Prediction of Masked Hypertension and Masked Uncontrolled Hypertension Using Machine Learning

Abstract

Objective: This study aimed to develop machine learning-based prediction models to predict masked hypertension and masked uncontrolled hypertension using the clinical characteristics of patients at a single outpatient visit.Methods: Data were derived from two cohorts in Taiwan. The first cohort included 970 hypertensive patients recruited from six medical centers between 2004 and 2005, which were split into a training set (n = 679), a validation set (n = 146), and a test set (n = 145) for model development and internal validation. The second cohort included 416 hypertensive patients recruited from a single medical center between 2012 and 2020, which was used for external validation. We used 33 clinical characteristics as candidate variables to develop models based on logistic regression (LR), random forest (RF), eXtreme Gradient Boosting (XGboost), and artificial neural network (ANN).Results: The four models featured high sensitivity and high negative predictive value (NPV) in internal validation (sensitivity = 0.914–1.000; NPV = 0.853–1.000) and external validation (sensitivity = 0.950–1.000; NPV = 0.875–1.000). The RF, XGboost, and ANN models showed much higher area under the receiver operating characteristic curve (AUC) (0.799–0.851 in internal validation, 0.672–0.837 in external validation) than the LR model. Among the models, the RF model, composed of 6 predictor variables, had the best overall performance in both internal and external validation (AUC = 0.851 and 0.837; sensitivity = 1.000 and 1.000; specificity = 0.609 and 0.580; NPV = 1.000 and 1.000; accuracy = 0.766 and 0.721, respectively).Conclusion: An effective machine learning-based predictive model that requires data from a single clinic visit may help to identify masked hypertension and masked uncontrolled hypertension.

Identification of nine signature proteins involved in periodontitis by integrated analysis of TMT proteomics and transcriptomics

“…Therefore, we attempted to further verify the accuracy of the 9 signature proteins involved in the pathogenesis of periodontitis obtained from our gingival tissue proteome using an ANN model (48). ANNs are one of the current tools with intelligent pattern recognition ability, and their application in the classification and diagnosis of infectious diseases, tumors, hypertension and related diseases (49)(50)(51), but not periodontitis, has been reported. Considering that the size of our periodontitis proteomics sample was only 15 and the proteomics sample size was also small in previous reports, we were unable to establish an effective ANN model to classify periodontitis utilizing existing proteomic data.…”

Prediction of Masked Uncontrolled Hypertension Detected by Ambulatory Blood Pressure Monitoring

“…The present study shows that: (1) male sex, smoking habit, LV hypertrophy, a clinic systolic BP in the range of 130–139 mmHg, and/or a clinic diastolic BP in the range of 85–89 mmHg are associated with MUCH defined by both daytime and 24 h BP thresholds; (2) prediction models based on the abovementioned variables were appropriate in identifying the presence of MUCH; (3) internal validation indicated a good predictive performance of the models. Though characteristics of patients with MUCH have been described in previous studies [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 31 , 32 , 33 ], few reports [ 34 , 35 ] have attempted to provide prediction models. Kim et al [ 34 ], studied 854 treated hypertensive patients with normal clinic BPs (<140/90 mmHg) enrolled in the Korean Ambulatory BP Monitoring Registry.…”

“…According to their scoring system, a score ≥ 9.6 points had a sensitivity and specificity of 79% and 78%, respectively. Hung et al [ 35 ], evaluated the characteristics of patients that could be able to predict masked hypertension and MUCH. They studied a cohort of 970 hypertensive patients (six medical centers in Taiwan) which were used for model development and internal validation and a cohort of 416 hypertensive patients (one medical center) which was used for external validation.…”

“…This aspect, however, could be explained by different populations’ characteristics and different ethnicities. Moreover, in the study by Hung et al [ 35 ], masked hypertension and MUCH were analyzed together. When we applied to our population the prediction models suggested by Kim et al [ 34 ] and Hung et al [ 35 ], their diagnostic accuracy tended to be lower.…”

“…In this context, it would be useful to find prediction models that could help to select patients eligible for out-of-office BP evaluation. Though factors associated with MUCH have been described in previous studies [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 31 , 32 , 33 ], few reports [ 34 , 35 ] have attempted to provide prediction models. Thus, other studies could be helpful to add further knowledge on this matter.…”