Effect of Passive Choice and Active Choice Interventions in the Electronic Health Record to Cardiologists on Statin Prescribing: A Cluster Randomized Clinical Trial

Pragmatic Trial of Messaging to Providers About Treatment of Hyperlipidemia (PROMPT-LIPID): A Randomized Clinical Trial

BACKGROUND

Lipid-lowering therapy (LLT) is underutilized for very high-risk atherosclerotic cardiovascular disease. PROMPT-LIPID (PRagmatic Trial of Messaging to Providers about Treatment of HyperLIPIDemia) sought to determine whether electronic health record (EHR) alerts improve 90-day LLT intensification in patients with very high-risk atherosclerotic cardiovascular disease.

METHODS

PROMPT-LIPID was a pragmatic trial in which cardiovascular and internal medicine clinicians within Yale New Haven Health (New Haven, CT) were cluster-randomized to receive an EHR alert with individualized LLT recommendations or no alert for outpatients with very high-risk atherosclerotic cardiovascular disease and LDL-C (low-density lipoprotein cholesterol), ≥70 mg/dL. The primary outcome was 90-day LLT intensification (change to high-intensity statin and addition of ezetimibe or PCSK9i [proprotein subtilisin/kexin type 9 inhibitors]). Secondary outcomes included LDL-C level, proportion of patients with LDL-C of <70 or < 55 mg/dL, rate of major adverse cardiovascular events, ED visit incidence, and 6-month mortality. Results were analyzed using logistic and linear regression clustered at the provider level.

RESULTS

The no-alert group included 47 clinicians and 1370 patients (median age, 71 years; 50.1% female, median LDL-C, 93 mg/dL); the alert group included 49 clinicians and 1130 patients (median age, 72 years; 47% female, median LDL-C 91, mg/dL). The primary outcome was observed in 14.1% of patients in the alert group as compared with 10.4% in the no-alert group. There were no differences in any secondary outcomes at 6 months. Among 542 patients whose clinicians (n=46) did not dismiss the EHR alert recommendations, LLT intensification was significantly greater (21.2% versus 10.4%, odds ratio, 2.33 [95% CI, 1.48-3.66]).

CONCLUSIONS

With a real-time, targeted, individualized EHR alert as compared with usual care, the proportion of patients with atherosclerotic cardiovascular disease with LLT intensification was numerically higher but not statistically significant. Among clinicians who did not dismiss the alert, there was a > 2-fold increase in LLT intensification. EHR alerts, coupled with strategies to reduce clinician dismissal, may help address persistent gaps in LDL-C management.

REGISTRATION

URL: https://www.clinicaltrials.gov; Unique identifier: NCT04394715, https://www.clinicaltrials.gov/ct2/show/study/NCT04394715.

Nudging a Nation - The Danish NUDGE Trial Concept

Danish NUDGE Trial ConceptRandomized encouragement trials randomize to an opportunity to receive treatment instead of to the treatment. Here, Johansen and colleagues combine randomized encouragement trials with several advantages inherent in the Danish health system.

Natural language processing to identify reasons for sex disparity in statin prescriptions

Background

Statins are the cornerstone of treatment of patients with atherosclerotic cardiovascular disease (ASCVD). Despite this, multiple studies have shown that women with ASCVD are less likely to be prescribed statins than men. The objective of this study was to use Natural Language Processing (NLP) to elucidate factors contributing to this disparity.

Methods

Our cohort included adult patients with two or more encounters between 2014 and 2021 with an ASCVD diagnosis within a multisite electronic health record (EHR) in Northern California. After reviewing structured EHR prescription data, we used a benchmark deep learning NLP approach, Clinical Bidirectional Encoder Representations from Transformers (BERT), to identify and interpret discussions of statin prescriptions documented in clinical notes. Clinical BERT was evaluated against expert clinician review in 20% test sets.

Results

There were 88,913 patients with ASCVD (mean age 67.8±13.1 years) and 35,901 (40.4%) were women. Women with ASCVD were less likely to be prescribed statins compared with men (56.6% vs 67.6%, p <0.001), and, when prescribed, less likely to be prescribed guideline-directed high-intensity dosing (41.4% vs 49.8%, p <0.001). These disparities were more pronounced among younger patients, patients with private insurance, and those for whom English is their preferred language. Among those not prescribed statins, women were less likely than men to have statins mentioned in their clinical notes (16.9% vs 19.1%, p <0.001). Women were less likely than men to have statin use reported in clinical notes despite absence of recorded prescription (32.8% vs 42.6%, p <0.001). Women were slightly more likely than men to have statin intolerance documented in structured data or clinical notes (6.0% vs 5.3%, p=0.003).

Conclusions

Women with ASCVD were less likely to be prescribed guideline-directed statins compared with men. NLP identified additional sex-based statin disparities and reasons for statin non-prescription in clinical notes of patients with ASCVD.

Identifying Reasons for Statin Nonuse in Patients With Diabetes Using Deep Learning of Electronic Health Records

Background Statins are guideline-recommended medications that reduce cardiovascular events in patients with diabetes. Yet, statin use is concerningly low in this high-risk population. Identifying reasons for statin nonuse, which are typically described in unstructured electronic health record data, can inform targeted system interventions to improve statin use. We aimed to leverage a deep learning approach to identify reasons for statin nonuse in patients with diabetes. Methods and Results Adults with diabetes and no statin prescriptions were identified from a multiethnic, multisite Northern California electronic health record cohort from 2014 to 2020. We used a benchmark deep learning natural language processing approach (Clinical Bidirectional Encoder Representations from Transformers) to identify statin nonuse and reasons for statin nonuse from unstructured electronic health record data. Performance was evaluated against expert clinician review from manual annotation of clinical notes and compared with other natural language processing approaches. Of 33 461 patients with diabetes (mean age 59±15 years, 49% women, 36% White patients, 24% Asian patients, and 15% Hispanic patients), 47% (15 580) had no statin prescriptions. From unstructured data, Clinical Bidirectional Encoder Representations from Transformers accurately identified statin nonuse (area under receiver operating characteristic curve [AUC] 0.99 [0.98-1.0]) and key patient (eg, side effects/contraindications), clinician (eg, guideline-discordant practice), and system reasons (eg, clinical inertia) for statin nonuse (AUC 0.90 [0.86-0.93]) and outperformed other natural language processing approaches. Reasons for nonuse varied by clinical and demographic characteristics, including race and ethnicity. Conclusions A deep learning algorithm identified statin nonuse and actionable reasons for statin nonuse in patients with diabetes. Findings may enable targeted interventions to improve guideline-directed statin use and be scaled to other evidence-based therapies.

Healthcare professionals' knowledge, attitude and its associated factors toward electronic personal health record system in a resource-limited setting: A cross-sectional study

Introduction

Electronic personal health record (e-PHR) system enables individuals to access their health information and manage it themselves. It helps patient engagement management of health information that is accessed and shared with their healthcare providers using the platform. This improves individual healthcare through the exchange of health information between patients and healthcare providers. However, less is known about e-PHRs among healthcare professionals.

Objective

Therefore, this study aimed to assess Health professionals' Knowledge and attitude and its associated factors toward e-PHR at the teaching hospital in northwest Ethiopia.

Methods

An institution-based cross-sectional study design was used to determine healthcare professionals' knowledge and attitude and their associated factors toward e-PHR systems in teaching hospitals of Amhara regional state, Ethiopia, from 20 July to 20 August 2022. Pretested structured self-administered questionnaires were used to collect the data. Descriptive statistic was computed based on sociodemographic and other variables presented in the form of table graphs and texts. Bivariable and multivariable logistic analyses were performed with an adjusted odds ratio (AOR) and 95% CI to identify predictor variables.

Result

Of the total study participants, 57% were males and nearly half of the respondents had a bachelor's degree. Out of 402 participants, ~65.7% [61–70%] and 55.5% [50–60%] had good knowledge and favorable attitude toward e-PHR systems, respectively. Having a social media account 4.3 [AOR = 4.3, 95% CI (2.3–7.9)], having a smartphone 4.4 [AOR = 4.4, 95% CI (2.2–8.6)], digital literacy 8.8 [(AOR = 8.8, 95% CI (4.6–15.9)], being male 2.7 [AOR = 2.7, 95% CI (1.4–5.0)], and perceived usefulness 4.5 [(AOR = 4.5, 95% CI (2.5–8.5)] were positively associated with knowledge toward e-PHR systems. Similarly, having a personal computer 1.9 [AOR = 1.9, 95% CI (1.1–3.5)], computer training 3.9 [AOR = 3.9, 95% CI (1.8–8.3)], computer skill 19.8 [AOR = 19.8, 95% CI (10.7–36.9)], and Internet access 6.0 [AOR = 6.0, 95% CI (3.0–12.0)] were predictors for attitude toward e-PHR systems.

Conclusion

The findings from the study showed that healthcare professionals have good knowledge and a favorable attitude toward e-PHRs. Providing comprehensive basic computer training to improve healthcare professionals' expectation on the usefulness of e-PHR systems has a paramount contribution to the advancement of their knowledge and attitude toward successfully implementing e-PHRs.

US Trends in Cholesterol Screening, Lipid Levels, and Lipid-Lowering Medication Use in US Adults, 1999 to 2018

Background Understanding current trends in cholesterol screening, lipid levels, and lipid management therapies may inform health policy and practice. Methods and Results In 50 928 US adult National Health and Nutrition Examination Survey (NHANES) participants, trends were assessed in cholesterol screening, mean levels of total cholesterol, triglycerides, low-density-lipoprotein cholesterol, and lipid-lowering medication use from 1999 through 2018. Point estimates were also calculated using the 2017 to March 2020 prepandemic data set. The age- and sex-adjusted proportion of having cholesterol screened within 5 years increased from 63.2% (95% CI, 60.0-66.3) in 1999 to 2000 to 72.5% (95% CI, 69.5-75.3) in 2017 to 2018 (P<0.001 for linear trend). Mean total cholesterol decreased from 203.3 mg/dL (95% CI, 201.0-205.7) in 1999 to 2000 to 188.4 mg/dL in 2017 to 2018 (95% CI, 185.4-191.5) (P<0.001 for nonlinear trend). The mean triglyceride level decreased from 121.3 mg/dL (95% CI, 116.4-126.4) in 1999 to 2000 to 91.4 mg/dL (95% CI, 88.4-94.6) in 2017 to 2018 (P<0.001 for nonlinear trend). Low-density lipoprotein cholesterol decreased from 127.9 mg/dL (95% CI, 125.3-130.5) in 1999 to 2000 to 111.7 mg/dL (95% CI, 109.0-114.4) in 2017 to 2018 (P<0.001 for nonlinear trend). Among statin-eligible US adults, the proportion of statin use increased from 14.9% (95% CI, 12.2-17.9) in 1999 to 2000 to 27.8% (95% CI, 23.0-33.2) in 2017 to 2018 (P<0.001 for nonlinear trend). Statin use increased in adults with diabetes aged 40 to 75 years from 21.4% in 1999 to 2000 to 51.9% in 2017 to 2018 (P<0.001 for overall linear trend). Statin use plateaued in all other groups. The proportions of using ezetimibe and proprotein convertase subtilisin/kexin type 9 inhibitors were 3.7% (95% CI, 1.3-9.8) and 0.03% (95% CI, 0.01-0.15) in 2017 to March 2020, respectively. Conclusions From 1999 through 2018, cholesterol screening increased while mean total cholesterol, triglycerides, and low-density lipoprotein cholesterol levels decreased, with a modest increase in statin use and low uptake of nonstatin therapy in the US population.

Rationale and design of a pragmatic trial aimed at improving treatment of hyperlipidemia in outpatients with very high risk atherosclerotic cardiovascular disease: A pragmatic trial of messaging to providers about treatment of hyperlipidemia (PROMPT-LIPID)

BACKGROUND

Despite guideline recommendations to optimize low-density lipoprotein cholesterol (LDL-C) reduction with intensification of lipid-lowering therapy (LLT) in patients with atherosclerotic cardiovascular disease (ASCVD), few of these patients achieve LDL-C < 70 mg/dL in practice.

PURPOSE

We developed a real-time, targeted electronic health record (EHR) alert with embedded ordering capability to promote intensification of evidence based LLT in outpatients with very high risk ASCVD.

METHODS

We designed a pragmatic, multicenter, single-blind, cluster randomized trial to test the effectiveness of an EHR-based LLT intensification alert. The study will enroll about 100 providers who will be randomized to either receive the alert or undergo usual care for outpatients with high risk ASCVD with LDL-C > 70 mg/dL. Total enrollment will include 2,500 patients. The primary outcome will be the proportion of patients with LLT intensification at 90 days. Secondary outcomes include achieved LDL-C at 6 months and the proportion of patients with LDL-C < 70 mg/dL or < 55 mg/dL at 6 months.

RESULTS

Enrollment of 1,250 patients (50% of goal) was reached within 47 days (50% women, mean age 72, median LDL-C 91). At baseline, 71%, 9%, and 3% were on statins, ezetimibe, or proprotein convertase subtilisin/kexin type 9 inhibitors, respectively.

CONCLUSIONS

PRagmatic Trial of Messaging to Providers about Treatment of HyperLIPIDemia has rapidly reached 50% enrollment of patients with very high risk ASCVD, demonstrating low baseline LLT utilization. This pragmatic, EHR-based trial will determine the effectiveness of a real-time, targeted EHR alert with embedded ordering capability to promote LLT intensification. Findings from this low-cost, widely scalable intervention to improve LDL-C may have important public health implications.

TRIAL REGISTRATION

clinicaltrials.gov NCT04394715.

LDL-C target attainment in secondary prevention of ASCVD in the United States: barriers, consequences of nonachievement, and strategies to reach goals

Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of death in the United States. Elevated low-density lipoprotein cholesterol (LDL-C) is a major causal risk factor for ASCVD. Current evidence overwhelmingly demonstrates that lowering LDL-C reduces the risk of secondary cardiovascular events in patients with previous myocardial infarction or stroke. There is no lower limit for LDL-C: large, randomized studies and meta-analyses have found continuous benefit and no safety concerns in patients achieving LDL-C levels <25 mg/dL. As 'Time is plaque' in patients with ASCVD, early, sustained reductions in LDL-C are critical to slow or halt disease progression. However, despite use of lipid-lowering medications, <30% of patients with ASCVD achieve guideline-recommended reductions in LDL-C, resulting in a substantial societal burden of preventable cardiovascular events and early mortality. LDL-C goals are not met due to several factors: lipid-lowering therapy is not initiated and intensified as directed by clinical guidelines (clinical inertia); most patients do not adhere to prescribed medications; and high-risk patients are frequently denied access to add-on therapies by their insurance providers. Promoting patient and clinician education, multidisciplinary collaboration, and other interventions may help to overcome these barriers. Ultimately, achieving population-level guideline-recommended reductions in LDL-C will require a collaborative effort from patients, clinicians, relevant professional societies, drug manufacturers, and payers.

A New Technology-Enabled Care Model for Pediatric Type 1 Diabetes

In July 2018, pediatric type 1 diabetes (T1D) care at Stanford suffered many of the problems that plague U.S. health care. Patient outcomes lagged behind those of peer European nations, care was delivered primarily on a fixed cadence rather than as needed, continuous glucose monitors (CGMs) were largely unavailable for individuals with public insurance, and providers' primary access to CGM data was through long printouts. Stanford developed a new technology-enabled, telemedicine-based care model for patients with newly diagnosed T1D. They developed and deployed Timely Interventions for Diabetes Excellence (TIDE) to facilitate as-needed patient contact with the partially automated analysis of CGM data and used philanthropic funding to facilitate full access to CGM technology for publicly insured patients, for whom CGM is not readily available in California. A study of the use of CGM for patients with new-onset T1D (pilot Teamwork, Targets, and Technology for Tight Control [4T] study), which incorporated the use of TIDE, was associated with a 0.5%-point reduction in hemoglobin A1c compared with historical controls and an 86% reduction in screen time for providers reviewing patient data. Based on this initial success, Stanford expanded the use of TIDE to a total of 300 patients, including many outside the pilot 4T study, and made TIDE freely available as open-source software. Next steps include expanding the use of TIDE to support the care of approximately 1,000 patients, improving TIDE and the associated workflows to scale their use to more patients, incorporating data from additional sensors, and partnering with other institutions to facilitate their deployment of this care model.

Contemporary Management of Dyslipidemia

The treatment of dyslipidemia continues to be a dynamic and controversial topic. Even the most appropriate therapeutic range for lipid levels-including that of triglycerides and low-density lipoprotein cholesterol-remain actively debated. Furthermore, with ever-increasing options and available treatment modalities, the management of dyslipidemia has progressed in both depth and complexity. An understanding of appropriate lipid-lowering therapy remains an essential topic of review for practitioners across medical specialties. The goal of this review is to provide an overview of recent research developments and recommendations for patients with dyslipidemia as a means of better informing the clinical practice of lipid management. By utilizing a guideline-directed approach, we provide a reference point on optimal lipid-lowering therapies across the spectrum of dyslipidemia. Special attention is paid to long-term adherence to lipid-lowering therapies, and the benefits derived from instituting appropriate medications in a structured manner alongside monitoring. Novel therapies and their impact on lipid lowering are discussed in detail, as well as potential avenues for research going forward. The prevention of cardiovascular disease remains paramount, and this review provides a roadmap for instituting appropriate therapies in cardiovascular disease prevention.

Gaps and Disparities in Primary Prevention Statin Prescription During Outpatient Care

The 2018 American College of Cardiology/American Heart Association Guideline on the Management of Blood Cholesterol recommends statin therapy for eligible patients to reduce the risk of atherosclerotic cardiovascular disease (ASCVD). We extracted electronic health record data for patients with at least one primary care or cardiology visit between October 2018 and January 2020 at an urban, academic medical center in New York City. Clinical and demographic data were used to identify patients eligible for primary prevention statin therapy. Statin prescription status was extracted from the electronic health record, and multivariate logistic regression was used to assess the association between statin prescription and age, gender, race, ethnicity, and other clinical and demographic covariables. In 7,550 patients eligible for primary prevention statin therapy, 3,994 (52.9%) were prescribed statins on at least 1 visit. Statin prescription was highest in patients with diabetes mellitus (73.6%) and with a 10-year ASCVD risk ≥20% (60.6%) and was lowest for those with a 10-year ASCVD risk between 5% and 7.5% (18.7%). Compared with those never prescribed statins, patients prescribed statins were less likely to be women, mainly driven by lower statin prescription rates for women with diabetes. In a fully adjusted model, women remained less likely to be prescribed statin therapy (adjusted odds ratios 0.79, 95% confidence interval 0.71 to 0.88). In conclusion, primary prevention statin therapy remains underutilized.

The Role of Behavioral Economics in Improving Cardiovascular Health Behaviors and Outcomes

PURPOSE OF REVIEW

Behavioral economics represents a promising set of principles to inform the design of health-promoting interventions. Techniques from the field have the potential to increase quality of cardiovascular care given suboptimal rates of guideline-directed care delivery and patient adherence to optimal health behaviors across the spectrum of cardiovascular care delivery.

RECENT FINDINGS

Cardiovascular health-promoting interventions have demonstrated success in using a wide array of principles from behavioral economics, including loss framing, social norms, and gamification. Such approaches are becoming increasingly sophisticated and focused on clinical cardiovascular outcomes in addition to health behaviors as a primary endpoint. Many approaches can be used to improve patient decisions remotely, which is particularly useful given the shift to virtual care in the context of the COVID-19 pandemic. Numerous applications for behavioral economics exist in the cardiovascular care delivery space, though more work is needed before we will have a full understanding of ways to best leverage such applications in each clinical context.

Algorithm-Enabled, Personalized Glucose Management for Type 1 Diabetes at the Population Scale: A Prospective Evaluation in Clinical Practice (Preprint)

Background

The use of continuous glucose monitors (CGMs) is recommended as the standard of care by the American Diabetes Association for individuals with type 1 diabetes (T1D). Few hardware-agnostic, open-source, whole-population tools are available to facilitate the use of CGM data by clinicians such as physicians and certified diabetes educators.

Objective

This study aimed to develop a tool that identifies patients appropriate for contact using an asynchronous message through electronic medical records while minimizing the number of patients reviewed by a certified diabetes educator or physician using the tool.

Methods

We used consensus guidelines to develop timely interventions for diabetes excellence (TIDE), an open-source hardware-agnostic tool to analyze CGM data to identify patients with deteriorating glucose control by generating generic flags (eg, mean glucose [MG] >170 mg/dL) and personalized flags (eg, MG increased by >10 mg/dL). In a prospective 7-week study in a pediatric T1D clinic, we measured the sensitivity of TIDE in identifying patients appropriate for contact and the number of patients reviewed. We simulated measures of the workload generated by TIDE, including the average number of time in range (TIR) flags per patient per review period, on a convenience sample of eight external data sets, 6 from clinical trials and 2 donated by research foundations.

Results

Over the 7 weeks of evaluation, the clinical population increased from 56 to 64 patients. The mean sensitivity was 99% (242/245; SD 2.5%), and the mean reduction in the number of patients reviewed was 42.6% (182/427; SD 10.9%). The 8 external data sets contained 1365 patients with 30,017 weeks of data collected by 7 types of CGMs. The rates of generic and personalized TIR flags per patient per review period were, respectively, 0.15 and 0.12 in the data set with the lowest average MG (141 mg/dL) and 0.95 and 0.22 in the data set with the highest average MG (207 mg/dL).

Conclusions

TIDE is an open-source hardware-agnostic tool for personalized analysis of CGM data at the clinical population scale. In a pediatric T1D clinic, TIDE identified 99% of patients appropriate for contact using an asynchronous message through electronic medical records while reducing the number of patients reviewed by certified diabetes care and education specialists by 43%. For each of the 8 external data sets, simulation of the use of TIDE produced fewer than 0.25 personalized TIR flags per patient per review period. The use of TIDE to support telemedicine-based T1D care may facilitate sensitive and efficient guideline-based population health management.