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 Screening the Influence of Biomarkers for Metabolic Syndrome in Occupational Population Based on the Lasso Algorithm

2021 ◽  
Vol 9 ◽  
Author(s):  
Qiao-Ying Xie ◽  
Ming-Wei Wang ◽  
Zu-Ying Hu ◽  
Cheng-Jian Cao ◽  
Cong Wang ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Metabolic Syndrome ◽  
Logistic Regression ◽  
Prediction Model ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Area Under The Curve ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Lasso Regression

Aim: Metabolic syndrome (MS) screening is essential for the early detection of the occupational population. This study aimed to screen out biomarkers related to MS and establish a risk assessment and prediction model for the routine physical examination of an occupational population.Methods: The least absolute shrinkage and selection operator (Lasso) regression algorithm of machine learning was used to screen biomarkers related to MS. Then, the accuracy of the logistic regression model was further verified based on the Lasso regression algorithm. The areas under the receiving operating characteristic curves were used to evaluate the selection accuracy of biomarkers in identifying MS subjects with risk. The screened biomarkers were used to establish a logistic regression model and calculate the odds ratio (OR) of the corresponding biomarkers. A nomogram risk prediction model was established based on the selected biomarkers, and the consistency index (C-index) and calibration curve were derived.Results: A total of 2,844 occupational workers were included, and 10 biomarkers related to MS were screened. The number of non-MS cases was 2,189 and that of MS was 655. The area under the curve (AUC) value for non-Lasso and Lasso logistic regression was 0.652 and 0.907, respectively. The established risk assessment model revealed that the main risk biomarkers were absolute basophil count (OR: 3.38, CI:1.05–6.85), platelet packed volume (OR: 2.63, CI:2.31–3.79), leukocyte count (OR: 2.01, CI:1.79–2.19), red blood cell count (OR: 1.99, CI:1.80–2.71), and alanine aminotransferase level (OR: 1.53, CI:1.12–1.98). Furthermore, favorable results with C-indexes (0.840) and calibration curves closer to ideal curves indicated the accurate predictive ability of this nomogram.Conclusions: The risk assessment model based on the Lasso logistic regression algorithm helped identify MS with high accuracy in physically examining an occupational population.

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 Establishment of Metabolic Syndrome Prediction Model for Occupational Population based on the Lasso Regression Algorithm

2020 ◽  
Author(s):  
Qiao-Ying Xie ◽  
Ming-Wei Wang ◽  
Zu-Ying Hu ◽  
Yan-Ming Chu ◽  
Cheng-Jian Cao ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Metabolic Syndrome ◽  
Logistic Regression ◽  
Prediction Model ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Area Under The Curve ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Lasso Regression

Abstract Background: Metabolic syndrome (MS) screening is important for the early detection of occupational population. This study aimed to screen out biomarkers related to MS and establish a risk assessment and prediction model for the routine physical examination of an occupational population.Methods: The least absolute shrinkage and selection operator (Lasso) regression algorithm of machine learning was used to screen biomarkers related to MS. Then, the accuracy of the logistic regression model was further verified based on the Lasso regression algorithm. Finally, the screened biomarkers were used to establish a logistic regression model and calculate the odds ratio (OR) of the corresponding biomarkers. Results: A total of 2844 occupational workers were included, and 10 biomarkers related to MS were screened. The area under the curve (AUC) value for non-Lasso and Lasso regression was 0.652 and 0.907, respectively. The established risk assessment model revealed that the main risk factors were basophil absolute count (OR: 3.38), platelet packed volume (OR: 2.63), leukocyte count (OR: 2.01), red blood cell count (OR: 1.99), and alanine aminotransferase level (OR: 1.53). Conclusion: The risk assessment model based on the Lasso regression algorithm helped identify Metabolic syndrome with high accuracy in physically examining an occupational population.

Establishment of Prediction Models for venous thromboembolism in non-oncological urological inpatients--a single center experience

2020 ◽  
Author(s):  
Kaixuan Li ◽  
Haozhen Li ◽  
Quan Zhu ◽  
Ziqiang Wu ◽  
Zhao Wang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Venous Thromboembolism ◽  
Prediction Model ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Prediction Models ◽  
Predictive Ability ◽  
Multivariate Logistic Regression Model ◽  
Single Center Experience

Abstract Background To establish prediction models for venous thromboembolism (VTE) in non-oncological urological inpatients. Methods A retrospective analysis of 1453 inpatients was carried out and the risk factors for VTE had been clarified our previous studies. Results Risk factors included the following 5 factors: presence of previous VTE (X1), presence of anticoagulants or anti-platelet agents treatment before admission (X2), D-dimer value (≥ 0.89 µg/ml, X3), presence of lower extremity swelling (X4), presence of chest symptoms (X5). The logistic regression model is Logit (P) = − 5.970 + 2.882 * X1 + 2.588 * X2 + 3.141 * X3 + 1.794 * X4 + 3.553 * X5. When widened the p value to not exceeding 0.1 in multivariate logistic regression model, two addition risk factors were enrolled: Caprini score (≥ 5, X6), presence of complications (X7). The prediction model turns into Logit (P) = − 6.433 + 2.696 * X1 + 2.507 * X2 + 2.817 * X3 + 1.597 * X4 + 3.524 * X5 + 0.886 * X6 + 0.963 * X7. Internal verification results suggest both two models have a good predictive ability, but the prediction accuracy turns to be both only 43.0% when taking the additional 291 inpatients’ data in the two models. Conclusion We built two similar novel prediction models to predict VTE in non-oncological urological inpatients. Trial registration: This trial was retrospectively registered at http://www.chictr.org.cn/index.aspx under the public title“The incidence, risk factors and establishment of prediction model for VTE n urological inpatients” with a code ChiCTR1900027180 on November 3, 2019. (Specific URL to the registration web page: http://www.chictr.org.cn/showproj.aspx?proj=44677).

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 Two-stage prediction model for in-hospital mortality of patients with influenza infection

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chan-Wa Cheong ◽  
Chien-Lin Chen ◽  
Chih-Huang Li ◽  
Chen-June Seak ◽  
Hsiao-Jung Tseng ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Critical Care ◽  
Critical Illness ◽  
Hospital Mortality ◽  
Prediction Model ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Stage I ◽  
Multiple Logistic Regression ◽  
Two Stage

Abstract Background Infleunza is a challenging issue in public health. The mortality and morbidity associated with epidemic and pandemic influenza puts a heavy burden on health care system. Most patients with influenza can be treated on an outpatient basis but some required critical care. It is crucial for frontline physicians to stratify influenza patients by level of risk. Therefore, this study aimed to create a prediction model for critical care and in-hospital mortality. Methods This retrospective cohort study extracted data from the Chang Gung Research Database. This study included the patients who were diagnosed with influenza between 2010 and 2016. The primary outcome of this study was critical illness. The secondary analysis was to predict in-hospital mortality. A two-stage-modeling method was developed to predict hospital mortality. We constructed a multiple logistic regression model to predict the outcome of critical illness in the first stage, then S1 score were calculated. In the second stage, we used the S1 score and other data to construct a backward multiple logistic regression model. The area under the receiver operating curve was used to assess the predictive value of the model. Results In the present study, 1680 patients met the inclusion criteria. The overall ICU admission and in-hospital mortality was 10.36% (174 patients) and 4.29% (72 patients), respectively. In stage I analysis, hypothermia (OR = 1.92), tachypnea (OR = 4.94), lower systolic blood pressure (OR = 2.35), diabetes mellitus (OR = 1.87), leukocytosis (OR = 2.22), leukopenia (OR = 2.70), and a high percentage of segmented neutrophils (OR = 2.10) were associated with ICU admission. Bandemia had the highest odds ratio in the Stage I model (OR = 5.43). In stage II analysis, C-reactive protein (OR = 1.01), blood urea nitrogen (OR = 1.02) and stage I model’s S1 score were assocaited with in-hospital mortality. The area under the curve for the stage I and II model was 0.889 and 0.766, respectively. Conclusions The two-stage model is a efficient risk-stratification tool for predicting critical illness and mortailty. The model may be an optional tool other than qSOFA and SIRS criteria.

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