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 [HRadj] 1.54, 95% confidence interval [CI] 1.11-2.14) and all-cause hospitalization (HRadj 1.20, 95% CI 1.03-1.40) and patients who were Black had a higher adjusted risk of amputation (HRadj 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.

The association of healthcare disparities and patient-specific factors on clinical outcomes in peripheral artery disease.

BACKGROUND: PAD increases the risk of cardiovascular mortality and limb loss, and disparities in treatment and outcomes have been described. However, the association of patient-specific characteristics with variation in outcomes is less well known. METHODS: Patients with PAD from Duke University Health System (DUHS) between January 1, 2015 and March 31, 2016 were identified. PAD status was confirmed through ground truth adjudication and predictive modeling using diagnosis codes, procedure codes, and other administrative data. Symptom severity, lower extremity imaging, and ankle-brachial index (ABI) were manually abstracted from the electronic health record (EHR). Data was linked to Centers for Medicare and Medicaid Services data to provide longitudinal follow up. Primary outcome was major adverse vascular events (MAVE), a composite of all-cause mortality, myocardial infarction (MI), stroke, lower extremity revascularization and amputation. RESULTS: Of 1,768 patients with PAD, 31.6% were asymptomatic, 41.2% had intermittent claudication (IC), and 27.3% had chronic limb-threatening ischemia (CLTI). At 1 year, patients with CLTI had higher rates of MAVE compared with asymptomatic or IC patients. CLTI and Medicaid dual eligibility were independent predictors of mortality. CLTI and Black race were associated with amputation. CONCLUSIONS: Rates of MAVE were highest in patients with CLTI, but patients with IC or asymptomatic disease also had high rates of adverse events. Black and Medicaid dual-eligible patients were disproportionately present in the CLTI subgroup and were at higher risk of amputation and mortality, respectively. Future studies must focus on early identification of high-risk patient groups to improve outcomes in patients with PAD.

User-centered design principles in the development of clinical research tools.

BACKGROUND: Increasing and sustaining the engagement of participants in clinical research studies is a goal for clinical investigators, especially for studies that require long-term or frequent involvement of participants. Technology can be used to reduce barriers to participation by providing multiple options for clinical data entry and form submission. However, electronic systems used in clinical research studies should be user-friendly while also ensuring data quality. Directly involving study participants in evaluating the effectiveness and usability of electronic tools may promote wider adoption, maintain involvement, and increase user satisfaction of the technology. While developers of healthcare applications have incorporated user-centered designs, these methods remain uncommon in the design of clinical study tools such as patient-reported outcome surveys or electronic data capture digital health tools. METHODS: Our study evaluated whether the clinical research setting may benefit from implementing user-centered design principles. Study participants were recruited to test the web-based form for the Measurement to Understand the Reclassification of Disease of Cabarrus/Kannapolis (MURDOCK) Study Community Translational Population Health Registry and Biorepository that would enable them to complete their study forms electronically. The study enrollment form collects disease history, conditions, smoking status, medications, and other information. The system was initially evaluated by the data management team through traditional user-acceptance testing methods. During the tool evaluation phase, a decision was made to incorporate a small-scale usability study to directly test the system. RESULTS: Results showed that a majority of participants found the system easy to use. Of the eight required tasks, 75% were completed successfully. Of the 72 heuristics violated, language was the most frequent violation. CONCLUSION: Our study showed that user-centered usability methods can identify important issues and capture information that can enhance the participant's experience and may improve the quality of study tools.

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.

Pragmatic (trial) informatics: a perspective from the NIH Health Care Systems Research Collaboratory.

Pragmatic clinical trials (PCTs) are research investigations embedded in health care settings designed to increase the efficiency of research and its relevance to clinical practice. The Health Care Systems Research Collaboratory, initiated by the National Institutes of Health Common Fund in 2010, is a pioneering cooperative aimed at identifying and overcoming operational challenges to pragmatic research. Drawing from our experience, we present 4 broad categories of informatics-related challenges: (1) using clinical data for research, (2) integrating data from heterogeneous systems, (3) using electronic health records to support intervention delivery or health system change, and (4) assessing and improving data capture to define study populations and outcomes. These challenges impact the validity, reliability, and integrity of PCTs. Achieving the full potential of PCTs and a learning health system will require meaningful partnerships between health system leadership and operations, and federally driven standards and policies to ensure that future electronic health record systems have the flexibility to support research.

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.

Preparing for the ICD-10-CM Transition: Automated Methods for Translating ICD Codes in Clinical Phenotype Definitions.

BACKGROUND: The national mandate for health systems to transition from ICD-9-CM to ICD-10-CM in October 2015 has an impact on research activities. Clinical phenotypes defined by ICD-9-CM codes need to be converted to ICD-10-CM, which has nearly four times more codes and a very different structure than ICD-9-CM. METHODS: We used the Centers for Medicare & Medicaid Services (CMS) General Equivalent Maps (GEMs) to translate, using four different methods, condition-specific ICD-9-CM code sets used for pragmatic trials (n=32) into ICD-10-CM. We calculated the recall, precision, and F score of each method. We also used the ICD-9-CM and ICD-10-CM value sets defined for electronic quality measure as an additional evaluation of the mapping methods. RESULTS: The forward-backward mapping (FBM) method had higher precision, recall and F-score metrics than simple forward mapping (SFM). The more aggressive secondary (SM) and tertiary mapping (TM) methods resulted in higher recall but lower precision. For clinical phenotype definition, FBM was the best (F=0.67), but was close to SM (F=0.62) and TM (F=0.60), judging on the F-scores alone. The overall difference between the four methods was statistically significant (one-way ANOVA, F=5.749, p=0.001). However, pairwise comparisons between FBM, SM, and TM did not reach statistical significance. A similar trend was found for the quality measure value sets. DISCUSSION: The optimal method for using the GEMs depends on the relative importance of recall versus precision for a given use case. It appears that for clinically distinct and homogenous conditions, the recall of FBM is sufficient. The performance of all mapping methods was lower for heterogeneous conditions. Since code sets used for phenotype definition and quality measurement can be very similar, there is a possibility of cross-fertilization between the two activities. CONCLUSION: Different mapping approaches yield different collections of ICD-10-CM codes. All methods require some level of human validation.

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.

Use of RxNorm and NDF-RT to normalize and characterize participant-reported medications in an i2b2-based research repository.

The MURDOCK Study is longitudinal, large-scale epidemiological study for which participants' medication use is collected as free text. In order to maximize utility of drug data, while minimizing cost due to manual expert intervention, we have developed a generalizable approach to automatically coding medication data using RxNorm and NDF-RT and their associated application program interfaces (APIs). Of 130,273 entries, we were able to accurately map 122,523 (94%) to RxNorm concepts, and 106,135 (85%) of those drug concepts to nodes under the Drug by VA Class branch of NDF-RT. This approach has enabled use of drug data in combination with other complementary information for cohort identification within an i2b2-based participant registry. The method may be generalized to other projects requiring coding of medication data from free-text.

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.

Can prospective usability evaluation predict data errors?

Increasing amounts of clinical research data are collected by manual data entry into electronic source systems and directly from research subjects. For this manual entered source data, common methods of data cleaning such as post-entry identification and resolution of discrepancies and double data entry are not feasible. However data accuracy rates achieved without these mechanisms may be higher than desired for a particular research use. We evaluated a heuristic usability method for utility as a tool to independently and prospectively identify data collection form questions associated with data errors. The method evaluated had a promising sensitivity of 64% and a specificity of 67%. The method was used as described in the literature for usability with no further adaptations or specialization for predicting data errors. We conclude that usability evaluation methodology should be further investigated for use in data quality assurance.