Abstract
BackgroundSecondary hypertension is a kind of hypertension with a definite etiology and may be cured. Patients with suspected secondary hypertension can benefit from timely detection and treatment and, conversely, will have a higher risk of morbidity and mortality than those with primary hypertension.ObjectiveThe aim of this study was to develop and validate machine learning (ML) prediction models of common etiologies in patients with suspected secondary hypertension.MethodsThe analyzed data set was retrospectively extracted from electronic medical records of patients discharged from Fuwai Hospital between January 1, 2016, and June 30, 2019. A total of 7532 unique patients were included and divided into 2 data sets by time: 6302 patients in 2016-2018 as the training data set for model building and 1230 patients in 2019 as the validation data set for further evaluation. Extreme Gradient Boosting (XGBoost) was adopted to develop 5 models to predict 4 etiologies of secondary hypertension and occurrence of any of them (named as composite outcome), including renovascular hypertension (RVH), primary aldosteronism (PA), thyroid dysfunction, and aortic stenosis. Both univariate logistic analysis and Gini Impurity were used for feature selection. Grid search and 10-fold cross-validation were used to select the optimal hyperparameters for each model.ResultsValidation of the composite outcome prediction model showed good performance with an area under the receiver-operating characteristic curve (AUC) of 0.924 in the validation data set, while the 4 prediction models of RVH, PA, thyroid dysfunction, and aortic stenosis achieved AUC of 0.938, 0.965, 0.959, and 0.946, respectively, in the validation data set. A total of 79 clinical indicators were identified in all and finally used in our prediction models. The result of subgroup analysis on the composite outcome prediction model demonstrated high discrimination with AUCs all higher than 0.890 among all age groups of adults.ConclusionsThe ML prediction models in this study showed good performance in detecting 4 etiologies of patients with suspected secondary hypertension; thus, they may potentially facilitate clinical diagnosis decision making of secondary hypertension in an intelligent way.
Highlights
Hypertension is a common chronic disease worldwide, with 5%-10% of these patients being secondary hypertensive [1,2,3,4,5]
Baseline Characteristics Of the 7532 patients included in this study, 64.82% (4882/7532) were male, with a mean age of 47.70 (SD 14.77), a mean maximum systolic pressure of 173.00 (SD 29.50) mmHg, and a mean maximum diastolic pressure of 124.87 (SD 32.56) mmHg
Principal Results Based on the electronic medical record (EMR) from Fuwai Hospital, we developed 5 prediction models with good performance for 4 etiologies of secondary hypertension using extreme Gradient Boosting (XGBoost)
Summary
Hypertension is a common chronic disease worldwide, with 5%-10% of these patients being secondary hypertensive [1,2,3,4,5]. Patients with secondary hypertension who have high risks of morbidity and mortality if not diagnosed and treated timely are early onset cases, with higher blood pressure (BP) that is more difficult to be controlled than patients with primary hypertension [2,3,4,6]. It is necessary to focus on accurate diagnosis to capture the secondary hypertension of patients in order to provide effective evidence for clinical therapy [2,3,4,7]. There are no relevant published studies on AI model–aided diagnosis of secondary hypertension for detecting etiologies of disease and providing effective treatment. Patients with suspected secondary hypertension can benefit from timely detection and treatment and, will have a higher risk of morbidity and mortality than those with primary hypertension
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