019 ◽  
Vol 49 (3) ◽  
pp. 368-380 ◽  
Author(s):  
Mohammed Azab ◽  
Abdel-Ellah Al-Shudifat ◽  
Lana Agraib ◽  
Sabika Allehdan ◽  
Reema Tayyem
Keyword(s):  
Coronary Heart Disease ◽  
Logistic Regression ◽  
Heart Disease ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Multinomial Logistic Regression ◽  
Daily Intake ◽  
Dietary Intakes ◽  
Content Type ◽  
Study Results

Purpose The purpose of this study was to examine the relationship between micronutrient intake and coronary heart disease (CHD) in middle-aged Jordanian participants. Design/methodology/approach A case-control study was conducted among patients referring for elective coronary angiography. A total of 400 patients were enrolled in this study. Face-to-face interview was used to complete food frequency questionnaire from which the authors derived usual daily intake of micronutrients. The mean age of participates was 52 years and their average BMI was 30.7 kg/m2. Multinomial logistic regression model and linear logistic regression model were used to calculate odd ratios (OR) and its 95 per cent confidence interval (CI) and p-value for trend, respectively. The association between the risk of CHD and micronutrients intake was adjusted for the age, gender, BMI, smoking, physical activity, total energy intake, occupation, education level, marital status and family history. Findings The study results showed no significant differences between cases and controls for dietary intakes of micronutrients, except for the intake of calcium (p < 0.005), magnesium (p < 0.025), phosphorus (p < 0.023) and potassium (p < 0.006) which were lower in cases than controls. Although no significant trend was observed between most of the dietary intake of micronutrients and the risk of developing CHD, a significant protective effect of magnesium [OR 0.52; 95 per cent CI (0.29-0.95)], phosphorus [OR 0.44; 95 per cent CI (0.24-0.80)] and potassium [OR 0.41; 95 per cent CI (0.22-0.74)] against the risk of CHD was detected. Originality/value The findings from this study provide strong evidence that the intake of micronutrients such as calcium, magnesium, phosphorus and potassium has no significant associations with the risk of CHD.

scholarly journals Emergency department routine data and the diagnosis of acute ischemic heart disease in patients with atypical chest pain

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0241920
Author(s):  
Ki Hong Kim ◽  
Jeong Ho Park ◽  
Young Sun Ro ◽  
Ki Jeong Hong ◽  
Kyoung Jun Song ◽  
...  
Keyword(s):  
Emergency Department ◽  
Logistic Regression ◽  
Heart Disease ◽  
Chest Pain ◽  
Ischemic Heart Disease ◽  
Prediction Model ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Single Variable ◽  
Atypical Chest Pain

Background Due to an aging population and the increasing proportion of patients with various comorbidities, the number of patients with acute ischemic heart disease (AIHD) who present to the emergency department (ED) with atypical chest pain is increasing. The aim of this study was to develop and validate a prediction model for AIHD in patients with atypical chest pain. Methods and results A chest pain workup registry, ED administrative database, and clinical data warehouse database were analyzed and integrated by using nonidentifiable key factors to create a comprehensive clinical dataset in a single academic ED from 2014 to 2018. Demographic findings, vital signs, and routine laboratory test results were assessed for their ability to predict AIHD. An extreme gradient boosting (XGB) model was developed and evaluated, and its performance was compared to that of a single-variable model and logistic regression model. The area under the receiver operating characteristic curve (AUROC) was calculated to assess discrimination. A calibration plot and partial dependence plots were also used in the analyses. Overall, 4,978 patients were analyzed. Of the 3,833 patients in the training cohort, 453 (11.8%) had AIHD; of the 1,145 patients in the validation cohort, 166 (14.5%) had AIHD. XGB, troponin (single-variable), and logistic regression models showed similar discrimination power (AUROC [95% confidence interval]: XGB model, 0.75 [0.71–0.79]; troponin model, 0.73 [0.69–0.77]; logistic regression model, 0.73 [0.70–0.79]). Most patients were classified as non-AIHD; calibration was good in patients with a low predicted probability of AIHD in all prediction models. Unlike in the logistic regression model, a nonlinear relationship-like threshold and U-shaped relationship between variables and the probability of AIHD were revealed in the XGB model. Conclusion We developed and validated an AIHD prediction model for patients with atypical chest pain by using an XGB model.

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.

Sign in / Sign up

Export Citation Format

Share Document