Hypothesis: We hypothesized that outpatient management of patients at risk for a HF hospitalization is associated with lower mortality following an incident HF hospitalization. Methods: Patterns of outpatient visits prior to incident HF hospitalization were assessed among CMS Medicare beneficiaries with continuous fee-for-service eligibility residing during 2003-2006 in four geographic areas of CVD surveillance conducted by the ARIC Study. Incident HF hospitalization was defined as hospitalization with ICD9 code 428.x with no HF hospitalizations in preceding 2 years. Outpatient visits to primary care physicians, general internists, or cardiologists were identified from Carrier files. A comorbidity score was calculated from ICD9 codes at the time of incident HF hospitalization. Cox proportional hazard models adjusted for age, comorbidity score, gender, and race were used to estimate mortality. Results: Mean age among beneficiaries with observed incident HF hospitalization (n=2006; 90.4% white, 45.1% male) was 79.8 years (SD 7.4). Mean comorbidity score was 3.6 (SD 1.9). Mean number of outpatient physician visits occurring in two years preceding the incident HF hospitalization, was 9.6 (SD 9.0); 19.6% beneficiaries had no observed prior outpatient physician visits. Risk of death within one year of incident HF hospitalization was greater among those with no preceding outpatient physician visits as compared to those with at least one physician visit (adjusted HR=1.81 (95% CI 1.50, 2.18); Figure). Adjustment for the presence of an outpatient visit within 2 weeks following the HF hospitalization attenuated the risk of death (HR=1.56 (1.29, 1.89)). Conclusion: Lack of outpatient care in two years prior to a HF-related hospitalization is associated with increased mortality within one year following hospitalization. Further inquiry is warranted to assess whether the association reflects diversity in causes/manifestations of HF, ambulatory care received in ED settings, or benefits associated with outpatient care.
Chronic transfusions are recommended for children with sickle cell anemia (SCA) and abnormal transcranial Doppler (TCD) velocities (≥ 200 cm/sec) to help prevent the occurrence of a primary stroke [1]. The goal is usually to maintain the sickle hemoglobin concentration (HbS) <30%; however, this goal is often difficult to achieve in clinical practice. The NHLBI-sponsored trial "TCD With Transfusions Changing to Hydroxyurea (TWiTCH)" will compare standard therapy (transfusions) to alternative therapy (hydroxyurea) for the reduction of primary stroke risk in this patient population. Transfusions will be given according to current transfusion practices at participating sites. To determine current academic community standards for primary stroke prophylaxis in children with SCA, 32 clinical sites collected data on 340 children with abnormal TCD velocities receiving chronic transfusions to help prevent primary stroke. The average (mean ± 1 SD) pretransfusion HbS was 34 ± 11% (institutional average 23–48%); the 75th and 90th percentiles were 41 and 50%, respectively. Lower %HbS was associated with higher pretransfusion Hb values and receiving transfusions on time. These data indicate variable current transfusion practices among academic pediatric institutions and in practice, 30% HbS may not be an easily attainable goal in this cohort of children with SCA and abnormal TCD. Children with sickle cell anemia (SCA) compose a high risk group for the development of stroke. If untreated, 11% will experience a clinical stroke by 20 years of age [2]. Adams et al. have shown that children with SCA who are at risk for primary stroke can be identified by measuring time-averaged mean blood flow velocities in the internal carotid or middle cerebral arteries by TCD [3]. Abnormal TCD velocities (≥200 cm/sec) are associated with high risk for stroke and warrant transfusion therapy to reduce the risk of primary stroke. First stroke can be successfully prevented in 90% of children with SCA and abnormal TCD velocities by the use of chronic transfusion therapy, with a goal of keeping HbS concentrations less than 30% [1]. TCD with Transfusions Changing to Hydroxyurea (TWiTCH) is an NHLBI-sponsored, Phase III, multicenter trial comparing standard therapy (monthly transfusions) to alternative therapy (daily hydroxyurea) to reduce the risk of primary stroke in children with SCA and documented abnormal TCD velocities. Since transfusions compose the standard treatment arm, accurate %HbS values achieved in actual clinical practice were needed for protocol development. The majority of our information about transfusing patients with SCA to prevent stroke comes from secondary stroke prevention, i.e., the use of chronic red blood cell transfusions to prevent a second strokeafter a first clinical stroke has occurred. Classically, transfusions are administered at 4-week intervals to maintain HbS at less than 30%. After several years of transfusion therapy, a few centers increase transfusion interval to 5–6 weeks and allow HbS to increase toward 50% in selected patients [4, 5]. Our previous study in 295 children with SCA who received transfusions for secondary stroke prevention revealed an average pretransfusion HbS of 35 ± 11% with highly variable institutional %HbS levels ranging from 22 to 51% [6] In order to determine the current clinical standard of transfusion therapy for primary stroke prevention for elevated TCD velocities, we performed a larger survey of potential TWiTCH sites. We hypothesized that average pretransfusion HbS values achieved at pediatric academic centers would be higher than 30%. This study defines the current practice at academic medical centers in provision of chronic transfusion therapy to help reduce the risk of primary stroke in children with SCA. A total of 340 children with SCA and history of abnormal TCD velocities receiving chronic PRBC transfusions for primary stroke prophylaxis were identified at 32 institutions (Table I). The number of patients per site ranged from 3 to 33 (median 9 per site). A total of 3,970 transfusions were administered over the 12-month period, with a mean of 11.7 ± 2.8 transfusions per patient. Results were similar when analyzed by each patient contributing equally or each transfusion contributing equally (Table II). The predominant transfusion type by patient was defined as the technique used ≥6 times over the 12-month period. Most children (79%) received primarily simple transfusions, while 19% had primarily exchange transfusions (11% partial /manual exchange, 8% erythrocytapheresis), and 2% multiple transfusion types. The transfusion goal was <30% at almost all sites (84%), while at five sites, the %HbS was allowed in selected patients to increase to 50% after a period of clinical stability. The majority (95%) of the transfusions were administered within the defined 7-day window. On average, late transfusions were given 1.3 ± 5.5 days after the defined 7-day window. Thirty percent of the patients had at least one late transfusion and 14% had 2 or more late transfusions in the 1-year period. For the 3,653 transfusions with reported %HbS values (representing 92% of the 3,970 transfusions), the mean pretransfusion HbS percentage was 33.2 ± 14.0% (median 32%). The 75th percentile for HbS values was 41%, while the 90th percentile was 51%. There were substantial differences among institutional pretransfusion %HbS values, ranging from 23 ± 14% HbS at one institution where HbS was reported for 103 transfusions given to nine patients during the 12-month period, to 48 ± 15% at another institution where HbS was reported for 95 transfusions administered to nine patients during the same time frame (Table III). The five sites with increased HbS goals to 50% in selected patients did not have higher values than others. For each transfusion, subjects were less likely to have pretransfusion HbS <30% if they were older [OR 0.92 for each year increase in age, 95% CI (0.89, 0.96)] and on transfusions for a longer period of time [OR 0.90 for each year increase in duration, 95% CI (0.86, 0.94)]. Patients with higher pretransfusion Hb levels were more likely to have pretransfusion HbS <30% [OR 1.63 for each g/dL increase in Hb, 95% CI (1.46, 1.83)] and late transfusions were less likely to be associated with a pretransfusion HbS <30% [OR 0.27, 95% CI (0.18, 0.41)]. The Hb result does not appear to be a function of late transfusions since both covariates remained significant when modeled jointly. History of allo- or autoantibodies, TCD velocity, and erythrocytapheresis use were not significant predictors of a pretransfusion HbS <30%. During the initial STOP study, transfusions were given to maintain pretransfusion HbS values at less than 30% [3]. However, there were frequent transient rises of HbS above this level [7]. Furthermore, extended follow-up results from the STOP study showed that pretransfusion %HbS values during the post-trial follow-up were higher than those during the STOP study [8]. The average %HbS per patient was 27.5 ± 12.4, still within the desired goal of 30%. However, pretransfusion HbS levels were 30–34.9% in 12%, 35–39.9% in 7%, and greater than 40% in 12% of the transfusions. In the STOP2 study, where children with abnormal TCD velocities whose Doppler readings became normal were randomly assigned to continue or stop transfusions, 24% of the patients had pretransfusion HbS levels greater than 30% [9]. These findings indicate that even in the context of a prospective clinical trial, maintaining HbS <30% was difficult to achieve. With the subsequent recommendation to treat all children with SCA who are at risk for primary stroke with transfusions to maintain HbS <30%, the feasibility of this approach in actual clinical practice is not known. Possible obstacles to achieving this goal include (1) physician and staff goals regarding TCD screening and chronic transfusion therapy; (2) family understanding of the need for on-time transfusions and other factors affecting patient adherence; (3) hypersplenism; (4) development of allo-and auto-antibodies; (5) difficulties with venous access. In a recent report describing the effects of chronic transfusion therapy on TCD velocities, the pretransfusion HbS levels for 88 children who were identified to have abnormal TCD velocities during screening for STOP2 was noted to be 37 ± 20% [10]. A single institution retrospective study from France involving 17 children with SCA with abnormal TCD velocities reported mean pretransfusion HbS levels were 30 ± 10% during chronic transfusion therapy, but with a follow-up period of only 39 patient-years [11]. We have conducted a similar survey analyzing chronic transfusion practice for 295 children with SCA and previous stroke at 23 US institutions [6]. Results of that study were very similar to the current study including an average pretransfusion %HbS of 35 ± 11%. Receiving scheduled transfusions on time was identified as the most significant factor to maintain HbS at ≤30%. Finally, in a prospective cohort study, the mean pretransfusion %HbS values of 39 children with SCA and stroke followed at seven academic centers was 26.9% (range, 3.94–48.3%) [12]. With a broad geographical distribution of 32 clinical sites and inclusion of all TWiTCH-eligible patients, our data provide an accurate "snapshot" of current therapy for children with SCA and abnormal TCD velocities receiving treatment at leading academic centers in North America. The mean age at start of chronic transfusions, frequency and duration of transfusions, compliance, and types of transfusions, and %HbS goals are similar to published reports and represent the clinical experience of pediatric hematologists at tertiary academic medical centers with large clinical transfusion programs. Importantly, %HbS was often not maintained at the accepted "gold standard" of <30%; the mean pretransfusion %HbS, calculated either by transfusion or by patient, was about 34%; the 75th and 90th percentiles were 41 and 50% HbS, respectively. Of all the variables studied, the only ones with a substantial impact on the probability of a transfusion achieving the 30% HbS goal were age of the patient, duration of transfusion therapy, pretransfusion Hb concentration, and whether transfusions were given on time. One limitation of this retrospective survey is information on other variables such as hypersplenism or infectious illnesses that may have caused deviations on %HbS levels was not collected. Of the variables analyzed, maintaining a higher Hb concentration and receiving transfusions on time appear to be the best ways to achieve the desired %HbS levels for children on chronic transfusion therapy. Deviations from accepted "gold standard" may change clinical outcomes such as TCD velocity reductions, change in cerebral vasculopathy, or stroke prevention. As a result of this analysis, the TWiTCH study will recommend chronic transfusions with the goal of maintaining HbS <30%, but only record a protocol violation when the HbS value exceeds 45%. After IRB approval at each institution, TWiTCH clinical investigators completed a transfusion survey for each potentially eligible TWiTCH participant at their site without including any protected health information. Eligibility criteria included the diagnosis of SCA, age 4.0–15.9 years, history of abnormal TCD velocities (≥200 cm/sec), and currently receiving chronic transfusion therapy to reduce the risk of primary stroke. Since protected health information was not collected, signed consent was typically not required. Participating clinical sites and personnel are listed in the Appendix. Subject and transfusion information were collected retrospectively for each patient over a 12-month period from September 1, 2008 to August 31, 2009. Subject data included year of birth, gender, the year chronic transfusions began, and TCD velocities that led to the start of transfusion therapy. Day and month of birth were not collected to ensure anonymized data. The transfusion goal for each patient (30% HbS versus 50% HbS) and a history of erythrocyte allo- and auto-antibodies were also recorded for each patient. Information collected from each transfusion during the 12-month period included the patient's weight (kg), pretransfusion hemoglobin concentration (Hb, gm/dL), pretransfusion %HbS, volume and type of transfusion (simple, partial exchange, erythrocytapheresis) and, and number of days beyond the scheduled date of transfusion. Children with SCA who are on chronic transfusion therapy usually receive a transfusion every 3–5 weeks to maintain the desired %HbS. Every subject is on an individual schedule based on his/her %HbS levels. For the purposes of this survey, a transfusion was defined as "late" if it was administered more than 7 days beyond the scheduled date. Categorical variables were summarized using frequency counts and percentages. Continuous variables were summarized using one or more of the following: mean, standard deviation, median, minimum, and maximum, 75th and 90th percentile, ignoring missing data when applicable. Age at survey, age at start of chronic transfusion therapy, average weight, average pretransfusion Hb and %HbS, average transfusion volume, duration of transfusion therapy and predominant transfusion type were summarized with each patient contributing equally. Weight, pretransfusion Hb and %HbS, transfusion volume, and days late were also summarized with each transfusion contributing equally. The probability of pretransfusion %HbS less than 30% was modeled using generalized estimating equations to account for the lack of independence induced by multiple transfusions per subject. The models were used to generate estimates of the odds ratio (OR) and 95% confidence intervals. Results were considered statistically significant if the confidence interval excluded one. The authors thank the entire TWiTCH Trial Group (members listed in "Appendix") and Rho Federal Systems Division, Inc. (Nancy Yovetich PhD, Christopher Woods, Jamie Spencer, Marsha McMurray) for their valuable contributions to the study. TWiTCH Trial investigators and key contributors include the following: Brigitta Mueller and Bogdan Dinu (Baylor College of Medicine, Houston, TX); Kusum Viswanathan and Natalie Sommerville-Brooks (Brookdale Hospital Medical Center, Brooklyn, NY); Clark Brown and Betsy Record (Children's Healthcare of Atlanta, Atlanta, GA); Matthew Heeney and Meredith Anderson (Children's Hospital Boston, Boston, MA); Janet L. Kwiatkowski, Jeffrey Olson and Martha Brown, (Children's Hospital of Philadelphia, Philadelphia, PA); Lakshmanan Krishnamurti and Regina McCollum (Children's Hospital of Pittsburgh, Pittsburgh, PA); Kamar Godder and Jennifer Newlin (Children's Hospital of Richmond, Richmond, VA); William Owen (Children's Hospital of the King's Daughters); Stephen Nelson (Children's Hospitals and Clinics of Minnesota, Minneapolis, MN); Alexis A. Thompson and Katie Bianchi (Children's Memorial Hospital, Chicago, IL); Lori Luchtman-Jones and Sheronda Brown (Children's National Medical Center, Washington, DC); Margaret Lee (Columbia University, New York, NY); Courtney Thornburg (Duke University Medical Center, Durham, NC); Charles Daeschner and Cynthia Brown (East Carolina University, Greenville, NC); Sherron Jackson and Lisa Kuisel (Medical University of South Carolina, Charleston, SC); Ramamoorthy Nagasubramanian (Nemours Children's Clinic Orlando, Orlando, FL); Cynthia Gauger (Nemours Children's Clinic, Jacksonville, FL); Brian Berman and Mary DeBarr (Rainbow Babies and Children's Hospital, Cleveland, OH); Sharon Singh and Antonella Farrell (Schneider Children's Hospital, New Hyde Park, NY); Banu Aygun and Eileen Hansbury (St. Jude Children's Research Hospital, Memphis, TN); Scott Miller and Kathy Rey (SUNY Downstate, Brooklyn, NY); Isaac Odame, Nagina Parmar, and Manuella Merelles-Pulcinni (The Hospital for Sick Children, Toronto ON Canada); Zora R Rogers and Leah Adix (The University of Texas Southwestern Medical Center, Dallas, TX); Lee Hilliard and Jeanine Dumas (University of Alabama, Birmingham, AL); Michelle Neier and Stephanie Farias (University of Medicine and Dentistry of New Jersey, New Brunswick, NJ); Ofelia Alvarez and Patrice Williams (University of Miami, Miami, FL); Rathi Iyer and Mary T. Walker (University of Mississippi Medical Center, Jackson, MS); Hamayun Imran and Stephanie Durggin (University of South Alabama, Mobile, AL); Elizabeth Yang (Vanderbilt University, Nashville, TN); Sharada Sarnaik and Mary Murphy (Wayne State University, Detroit, MI).
Introduction: Among older adults with diabetes, cognitive dysfunction is of particular concern as it has implications for treatment adherence and diabetes self-management. The prevalence of cognitive dysfunction has not been well characterized in this population. Methods: We conducted a cross-sectional analysis of 5509 participants (1815 with diabetes) from visit 5 (2011-2013) of the ARIC Study. Diabetes was defined based on self-reported physician diagnosis, diabetes medication use, or HbA1c ≥ 6.5%. Cognitive function was measured using 8 neuropsychological tests, which were grouped into three cognitive domains representing memory, executive function, and language. Participants were categorized as having cognitive dysfunction if test scores were more than 1.5 standard deviations below age-, race-, and education-adjusted scores derived from a cognitively healthy population. We calculated crude prevalence estimates and used Poisson regression to estimate adjusted prevalence ratios (PRs), comparing cognitive dysfunction in persons with and without diabetes. We adjusted for demographic and clinical characteristics. Results: The mean age of participants was 75 years, 59% were female, 79% were white, and 33% had diabetes. In each domain, the prevalence of cognitive dysfunction among persons with diabetes ranged from 14% to 27%. Persons with diabetes were more likely than persons without diabetes to have dysfunction in multiple domains (PR = 1.29, 95% CI: 1.12, 1.49). Prevalence of cognitive dysfunction was significantly higher in persons with versus without diabetes for memory (PR=1.13, 95% CI: 1.02, 1.25), language (PR=1.24, 95% CI: 1.09, 1.45), and executive function (PR=1.10, 95% CI: 1.00, 1.22)(Figure). PRs were similar in crude models. Conclusions: The prevalence of cognitive dysfunction among older adults with diabetes is high. These results have implications for how physicians educate patients in appropriate self-management practices and for the prevention of diabetes-related complications.
Background: Atrial fibrillation (AF) is associated with increased risk of hospitalization. However, little is known about the impact of AF on non-inpatient healthcare utilization or about sex or race differences in AF-related utilization. We examined rates of inpatient and outpatient utilization by AF status in the Atherosclerosis Risk in Communities (ARIC) study. Methods and Results: ARIC cohort participants with incident AF enrolled in fee-for-service Medicare, Parts A and B, for at least 12 continuous months between 1991 and 2009 were matched on age, sex, race and center to up to three participants without AF. Healthcare utilization was ascertained from inpatient and outpatient Medicare claims and classified based on primary ICD-9 code. The analysis included 944 AF and 2,761 non-AF participants. The average number of days hospitalized per year was 13.1 (95% confidence interval [CI]: 11.5-15.0) and 2.8 (95% CI: 2.5-3.1) for those with and without AF, respectively. The corresponding number of outpatient claims per year was 53.2 (95% CI: 50.4-56.1) and 23.0 (95% CI: 22.2-23.8) for those with and without AF, respectively (Table). Most utilization in AF patients was attributable to non-AF conditions, particularly other-cardiovascular disease (CVD)-related reasons; the adjusted rate ratio for days hospitalized per year for other-CVD-related reasons was 4.76 (95% CI: 3.51 - 6.44) for those with compared to those without AF. There was suggestive evidence that sex modified the association between AF and inpatient utilization, with AF related to greater utilization in women than men. The association between AF and healthcare utilization was similar in whites and blacks. Conclusions: This study highlights the considerably greater healthcare utilization (inpatient and outpatient) among those with AF; the differential in utilization due to other-CVD-related reasons was substantial. In addition to recommended heart rate or rhythm treatment, accompanying cardiovascular comorbidities should be evaluated and managed.
Abstract Background Dementia disproportionately impacts people of color in the U.S.. Whether these disparities are due to differences in vascular risk factors, cognitive reserve, or AD pathophysiology remains unclear. Method Black and White participants from four U.S. communities were recruited into the Atherosclerosis Risk in Communities (ARIC) study at ages 45‐64. Vascular risk factors were evaluated in midlife and at multiple visits over 30+ years, as were markers of cognitive reserve, with expert classification of dementia. In participants with brain MRI, brain volumes were evaluated in association with dementia. In nondemented participants in the ARIC‐PET ancillary study, florbetapir PET scans were used to quantify brain cortical amyloid; differences in levels by race, vascular risk factors, and white matter hyperintensities (WMH) were evaluated. Result In 15744 ARIC participants, dementia was more common in Black vs White participants. Although midlife vascular risk factors were related to dementia, the impact of these risk factors on dementia risk and cortical volumes didn’t differ by race. Lower education was nonsignificantly more strongly associated with dementia in Blacks vs Whites, and an APOE e4 allele was more strongly associated with dementia in Whites. In ARIC‐PET (N=322), vascular risk factors from midlife were associated with PET amyloid, without similar associations for late‐life risk factors; these associations did not significantly differ by race. WMH volume was more strongly associated (cross‐sectionally) with PET amyloid in Blacks vs Whites, but differences were not statistically significantly different. Finally, Blacks had an over two‐fold increased odds of elevated florbetapir PET vs Whites, independent of vascular risk factors, demographics, WMH volume, cognitive status, and APOE genotype. Conclusion Consideration of racial disparities in dementia should consider vascular risk, cognitive reserve, and social determinants of health, across the life course. Vascular risk factors do not appear to differentially impact dementia risk or brain amyloid in Blacks vs Whites, but the greater prevalence of these risk factors in Blacks must be considered. Although the A/T/N framework may clarify underlying biology contributing to Alzheimer’s disease, inclusion of these additional factors is needed to understand and reduce racial disparities in dementia rates.
