Use of machine-learning algorithms to determine features of systolic blood pressure variability that predict poor outcomes in hypertensive patients

Abstract

Background We re-analyzed data from the Systolic Blood Pressure Intervention Trial (SPRINT) trial to identify features of systolic blood pressure (SBP) variability that portend poor cardiovascular outcomes using a nonlinear machine-learning algorithm. Methods We included all patients who completed 1 year of the study without reaching any primary endpoint during the first year, specifically: myocardial infarction, other acute coronary syndromes, stroke, heart failure or death from a cardiovascular event ( n = 8799; 94%). In addition to clinical variables, features representing longitudinal SBP trends and variability were determined and combined in a random forest algorithm, optimized using cross-validation, using 70% of patients in the training set. Area under the curve (AUC) was measured using a 30% testing set. Finally, feature importance was determined by minimizing node impurity averaging over all trees in the forest for a specific feature. Results A total of 365 patients (4.1%) reached the combined primary outcome over 37 months of follow-up. The random forest classifier had an AUC of 0.71 on the testing set. The 10 most significant features selected in order of importance by the automated algorithm included the urine albumin/creatinine (CR) ratio, estimated glomerular filtration rate, age, serum CR, history of subclinical cardiovascular disease (CVD), cholesterol, a variable representing SBP signals using wavelet transformation, high-density lipoprotein, the 90th percentile of SBP and triglyceride level. Conclusions We successfully demonstrated use of random forest algorithm to define best prognostic longitudinal SBP representations. In addition to known risk factors for CVD, transformed variables for time series SBP measurements were found to be important in predicting poor cardiovascular outcomes and require further evaluation.

SENIOR: An Intelligent Web-Based Ecosystem to Predict High Blood Pressure Adverse Events Using Biomarkers and Environmental Data

“…One relevant scenario has been the identification of features that can negatively impact this disease. In this sense, Lacson et al [12] studied the Systolic Blood Pressure Intervention Trial (SPRINT) dataset containing electronic health records, including a wide variety of characteristics in categories such as demographics, medication, or laboratory parameters.…”

Potential predictors of type-2 diabetes risk: machine learning, synthetic data and wearable health devices

“…ML is becoming a popular and efficient approach to evaluate multidimensional longitudinal health data in different fields of medical research. Examples of this kind of studies include the diagnosis of asymptomatic liver disease [ 16 ], the prediction of opioid dependence [ 17 ], the evaluation of sociodemographic determinants of health status in aging [ 18 ], the prediction of the mobility of medical rescue-vehicles [ 19 ], forecasting adverse perioperative outcomes [ 20 ], the measure of caloric intake at the population level [ 21 ], the personalisation of oncological treatment in radiogenomics [ 22 ], the determination of features of systolic blood pressure variability [ 23 ], the identification of clinical variables in bipolar disorder [ 24 ] and, interestingly, a specific interest in uncovering potential predictors of diabetes (type 1 and 2) using large set of data [ 25 – 32 ]. ML can also support global efforts in various fields of epidemic outbreaks of infectious diseases, developing up-to-date text and data-mining techniques to assist COVID-19-related research, especially by developing drugs faster (screening and detecting antibody virus interactions and detect viral antigens), understanding viruses better, mapping where viruses come from, and hopefully predicting the next pandemic [ 33 , 34 ].…”

Ckj consolidation among Q1 Urology and Nephrology journals

“…The different patterns of progression of later stages of CKD were also characterized [ 26 ], as well as the relationship between obesity, the new adipokine C1q/tumour necrosis factor-related protein-1 and CKD progression [ 35 ]. Use of machine-learning algorithms was explored to predict poor outcomes in hypertensive patients based on blood pressure variability [ 37 ]. Another text studied the usefulness of antiphospholipase 2 receptor levels to predict complete spontaneous remission in untreated membranous nephropathy, and thus to help in initial decision-making regarding initiation of immunosuppressive therapy [ 39 ].…”