Application of a decision tree to establish factors associated with a nomogram of aortic stiffness

Construction of a multi-classified decision tree model for identifying malignant pleural effusion and tuberculous pleural effusion

OBJECTIVE

Pleural effusion (PE) is a common clinical complication associated with various disorders. We aimed to utilize laboratory variables and their corresponding ratios in serum and PE for the differential diagnosis of multiple types of PE based on a decision tree (DT) algorithm.

METHODS

A total of 1435 untreated patients with PE admitted to The First Affiliated Hospital of Ningbo University were enrolled. The demographic and laboratory variables were collected and compared. The receiver operating characteristic curve was used to select important variables for diagnosing malignant pleural effusion (MPE) or tuberculous pleural effusion (TPE) and included in the DT model. The data were divided into the training set and the test set at a ratio of 7:3. The training data was used to develop the DT model, and the test data was for evaluating the model. Independent data was collected as external validation.

RESULTS

Three PE indicators (carcinoembryonic antigen, adenosine deaminase [ADA], and total protein), two serum indicators (neuron-specific enolase and cytokeratin 19 fragments), and two ratios [high-sensitivity C-reactive protein (hsCRP)/ PE lymphocyte and hsCRP/PE ADA] were used to construct the DT model. The area under the curve (AUC), sensitivity, and specificity for diagnosing MPE were 0.963, 84.0%, 91.6% in the training set, 0.976, 84.1%, 88.6% in the test set, and 0.955,83.3%, 86.7% in the external validation set. The AUC, sensitivity, and specificity of diagnosing TPE were 0.898, 86.8%, 92.3% in the training set, 0.888, 88.8%, 92.7% in the test set, and 0.778, 84.8%, 94.3% in the external validation set.

CONCLUSION

The DT model showed good diagnostic efficacy and could be applied for the differential diagnosis of MPE and TPE in clinical settings.

Arterial stiffness and biological parameters: A decision tree machine learning application in hypertensive participants

Arterial stiffness, measured by arterial stiffness index (ASI), could be considered a main denominator in target organ damage among hypertensive subjects. Currently, no reported ASI normal references have been reported. The index of arterial stiffness is evaluated by calculation of a stiffness index. Predicted ASI can be estimated regardless to age, sex, mean blood pressure, and heart rate, to compose an individual stiffness index [(measured ASI-predicted ASI)/predicted ASI]. A stiffness index greater than zero defines arterial stiffness. Thus, the purpose of this study was 1) to determine determinants of stiffness index 2) to perform threshold values to discriminate stiffness index and then 3) to determine hierarchical associations of the determinants by performing a decision tree model among hypertensive participants without CV diseases. A study was conducted from 53,363 healthy participants in the UK Biobank survey to determine predicted ASI. Stiffness index was applied on 49,452 hypertensives without CV diseases to discriminate determinants of positive stiffness index (N = 22,453) from negative index (N = 26,999). The input variables for the models were clinical and biological parameters. The independent classifiers were ranked from the most sensitives: HDL cholesterol≤1.425 mmol/L, smoking pack years≥9.2pack-years, Phosphate≥1.172 mmol/L, to the most specifics: Cystatin c≤0.901 mg/L, Triglycerides≥1.487 mmol/L, Urate≥291.9 μmol/L, ALT≥22.13 U/L, AST≤32.5 U/L, Albumin≤45.92 g/L, Testosterone≥5.181 nmol/L. A decision tree model was performed to determine rules to highlight the different hierarchization and interactions between these classifiers with a higher performance than multiple logistic regression (p<0.001). The stiffness index could be an integrator of CV risk factors and participate in future CV risk management evaluations for preventive strategies. Decision trees can provide accurate and useful classification for clinicians.

Association between lifetime cannabis use and arterial stiffness in a middle-aged general population

BACKGROUND

No studies have investigated the association between arterial stiffness index (ASI) and cannabis use. The purpose of this study is to examine sex-stratified associations of cannabis use and ASI levels in a middle-aged general population.

METHODS

Cannabis use of 46 219 volunteers of the middle-aged UK Biobank population was assessed by questionnaire and investigates several aspects of cannabis use (lifetime, frequency and current status). Associations between cannabis use and ASI were estimated using sex multiple linear regressions. Covariates were tobacco status, diabetes dyslipidaemia, alcohol status, BMI categories, hypertension, mean blood pressure and heart rate.

RESULTS

Men presented higher ASI levels compared with women (respectively, 9.826 vs. 8.578 m/s, P < 0.001), a higher heavy lifetime cannabis users (4.0 vs. 1.9%, P < 0.001), higher current cannabis users (3.1 vs. 1.7%, P < 0.001), higher current smokers (8.4 vs. 5.8%, P < 0.001) and higher alcohol users (95.6 vs. 93.4%, P < 0.001). After adjustment for all covariates in sex models, heavy lifetime cannabis users were associated with higher ASI levels in men [b = 0.19, 95% confidence interval (0.02; 0.35)] but not in women [b = -0.02 (-0.23; 0.19)]. Current cannabis users were associated with higher ASI levels in men [b = 0.17 (0.01; 0.32)] but not in women [b = -0.01 (-0.20; 0.18)] and among cannabis users, every day frequency was associated with increased ASI levels in men [b = 0.29 (0.07; 0.51)] but not in women [b = 0.10 (-0.17; 0.37)].

CONCLUSION

The observed association between cannabis use and ASI could participate in accurate and appropriate cardiovascular risk reduction strategies among cannabis users.

Associations Among Multimorbid Conditions in Hospitalized Middle-aged and Older Adults in China: Statistical Analysis of Medical Records

BACKGROUND

Multimorbidity has become a new challenge for medical systems and public health policy. Understanding the patterns of and associations among multimorbid conditions should be given priority. It may assist with the early detection of multimorbidity and thus improve quality of life in older adults.

OBJECTIVE

This study aims to comprehensively analyze and compare associations among multimorbid conditions by age and sex in a large number of middle-aged and older Chinese adults.

METHODS

Data from the home pages of inpatient medical records in the Shenzhen National Health Information Platform were evaluated. From January 1, 2017, to December 31, 2018, inpatients aged 50 years and older who had been diagnosed with at least one of 40 conditions were included in this study. Their demographic characteristics (age and sex) and inpatient diagnoses were extracted. Association rule mining, Chi-square tests, and decision tree analyses were combined to identify associations between multiple chronic conditions.

RESULTS

In total, 306,264 hospitalized cases with available information on related chronic conditions were included in this study. The prevalence of multimorbidity in the overall population was 76.46%. The combined results of the 3 analyses showed that, in patients aged 50 years to 64 years, lipoprotein metabolism disorder tended to be comorbid with multiple chronic conditions. Gout and lipoprotein metabolism disorder had the strongest association. Among patients aged 65 years or older, there were strong associations between cerebrovascular disease, heart disease, lipoprotein metabolism disorder, and peripheral vascular disease. The strongest associations were observed between senile cataract and glaucoma in men and women. In particular, the association between osteoporosis and malignant tumor was only observed in middle-aged and older men, while the association between anemia and chronic kidney disease was only observed in older women.

CONCLUSIONS

Multimorbidity was prevalent among middle-aged and older Chinese individuals. The results of this comprehensive analysis of 4 age-sex subgroups suggested that associations between particular conditions within the sex and age groups occurred more frequently than expected by random chance. This provides evidence for further research on disease clusters and for health care providers to develop different strategies based on age and sex to improve the early identification and treatment of multimorbidity.

Arterial stiffness nomogram identification by cluster analysis: A new approach of vascular phenotype modeling

Arterial stiffness, measured by arterial stiffness index (ASI), can be considered as a major denominator in cardiovascular diseases. Thus, it remains essential to highlight patient phenotyping profiles with high ASI values. A nomogram of arterial stiffness was evaluated by calculation of ASI nomogram. Theoretical ASI can be performed according to age, sex, mean blood pressure, and heart rate, allowing to form an individual ASI nomogram [(measured ASI - theoretical ASI)/theoretical ASI]. An ASI nomogram > 0 defined AS. This study investigates among UK Biobank participants without cardiovascular diseases, the hypothesis that K-means cluster analysis can be used to identify homogeneous phenotyping subgroups of participants according to ASI levels and then, the phenotype differences observed between these clusters. ASI nomogram was applied on 132 851 participants. K-means clustering was implemented with 10 clusters (optimal CCC value of 105.246). One cluster showed 100% rate of AS, corresponding to 25 393 participants (41.6% of the AS participants) with ASI nomogram = .26 (.22), ASI = 11.6 (2.3)m/s. A second cluster showed a 100% of non-AS, corresponding to 27 844 participants (38.8% of the participants with no arterial stiffness) with ASI nomogram = -.22 (.13), ASI = 7.1 (1.44)m/s. Threshold values of independent factors for differencing these two clusters were total cholesterol > 5.409 mmol/L (P < .001), triglycerides > 1.286 mmol/L (P < .001), smoking pack years > 11.8 pack/years, CRP > .99 (P < .001), daily alcohol consumption > 1.794 units/days and BMI > 26.641 kg/m² (P < .001). Cluster analysis allowed to highlight homogeneous participants profile with or without AS. Determine the markers differencing these clusters participates in the management of cardiovascular preventive strategies.

Arterial Stiffness and the Canonical WNT/β-catenin Pathway

PURPOSE OF REVIEW

Arterial stiffness (AS) was mainly associated with cardiovascular morbidity and mortality in a hypertensive patient. Some risk factors contribute to the development of AS, such as aging, high blood pressure, vascular calcification, inflammation, and diabetes mellitus. The WNT/β-catenin pathway is implicated in numerous signaling and regulating pathways, including embryogenesis, cell proliferation, migration and polarity, apoptosis, and organogenesis. The activation of the WNT/β-catenin pathway is associated with the development of these risk factors.

RECENT FINDINGS

Aortic pulse wave velocity (PWV) is measured to determine AS, and in peripheral artery disease patients, PWV is higher than controls. An augmentation in PWV by 1 m/s has been shown to increase the risk of cardiovascular events by 14%. AS measured by PWV is characterized by the deregulation of the WNT/β-catenin pathway by the inactivation of its two inhibitors, i.e., DKK1 and sclerostin. Thus, this review focuses on the role of the WNT/β-catenin pathway which contributes to the development of arterial stiffness.

Association between serum uric acid and arterial stiffness in a large-aged 40-70 years old population

Arterial stiffness (AS), measured by arterial stiffness index (ASI), is a determinant in cardiovascular (CV) diseases. A high serum uric acid (SUA) level is a known risk factor for CV disease. The authors investigated the relationship between SUA and ASI in the middle-age UK Biobank population study. AS was defined as ASI > 10 m/s. A cross-sectional study was conducted from 126 663 participants. Participants were divided into four quartiles according to SUA levels and sex. Sex multivariate analyses were performed with adjustment for confounding factors. The average ASI for overall participants was 9.3 m/s (SD: 2.9); 9.9 m/s (SD: 2.8) for men and 8.7 m/s (SD: 2.9) for women (P < .001). Men presented higher SUA rate (351.3 mmol/L (SD:67.9)) than women (270.7 mmol/L (SD:64.4)), P < .001. In men multivariate analysis, SUA remained a determinant of AS, with an increase in the strength of the association between the quartiles, Q4 versus Q1, OR = 1.10 [1.05-1.16], P < .001, Q3 versus Q1, OR = 1.09 [1.04-1.14], P < .001 but not between Q2 and Q1 (P = .136). In women, SUA remained significant for AS, with an increase in the strength of the association between the quartiles, Q4 versus Q1, OR = 1.22 [1.15-1.30], P < .001, Q3 versus Q1, OR = 1.13 [1.07-1.19], P < .001 and no difference between Q2 and Q1 (P = .101). When applying continuous SUA values in the multivariate analysis, SUA remained significant (P < .001), with a Youden index value for men = 338.3 mmol/L and for women = 267.3 mmol/L. High SUA levels were associated with AS, suggesting that SUA could be used as a predictor of atherosclerosis.

Arterial Stiffness Determinants for Primary Cardiovascular Prevention among Healthy Participants

Background: Arterial stiffness (AS), measured by arterial stiffness index (ASI), can be considered as a major denominator in cardiovascular (CV) diseases. Thus, it remains essential to highlight the risk factors influencing its increase among healthy participants. Methods: According to European consensus, AS is defined as ASI > 10 m/s. The purpose of this study was to investigate the determinants of the arterial stiffness (ASI > 10 m/s) among UK Biobank normotensive and healthy participants without comorbidities and previous CV diseases. Thus, a cross-sectional study was conducted on 22,452 healthy participants. Results: Participants were divided into two groups, i.e., ASI > 10 m/s (n = 5782, 25.8%) and ASI < 10 m/s (n = 16,670, 74.2%). All the significant univariate covariables were included in the multivariate analysis. The remaining independent factors associated with AS were age (OR = 1.063, threshold = 53.0 years, p < 0.001), BMI (OR = 1.0450, threshold = 24.9 kg/m2, p < 0.001), cystatin c (OR = 1.384, threshold = 0.85 mg/L, p = 0.011), phosphate (OR = 2.225, threshold = 1.21 mmol/L, p < 0.001), triglycerides (OR = 1.281, threshold = 1.09 mmol/L, p < 0.001), mean BP (OR = 1.028, threshold = 91.2 mmHg, p < 0.001), HR (OR = 1.007, threshold = 55 bpm, p < 0.001), Alkaline phosphate (OR = 1.002, threshold = 67.9 U/L, p = 0.004), albumin (OR = 0.973, threshold = 46.0 g/L, p < 0.001), gender (male, OR = 1.657, p < 0.001) and tobacco use (current, OR = 1.871, p < 0.001). Conclusion: AS is associated with multiple parameters which should be investigated in future prospective studies. Determining the markers of increased ASI among healthy participants participates in the management of future CV risk for preventive strategies.

Severe Aortic Valve Stenosis and Pulmonary Hypertension: A Systematic Review of Non-Invasive Ways of Risk Stratification, Especially in Patients Undergoing Transcatheter Aortic Valve Replacement

Patients with severe aortic valve stenosis and concomitant pulmonary hypertension show a significantly reduced survival prognosis. Right heart catheterization as a preoperative diagnostic tool to determine pulmonary hypertension has been largely abandoned in recent years in favor of echocardiographic criteria. Clinically, determination of echocardiographically estimated systolic pulmonary artery pressure falls far short of invasive right heart catheterization data in terms of accuracy. The aim of the present systematic review was to highlight noninvasive possibilities for the detection of pulmonary hypertension in patients with severe aortic valve stenosis, with a special focus on cardiovascular biomarkers. A total of 525 publications regarding echocardiography, cardiovascular imaging and biomarkers related to severe aortic valve stenosis and pulmonary hypertension were analyzed in a systematic database analysis using PubMed Central®. Finally, 39 publications were included in the following review. It was shown that the current scientific data situation, especially regarding cardiovascular biomarkers as non-invasive diagnostic tools for the determination of pulmonary hypertension in severe aortic valve stenosis patients, is poor. Thus, there is a great scientific potential to combine different biomarkers (biomarker scores) in a non-invasive way to determine the presence or absence of PH.

Association Between Lipids and Arterial Stiffness for Primary Cardiovascular Prevention in a General Middle-Aged European Population

Background

Dyslipidemia contributes to the progression of arterial stiffness (AS). The purpose of this study was to investigate the association of the different lipid parameters with arterial stiffness index (ASI) in a middle-aged population free of cardiovascular (CV) disease.

Methods

Among 71,326 volunteers from the UK Biobank population, total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), lipoprotein (a) [Lp(a)], apolipoproteins A and B (Apo A and Apo B), and ASI were measured. Values for non-HDL, TC/HDL, TG/HDL, and LDL/HDL were calculated. AS was defined as an ASI > 10 m/s. Associations between lipid parameters and ASI were performed using multiple linear logistic regressions. The results reported from univariate models were the squared partial correlation coefficient, r ², and from multivariate models, the adjusted coefficient of determination, R ², to describe the contribution of ASI variability for each lipid parameter.

Results

We found that TG/HDL was mainly associated with ASI [β = 0.53 (0.01), r ² = 3.66%, p < 0.001 and adjusted β = 0.21 (0.01), R ² = 13.58%, p < 0.001] and AS [odds ratio (OR) = 1.86 (1.80-1.92), r ² = 1.65%, p < 0.001 and adjusted OR = 1.15 (1.13-1.17), R ² = 8.54%, p < 0.001] rather than the others. TG/HDL remained the only lipid parameter that showed added value in linear multivariate models. TG/HDL remained less associated with AS than age (r ² = 5.55%, p < 0.001), mean blood pressure (BP; r ² = 5.31%, p < 0.001), and gender (r ² = 4.44%, p < 0.001), but more highly associated than body mass index (BMI; r ² = 1.95%, p < 0.001), heart rate (HR; r ² = 0.81%, p < 0.001), fasting glucose (r ² = 0.18%, p < 0.001), tobacco (r ² = 0.05%, p < 0.001), and glomerular filtration rate (GFR; r ² = 0.01%, p < 0.001).

Conclusions

In primary CV prevention, lipids, especially through the TG/HDL ratio, could be more instructive in preventing the increase in AS than other modifiable factors.

Estimated Arterial Stiffness and Prediction of Vascular Aging: The Rising of a New Era

Arterial stiffness has been associated with cardiovascular events and correlated with cardiovascular risk factors. In the new guidelines of the European Society of Hypertension, the evaluation of arterial stiffness, and specifically carotid-femoral pulse wave velocity (c-f PWV), was taken into account for the detailed screening of the hypertensive population. Despite the importance of arterial stiffness as a target organ damage, the measurement is time-consuming, not practical, needs expensive equipment and experienced staff. For all these reasons, c-f PWV is not recommended for the everyday clinical practice and its current use is restricted for research purposes. The importance of arterial properties in clinical practice and cardiovascular prevention is well known. Hence, the estimation of arterial stiffness and vascular health based on parameters that affect arterial stiffness, but without the use of a machine, is a new promising field. Furthermore, the relationship between age-related MRI abnormalities as well as ultrafast ultrasound with vascular effect gives a new promise for future vascular aging assessment.

An effective frequency-domain feature of atrial fibrillation based on time-frequency analysis

Background

Atrial fibrillation is a type of persistent arrhythmia that can lead to serious complications. Therefore, accurate and quick detection of atrial fibrillation by surface electrocardiogram has great importance on further treatment. The practical electrocardiogram signals contain various interferences in different frequencies, such as myoelectricity interference, power interference and so on. Detection speed and accuracy largely depend on the atrial fibrillation signal features extracted by the algorithm. But some of the discovered atrial fibrillation features are not well distinguishable, resulting in poor classification effect.

Methods

This paper proposed a high distinguishable frequency feature—the frequency corresponding to the maximum amplitude in the frequency spectrum. We used the R–R interval detection method optimized with the mathematical morphology method and combined with the wavelet transform method for analysis. According to the two features—the maximum amplitude in the frequency spectrum and R–R interval irregular, we could recognize atrial fibrillation signals in electrocardiogram signals by decision tree classification algorithm.

Results

The data used in the experiment come from the MIT-BIH database, which is publicly accessible via the web and with ethical approval and consent. Based on the input of time-domain and frequency-domain features, we classified sinus rhythm signals and AF signals using the decision tree generated by classification and regression tree (CART) algorithm. From the confusion matrix, we got the accuracy was 98.9%, sensitivity was 97.93% and specificity was 99.63%.

Conclusions

The experimental results can prove the validity of the maximum amplitude in the frequency spectrum and the practicability and accuracy of the detection method, which applied this frequency-domain feature. Through the detection method, we obtained good accuracy of classifying sinus rhythm signals and atrial fibrillation signals. And the sensitivity and specificity of our method were pretty good by comparison with other studies.