Prognostic value of plasma NT-proBNP levels in very old patients with moderate renal insufficiency in China

Diagnostic and Prognostic Value of Aminoterminal Prohormone of Brain Natriuretic Peptide in Heart Failure with Mildly Reduced Ejection Fraction Stratified by the Degree of Renal Dysfunction

Limited data concerning the diagnostic and prognostic value of blood-derived biomarkers in heart failure with mildly reduced ejection fraction (HFmrEF) is available. This study investigates the diagnostic and prognostic value of aminoterminal prohormone of brain natriuretic peptide (NT-proBNP) in patients with HFmrEF, stratified by the estimated glomerular filtration rate (eGFR). Consecutive patients with HFmrEF were retrospectively included at one institution from 2016 to 2022. First, the diagnostic value of NT-proBNP for acute decompensated heart failure (ADHF) was tested. Thereafter, the prognostic value of NT-proBNP levels was tested for 30-months all-cause mortality in patients with ADHF. From a total of 755 patients hospitalized with HFmrEF, the rate of ADHF was 42%. Patients with ADHF revealed higher NT-proBNP levels compared to patients without (median 5394 pg/mL vs. 1655 pg/mL; p = 0.001). NT-proBNP was able to discriminate ADHF with an area under the curve (AUC) of 0.777 (p = 0.001), with the highest AUC in patients with eGFR ≥ 60 mL/min (AUC = 0.800; p = 0.001), and no diagnostic value was seen in eGFR < 30 mL/min (AUC = 0.576; p = 0.210). Patients with NT-proBNP levels > 3946 pg/mL were associated with higher rates of all-cause mortality at 30 months (57.7% vs. 34.4%; HR = 2.036; 95% CI 1.423-2.912; p = 0.001), even after multivariable adjustment (HR = 1.712; 95% CI 1.166-2.512; p = 0.006). In conclusion, increasing NT-proBNP levels predicted the risk of ADHF and all-cause mortality in patients with HFmrEF and preserved renal function; however, NT-proBNP levels were not predictive in patients with HFmrEF and eGFR < 30 mL/min.

An Application of Machine Learning to Etiological Diagnosis of Secondary Hypertension: Retrospective Study Using Electronic Medical Records

Background

Secondary 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.

Objective

The aim of this study was to develop and validate machine learning (ML) prediction models of common etiologies in patients with suspected secondary hypertension.

Methods

The 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.

Results

Validation 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.

Conclusions

The 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.