Aortic aneurysms are usually asymptomatic until catastrophic rupture occurs. Ruptured abdominal aortic aneurysms classically present with acute back pain, shock, and a pulsatile abdominal mass. The natural history of some aortic aneurysms also includes a stage of contained rupture. This occurs when extravasation of blood from the ruptured aneurysm is contained by surrounding tissues. Here, the authors report the case of a chronic contained abdominal aortic aneurysm rupture that resulted in erosion of the spine.

BACKGROUND: Concerns about antipsychotic prescribing for children, particularly those enrolled in Medicaid and with Supplemental Security Income (SSI), continue despite recent calls for selective use within established guidelines. OBJECTIVES: To (a) examine the application of 6 quality measures for antipsychotic medication prescribing in children and adolescents receiving Medicaid and (b) understand distinctive patterns across eligibility categories in order to inform ongoing quality management efforts to support judicious antipsychotic use. METHODS: Using data for 10 states from the 2008 Medicaid Analytic Extract (MAX), a cross-sectional assessment of 144,200 Medicaid beneficiaries aged < 21 years who received antipsychotics was conducted to calculate the prevalence of 6 quality measures for antipsychotic medication management, which were developed in 2012-2014 by the National Collaborative for Innovation in Quality Measurement. These measures addressed antipsychotic polypharmacy, higher-than-recommended doses of antipsychotics, use of psychosocial services before antipsychotic initiation, follow-up after initiation, baseline metabolic screening, and ongoing metabolic monitoring. RESULTS: Compared with children eligble for income-based Medicaid, children receiving SSI and in foster care were twice as likely to receive higher-than-recommended doses of antipsychotics (adjusted odds ratio [AOR] = 2.4, 95% CI = 2.3-2.6; AOR = 2.5, 95% CI = 2.4-2.6, respectively) and multiple concurrent antipsychotic medications (AOR = 2.2, 95% CI = 2.0-2.4; AOR = 2.2, 95% CI = 2.0-2.4, respectively). However, children receiving SSI and in foster care were more likely to have appropriate management, including psychosocial visits before initiating antipsychotic treatment and ongoing metabolic monitoring. While children in foster care were more likely to experience baseline metabolic screening, SSI children were no more likely than children eligible for income-based aid to receive baseline screening. CONCLUSIONS: While indicators of overuse were more common in SSI and foster care groups, access to follow-up, metabolic monitoring, and psychosocial services was somewhat better for these children. However, substantial quality shortfalls existed for all groups, particularly metabolic screening and monitoring. Renewed efforts are needed to improve antipsychotic medication management for all children. DISCLOSURES: This project was supported by grant number U18HS020503 from the Agency for Healthcare Research and Quality (AHRQ) and Centers for Medicare & Medicaid Services (CMS). Additional support for Rutgers-based participants was provided from AHRQ grants R18 HS019937 and U19HS021112, as well as the New York State Office of Mental Health. The content of this study is solely the responsibility of the authors and does not necessarily represent the official views of AHRQ, CMS, or the New York State Office of Mental Health. Finnerty has been the principle investigator on research grants/contracts from Bristol Myers Squibb and Sunovion, but her time on these projects is fully supported by the New York State Office of Mental Health. Scholle, Byron, and Morden work for the National Committee for Quality Assurance, a not-for-profit organization that develops and maintains quality measures. Neese-Todd was at Rutgers University at the time of this study and is now employed by the National Committee for Quality Assurance. The other authors have no financial relationships relevant to this article to disclose. Study concept and design were contributed by Finnerty, Neese-Todd, and Crystal, assisted by Scholle, Leckman-Westin, Horowitz, and Hoagwood. Scholle, Byron, Morden, and Hoagwood collected the data, and data interpretation was performed by Pritam, Bilder, Leckman-Westin, and Finnerty, with assistance from Scholle, Byron, Crystal, Kealey, and Neese-Todd. The manuscript was written by Leckman-Westin, Kealey, and Horowitz and revised by Layman, Crystal, Leckman-Westin, Finnerty, Scholle, Neese-Todd, and Horowitz, along with the other authors.

BACKGROUND: Regulatory approval of novel therapies by the FDA does not guarantee insurance coverage requisite for most clinical use. In the United States, the largest health insurance payer is the Centers for Medicare & Medicaid Services (CMS), which provides Part D prescription drug benefits to over 43 million Americans. While the FDA and CMS have implemented policies to improve the availability of novel therapies to patients, the time required to secure Medicare prescription drug benefit coverage—and accompanying restrictions—has not been previously described. OBJECTIVE: To characterize Medicare prescription drug plan coverage of novel therapeutic agents approved by the FDA between 2006 and 2012. METHODS: This is a cross-sectional study of drug coverage using Medicare Part D prescription drug benefit plan data from 2007 to 2015. Drug coverage was defined as inclusion of a drug on a plan formulary, evaluated at 1 and 3 years after FDA approval. For covered drugs, coverage was categorized as unrestrictive or restrictive, which was defined as requiring step therapy or prior authorization. Median coverage was estimated at 1 and 3 years after FDA approval, overall, and compared with a number of drug characteristics, including year of approval, CMS-protected class status, biologics versus small molecules, therapeutic area, orphan drug status, FDA priority review, and FDA-accelerated approval. RESULTS: Among 144 novel therapeutic agents approved by the FDA between 2006 and 2012, 14% (20 of 144) were biologics; 40% (57 of 144) were included in a CMS-protected class; 31% (45 of 144) were approved under an orphan drug designation; 42% (60 of 144) received priority review; and 11% (16 of 144) received accelerated approval. The proportion of novel therapeutics covered by at least 1 Medicare prescription drug plan was 90% (129 of 144) and 97% (140 of 144) at 1 year and 3 years after approval, respectively. At 3 years after approval, 28% (40 of 144) of novel therapeutics were covered by all plans. Novel therapeutic agents were covered by a median of 61% (interquartile range [IQR] = 39%-90%) of plans at 1 year and 79% (IQR = 57%-100%) at 3 years ( P < 0.001). When novel therapeutics were covered, many plans restricted coverage through prior authorization or step therapy requirements. The median proportion of unrestrictive coverage was 29% (IQR = 13%-54%) at 3 years. Several drug characteristics, including therapeutic area, FDA priority review, FDA-accelerated approval, and CMS-protected drug class, were associated with higher rates of coverage, whereas year of approval, drug type, and orphan drug status were not. CONCLUSIONS: Most Medicare prescription drug plans covered the majority of novel therapeutics in the year following FDA approval, although access was often restricted through prior authorization or step therapy and was dependent on plan choice. DISCLOSURES: Funding for this study was contributed by a student research grant awarded to Shaw and provided by the Yale School of Medicine Office of Student Research under National Institutes of Health training grant award T35DK104689. Ross reports research grants to Yale University from the U.S. Food and Drug Administration (U01FD005938, U01FD004585), Medtronic, Johnson & Johnson, Centers for Medicare & Medicaid Services (HHSM-500-2013-13018I), Blue Cross-Blue Shield Association, Laura and John Arnold Foundation, Agency for Healthcare Research and Quality (R01HS022882), and National Institutes of Health (R01HS025164), unrelated to this study. Dhruva has nothing to disclose. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

BACKGROUND: Recent changes in the health care delivery landscape have expanded opportunities for clinical pharmacists in the ambulatory care setting. This article describes the successful integration of a clinical pharmacist-led chronic disease management service in a patient-centered medical home (PCMH) and accountable care organization (ACO) environment. PROGRAM DESCRIPTION: In 2008, the year before PCMH implementation, 36% of patients who were hospitalized at Advocate Trinity Hospital for a heart failure exacerbation were readmitted within 30 days of their hospital stay for heart failure exacerbation. This high rate of heart failure hospital readmissions, compared with national standards, drove the implementation of the PCMH at Advocate Medical Group – Southeast Center (AMG-SE), the adjoining outpatient medical clinic. A clinical pharmacist was added to the health care team to help achieve the collective goal of improving patient outcomes and decreasing hospitalizations. OBSERVATIONS: From November 1, 2009, through August 30, 2010, the clinical pharmacist conducted visits and intervened in the care of 111 chronic heart failure patients. A pre/post analysis of those 111 patients during the 10 months before and after the integration of the clinical pharmacist showed that those patients were hospitalized 63 times in the 10 months before having regularly scheduled visits with the clinical pharmacist and 30 times in the 10 months after establishing care. This reduction from 63 to 30 visits translated to an approximate 50% decrease in heart failure hospitalizations in patients being followed by the clinical pharmacist within the first 10 months. Once the clinical pharmacist became better integrated into the workflow through development of rapport with the medical team, the outcomes improved further. In an 18-month analysis from May 1, 2010, through November 30, 2011, only 2% of patients (3 of 153) designated as high-risk patients managed by the clinical pharmacist had a 30-day readmission for heart failure exacerbation. IMPLICATIONS: Outcomes-based models have expanded opportunities for clinical pharmacist involvement and can provide unique reimbursement options. Demonstration of cost savings and an improvement in quality measures are paramount to establishing and justifying the clinical pharmacist’s role in a team-based model of care. DISCLOSURES: No outside funding supported this research. The authors have no conflicts of interest to disclose.

BACKGROUND: Nonadherence to essential chronic medications has been identified as a potential driver of high health care costs in superutilizers of inpatient services. Few studies, however, have documented the levels of nonadherence and factors associated with nonadherence in this high-cost, vulnerable population. OBJECTIVE: To examine the factors associated with nonadherence to essential chronic medications, with special emphasis on mental illness and use of opioid medications. METHODS: This study was a retrospective panel analysis of 2-year baseline data for Medicare Part D beneficiaries eligible for the SafeMed care transitions program in Memphis, Tennessee, from February 2013 to December 2014. The 2-year baseline data for each patient were divided into four, 6-month patient periods. The study included Medicare superutilizers (defined as patients with ≥ 3 hospitalizations or ≥ 2 hospitalizations with ≥ 2 emergency visits in 6 months) with continuous Part D coverage who had filled at least 1 drug class used to treat hypertension, diabetes mellitus, congestive heart failure, coronary artery disease, or chronic lung disease. The outcome included medication nonadherence assessed using proportion of days covered (PDC), with PDC < 80% defined as nonadherent, and the main exposure variables included mental illness (defined as a diagnosis of depression or anxiety or ≥ 1 anxiolytic or antidepressant fill) and opioid medication fills assessed in each 6-month period. Pooled observations from the four 6-month periods were used for multivariable analyses using the patient periods as the unit of analysis. A random effects model with robust standard errors and a binary distribution were used to examine associations between independent variables (time invariant and time variant factors) and medication nonadherence. The model included lagged effects of time variant factors measured in each period. RESULTS: Overall nonadherence to essential chronic medications ranged from 39.3% to 58.4%, with the highest for chronic lung disease medications (49.1%-64.4%). Factors associated with nonadherence included ≥ 4 opioid medication fills in the previous 6-month period (adjusted odds ratio [OR] = 1.90, 95% CI = 1.32-2.73); age 22-44 and 45-64 years vs. ≥ 65 years (OR = 3.57, 95% CI = 2.07-6.16, and OR = 2.07, 95% CI = 1.49-2.88); and a higher number of unique prescribers (OR = 1.10, 95% CI = 1.04-1.17). Factors protecting against nonadherence included higher number of unique medications filled (OR = 0.95, 95% CI = 0.92-0.98) and ≥ 1 physician office visit in the previous 6-month period (OR = 0.66, 95% CI = 0.46-0.94). CONCLUSIONS: This study demonstrated that high levels of opioid medication use are significantly associated with essential chronic disease medication nonadherence among superutilizers. Other risk factors for nonadherence were aged < 65 years, low-income status, and a higher number of unique prescribers. Factors protecting against nonadherence were physician office visits and filling higher number of medications. Medication management interventions targeting superutilizers should focus on supporting chronic disease medication adherence. DISCLOSURES: This project was supported by Funding Opportunity Number 1C1CMS331067-01-00 from the Centers for Medicare & Medicaid Services, Center for Medicare and Medicaid Innovation. Support was also provided by the Pharmaceutical Research and Manufacturers of America Foundation. The content of this study is solely the responsibility of the authors. The authors declare no relevant conflicts of interest or financial relationships. Study concept and design were contributed by Surbhi, Bailey, and Graetz. Surbhi and Wan collected the data, and data interpretation was performed primarily by Surbhi, along with Graetz, Bailey, and Gatwood. The manuscript was primarily written by Surbhi, with assistance from Bailey and Graetz, and revised by Bailey, Graetz, Gatwood, and Surbhi. This study was presented as a poster at the Academy Health Annual Research Meeting in Boston, Massachusetts, on June 26-28, 2016.

BACKGROUND: With increasing health care costs that have outpaced those of other industries, payers of health care are moving from a fee-for-service payment model to one in which reimbursement is tied to outcomes. Chronic obstructive pulmonary disease (COPD) is a disease where this payment model has been implemented by some payers, and COPD exacerbations are a quality metric that is used. Under an outcomes-based payment model, it is important for health systems to be able to identify patients at risk for poor outcomes so that they can target interventions to improve outcomes. OBJECTIVE: To develop and evaluate predictive models that could be used to identify patients at high risk for COPD exacerbations. METHODS: This study was retrospective and observational and included COPD patients treated with a bronchodilator-based combination therapy. We used health insurance claims data to obtain demographics, enrollment information, comorbidities, medication use, and health care resource utilization for each patient over a 6-month baseline period. Exacerbations were examined over a 6-month outcome period and included inpatient (primary discharge diagnosis for COPD), outpatient, and emergency department (outpatient/emergency department visits with a COPD diagnosis plus an acute prescription for an antibiotic or corticosteroid within 5 days) exacerbations. The cohort was split into training (75%) and validation (25%) sets. Within the training cohort, stepwise logistic regression models were created to evaluate risk of exacerbations based on factors measured during the baseline period. Models were evaluated using sensitivity, specificity, and positive and negative predictive values. The base model included all confounding or effect modifier covariates. Several other models were explored using different sets of observations and variables to determine the best predictive model. RESULTS: There were 478,772 patients included in the analytic sample, of which 40.5% had exacerbations during the outcome period. Patients with exacerbations had slightly more comorbidities, medication use, and health care resource utilization compared with patients without exacerbations. In the base model, sensitivity was 41.6% and specificity was 85.5%. Positive and negative predictive values were 66.2% and 68.2%, respectively. Other models that were evaluated resulted in similar test characteristics as the base model. CONCLUSIONS: In this study, we were not able to predict COPD exacerbations with a high level of accuracy using health insurance claims data from COPD patients treated with bronchodilator-based combination therapy. Future studies should be done to explore predictive models for exacerbations. DISCLOSURES: No outside funding supported this study. Samp is now employed by, and owns stock in, AbbVie. The other authors have nothing to disclose. Study concept and design were contributed by Joo and Pickard, along with the other authors. Samp and Lee performed the data analysis, with assistance from the other authors. Samp wrote the manuscript, which was revised by Schumock and Calip, along with the other authors.