Impact of risk factor control on peripheral artery disease outcomes and health disparities

BACKGROUND

Peripheral artery disease (PAD) is associated with modifiable atherosclerotic risk factors like hypertension, diabetes, hyperlipidemia, and smoking. However, the effect of risk factor control on outcomes and disparities in achieving control is less well understood.

METHODS

All patients in an integrated, regional health system with PAD-related encounters, fee-for-service Medicare, and clinical risk factor control data were identified. Component risk factors were dichotomized into controlled and uncontrolled categories (control defined as low-density lipoprotein < 100 mg/dL, hemoglobin A1c < 7.0%, SBP < 140 mmHg, and current nonsmoker) and composite categories (none, 1, ⩾ 2 uncontrolled RFs) created. The primary outcome was major adverse vascular events (MAVE, a composite of all-cause mortality, myocardial infarction, stroke, and lower-extremity revascularization and amputation).

RESULTS

The cohort included 781 patients with PAD, average age 72.5 ± 9.8 years, of whom 30.1% were Black, and 19.1% were Medicaid dual-enrolled. In this cohort, 260 (33.3%) had no uncontrolled risk factors and 200 (25.6%) had two or more uncontrolled risk factors. Patients with the poorest risk factor control were more likely to be Black (p < 0.001), Medicaid dual-enrolled (p < 0.001), and have chronic limb-threatening ischemia (p = 0.009). Significant differences in MAVE by degree of risk factor control were observed at 30 days (none uncontrolled: 5.8%, 1 uncontrolled: 11.5%, ⩾ 2 uncontrolled: 13.6%; p = 0.01) but not at 1 year (p = 0.08). risk factor control was not associated with outcomes at 1 year after adjustment for patient and PAD-specific characteristics.

CONCLUSIONS

risk factor control is poor among patients with PAD. Significant disparities in achieving optimal risk factor control represent a potential target for reducing inequities in outcomes.

Clinician Specialty, Access to Care, and Outcomes Among Patients with Peripheral Artery Disease

BACKGROUND

Understanding the relationship between patterns of peripheral artery disease and outcomes is an essential step toward improving care and outcomes. We hypothesized that clinician specialty would be associated with occurrence of major adverse vascular events (MAVE).

METHODS

Patients with at least 1 peripheral artery disease-related encounter in our health system and fee-for-service Medicare were divided into groups based on the specialty of the clinician (ie, cardiologist, surgeon, podiatrist, primary care, or other) providing a plurality of peripheral artery disease-coded care in the year prior to index encounter. The primary outcome was MAVE (a composite of all-cause mortality, myocardial infarction, stroke, lower extremity revascularization, and lower extremity amputation).

RESULTS

The cohort included 1768 patients, of whom 30.0% were Black, 23.9% were Medicaid dual-enrollment eligible, and 31.1% lived in rural areas. Patients receiving a plurality of their care from podiatrists had the highest 1-year rates of MAVE (34.4%, P <.001), hospitalization (65.9%, P <.001), and amputations (22.6%, P <.001). Clinician specialty was not associated with outcomes after adjustment. Patients who were Medicaid dual-eligible had higher adjusted risks of mortality (adjusted hazard ratio [HR_adj] 1.54, 95% confidence interval [CI] 1.11-2.14) and all-cause hospitalization (HR_adj 1.20, 95% CI 1.03-1.40) and patients who were Black had a higher adjusted risk of amputation (HR_adj 1.49, 95% CI 1.03-2.15).

CONCLUSIONS

Clinician specialty was not associated with worse outcomes after adjustment, but certain socioeconomic factors were. The effects of clinician specialty and socioeconomic status were likely attenuated by the fact that all patients in this study had health insurance; these analyses require confirmation in a more representative cohort.

Model-Based Algorithms for Detecting Peripheral Artery Disease Using Administrative Data From an Electronic Health Record Data System: Algorithm Development Study

Background

Peripheral artery disease (PAD) affects 8 to 10 million Americans, who face significantly elevated risks of both mortality and major limb events such as amputation. Unfortunately, PAD is relatively underdiagnosed, undertreated, and underresearched, leading to wide variations in treatment patterns and outcomes. Efforts to improve PAD care and outcomes have been hampered by persistent difficulties identifying patients with PAD for clinical and investigatory purposes.

Objective

The aim of this study is to develop and validate a model-based algorithm to detect patients with peripheral artery disease (PAD) using data from an electronic health record (EHR) system.

Methods

An initial query of the EHR in a large health system identified all patients with PAD-related diagnosis codes for any encounter during the study period. Clinical adjudication of PAD diagnosis was performed by chart review on a random subgroup. A binary logistic regression to predict PAD was built and validated using a least absolute shrinkage and selection operator (LASSO) approach in the adjudicated patients. The algorithm was then applied to the nonsampled records to further evaluate its performance.

Results

The initial EHR data query using 406 diagnostic codes yielded 15,406 patients. Overall, 2500 patients were randomly selected for ground truth PAD status adjudication. In the end, 108 code flags remained after removing rarely- and never-used codes. We entered these code flags plus administrative encounter, imaging, procedure, and specialist flags into a LASSO model. The area under the curve for this model was 0.862.

Conclusions

The algorithm we constructed has two main advantages over other approaches to the identification of patients with PAD. First, it was derived from a broad population of patients with many different PAD manifestations and treatment pathways across a large health system. Second, our model does not rely on clinical notes and can be applied in situations in which only administrative billing data (eg, large administrative data sets) are available. A combination of diagnosis codes and administrative flags can accurately identify patients with PAD in large cohorts.

A Framework to Support the Sharing and Reuse of Computable Phenotype Definitions Across Health Care Delivery and Clinical Research Applications

Introduction:

The ability to reproducibly identify clinically equivalent patient populations is critical to the vision of learning health care systems that implement and evaluate evidence-based treatments. The use of common or semantically equivalent phenotype definitions across research and health care use cases will support this aim. Currently, there is no single consolidated repository for computable phenotype definitions, making it difficult to find all definitions that already exist, and also hindering the sharing of definitions between user groups.

Method:

Drawing from our experience in an academic medical center that supports a number of multisite research projects and quality improvement studies, we articulate a framework that will support the sharing of phenotype definitions across research and health care use cases, and highlight gaps and areas that need attention and collaborative solutions.

Framework:

An infrastructure for re-using computable phenotype definitions and sharing experience across health care delivery and clinical research applications includes: access to a collection of existing phenotype definitions, information to evaluate their appropriateness for particular applications, a knowledge base of implementation guidance, supporting tools that are user-friendly and intuitive, and a willingness to use them.

Next Steps:

We encourage prospective researchers and health administrators to re-use existing EHR-based condition definitions where appropriate and share their results with others to support a national culture of learning health care. There are a number of federally funded resources to support these activities, and research sponsors should encourage their use.

Improving population representation through geographic health information systems: mapping the MURDOCK study

This paper highlights methods for using geospatial analysis to assess, enhance, and improve recruitment efforts to ensure representativeness in study populations. We apply these methods to the Measurement to Understand Reclassification of Disease of Cabarrus/Kannapolis (MURDOCK) study, a longitudinal population health study focused on the city of Kannapolis and Cabarrus County, NC. Although efforts have been made to recruit a participant registry that is representative of the 18 ZIP code catchment region inclusive of Cabarrus County and Kannapolis, bias in such recruitment is inevitable. Participants in the MURDOCK study are geospatially referenced at entry, providing information that can be used to monitor and guide recruitment efforts. MURDOCK participant population representativeness was assessed using chi-squared tests to compare the MURDOCK population with 2010 Census data, relative to both the entire 18 ZIP code catchment area and for individual Census tracts. A logistic regression model was fit to characterize Census tracts with low recruitment, defined by fewer than 56 participants from that tract. The distance to the site at which participants enrolled was calculated, and median distance to enrollment site was used in the logistic regression. Tracts with low recruitment rates contained higher minority and younger populations, suggesting specific strategies for improving recruitment in these areas. Areal units farther away from enrollment sites were also not well-sampled, despite being in the specified study area, indicating that distance traveled to enrollment may be a barrier. These results have implications for targeting recruitment efforts and representative samples more generally, including in other population-based studies.

Electronic health records based phenotyping in next-generation clinical trials: a perspective from the NIH Health Care Systems Collaboratory

Widespread sharing of data from electronic health records and patient-reported outcomes can strengthen the national capacity for conducting cost-effective clinical trials and allow research to be embedded within routine care delivery. While pragmatic clinical trials (PCTs) have been performed for decades, they now can draw on rich sources of clinical and operational data that are continuously fed back to inform research and practice. The Health Care Systems Collaboratory program, initiated by the NIH Common Fund in 2012, engages healthcare systems as partners in discussing and promoting activities, tools, and strategies for supporting active participation in PCTs. The NIH Collaboratory consists of seven demonstration projects, and seven problem-specific working group 'Cores', aimed at leveraging the data captured in heterogeneous 'real-world' environments for research, thereby improving the efficiency, relevance, and generalizability of trials. Here, we introduce the Collaboratory, focusing on its Phenotype, Data Standards, and Data Quality Core, and present early observations from researchers implementing PCTs within large healthcare systems. We also identify gaps in knowledge and present an informatics research agenda that includes identifying methods for the definition and appropriate application of phenotypes in diverse healthcare settings, and methods for validating both the definition and execution of electronic health records based phenotypes.