Search

ObjectiveTo explore trends in blood pressure (BP) control before and during the COVID-19 pandemic.Patients and methodsHealth systems participating in the National Patient-Centered Clinical Research Network (PCORnet) Blood Pressure Control Laboratory Surveillance System responded to data queries, producing 9 BP control metrics. Averages of the BP control metrics (weighted by numbers of observations in each health system) were calculated and compared between two 1-year measurement periods (January 1, 2019, through December 31, 2019, and January 1, 2020, through December 31, 2020).ResultsAmong 1,770,547 hypertensive persons in 2019, BP control to

Background Uncontrolled blood pressure (BP) remains a leading cause of death in the United States. The American Medical Association developed a quality improvement program to improve BP control, but it is unclear how to efficiently implement this program at scale across multiple health systems. Methods and Results We conducted BP MAP (Blood Pressure Measure Accurately, Act Rapidly, and Partner With Patients), a comparative effectiveness trial with clinic-level randomization to compare 2 scalable versions of the quality improvement program: Full Support (with support from quality improvement expert) and Self-Guided (using only online materials). Outcomes were clinic-level BP control (

SARS-CoV-2 accesses host cells via angiotensin-converting enzyme-2, which is also affected by commonly used angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs), raising concerns that ACEI or ARB exposure may portend differential COVID-19 outcomes. In parallel cohort studies of outpatient and inpatient COVID-19-diagnosed adults with hypertension, we assessed associations between antihypertensive exposure (ACEI/ARB vs. non-ACEI/ARB antihypertensives, as well as between ACEI- vs. ARB) at the time of COVID-19 diagnosis, using electronic health record data from PCORnet health systems. The primary outcomes were all-cause hospitalization or death (outpatient cohort) or all-cause death (inpatient), analyzed via Cox regression weighted by inverse probability of treatment weights. From February 2020 through December 9, 2020, 11,246 patients (3477 person-years) and 2200 patients (777 person-years) were included from 17 health systems in outpatient and inpatient cohorts, respectively. There were 1015 all-cause hospitalization or deaths in the outpatient cohort (incidence, 29.2 events per 100 person-years), with no significant difference by ACEI/ARB use (adjusted HR 1.01; 95% CI 0.88, 1.15). In the inpatient cohort, there were 218 all-cause deaths (incidence, 28.1 per 100 person-years) and ACEI/ARB exposure was associated with reduced death (adjusted HR, 0.76; 95% CI, 0.57, 0.99). ACEI, versus ARB exposure, was associated with higher risk of hospitalization in the outpatient cohort, but no difference in all-cause death in either cohort. There was no evidence of effect modification across pre-specified baseline characteristics. Our results suggest ACEI and ARB exposure have no detrimental effect on hospitalizations and may reduce death among hypertensive patients diagnosed with COVID-19.

Background The National Patient-Centered Clinical Research Network Blood Pressure Control Laboratory Surveillance System was established to identify opportunities for blood pressure (BP) control improvement and to provide a mechanism for tracking improvement longitudinally. Methods and Results We conducted a serial cross-sectional study with queries against standardized electronic health record data in the National Patient-Centered Clinical Research Network (PCORnet) common data model returned by 25 participating US health systems. Queries produced BP control metrics for adults with well-documented hypertension and a recent encounter at the health system for a series of 1-year measurement periods for each quarter of available data from January 2017 to March 2020. Aggregate weighted results are presented overall and by race and ethnicity. The most recent measurement period includes data from 1 737 995 patients, and 11 956 509 patient-years were included in the trend analysis. Overall, 15% were Black, 52% women, and 28% had diabetes. BP control (

BackgroundUncontrolled blood pressure (BP) is a leading preventable cause of death that remains common in the US population despite the availability of effective medications. New technology and program innovation has high potential to improve BP but may be expensive and burdensome for patients, clinicians, health systems, and payers and may not produce desired results or reduce existing disparities in BP control.Methods and resultsThe PCORnet Blood Pressure Control Laboratory is a platform designed to enable national surveillance and facilitate quality improvement and comparative effectiveness research. The platform uses PCORnet, the National Patient-Centered Clinical Research Network, for engagement of health systems and collection of electronic health record data, and the Eureka Research Platform for eConsent and collection of patient-reported outcomes and mHealth data from wearable devices and smartphones. Three demonstration projects are underway: BP track will conduct national surveillance of BP control and related clinical processes by measuring theory-derived pragmatic BP control metrics using electronic health record data, with a focus on tracking disparities over time; BP MAP will conduct a cluster-randomized trial comparing effectiveness of 2 versions of a BP control quality improvement program; BP Home will conduct an individual patient-level randomized trial comparing effectiveness of smartphone-linked versus standard home BP monitoring. Thus far, BP Track has collected electronic health record data from over 826 000 eligible patients with hypertension who completed ≈3.1 million ambulatory visits. Preliminary results demonstrate substantial room for improvement in BP control (

Objectives Heart failure (HF) impacts millions of patients worldwide, yet the variability in treatment responses remains a major challenge for healthcare professionals. The current treatment strategies, largely derived from population based evidence, often fail to consider the unique characteristics of individual patients, resulting in suboptimal outcomes. This study aims to develop computational models that are patient-specific in predicting treatment outcomes, by utilizing a large Electronic Health Records (EHR) database. The goal is to improve drug response predictions by identifying specific HF patient subgroups that are likely to benefit from existing HF medications. Materials and Methods A novel, graph-based model capable of predicting treatment responses, combining Graph Neural Network and Transformer was developed. This method differs from conventional approaches by transforming a patient's EHR data into a graph structure. By defining patient subgroups based on this representation via K-Means Clustering, we were able to enhance the performance of drug response predictions. Results Leveraging EHR data from 11 627 Mayo Clinic HF patients, our model significantly outperformed traditional models in predicting drug response using NT-proBNP as a HF biomarker across five medication categories (best RMSE of 0.0043). Four distinct patient subgroups were identified with differential characteristics and outcomes, demonstrating superior predictive capabilities over existing HF subtypes (best mean RMSE of 0.0032). Discussion These results highlight the power of graph-based modeling of EHR in improving HF treatment strategies. The stratification of patients sheds light on particular patient segments that could benefit more significantly from tailored response predictions. Conclusions Longitudinal EHR data have the potential to enhance personalized prognostic predictions through the application of graph-based AI techniques. [ABSTRACT FROM AUTHOR]

BACKGROUND: Senescence, a mechanism of cellular aging, which is characterized by irreversible proliferation arrest and a proinflammatory secretory phenotype, has been documented in women with preeclampsia. As cellular senescence can persist and progress, we postulated that it is associated with accelerated aging phenotype and accumulation of comorbidities in women with a history of preeclampsia. METHODS: We included a cohort of women with a history of preeclampsia (n=40) age- and parity-matched to a group of referent women with normotensive pregnancies (n=40). Women with prior major cardiovascular events, neurological, or autoimmune conditions were excluded. We collected urine and blood samples to study markers of aging, data on multimorbidity at the time of enrollment, and prospectively followed them for events over the course of 6 years, on average. RESULTS: Women with a history of preeclampsia exhibited unfavorable aging profiles compared with referent women, including decreased urinary α-Klotho (P=0.018); increased leptin (P=0.016) and leptin/adiponectin ratio (P=0.027), and increased extracellular vesicles positive for tissue factor (P=0.025). Women with a history of preeclampsia likewise had a higher rate of comorbidities at the time of enrollment (P=0.003) and had a 4× higher risk of developing major cardiovascular events compared with referent women (P=0.003). CONCLUSIONS: Our data suggest that a history of preeclampsia is associated with accelerated aging as indicated by senescence marker differences and the accumulation of multimorbidity later in life. Targeting cellular senescence may offer novel, mechanism-based approaches for the diagnosis and treatment of adverse health outcomes in women with a history of preeclampsia. [ABSTRACT FROM AUTHOR]

The article presents a study which sought to automate Medication Regimen Complexity Index (MRCI) calculation in a database of people with mild cognitive impairment (MCI) or dementia and to examine contributions of specific MRCI components to overall complexity. Topics include calculation of MRCI scores, MRCI by patient characteristics, and possible impact of complex medications on the risk of poor health outcomes.

Background: Multimorbidity and functional limitation are associated with poor outcomes in heart failure (HF). However, the individual and combined effect of these on health‐related quality of life in patients with HF is not well understood. Methods: Patients aged ≥30 years with two or more HF diagnostic codes and one or more HF‐related prescription drugs from four U.S. institutions were mailed a survey to measure patient‐centric factors including functional status (activities of daily living [ADLs]) and health‐related quality of life (PROMIS‐29 Health Profile). Patients with HF from January 1, 2013 to February 1, 2018 were included. Multimorbidity was defined as ≥2 non‐cardiovascular comorbidities; functional limitation as any limitation in at least one of eight ADLs. Patients were categorized into four groups by multimorbidity (Yes/No) and functional limitation (Yes/No). We dichotomized the PROMIS‐29 sub‐scale scores at the median and calculated odd ratios for the four multimorbidity/functional limitation groups. Results: A total of 3330 patients with HF returned the survey (response rate 31%); 3020 completed the questions of interest and were retained. Among these patients (45% female; mean age 73 [standard deviation: 12] years), 29% had neither multimorbidity nor functional limitation, 24% had multimorbidity only, 22% had functional limitation only, and 25% had both. After adjustment, having functional limitation only was associated with higher anxiety (odds ratio [OR]: 3.44, 95% confidence interval [CI]: 2.66–4.45), depression (OR: 3.11, 95% CI: 2.39–4.06), and fatigue (OR: 4.19, 95% CI: 3.25–5.40); worse sleep (OR: 2.14, 95% CI: 1.69–2.72) and pain (OR: 6.73, 95% CI: 5.15–8.78); and greater difficulty with social activities (OR: 9.40, 95% CI: 7.19–12.28) compared with having neither. Results were similar for having both multimorbidity and functional limitation. Conclusion: Patients with only functional limitation have similar poor health‐related quality of life scores as those with both multimorbidity and functional limitation, underscoring the important role that physical functioning plays in the well‐being of patients with HF. [ABSTRACT FROM AUTHOR]

In April 2023, the National Cancer Institute offered a roadmap for cancer research to achieve Cancer Moonshot goals. To reach these goals requires making progress for all cancers, not just those that are most common. Achieving progress against rare cancers, as well as common cancers, requires involvement of large clinical research networks. In 2020, the Patient‐Centered Outcomes Research Institute (PCORI) launched an initiative on Conducting Rare Disease Research using PCORnet, the National Patient‐Centered Clinical Research Network. The purpose of this commentary is to introduce the broader community of cancer researchers to the PCORnet NET‐PRO study (comparing the effects of different treatment approaches for neuroendocrine tumors on patient‐reported outcomes) thereby demonstrating how researchers can use the PCORnet infrastructure to conduct large‐scale patient‐centered studies of rare cancers. Achieving progress against rare cancers requires involvement of large clinical research networks. This commentary showcases the NET‐PRO study (comparing the effects of different treatment approaches for neuroendocrine tumors on patient‐reported outcomes) to demonstrate how cancer researchers can conduct large‐scale patient‐centered studies of rare cancers using PCORnet. [ABSTRACT FROM AUTHOR]

ObjectivesTo estimate the risk of fatal and non-fatal myocardial infarction (MI) and stroke in patients with bipolar I disorder compared to people without bipolar I disorder.MethodUtilizing a records-linkage system spanning 30 years (1966-1996), a population-based cohort of 334 subjects with bipolar I disorder and 334 age and sex-matched referents from Olmsted County, Minnesota, U.S. was identified. Longitudinal follow-up continued until incident MI or stroke (confirmed by board-certified cardiologist/neurologist), death, or study end date (December 31, 2013). Cox proportional hazards models assessed the hazard ratio (HR) for MI or stroke, adjusting for potential confounders.ResultsThere was an increased risk of fatal or non-fatal MI or stroke (as a composite outcome) in patients with bipolar I disorder [HR 1.54, 95% confidence interval (CI) 1.02, 2.33; p=0.04]. However, after adjusting for baseline cardiovascular risk factors (alcoholism, hypertension, diabetes, and smoking), the risk was no longer significantly increased (HR 1.19, 95% CI 0.76, 1.86; p=0.46).LimitationsSmall sample size for the study design. Findings were not retained after adjustment for cardiovascular disease risk factors. Psychotropic medication use during the follow-up was not ascertained and was not included in the analyses.ConclusionThis study in a geographically defined region in the U.S. demonstrated a significant increased risk of MI or stroke in bipolar I disorder, which was no longer significant after adjustment for cardiovascular risk factors.