Cardiovascular outcomes according to on-treatment systolic blood pressure in older hypertensive patients: a multicenter cohort using a common data model.

OBJECTIVE: In the growing population of older patients with hypertension, limited evidence supports an association between lowering systolic blood pressure (SBP) and decreased adverse events. We aimed to investigate cardiovascular outcomes according to on-treatment SBP in older hypertensive patients. METHODS: This multicenter, retrospective study used data from the Korea University Medical Center database built on electronic health records from 2017 to 2022. Patients initiated on at least two antihypertensive drugs in combination were followed for three years. The patients were grouped by average on-treatment SBP in 10-mmHg increments from <110 to 160 mmHg or more. The primary outcome was a composite of all-cause death, myocardial infarction, stroke, and hospitalization due to heart failure. RESULTS: A total of 6427 patients aged ≥75 years (mean age, 80 years) were identified. The incidence of the primary outcome was lowest in individuals with an SBP of 120-129 mmHg (14.0%, P  < 0.001), and the adjusted hazard ratio for the primary outcome showed a J-shaped relationship with on-treatment SBP. Achieving an SBP of 120-129 mmHg showed acceptable safety profiles, including electrolyte imbalance, acute kidney injury, new-onset atrial fibrillation, and new-onset dementia or Alzheimer's disease when compared to the group with SBP of 130-139 mmHg. CONCLUSIONS: An average on-treatment SBP of less than 130 mmHg was associated with improved outcomes in older hypertensive patients without raising safety concerns. These findings support the target SBP of 130 mmHg in older patients, if tolerated.

Safety and cardiovascular effectiveness of olmesartan in combination therapy for advanced hypertension: an electronic health record-based cohort study.

OBJECTIVE: Compared with placebo, olmesartan has been linked to numerical imbalances in cardiovascular mortality. There is a paucity of contemporary real-world evidence on this agent for different study populations. This study investigated the clinical outcomes of olmesartan and other antihypertensives in patients with advanced hypertension. METHODS: This multicenter retrospective study used data from the Korea University Medical Center database, built from electronic health records. Patients prescribed at least two antihypertensive medications as a combined therapy were followed-up for 3 years. The primary outcome was a composite of all-cause mortality, myocardial infarction (MI), stroke, and hospitalization for heart failure. Adjusted outcomes were compared using propensity score (PS) matching. RESULTS: Among 24 806 patients, 4050 (16.3%) were olmesartan users between January 2017 and December 2018. The average patient age was 64 years, 45% were women, and 41% had diabetes. Olmesartan users were younger and less likely to have diabetes mellitus or chronic kidney disease. In PS-matched cohort, the 3-year cumulative incidences of the primary outcome were similar between the two groups ( P  = 0.91). The cumulative incidence of MI at 3 years was 1.4% in olmesartan users (4.8 per 1000 person-years) and 1.5% in active comparators (5.2 per 1000 person-years; P  = 0.74). Olmesartan also showed similar safety profiles, including acute kidney injury and newly started dialysis. CONCLUSIONS: In real-world practice, olmesartan use in combination therapy resulted in similar cardiovascular outcomes when compared with those of active comparators, and our findings did not show any conclusive evidence that olmesartan is harmful in patients with hypertension.

Effects of metabolic parameters' variability on cardiovascular outcomes in diabetic patients.

BACKGROUND: Metabolic abnormalities such as dyslipidemia, glucose and high blood pressure are common in diabetic patients. Visit-to-visit variabilities in these measures have been reported as potential residual cardiovascular risk factors. However, the relationship between these variabilities and their effects on cardiovascular prognosis have not been studied. METHODS: A total of 22,310 diabetic patients with ≥ 3 measurements of systolic blood pressure (SBP), blood glucose, total cholesterol (TC), and triglyceride (TG) levels during a minimum of three years at three tertiary general hospitals were selected. They were divided into high/low variability groups for each variable based on the coefficient of variation (CV) values. The primary outcome was the incidence of major adverse cardiovascular events (MACE), a composite of cardiovascular death, myocardial infarction, and stroke. RESULTS: All high CV groups had a higher incidence of MACE than those with low CV (6.0% vs. 2.5% for SBP-CV groups, 5.5% vs. 3.0% for TC-CV groups, 4.7% vs. 3.8% for TG-CV groups, 5.8% vs. 2.7% for glucose-CV groups). In multivariable Cox regression analysis,, high SBP-CV (HR 1.79 [95% CI 1.54-2.07], p < 0.01), high TC-CV (HR 1.54 [95% CI 1.34-1.77], p < 0.01), high TG-CV (HR 1.15 [95% CI 1.01-1.31], p = 0.040) and high glucose-CV (HR 1.61 [95% CI 1.40-1.86], p < 0.01) were independent predictors of MACE. CONCLUSION: Variability of SBP, TC, TG and glucose are important residual risk factors for cardiovascular events in diabetic patients.

Deep Learning Algorithm of 12-Lead Electrocardiogram for Parkinson Disease Screening.

BACKGROUND: Although idiopathic Parkinson's disease (IPD) is increasing with the aging population, there is no adequate screening test for early diagnosis of IPD. Cardiac autonomic dysfunction begins in the early stages of IPD, and an electrocardiogram (ECG) contains precise information on the heart. OBJECTIVE: This study is to develop an ECG deep learning algorithm that can efficiently screen for IPD. METHODS: Data were collected from 751 IPD patients (2,138 ECGs), 751 age and sex-matched non-IPD patients (2,673 ECGs) as a control group, and 297 drug-induced Parkinsonism (DPD) patients (875 ECGs) as a disease control group. ECG data were randomly divided into training set, validation set, and test set at a ratio of 6:2:2. We developed a deep-convolutional neural network (CNN) consisting of 16 layers with Bayesian optimization that classified IPD patients by ECG data. The robustness of the deep learning model was verified through 5-fold cross-validation. RESULTS: The AUROC of the model for detection of IPD was 0.924 (95% CI, 0.913-0.936) in the test set. That for detecting DPD was 0.473 (95% CI, 0.453-0.504). The sensitivities of the model according to Unified Parkinson's Disease Rating Scale III and Hoehn & Yahr scale were also similar. CONCLUSION: In conclusion, the CNN-based deep learning model using ECG data showed quite good performance in identifying IPD patients. Standardized 12-lead ECG test could be one of the clinically feasible candidate methods for early screening of IPD in the future.

Comparison of the effects of triglyceride variability and exposure estimate on clinical prognosis in diabetic patients.

BACKGROUND: Hypertriglyceridemia is an important feature of dyslipidemia in type 1 and type 2 diabetic patients and associated with the development of atherosclerotic cardiovascular disease. Recently, variability of lipid profile has been suggested as a residual risk factor for cardiovascular disease. This study compared the clinical impact of serum triglyceride variability, and their cumulative exposure estimates on cardiovascular prognosis in diabetic patients. METHODS: A total of 25,933 diabetic patients who had serum triglyceride levels measured at least 3 times and did not have underlying malignancy, myocardial infarction (MI), and stroke during the initial 3 years (modeling phase) were selected from three tertiary hospitals. They were divided into a high/low group depending on their coefficient of variation (CV) and cumulative exposure estimate (CEE). Incidence of major adverse event (MAE), a composite of all-cause death, MI, and stroke during the following 5 years were compared between groups by multivariable analysis after propensity score matching. RESULTS: Although there was a slight difference, both the high CV group and the high CEE group had a higher cardiovascular risk profile including male-dominance, smoking, alcohol, dyslipidemia, and chronic kidney disease compared to the low groups. After the propensity score matching, the high CV group showed higher MAE incidence compared to the low CV group (9.1% vs 7.7%, p = 0.01). In contrast, there was no significant difference of MAE incidence between the high CEE group and the low CEE group (8.6% vs 9.1%, p = 0.44). After the multivariable analysis with further adjustment for potential residual confounding factors, the high CV was suggested as an independent risk predictor for MAE (HR 1.19 [95% CI 1.03-1.37]). CONCLUSION: Visit-to-visit variability of triglyceride rather than their cumulative exposure is more strongly related to the incidence of MAE in diabetic patients.