Objective: The Framingham Heart Study showed an increased risk of cardiovascular disease with increasing pulse pressure (PP). Elevated PP is frequently observed with increasing age and may reflect the loss of elastic properties in large arteries. We investigated the effects of the MobiusHD-device on office systolic BP (SBP) and on 24-hour ambulatory BP (ABPM) in patients with high PP. Design and method: MobiusHD is designed to passively amplify pulsatile strain at the carotid sinus and reduce BP through increased baroreceptor activation, and consequently increase sympathoinhibition. A total of 40 patients were treated with MobiusHD for therapy-resistant hypertension and had BP measured at discharge, 1, 3, and 6 months. For analyses, patients were grouped according to baseline PP (high PP:>70 mmHg, n = 25; low PP:<70 mmHg, n = 15). Responsiveness at 6 months to MobiusHD treatment was defined as decrease in SBP of more than 10 mmHg, and also as decrease in ABPM of more than 5 mmHg. Linear mixed models were used to compare mean changes in BP over time between groups, chi-square tests were used to assess responsiveness. Results: Mean ± SD age was 53 ± 12 years and 50% were female. Baseline mean SBP, PP, and ABPM were 182 ± 17 mmHg, 74 ± 16 mmHg, and 165 ± 16 mmHg, respectively. Upon implantation of MobiusHD, SBP and PP were significantly reduced at 1,3,6 months by 21,29,25 mmHg (p<0.001) and by 13,16,12 mmHg (p < 0.001) in the high PP group and by 24,16,25 mmHg (p = 0.001) and by 11,6,11 mmHg (p = 0.009) in the low PP group, respectively. ABPM was significantly reduced at 3,6 months by 15,19 mmHg (p < 0.001) in the high PP group and by 14,22 mmHg (p = 0.001) in the low PP group. There were no significant differences in overall reductions in SBP, PP, ABPM between the high and low groups (p = 0.64, p = 0.28, and p = 0.90 respectively). No significant differences in SBP responsiveness were observed between high and low baseline PP groups (68%vs.80%, p = 0.41), as well as ABPM responsiveness between high and low baseline PP groups (76%vs.73%, p = 0.85). Conclusions: The MobiusHD-device effectively reduced SBP, PP, and ABPM in patients with therapy-resistant hypertension. The SBP and ABPM responses to MobiusHD were not different between patients with high or low PP.

We analyze situations where players build reputations for honesty rather than for playing particular actions. A patient player faces a sequence of short-run opponents. Before players act, the patient player announces their intended action after observing both a private payoff shock and a signal of what actions will be feasible that period. The patient player is either an honest type who keeps their word whenever their announced action is feasible, or an opportunistic type who freely chooses announcements and feasible actions. Short-run players only observe the current-period announcement and whether the patient player has kept their word in the past. We provide sufficient conditions under which the patient player can secure their optimal commitment payoff by building a reputation for honesty. Our proof introduces a novel technique based on concentration inequalities.

To develop an artificial intelligence, machine learning prediction model for estimating in-hospital mortality and stroke in patients undergoing balloon aortic valvuloplasty (BAV).The National Inpatient Sample (NIS) database was used to identify patients who underwent BAV from 2005 to 2017. Outcomes analyzed were in-hospital all-cause mortality and stroke after BAV. Predictors of mortality and stroke were selected using LASSO regularization. A conventional logistic regression and a random forest machine learning algorithm were used to train the models for predicting outcomes. The performance of all the modeling algorithms for predicting in-hospital mortality and stroke was compared between models using c-statistic, F1 score, brier score loss, diagnostic accuracy, and Kolmogorov-Smirnov plots.A total of 6962 patients with severe aortic stenosis who underwent BAV were identified. The performance of random forest classifier was comparable with logistic regression for predicting in-hospital mortality for all measures of performance (F1 score 0.422 vs 0.409, ROC-AUC 0.822 [95 % CI 0.787–0.855] vs 0.815 [95 % CI 0.779–0.849], diagnostic accuracy 70.42 % vs 70.93 %, KS-statistic 0.513 vs 0.494 and brier score loss 0.295 vs 0.291). The random forest algorithm significantly outperformed logistic regression in predicting in-hospital stroke with respect to all performance metrics: F1 score 0.225 vs 0.095, AUC 0.767 [0.662–0.858] vs 0.637 [0.499–0.754], brier score loss [0.399 vs 0.407], and KS-statistic [0.465 vs 0.254].The good discrimination of machine learning models reveal the potential of artificial intelligence to improve patient risk stratification for BAV.