For each threshold number of visits in the preceding six months, the unadjusted risk of return visit was more than double among frequent visitors as compared to non-frequent visitors. The remainder of the analysis uses two or more previous visits as the threshold defining frequent visitor, unless otherwise specified. This retrospective analysis of almost seven million patient visits found that recent previous ED visits was the strongest predictor of an ED return visit. This finding held true across multiple cutoffs defining frequent use, and also under both univariate analysis and a multivariate model including patient, visit, hospital, and county characteristics. Along with recent frequent use, public insurance and three diagnoses were associated with an increased risk of a return visit. This suggests that our understanding of short-term revisits could be informed by considering frequency of ED use. A parallel thread in the literature has investigated frequent users and interventions designed to decrease ED use.Previous studies have evaluated predictors of ED revisit using patient-level data such as age, sex, race, insurance status, and diagnosis at initial ED visit, as well as hospital-level data. Surprisingly, the relationship between frequent ED use and risk of revisit after discharge is poorly characterized.Further, there is no consensus on what defines “frequent,” with definitions ranging from 2–12 visits per year.We had the striking finding that even one previous visit increased risk of return by a clinically-significant margin. This finding held true even when accounting for patient, visit, hospital, and community characteristics. Our definition focused on visits within the previous six months because other work has shown that episodes of frequent ED use are usually self-limited,4×4 grow tray which suggests that the recent past is more relevant to current health and risk of short-term return visit.

A second, related finding is that the threshold used to define frequent visitors is arbitrary with respect to risk of return visit. In the hope of informing the wide range in the literature on the number of visits or length of time used to define frequent users, we considered our definition of frequent user in relation to risk of return visit. We had the surp finding that any number of previous visits used to define frequent vs non-frequent ED users predicted an increased risk of revisit. Given that the reason to label certain patients as frequent visitors is often in order to identify them for interventions, future work may consider an outcome-based definition of frequent users and define the term “frequent” with a qualifier – eg, with respect to propensity to revisit after a visit, risk of becoming a persistent frequent user, or risk of death. As with existing literature, we transformed the number of previous visits from a continuous variable to a binary one. This has the disadvantage of losing some information, but is standard in the literature regarding frequent ED use, and can easily be applied in the midst of clinical practice.Our sensitivity analysis demonstrated that any threshold was significantly associated with return visits, suggesting that knowing whether a patient had four vs three previous visits would provide marginally more information than simply knowing the patient had more than two previous ED visits. As with the definition of frequent user, the time to return visit defining a return visit is somewhat arbitrary. While the risk of return visit is highest on the first day following the ED visit, the risk gradually decreases and, as found previously by Rising et al., there is no clear timeline that defines a return visit.This finding may suggest something other than inadequate care at the index visit is the driving factor for most short-term revisits, and that both frequent use and revisits may simply be proxies for certain patients with increased healthcare-seeking behavior. Further complicating this issue is that patients may be instructed to return to the ED for a re-evaluation.

Thus, an ED in a setting with limited outpatient resources might appear to give poor care as measured by revisits when in fact it serves to provide followup care that patients otherwise would not obtain. Despite the variation in the literature and thus our broad range of models, we consistently found that the strongest predictor of a revisit is a high number of previous visits. This finding held true in our sensitivity analysis using different thresholds for number of previous visits and also days after index visit. The observation that previous visits predicts future visits may seem obvious or mechanical, but it does not necessarily follow that a patient with one or two visits in the prior six months would be at double the risk of a revisit within three days. Further, that this relationship was stronger than any other patient, hospital, or community characteristic is an important finding that has been overlooked in the literature regarding revisits. In fact, it appears that the literature on frequent visitors and the literature regarding revisits have to this point largely functioned in parallel and have not yet begun to inform each other. Whether frequent users are merely frequently-ill people, and whether sicker patients are at increased risk of short-term revisits deserves future research. Likewise, future work should investigate the extent to which patients are frequent users because they received poor care or face limitations in their ability to obtain outpatient resources, the extent to which revisits are avoidable, and the degree to which frequent use persists over time. Understanding the extent to which follow-up with primary care, referrals to specialists, and ability to obtain further evaluation such as advanced imaging, cardiac stress test, or even a wound check is essential to understanding why patients return to the ED. The data for this study were obtained from a single multi-state physician partnership and do not necessarily generalize to other providers or provider groups, or to other populations. However, the sample size was large and spans many cities and rural areas across several states, includes a broad set of hospital owner types, a large range of hospital sizes, and both teaching and non-teaching hospitals. This source of data may lead to a biased sample with respect to patient population, hospital characteristics, and provider characteristics. In particular, the income distribution is narrower than the distribution for the entire U.S., so the patient population could have a lower proportion of low- and high income patients than typical for the U.S.

We addressed these potential sources of bias by controlling for patient demographics, patient insurance, and local income; hospital characteristics including volume and a performance metric, and clinician degree. Second, because not all hospitals within a region were observed, measures of frequent visitors and repeat visits may underestimate the actual numbers of frequent visitors and repeat visits, as patients may have gone to another ED either prior to or after the observed index visit. This limitation is typical of this research,and in this dataset patients were linked across hospitals, although this was limited to the hospitals served by this company. Thus, it is unknown whether patients had an unobserved revisit at another ED, or whether what was considered an index visit actually represented a revisit after an initial visit at another ED. Next, we were unable to distinguish between planned and unplanned return visits. Thus, a patient who is instructed to return for a check over the weekend to ensure their illness is improving, for example, would appear to be a revisit, but this should not imply that their initial treatment was inadequate or inappropriate in any way. Research using administrative datasets, such as HCUP, likewise suffers from this limitation. Finally, as with related research, this study does not identify the extent to which high rates of frequent visits and revisits are driven by patient factors, ED care, or non-ED healthcare resources. This analysis was limited in its ability to examine patient psychosocial attributes or local resources, which are likely to contribute to ED visits and revisits, although we did consider proxies for access to care: patient insurance and community-level factors such as income and number of hospitals in the county. The condition covers a large spectrum of disease, ranging from mild exacerbations with gradual increases in edema to cardiogenic shock. HF affects close to six million people in the United States and increases in prevalence with age.6-11 Currently, the emergency department initiates the evaluation and treatment of over 80% of patients with AHF in the U.S.As the population ages, increasing numbers of patients with HF will present to the ED for evaluation and management. However,greenhouse racking making the correct diagnosis can be challenging due to the broad differential diagnosis associated with presenting symptoms and variations in patient presentations. Over one million patients are admitted for HF in the U.S. and Europe annually.In the U.S. population, people have a 20% risk of developing HF by 40 years of age.HF is more common in males until the age of 65, at which time Brooke Army Medical Center, Department of Emergency Medicine, Fort Sam Houston, Texas University of Texas Southwestern Medical Center, Department of Emergency Medicine, Dallas, Texas Rush University Medical Center, Department of Emergency Medicine, Chicago, Illinois males and females are equally affected.Patients with HF average at least two hospital admissions per year.

Among patients who are admitted with AHF, over 80% have a prior history of HF, referred to as decompensated heart failure.De novo HF is marked by no previous history of HF combined with symptom appearance after an acute event.Mortality in patients with HF can be severe, with up to half of all patients dying within five years of disease diagnosis.Other studies have found that post-hospitalization mortality rates at 30 days, one year, and five years are 10.4%, 22%, and 42.3%, respectively.AHF expenditures approach $39 billion per year, which is expected to almost double by 2030.Normal cardiac physiology is dependent on appropriately functioning ventricular contraction, ventricular wall structural integrity, and valvular competence.At normal functional status, a person’s stroke volume is approximately one milliliter per kilogram for every heartbeat.SV is dependent upon the preload , after load , and contractility . In patients with HF, left ventricular dysfunction can be due to impaired LV contraction and ejection , impaired relaxation and filling , or a combination of both.An alternate way of defining this would be by the effect on ejection fraction . HF with preserved EF refers to patients with an EF > 50%, while HF with reduced EF refers to patients with an EF < 40%. Borderline preserved EF is defined by HF with an EF of 41-50%.The most common form is HF with reduced EF, which is primarily related to a decrease in the functional myocardium .Additional causes include excessive pressure overload from hypertension, valvular incompetence, and cardiotoxic medications. HF with preserved EF occurs due to impaired ventricle relaxation and filling, which accounts for 30-45% of all HF cases. This form of HF results in increased end-systolic and diastolic volumes and pressures and is most commonly associated with chronic hypertension, coronary artery disease, diabetes mellitus, cardiomyopathy, and valvular disease. Both systolic and diastolic HF can present with similar symptoms due to elevated, left-sided intracardiac pressures and pulmonary congestion.Right ventricular failure most commonly results from LV failure. As the right side of the heart fails, increased pressure in the vena caval system elevates pressure in the venous system of the gastrointestinal tract, liver, and extremities, resulting in edema, jugular venous distension, hepatomegaly, bloating, abdominal pain, and nausea.High-output HF is associated with normal or greater-than-normal cardiac output and decreased systemic vascular resistance.The associated decrease in after load reduces arterial blood pressure and also activates neurohormones, which increase salt and water retention. Diseases that may result in high-output HF include anemia, large arteriovenous fistula or multiple small fistulas, severe hepatic or renal disease, hyperthyroidism, beriberi disease, and septic shock.In AHF, peripheral vascular flow and end-organ perfusion decrease, causing the body to compensate by neurohormonal activation , ventricular remodeling, and release of natriuretic peptides. These mechanisms are chronically activated in HF, but worsen during acute exacerbations, resulting in hemodynamic abnormalities leading to further deterioration. Continued progression can result in a critical reduction to end-organ blood flow, leading to severe morbidity and mortality.Patients with HF are classified into one of four classes, primarily determined by daily function, using the New York Heart Association, American College of Cardiology/American Heart Association, or European Society of Cardiology Guidelines .