RESUMEN
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.
Asunto(s)
Accesibilidad a los Servicios de Salud , Disparidades en Atención de Salud , Enfermedad Arterial Periférica/terapia , Médicos/clasificación , Anciano , Estudios de Cohortes , Procedimientos Endovasculares , Femenino , Hospitalización , Humanos , Seguro de Salud , Extremidad Inferior/cirugía , Masculino , Persona de Mediana Edad , Modelos de Riesgos Proporcionales , Factores de Riesgo , Clase Social , Resultado del Tratamiento , Estados UnidosRESUMEN
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.
Asunto(s)
Amputación Quirúrgica/estadística & datos numéricos , Disparidades en Atención de Salud/organización & administración , Extremidad Inferior , Infarto del Miocardio/epidemiología , Enfermedad Arterial Periférica , Accidente Cerebrovascular/epidemiología , Procedimientos Quirúrgicos Vasculares , Enfermedades Asintomáticas/epidemiología , Población Negra/estadística & datos numéricos , Femenino , Necesidades y Demandas de Servicios de Salud , Humanos , Extremidad Inferior/irrigación sanguínea , Extremidad Inferior/cirugía , Masculino , Medicaid/estadística & datos numéricos , Persona de Mediana Edad , Mortalidad , Enfermedad Arterial Periférica/complicaciones , Enfermedad Arterial Periférica/diagnóstico , Enfermedad Arterial Periférica/epidemiología , Enfermedad Arterial Periférica/fisiopatología , Factores de Riesgo , Estados Unidos/epidemiología , Procedimientos Quirúrgicos Vasculares/métodos , Procedimientos Quirúrgicos Vasculares/estadística & datos numéricosRESUMEN
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.