Relationship between blood pressure values, depressive symptoms and cardiovascular outcomes in patients with cardiometabolic disease

We studied joint effect of blood pressure-BP and depression on risk of major adverse cardiovascular outcome in patients with existing cardiometabolic disease. A cohort of 35537 patients with coronary heart disease, diabetes or stroke underwent depression screening and BP was recorded concurrently. We used Cox’s proportional hazards to calculate risk of major adverse cardiovascular event-MACE (myocardial infarction/heart failure/stroke or cardiovascular death) over 4 years associated with baseline BP and depression. 11% (3939) had experienced MACE within 4 years. Patients with very high systolic BP-SBP (160-240) hazard ratio-HR 1.28 and with depression (HR 1.22) at baseline had significantly higher adjusted risk. Depression had significant interaction with SBP in risk prediction (p=0.03). Patients with combination of SBP and depression at baseline had 83% higher adjusted risk of MACE, as compared to patients with reference SBP and without depression. Patients with cardiometabolic disease and comorbid depression may benefit from closer monitoring of SBP

Prognostic Predictive Model to Estimate the Risk of Multiple Chronic Diseases: Constructing Copulas Using Electronic Medical Record Data

Introduction: Multimorbidity, the presence of two or more chronic diseases in an individual, is a pressing medical condition. Novel prevention methods are required to reduce the incidence of multimorbidity. Prognostic predictive models estimate a patient’s risk of developing chronic disease. This thesis developed a single predictive model for three diseases associated with multimorbidity: diabetes, hypertension, and osteoarthritis. Methods: Univariate logistic regression models were constructed, followed by an analysis of the dependence that existed using copulas. All analyses were based on data from the Canadian Primary Care Sentinel Surveillance Network. Results: All univariate models were highly predictive, as demonstrated by their discrimination and calibration. Copula models revealed the dependence between each disease pair. Discussion: By estimating the risk of multiple chronic diseases, prognostic predictive models may enable the prevention of chronic disease through identification of high-risk individuals or delivery of individualized risk assessments to inform patient and health care provider decision-making

Effect of Home Telemonitoring on Heart Failure Hospital Readmissions Among Adult Hispanics

Heart failure is ranked as one of the leading causes of hospitalizations and mortality among adults of all racial/ethnic groups in the United States. Telemonitoring, as a homecare intervention for heart failure management, has been used across all groups although the benefit for Hispanics not established. The purpose of this retrospective, quantitative study was to determine the differences in hospital readmission between Hispanic, non-Hispanic Black, and non-Hispanic White patients with heart failure disease who either received or did not receive home telemonitoring services from a homecare agency in Connecticut. The research questions for this study examined the effect of home telemonitoring, race, age, gender, and insurance on heart failure hospitalization across the 3 groups. The chronic care model was used as the theoretical framework for this study because it offers a method for reforming healthcare to ensure optimization in chronic disease management. A purposive sample of 138 records of patients admitted between January 1, 2012 and June 30, 2017 with a diagnosis of heart failure provided the data for the study. Data were analyzed by conducting a simple and multiple logistic regression analysis. The key findings of the simple logistic analysis showed that only Hispanics who used telemonitoring were almost 4 times less likely to be readmitted to the hospital compared to Hispanics who did not use telemonitoring (p = 0.04). The multiple logistic analysis revealed race, age, gender, and insurance were not significant predictors of readmissions (p \u3e 0.05). The findings from this study may promote positive social change by providing healthcare providers with a better understanding of the effects of home telemonitoring for treating adult Hispanic patients with heart failure disease

Rationale and design of a multicenter Chronic Kidney Disease (CKD) and at-risk for CKD electronic health records-based registry: CURE-CKD.

BACKGROUND: Chronic kidney disease (CKD) is a global public health problem, exhibiting sharp increases in incidence, prevalence, and attributable morbidity and mortality. There is a critical need to better understand the demographics, clinical characteristics, and key risk factors for CKD; and to develop platforms for testing novel interventions to improve modifiable risk factors, particularly for the CKD patients with a rapid decline in kidney function. METHODS: We describe a novel collaboration between two large healthcare systems (Providence St. Joseph Health and University of California, Los Angeles Health) supported by leadership from both institutions, which was created to develop harmonized cohorts of patients with CKD or those at increased risk for CKD (hypertension/HTN, diabetes/DM, pre-diabetes) from electronic health record data. RESULTS: The combined repository of candidate records included more than 3.3 million patients with at least a single qualifying measure for CKD and/or at-risk for CKD. The CURE-CKD registry includes over 2.6 million patients with and/or at-risk for CKD identified by stricter guide-line based criteria using a combination of administrative encounter codes, physical examinations, laboratory values and medication use. Notably, data based on race/ethnicity and geography in part, will enable robust analyses to study traditionally disadvantaged or marginalized patients not typically included in clinical trials. DISCUSSION: CURE-CKD project is a unique multidisciplinary collaboration between nephrologists, endocrinologists, primary care physicians with health services research skills, health economists, and those with expertise in statistics, bio-informatics and machine learning. The CURE-CKD registry uses curated observations from real-world settings across two large healthcare systems and has great potential to provide important contributions for healthcare and for improving clinical outcomes in patients with and at-risk for CKD

Electronic health record phenotyping improves detection and screening of type 2 diabetes in the general United States population: A cross-sectional, unselected, retrospective study

Objectives: In the United States, 25% of people with type 2 diabetes are undiagnosed. Conventional screening models use limited demographic information to assess risk. We evaluated whether electronic health record (EHR) phenotyping could improve diabetes screening, even when records are incomplete and data are not recorded systematically across patients and practice locations. Methods: In this cross-sectional, retrospective study, data from 9,948 US patients between 2009 and 2012 were used to develop a pre-screening tool to predict current type 2 diabetes, using multivariate logistic regression. We compared (1) a full EHR model containing prescribed medications, diagnoses, and traditional predictive information, (2) a restricted EHR model where medication information was removed, and (3) a conventional model containing only traditional predictive information (BMI, age, gender, hypertensive and smoking status). We additionally used a random-forests classification model to judge whether including additional EHR information could increase the ability to detect patients with Type 2 diabetes on new patient samples. Results: Using a patient's full or restricted EHR to detect diabetes was superior to using basic covariates alone (p<0.001). The random forests model replicated on out-of-bag data. Migraines and cardiac dysrhythmias were negatively associated with type 2 diabetes, while acute bronchitis and herpes zoster were positively associated, among other factors. Conclusions: EHR phenotyping resulted in markedly superior detection of type 2 diabetes in a general US population, could increase the efficiency and accuracy of disease screening, and are capable of picking up signals in real-world records

Patient Engagement: The Impact of Electronic Patient Portal Use on Missed Appointments in Patients with Diabetes, a Retrospective Study

Background: This retrospective observational chart review evaluated the use of the MyChart® patient portal as a viable tool for engaging patients. Engagement was measured as fewer missed appointments (no-shows and same-day cancellations). Objectives: To determine who uses the MyChart® patient portal in a chronically ill population of adult patients with diabetes and assess the association of portal use with missed appointments. Methods: The medical records of adult patients (18-80) with a diagnosis of Type 1 and/or Type 2 Diabetes Mellitus (DM) were reviewed (N=7,795). The efficacy of the MyChart® patient portal at reducing missed appointments was assessed by comparing patients who use the portal (evidenced by two or more log-ins during the study period) to those who do not. Results: In this study, 43.7% of adult patients with diabetes used a portal account. Portal users were predominantly female, non-Black, married, non-smokers, and had at least one of the comorbidities often associated with diabetes (hypertension, hyperlipidemia, and/or obesity). Portal users were on average 58.8 years old. Use of the MyChart® patient portal was independently associated with a reduced no-show rate (4.7% for portal users compared to 12.4% for nonusers). However, when patients who activated a portal account during the study period were subjected to a within-subjects analysis, the mean missed appointment percentage was not statistically significantly different when patients had an activated portal account compared to when they did not. Thus, the portal may be a useful tool for engaging chronically ill patients but it is only one component to appointment arrivals. Conclusion: Conclusions from this study are limited given the retrospective design. Nonetheless, the findings suggest that the patient portal is effective at engaging chronically ill patients and thus warrants greater merit. The portal may also be a useful tool for reducing missed appointments in patients with chronic illness who would greatly benefit from appointment adherence. Future research should focus on testing the hypotheses generated in a prospective manner

Complex Care Management Program Overview

This report includes brief updates on various forms of complex care management including: Aetna - Medicare Advantage Embedded Case Management ProgramBrigham and Women's Hospital - Care Management ProgramIndependent Health - Care PartnersIntermountain Healthcare and Oregon Health and Science University - Care Management PlusJohns Hopkins University - Hospital at HomeMount Sinai Medical Center -- New York - Mount Sinai Visiting Doctors Program/ Chelsea-Village House Calls ProgramsPartners in Care Foundation - HomeMeds ProgramPrinceton HealthCare System - Partnerships for PIECEQuality Improvement for Complex Chronic Conditions - CarePartner ProgramSenior Services - Project Enhance/EnhanceWellnessSenior Whole Health - Complex Care Management ProgramSumma Health/Ohio Department of Aging - PASSPORT Medicaid Waiver ProgramSutter Health - Sutter Care Coordination ProgramUniversity of Washington School of Medicine - TEAMcar

Protocol for the 'e-Nudge trial' : a randomised controlled trial of electronic feedback to reduce the cardiovascular risk of individuals in general practice [ISRCTN64828380]

Background: Cardiovascular disease (including coronary heart disease and stroke) is a major cause of death and disability in the United Kingdom, and is to a large extent preventable, by lifestyle modification and drug therapy. The recent standardisation of electronic codes for cardiovascular risk variables through the United Kingdom's new General Practice contract provides an opportunity for the application of risk algorithms to identify high risk individuals. This randomised controlled trial will test the benefits of an automated system of alert messages and practice searches to identify those at highest risk of cardiovascular disease in primary care databases. Design: Patients over 50 years old in practice databases will be randomised to the intervention group that will receive the alert messages and searches, and a control group who will continue to receive usual care. In addition to those at high estimated risk, potentially high risk patients will be identified who have insufficient data to allow a risk estimate to be made. Further groups identified will be those with possible undiagnosed diabetes, based either on elevated past recorded blood glucose measurements, or an absence of recent blood glucose measurement in those with established cardiovascular disease. Outcome measures: The intervention will be applied for two years, and outcome data will be collected for a further year. The primary outcome measure will be the annual rate of cardiovascular events in the intervention and control arms of the study. Secondary measures include the proportion of patients at high estimated cardiovascular risk, the proportion of patients with missing data for a risk estimate, and the proportion with undefined diabetes status at the end of the trial