Machine learning approaches to constructing predictive models of vitamin D deficiency in a hypertensive population: a comparative study.

Objective: Given the association between vitamin D deficiency and risk for cardiovascular disease, we used machine learning approaches to establish a model to predict the probability of deficiency. Determination of serum levels of 25-hydroxy vitamin D (25(OH)D) provided the best assessment of vitamin D status, but such tests are not always widely available or feasible. Thus, our study established predictive models with high sensitivity to identify patients either unlikely to have vitamin D deficiency or who should undergo 25(OH)D testing.Methods: We collected data from 1002 hypertensive patients from a Spanish university hospital. The elastic net regularization approach was applied to reduce the dimensionality of the dataset. The issue of determining vitamin D status was addressed as a classification problem; thus, the following classifiers were applied: logistic regression, support vector machine (SVM), random forest, naive Bayes, and Extreme Gradient Boost methods. Classification accuracy, sensitivity, specificity, and predictive values were computed to assess the performance of each method.Results: The SVM-based method with radial kernel performed better than the other algorithms in terms of sensitivity (98%), negative predictive value (71%), and classification accuracy (73%).Conclusion: The combination of a feature-selection method such as elastic net regularization and a classification approach produced well-fitted models. The SVM approach yielded better predictions than the other algorithms. This combination approach allowed us to develop a predictive model with high sensitivity but low specificity, to identify the population that could benefit from laboratory determination of serum levels of 25(OH)D.

Use of a K-nearest neighbors model to predict the development of type 2 diabetes within 2 years in an obese, hypertensive population.

Prediabetes is a type of hyperglycemia in which patients have blood glucose levels above normal but below the threshold for type 2 diabetes mellitus (T2DM). Prediabetic patients are considered to be at high risk for developing T2DM, but not all will eventually do so. Because it is difficult to identify which patients have an increased risk of developing T2DM, we developed a model of several clinical and laboratory features to predict the development of T2DM within a 2-year period. We used a supervised machine learning algorithm to identify at-risk patients from among 1647 obese, hypertensive patients. The study period began in 2005 and ended in 2018. We constrained data up to 2 years before the development of T2DM. Then, using a time series analysis with the features of every patient, we calculated one linear regression line and one slope per feature. Features were then included in a K-nearest neighbors classification model. Feature importance was assessed using the random forest algorithm. The K-nearest neighbors model accurately classified patients in 96% of cases, with a sensitivity of 99%, specificity of 78%, positive predictive value of 96%, and negative predictive value of 94%. The random forest algorithm selected the homeostatic model assessment-estimated insulin resistance, insulin levels, and body mass index as the most important factors, which in combination with KNN had an accuracy of 99% with a sensitivity of 99% and specificity of 97%. We built a prognostic model that accurately identified obese, hypertensive patients at risk for developing T2DM within a 2-year period. Clinicians may use machine learning approaches to better assess risk for T2DM and better manage hypertensive patients. Machine learning algorithms may help health care providers make more informed decisions.

Logistic LASSO and Elastic Net to Characterize Vitamin D Deficiency in a Hypertensive Obese Population.

Aim: The primary objective of our research was to compare the performance of data analysis to predict vitamin D deficiency using three different regression approaches and to evaluate the usefulness of incorporating machine learning algorithms into the data analysis in a clinical setting. Methods: We included 221 patients from our hypertension unit, whose data were collected from electronic records dated between 2006 and 2017. We used classical stepwise logistic regression, and two machine learning methods [least absolute shrinkage and selection operator (LASSO) and elastic net]. We assessed the performance of these three algorithms in terms of sensitivity, specificity, misclassification error, and area under the curve (AUC). Results: LASSO and elastic net regression performed better than logistic regression in terms of AUC, which was significantly better in both penalized methods, with AUC = 0.76 and AUC = 0.74 for elastic net and LASSO, respectively, than in logistic regression, with AUC = 0.64. In terms of misclassification rate, elastic net (18%) outperformed LASSO (22%) and logistic regression (25%). Conclusion: Compared with a classical logistic regression approach, penalized methods were found to have better performance in predicting vitamin D deficiency. The use of machine learning algorithms such as LASSO and elastic net may significantly improve the prediction of vitamin D deficiency in a hypertensive obese population.

Pulse Wave Velocity and Machine Learning to Predict Cardiovascular Outcomes in Prediabetic and Diabetic Populations.

Few studies have addressed the predictive value of arterial stiffness determined by pulse wave velocity (PWV) in a high-risk population with no prevalent cardiovascular disease and with obesity, hypertension, hyperglycemia, and preserved kidney function. This longitudinal, retrospective study enrolled 88 high-risk patients and had a follow-up time of 12.4 years. We collected clinical and laboratory data, as well as information on arterial stiffness parameters using arterial tonometry and measurements from ambulatory blood pressure monitoring. We considered nonfatal, incident cardiovascular events as the primary outcome. Given the small size of our dataset, we used survival analysis (i.e., Cox proportional hazards model) combined with a machine learning-based algorithm/penalization method to evaluate the data. Our predictive model, calculated with Cox regression and least absolute shrinkage and selection operator (LASSO), included body mass index, diabetes mellitus, gender (male), and PWV. We recorded 16 nonfatal cardiovascular events (5 myocardial infarctions, 5 episodes of heart failure, and 6 strokes). The adjusted hazard ratio for PWV was 1.199 (95% confidence interval: 1.09-1.37, p < 0.001). Arterial stiffness was a predictor of cardiovascular disease development, as determined by PWV in a high-risk population. Thus, in obese, hypertensive, hyperglycemic patients with preserved kidney function, PWV can serve as a prognostic factor for major adverse cardiac events.

Relevant Features in Nonalcoholic Steatohepatitis Determined Using Machine Learning for Feature Selection.

Aim: We investigated the prevalence and the most relevant features of nonalcoholic steatohepatitis (NASH), a stage of nonalcoholic fatty liver disease, (NAFLD) in which the inflammation of hepatocytes can lead to increased cardiovascular risk, liver fibrosis, cirrhosis, and the need for liver transplant. Methods: We analyzed data from 2239 hypertensive patients using descriptive statistics and supervised machine learning algorithms, including the least absolute shrinkage and selection operator and random forest classifier, to select the most relevant features of NASH. Results: The prevalence of NASH among our hypertensive patients was 11.3%. In univariate analyses, it was associated with metabolic syndrome, type 2 diabetes, insulin resistance, and dyslipidemia. Ferritin and serum insulin were the most relevant features in the final model, with a sensitivity of 70%, specificity of 79%, and area under the curve of 0.79. Conclusion: Ferritin and insulin are significant predictors of NASH. Clinicians may use these to better assess cardiovascular risk and provide better management to hypertensive patients with NASH. Machine-learning algorithms may help health care providers make decisions.

Identification of clinically relevant features in hypertensive patients using penalized regression: a case study of cardiovascular events.

Appropriate management of hypertensive patients relies on the accurate identification of clinically relevant features. However, traditional statistical methods may ignore important information in datasets or overlook possible interactions among features. Machine learning may improve the prediction accuracy and interpretability of regression models by identifying the most relevant features in hypertensive patients. We sought the most relevant features for prediction of cardiovascular (CV) events in a hypertensive population. We used the penalized regression models least absolute shrinkage and selection operator (LASSO) and elastic net (EN) to obtain the most parsimonious and accurate models. The clinical parameters and laboratory biomarkers were collected from the clinical records of 1,471 patients receiving care at Mostoles University Hospital. The outcome was the development of major adverse CV events. Cox proportional hazards regression was performed alone and with penalized regression analyses (LASSO and EN), producing three models. The modeling was performed using 10-fold cross-validation to fit the penalized models. The three predictive models were compared and statistically analyzed to assess their classification accuracy, sensitivity, specificity, discriminative power, and calibration accuracy. The standard Cox model identified five relevant features, while LASSO and EN identified only three (age, LDL cholesterol, and kidney function). The accuracies of the models (prediction vs. observation) were 0.767 (Cox model), 0.754 (LASSO), and 0.764 (EN), and the areas under the curve were 0.694, 0.670, and 0.673, respectively. However, pairwise comparison of performance yielded no statistically significant differences. All three calibration curves showed close agreement between the predicted and observed probabilities of the development of a CV event. Although the performance was similar for all three models, both penalized regression analyses produced models with good fit and fewer features than the Cox regression predictive model but with the same accuracy. This case study of predictive models using penalized regression analyses shows that penalized regularization techniques can provide predictive models for CV risk assessment that are parsimonious, highly interpretable, and generalizable and that have good fit. For clinicians, a parsimonious model can be useful where available data are limited, as such a model can offer a simple but efficient way to model the impact of the different features on the prediction of CV events. Management of these features may lower the risk for a CV event. Graphical Abstract In a clinical setting, with numerous biological and laboratory features and incomplete datasets, traditional statistical methods may ignore important information and overlook possible interactions among features. Our aim was to identify the most relevant features to predict cardiovascular events in a hypertensive population, using three different regression approaches for feature selection, to improve the prediction accuracy and interpretability of regression models by identifying the relevant features in these patients.

Cardiovascular risk assessment in prediabetic patients in a hypertensive population: The role of cystatin C.

BACKGROUND: The aim of our study was to determine whether prediabetes increases cardiovascular (CV) risk compared to the non-prediabetic patients in our hypertensive population. Once this was achieved, the objective was to identify relevant CV prognostic features among prediabetic individuals. METHODS: We included hypertensive 1652 patients. The primary outcome was a composite of incident CV events: cardiovascular death, stroke, heart failure and myocardial infarction. We performed a Cox proportional hazard regression to assess the CV risk of prediabetic patients compared to non-prediabetic and to produce a survival model in the prediabetic cohort. RESULTS: The risk of developing a CV event was higher in the prediabetic cohort than in the non-prediabetic cohort, with a hazard ratio (HR) = 1.61, 95% CI 1.01-2.54, p = 0.04. Our Cox proportional hazard model selected age (HR = 1.04, 95% CI 1.02-1.07, p < 0.001) and cystatin C (HR = 2.4, 95% CI 1.26-4.22, p = 0.01) as the most relevant prognostic features in our prediabetic patients. CONCLUSIONS: Prediabetes was associated with an increased risk of CV events, when compared with the non-prediabetic patients. Age and cystatin C were found as significant risk factors for CV events in the prediabetic cohort.

An Interoperable System toward Cardiac Risk Stratification from ECG Monitoring.

Many indices have been proposed for cardiovascular risk stratification from electrocardiogram signal processing, still with limited use in clinical practice. We created a system integrating the clinical definition of cardiac risk subdomains from ECGs and the use of diverse signal processing techniques. Three subdomains were defined from the joint analysis of the technical and clinical viewpoints. One subdomain was devoted to demographic and clinical data. The other two subdomains were intended to obtain widely defined risk indices from ECG monitoring: a simple-domain (heart rate turbulence (HRT)), and a complex-domain (heart rate variability (HRV)). Data provided by the three subdomains allowed for the generation of alerts with different intensity and nature, as well as for the grouping and scrutinization of patients according to the established processing and risk-thresholding criteria. The implemented system was tested by connecting data from real-world in-hospital electronic health records and ECG monitoring by considering standards for syntactic (HL7 messages) and semantic interoperability (archetypes based on CEN/ISO EN13606 and SNOMED-CT). The system was able to provide risk indices and to generate alerts in the health records to support decision-making. Overall, the system allows for the agile interaction of research and clinical practice in the Holter-ECG-based cardiac risk domain.

Ontology for heart rate turbulence domain from the conceptual model of SNOMED-CT.

Electronic health record (EHR) automates the clinician workflow, allowing evidence-based decision support and quality management. We aimed to start a framework for domain standardization of cardiovascular risk stratification into the EHR, including risk indices whose calculation involves ECG signal processing. We propose the use of biomedical ontologies completely based on the conceptual model of SNOMED-CT, which allows us to implement our domain in the EHR. In this setting, the present study focused on the heart rate turbulence (HRT) domain, according to its concise guidelines and clear procedures for parameter calculations. We used 289 concepts from SNOMED-CT, and generated 19 local extensions (new concepts) for the HRT specific concepts not present in the current version of SNOMED-CT. New concepts included averaged and individual ventricular premature complex tachograms, initial sinus acceleration for turbulence onset, or sinusal oscillation for turbulence slope. Two representative use studies were implemented: first, a prototype was inserted in the hospital information system for supporting HRT recordings and their simple follow up by medical societies; second, an advanced support for a prospective scientific research, involving standard and emergent signal processing algorithms in the HRT indices, was generated and then tested in an example database of 27 Holter patients. Concepts of the proposed HRT ontology are publicly available through a terminology server, hence their use in any information system will be straightforward due to the interoperability provided by SNOMED-CT.

Spectral analysis of intracardiac electrograms during induced and spontaneous ventricular fibrillation in humans.

AIMS: Very limited data are available on the differences between spontaneous and induced episodes of ventricular fibrillation (VF) in humans. The aim of the study was to compare the spectral characteristics of the electrical signal recorded by an implantable cardioverter defibrillator (ICD) during both types of episodes. METHODS AND RESULTS: Thirteen ICD patients with at least one spontaneous and one induced VF recorded by the device were included in the study. A spectral representation was obtained for the first 3 s of the intracardiac unipolar electrogram during VF. The dominant frequency (f(d)), the peak power at f(d), an organization index (OI), a bandwidth measurement, and an estimate of the correlation with a sinusoidal wave (leakage) were estimated for each episode. The f(d) was higher in induced episodes (4.75 +/- 0.57 vs. 3.95 +/- 0.59 Hz for the spontaneous episodes, P = 0.002), as well as the degree of organization assessed by the OI, bandwidth, and leakage parameters. CONCLUSION: Clinical and induced VF episodes in humans have different spectral characteristics. Changes in the electrophysiological substrate or in the location of the arrhythmia wavefront at onset could play a role to explain the observed differences.