[A case of wild-type transthyretin cardiac amyloidosis].

Value of a Machine Learning Approach for Predicting Clinical Outcomes in Young Patients With Hypertension.

Risk stratification of young patients with hypertension remains challenging. Generally, machine learning (ML) is considered a promising alternative to traditional methods for clinical predictions because it is capable of processing large amounts of complex data. We, therefore, explored the feasibility of an ML approach for predicting outcomes in young patients with hypertension and compared its performance with that of approaches now commonly used in clinical practice. Baseline clinical data and a composite end point-comprising all-cause death, acute myocardial infarction, coronary artery revascularization, new-onset heart failure, new-onset atrial fibrillation/atrial flutter, sustained ventricular tachycardia/ventricular fibrillation, peripheral artery revascularization, new-onset stroke, end-stage renal disease-were evaluated in 508 young patients with hypertension (30.83±6.17 years) who had been treated at a tertiary hospital. Construction of the ML model, which consisted of recursive feature elimination, extreme gradient boosting, and 10-fold cross-validation, was performed at the 33-month follow-up evaluation, and the model's performance was compared with that of the Cox regression and recalibrated Framingham Risk Score models. An 11-variable combination was considered most valuable for predicting outcomes using the ML approach. The C statistic for identifying patients with composite end points was 0.757 (95% CI, 0.660-0.854) for the ML model, whereas for Cox regression model and the recalibrated Framingham Risk Score model it was 0.723 (95% CI, 0.636-0.810) and 0.529 (95% CI, 0.403-0.655). The ML approach was comparable with Cox regression for determining the clinical prognosis of young patients with hypertension and was better than that of the recalibrated Framingham Risk Score model.

Steroid metabolism gene variants and their genotype-phenotype correlations in Chinese early-onset hypertension patients.

The genetic factors related to early-onset hypertension are largely unknown. This study aimed to determine the spectrum of steroid metabolism gene variants and the clinical relationships of these variants to phenotypes in Chinese patients with early-onset hypertension. A total of 306 consecutive early-onset hypertensive patients were recruited. All coding exons and flanking intronic regions of KCNJ5, CYP11B1, and CYP17A1 were sequenced. Long-distance polymerase chain reaction was used to search for a CYP11B1/CYP11B2 chimeric gene. Pedigree investigations and genotype-phenotype analyses were performed for patients with rare variants. Nine rare variants were detected in eight patients (2.6%), but no CYP11B1/CYP11B2 chimeric gene was identified. One patient and two of her siblings were found to carry compound heterozygous mutations (C183Y and T390R) in CYP17A1 and were eventually diagnosed with atypical congenital adrenal hyperplasia. Patients with rare variants had younger ages of onset [17 (16, 20) vs. 30 (23, 35) years old, p = 0.010] and higher systolic blood pressure (148.5 ± 9.6 vs. 137.9 ± 17.8 mmHg, p = 0.021) than those without rare variants. Additionally, the patients and their relatives carrying rare variants exhibited increased serum free corticosterone [230.4 (7.4, 533.0) vs. 1.9 (0.9, 6.7)ng/ml, p = 0.001] and 11-deoxycorticosterone [16.16 (0.59, 33.23) vs. 0.77 (0.41, 0.96)ng/ml, p = 0.038] levels. Genetic testing is useful for the etiologic diagnosis of early-onset hypertension. Rare variants in steroid metabolism genes were associated with more severe clinical expression and abnormal circulating steroid metabolites in patients with early-onset hypertension.

[A case of wild-type transthyretin cardiac amyloidosis].

Value of a Machine Learning Approach for Predicting Clinical Outcomes in Young Patients With Hypertension.

Risk stratification of young patients with hypertension remains challenging. Generally, machine learning (ML) is considered a promising alternative to traditional methods for clinical predictions because it is capable of processing large amounts of complex data. We, therefore, explored the feasibility of an ML approach for predicting outcomes in young patients with hypertension and compared its performance with that of approaches now commonly used in clinical practice. Baseline clinical data and a composite end point-comprising all-cause death, acute myocardial infarction, coronary artery revascularization, new-onset heart failure, new-onset atrial fibrillation/atrial flutter, sustained ventricular tachycardia/ventricular fibrillation, peripheral artery revascularization, new-onset stroke, end-stage renal disease-were evaluated in 508 young patients with hypertension (30.83±6.17 years) who had been treated at a tertiary hospital. Construction of the ML model, which consisted of recursive feature elimination, extreme gradient boosting, and 10-fold cross-validation, was performed at the 33-month follow-up evaluation, and the model's performance was compared with that of the Cox regression and recalibrated Framingham Risk Score models. An 11-variable combination was considered most valuable for predicting outcomes using the ML approach. The C statistic for identifying patients with composite end points was 0.757 (95% CI, 0.660-0.854) for the ML model, whereas for Cox regression model and the recalibrated Framingham Risk Score model it was 0.723 (95% CI, 0.636-0.810) and 0.529 (95% CI, 0.403-0.655). The ML approach was comparable with Cox regression for determining the clinical prognosis of young patients with hypertension and was better than that of the recalibrated Framingham Risk Score model.

Steroid metabolism gene variants and their genotype-phenotype correlations in Chinese early-onset hypertension patients.

The genetic factors related to early-onset hypertension are largely unknown. This study aimed to determine the spectrum of steroid metabolism gene variants and the clinical relationships of these variants to phenotypes in Chinese patients with early-onset hypertension. A total of 306 consecutive early-onset hypertensive patients were recruited. All coding exons and flanking intronic regions of KCNJ5, CYP11B1, and CYP17A1 were sequenced. Long-distance polymerase chain reaction was used to search for a CYP11B1/CYP11B2 chimeric gene. Pedigree investigations and genotype-phenotype analyses were performed for patients with rare variants. Nine rare variants were detected in eight patients (2.6%), but no CYP11B1/CYP11B2 chimeric gene was identified. One patient and two of her siblings were found to carry compound heterozygous mutations (C183Y and T390R) in CYP17A1 and were eventually diagnosed with atypical congenital adrenal hyperplasia. Patients with rare variants had younger ages of onset [17 (16, 20) vs. 30 (23, 35) years old, p = 0.010] and higher systolic blood pressure (148.5 ± 9.6 vs. 137.9 ± 17.8 mmHg, p = 0.021) than those without rare variants. Additionally, the patients and their relatives carrying rare variants exhibited increased serum free corticosterone [230.4 (7.4, 533.0) vs. 1.9 (0.9, 6.7)ng/ml, p = 0.001] and 11-deoxycorticosterone [16.16 (0.59, 33.23) vs. 0.77 (0.41, 0.96)ng/ml, p = 0.038] levels. Genetic testing is useful for the etiologic diagnosis of early-onset hypertension. Rare variants in steroid metabolism genes were associated with more severe clinical expression and abnormal circulating steroid metabolites in patients with early-onset hypertension.