scholarly journals Incorporating Ensembled stacked approach into Automated Machine Learning in an attempt to predict acute ischemic heart disease in patients with atypical chest pain: Secondary analysis of a single-center retrospective cohort study

2021 ◽  
Author(s):  
Ali Haider Bangash ◽  
Arshiya Fatima ◽  
Saiqa Zehra ◽  
Ali Haider Shah ◽  
Syed Mohammad Mehmood Abbas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cohort Study ◽  
Heart Disease ◽  
Chest Pain ◽  
Ischemic Heart Disease ◽  
Retrospective Cohort ◽  
Secondary Analysis ◽  
Single Center ◽  
Atypical Chest Pain ◽  
Automated Machine Learning

Automated Machine Learning is explored to predict AIHD in patients presenting with atypical chest pain with an ensembled stacked approach incorporated.

scholarly journals Nonlinear Relationship Between HbA1c and Coronary Artery Calcium Score Progression: A Secondary Analysis Based on a Retrospective Cohort Study

2021 ◽  
Author(s):  
Jing Yu ◽  
Bo Gao
Keyword(s):  
Cohort Study ◽  
Heart Disease ◽  
Coronary Artery ◽  
Retrospective Cohort Study ◽  
Coronary Artery Calcium ◽  
Retrospective Cohort ◽  
Coronary Artery Calcium Score ◽  
Secondary Analysis ◽  
Calcium Score ◽  
Non Linear

Abstract ObjectiveSome previous studies was to clarify the correlation between glycated hemoglobin(HbA1c) and coronary heart disease (CHD) and the evidence regarding the correlation was still debated. However, there are fewer scientific dissertations about the correlation between HbA1c and coronary artery calcium score progression. Consequently, the present study was undertaken to explore the link of HbA1c on coronary artery calcium score progression in South Korea.MethodsThis study is a secondary analysis based on a retrospective cohort study. 8151 participants received a health check-up program at the Health Promotion Center of the Samsung Medical Center in Seoul, South Korea, from March 1, 2003 to December 31, 2013. We then used Cox proportional-hazards regression model to evaluate the independent relationship between HbA1c and coronary artery calcium score progression.ResultsAfter adjusting potential confounders (age, sex, BMI, height, weight, SBP, DBP, TC, LDL-C, HDL-C, triglycerides,smoking status, alcohol consumption, reflux esophagitis status, hypertension, diabetes, dyslipidemia, ischemic heart disease and cerebrovascular disease), non-linear relationship was detected between HbA1c and coronary artery calcium score progression, whose point was 5.8%. The effect sizes and the confidence intervals on the left and right sides of inflection point were 2.05 (1.85 to 2.27) and 1.04 (0.99 to 1.10) , respectively. ConclusionThe relationship between HbA1c and coronary artery calcium score progression is non-linear. HbA1c is positively related with coronary artery calcium score progression when HbA1c was less than 5.8%.

scholarly journals Incidence of Heart Failure among Diabetic Patients with Ischemic Heart Disease: A Cohort Study

2020 ◽  
Author(s):  
Senbeta Guteta Abdissa ◽  
Wakgari Deressa ◽  
Amit J Shah
Keyword(s):  
Diabetes Mellitus ◽  
Heart Failure ◽  
Risk Factor ◽  
Cohort Study ◽  
Heart Disease ◽  
Ischemic Heart Disease ◽  
Retrospective Cohort Study ◽  
Retrospective Cohort ◽  
Independent Risk Factor ◽  
Ischemic Heart

Abstract Background: In population studies of heart failure (HF), diabetes mellitus (DM) has been shown to be an independent risk factor. However, the evidence evaluating it as an independent risk factor in incident HF in patients with ischemic heart disease (IHD) is scarce. Our study aimed to assess the incidence of HF in diabetic IHD patients compared to non-diabetic IHD patients in Ethiopia. Methods: A retrospective cohort study was conducted among 306 patients with IHD followed-up at Tikur Anbessa Specialized Hospital in Addis Ababa, Ethiopia. The IHD patients who did not have HF at baseline were followed for 24 months beginning from November 30, 2015. We assessed the incidence of HF in patients with diabetic IHD versus the non-diabetic IHD. Cox proportional hazards models were used to assess the association between diabetic IHD and HF after controlling for important covariates. Hypertension was examined as a possible effect modifier as well. Results: The mean age was 56.8 years, 69% were male, and 31% were diabetic. During the 24 months follow-up period, 196 (64.1%) had incident HF. On multivariate Cox regression, DM was significantly associated with incident HF [Hazard Ratio = 2.04, 95% confidence interval (CI): 1.32-3.14, p = 0.001]. Furthermore, when the patients were stratified by hypertension (HTN) status, DM was associated with worse prognosis, and the strongest association was in those with co-existing DM and HTN [HR = 2.57,95% CI =1.66-3.98, p<0.0001], followed by the presence of DM without HTN [HR 2.27, 95% CI = 1.38-3.71, p=0.001] (compared to those with neither). Conclusion: DM is the strongest predictor of incident HF, compared to other traditional risk factors, in Ethiopian patients with IHD. Those with both DM and HTN are at the highest risk. Key Words: Ischemic heart disease; Heart failure; Incidence; Diabetes Mellitus; Retrospective cohort study

scholarly journals Adverse outcomes in COVID-19 and diabetes: a retrospective cohort study from three London teaching hospitals

2021 ◽  
Vol 9 (1) ◽  
pp. e001858
Author(s):  
Chioma Izzi-Engbeaya ◽  
Walter Distaso ◽  
Anjali Amin ◽  
Wei Yang ◽  
Oluwagbemiga Idowu ◽  
...  
Keyword(s):  
Cohort Study ◽  
Platelet Count ◽  
Heart Disease ◽  
Ischemic Heart Disease ◽  
Retrospective Cohort Study ◽  
Retrospective Cohort ◽  
Risk Of Death ◽  
Antiplatelet Medication ◽  
Patients With Diabetes ◽  
Poor Outcomes

IntroductionPatients with diabetes mellitus admitted to hospital with COVID-19 have poorer outcomes. However, the drivers of poorer outcomes are not fully elucidated. We performed detailed characterization of patients with COVID-19 to determine the clinical and biochemical factors that may be drivers of poorer outcomes.Research design and methodsThis is a retrospective cohort study of 889 consecutive inpatients diagnosed with COVID-19 between March 9 and April 22, 2020 in a large London National Health Service Trust. Unbiased multivariate logistic regression analysis was performed to determine variables that were independently and significantly associated with increased risk of death and/or intensive care unit (ICU) admission within 30 days of COVID-19 diagnosis.Results62% of patients in our cohort were of non-white ethnic background and the prevalence of diabetes was 38%. 323 (36%) patients met the primary outcome of death/admission to the ICU within 30 days of COVID-19 diagnosis. Male gender, lower platelet count, advancing age and higher Clinical Frailty Scale (CFS) score (but not diabetes) independently predicted poor outcomes on multivariate analysis. Antiplatelet medication was associated with a lower risk of death/ICU admission. Factors that were significantly and independently associated with poorer outcomes in patients with diabetes were coexisting ischemic heart disease, increasing age and lower platelet count.ConclusionsIn this large study of a diverse patient population, comorbidity (ie, diabetes with ischemic heart disease; increasing CFS score in older patients) was a major determinant of poor outcomes with COVID-19. Antiplatelet medication should be evaluated in randomized clinical trials among high-risk patient groups.

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.

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