An Analytics Approach to Guide Randomized Controlled Trials in Hypertension Management

Blood pressure (BP) is a significant controllable risk factor for cardiovascular disease (CVD), the leading cause of death worldwide. BP comprises two interrelated measurements: systolic and diastolic. CVD risk is minimized at intermediate BP values, a notion known as the J-curve effect. The J-curve effect imposes fundamental trade-offs in simultaneous management of systolic and diastolic BP; however, assessing a comprehensive set of joint systolic/diastolic BP treatment thresholds while explicitly considering the J-curve effect via randomized controlled trials (RCTs) is not feasible because of the time and cost-prohibitive nature of RCTs. In this study, we propose an analytics approach to identify promising joint systolic/diastolic BP threshold levels for antihypertensive treatment. More specifically, using one of the largest longitudinal BP progression data sets, we first build and fit Brownian motion processes to capture simultaneous progression of systolic/diastolic BP at the population level and externally validate our BP progression model on unseen data. We then analytically characterize the hazard ratio, which enables us to compute the optimal treatment decisions. Finally, building upon the optimal joint BP treatment thresholds, we devise a practical and easily implementable approximate policy. We estimate the potential impact of our findings through a simulation study, which indicates that the impact of explicitly considering the J-curve effect and joint systolic/diastolic BP in treatment decisions could be substantial. Specifically, we estimate that between approximately 3,000 and 9,000 premature deaths from cardiovascular disease in the United States could be prevented annually, a finding that could be tested empirically in randomized trials. This paper was accepted by Stefan Scholtes, healthcare management.

Obesity as a mortality risk factor in the medical ward: a case control study

Background Research regarding the association between severe obesity and in-hospital mortality is inconsistent. We evaluated the impact of body mass index (BMI) levels on mortality in the medical wards. The analysis was performed separately before and during the COVID-19 pandemic. Methods We retrospectively retrieved data of adult patients admitted to the medical wards at the Mount Sinai Health System in New York City. The study was conducted between January 1, 2011, to March 23, 2021. Patients were divided into two sub-cohorts: pre-COVID-19 and during-COVID-19. Patients were then clustered into groups based on BMI ranges. A multivariate logistic regression analysis compared the mortality rate among the BMI groups, before and during the pandemic. Results Overall, 179,288 patients were admitted to the medical wards and had a recorded BMI measurement. 149,098 were admitted before the COVID-19 pandemic and 30,190 during the pandemic. Pre-pandemic, multivariate analysis showed a “J curve” between BMI and mortality. Severe obesity (BMI > 40) had an aOR of 0.8 (95% CI:0.7–1.0, p = 0.018) compared to the normal BMI group. In contrast, during the pandemic, the analysis showed a “U curve” between BMI and mortality. Severe obesity had an aOR of 1.7 (95% CI:1.3–2.4, p < 0.001) compared to the normal BMI group. Conclusions Medical ward patients with severe obesity have a lower risk for mortality compared to patients with normal BMI. However, this does not apply during COVID-19, where obesity was a leading risk factor for mortality in the medical wards. It is important for the internal medicine physician to understand the intricacies of the association between obesity and medical ward mortality.

Prognostic Importance of Pulmonary Artery Pulsatility Index and Right Ventricular Stroke Work Index in End-Stage Heart Failure Patients

Background: Right ventricular (RV) failure is an important cause of morbidity and mortality in patients with left ventricular (LV) end-stage heart failure (ESHF). Pulmonary artery pulsatility index (PAPi) and right ventricular stroke work index (RVSWI) are invasive parameters related to RV function. This study aimed to investigate the prognostic impact of PAPi and RVSWI in these patients. Methods and Results: In this study, 416 patients with ESHF were included. The adverse cardiac event (ACE) was defined as left ventricular assist device (LVAD) implantation, urgent heart transplantation, or cardiac mortality. There were 218 ACE cases and 198 non-ACE cases over a median follow-up of 503.50 days. Patients with ACE had lower PAPi and similar RVSWI compared to those without ACE (3.1±1.9 vs. 3.7±2.3, P=0.003 and 7.3±4.9 vs. 6.9±4.4, P=0.422, respectively). According to the results of multivariate analysis, while PAPi (from 2 to 5.65) was associated with ACE, RVSWI (from 3.62 to 9.75) was not associated with ACE (HR: 0.75, 95% CI (0.55-0.95), P=0.031; HR: 0.79, 95% CI: (0.58-1.09), P=0.081, , respectively). Survival analysis revealed that PAPi ≤2.56 was associated with a higher ACE risk compared to PAPi >2.56 (HR: 1.46, 95% CI: 1.11-1.92, P=0.006). PAPi ≤2.56 could predict ACE with 56.7% sensitivity and 51.3% specificity at one year. Furthermore, the association between RVSWI and ACE was nonlinear (J-curve pattern). Low and high values seem to be associated with higher ACE risk compared to intermediate values. Conclusion: The low PAPi was an independent risk for ACE and it had a linear association with it. However, RVSWI seems to be have a nonlinear association with ACE (J-curve pattern).

Asymmetric J-curve in the Commodity Trade between Pakistan and China

The J-curve is a term used to describe short-run deterioration in the trade balance combined with long-run improvement subsequent to a currency devaluation or depreciation. While the majority of studies have tested the symmetric J-curve concept, the new direction is to test for an asymmetric J-curve. We tested both concepts for each of the 21 two-digit industries that trade between Pakistan and its major partner, China. While we found support for the symmetric J-curve in only six industries, we found support for the asymmetric J-curve in 13 industries. The two largest industries, coded 71 (machinery other than electric with 21.14% trade share) and 72 (electrical machinery, apparatus, and appliances with 16.87% trade share) were found to be in the list.

J-curve relationship between long term glycemic control and mortality in diabetic patients with acute myocardial infarction undergoing percutaneous coronary intervention

Background Intensive glycemic control is generally recommended for diabetic patients to reduce complications. However, the role of glycemic control in the mortality in diabetic patients with acute myocardial infarction (AMI) remained unclear. Methods We selected diabetic patients who measured HbA1c more than 3 times after AMI among 10,719 patients enrolled in the multicenter AMI registry. Patients (n = 1384) were categorized into five groups: according to mean HbA1c level: ≤ 6.5%, > 6.5 to ≤ 7.0%, > 7.0 to ≤ 7.5%, > 7.5 to ≤ 8.0% and > 8.0%. The primary endpoint was all-cause mortality. Results During a median follow-up of 6.2 years, the patients with a mean HbA1c of 6.5 to 7.0% had the lowest all-cause mortality. Compared to patients with mean HbA1c of 6.5 to 7.0%, the risk of all-cause mortality increased in subjects with mean HbA1c ≤ 6.5% (adjusted hazard ratio [HR] 2.00, 95% confidence interval [CI] 1.02–3.95) and in those with mean HbA1c > 8.0% (adjusted HR 3.35, 95% CI 1.78–6.29). In the subgroup analysis by age, the J-curve relationship between mean HbA1c and all-cause mortality was accentuated in elderly patients (age ≥ 65 years), while there was no difference in all-cause mortality across the HbA1c groups in younger patients (age < 65 years). Conclusions The less strict glycemic control in diabetic patients with AMI would be optimal for preventing mortality, especially in elderly patients.

Moderated mediation and J-curve emergence in path models: an information systems research perspective

Purpose J-curve relationship analyses can provide valuable insights to information systems (IS) researchers. This paper aims to discuss moderated mediation in IS research and the related emergence of J-curve relationships. Design/methodology/approach Building on an illustrative study in the field of IS, the author Lays out three steps to combine moderation and J-curve analyses, with the goal of more fully understanding the underlying moderated mediation relationships. The paper proposes a new segmentation delta method to test for J-curve emergence, as part of this framework. Findings The paper shows, in the context of this study, the complementarity of moderation and J-curve analyses. Research limitations/implications Currently, IS researchers rarely conduct moderation and J-curve analyses in a complementary way, even though there are software tools, and related methods, which allow them to do so in a relatively straightforward way. Originality/value The analyses were conducted with the software WarpPLS, a widely used tool that allows for moderated mediation and J-curve analyses, in a way that is fully compatible with the set of steps presented in this paper.