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The American Journal of Managed Care April 2017
Physician Variation in Lung Cancer Treatment at the End of Life
Jonas B. Green, MD, MPH, MSHS; Martin F. Shapiro, MD, PhD; Susan L. Ettner, PhD; Jennifer Malin, MD, PhD; Alfonso Ang, PhD; and Mitchell D. Wong, MD, PhD
Real-World Evidence and the Behavioral Economics of Physician Prescribing
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Provider Type and Management of Common Visits in Primary Care
Douglas W. Roblin, PhD; Hangsheng Liu, PhD; Lee F. Cromwell, MS; Michael Robbins, PhD; Brandi E. Robinson, MPH; David Auerbach, PhD; and Ateev Mehrotra, MD, MPH
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Joanna P. MacEwan, PhD; John J. Sheehan, PhD; Wes Yin, PhD; Jacqueline Vanderpuye-Orgle, PhD; Jeffrey Sullivan, MS; Desi Peneva, MS; Iftekhar Kalsekar, PhD; and Anne L. Peters, MD
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Andrew S. Hwang, MD, MPH; Jeffrey M. Ashburner, PhD, MPH; Clemens S. Hong, MD, MPH; Wei He, MS; and Steven J. Atlas, MD, MPH
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Patients' Preferences for Receiving Laboratory Test Results
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Medication Burden in Patients With Acute Coronary Syndromes
Eric A. Wright, PharmD, MPH; Steven R. Steinhubl, MD; J.B. Jones, PhD, MBA; Pinky Barua, MSc, MBA; Xiaowei Yan, PhD; Ryan Van Loan, BA; Glenda Frederick, BA; Durgesh Bhandary, MS; and David Cobden, Ph

Medication Burden in Patients With Acute Coronary Syndromes

Eric A. Wright, PharmD, MPH; Steven R. Steinhubl, MD; J.B. Jones, PhD, MBA; Pinky Barua, MSc, MBA; Xiaowei Yan, PhD; Ryan Van Loan, BA; Glenda Frederick, BA; Durgesh Bhandary, MS; and David Cobden, Ph
Patients endure heavy medication complexity following hospital discharge for acute coronary syndrome.
ABSTRACT
 
Objectives: Cardioprotective medications improve outcomes following acute coronary syndromes (ACS) but add to medication complexity. We set out to describe the use of these medications and quantify medication changes in patients admitted and discharged for ACS.

Study Design: Retrospective cohort study.

Methods: Using archived data from the electronic health record (EHR), we evaluated patients with ACS admitted to 1 of 2 hospitals between January 2008 and December 2012. Patients aged 18 to 89 years who were discharged with a principal diagnosis of ACS were included in the study. Descriptive statistics were compiled and medication use was compared at 3 time points: admission, discharge, and within 90 days post discharge.

Results: This study included 4767 patients. The mean number of total medications increased from 8.6 ± 6.5 to 11.4 ± 5.4 from admission to discharge, dropping slightly within 90 days post discharge (11.1 ± 5.2). Patients taking medications at least twice daily increased from 6.4 of 10 at admission to 9 of 10 at discharge. Cardioprotective medication use increased by a relative 76% for aspirin, 72% for statins, 85% for beta-blockers, and 29% for angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers from admission to discharge, whereas P2Y12 receptor inhibitor use increased 4-fold.

Conclusions: Medication complexity among patients with ACS are high, with notable changes from admission to discharge. Awareness of the extent of medication burden provides clinicians and policy makers with insight to help address medication use during the ACS peri-hospitalization period.

Am J Manag Care. 2017;23(4):e106-e112
Takeaway Points

  • Patients admitted for acute coronary syndrome (ACS) leave the hospital with an increased medication burden.
  • Among patients with ACS, the complexity of medication regimens increases from admission to discharge. 
  • Although medication use increased following admission for ACS, the majority of patients are not prescribed all recommended evidenced-based medications upon discharge.
Acute coronary syndromes (ACS), encompassing ST-segment elevation myocardial infarction (STEMI), non-ST-segment elevation myocardial infarction (NSTEMI), and unstable angina, are responsible for significant patient morbidity and mortality and are frequent causes of hospital admissions. Although the incidence of ACS and related coronary heart disease (CHD) has declined in recent decades,1 CHD remains the leading cause of mortality (approximately one-third of all deaths).2 Improvements in ACS outcomes have largely been attributed to reductions in major risk factors and advances in acute therapies, including coronary perfusion through percutaneous coronary intervention (PCI), coronary artery bypass grafting, and improved medication management using 1 or more of the 5 major classes of cardioprotective agents: aspirin, P2Y12 receptor inhibitors, beta-blockers, angiotensin-converting enzyme (ACE) inhibitors or angiotensin II receptor blockers (ARBs), and 3-hydroxy-3-methylglutaryl-coenzyme A reductase inhibitors (ie, statins).3,4 Optimal medication management during and following admission for ACS is critical to improving patient outcomes.4

High rates of medication nonadherence are major contributors to poor outcomes following ACS.5,6 Approximately one-third of patients will discontinue 1 or more cardiovascular medications within 3 months of discharge following admission for ACS,7 with over half reporting nonadherence at 10 months.8 Among other reasons, complex medication regimens and a high overall medication burden are directly related to medication nonadherence.7,9 Medication complexity, including an increased number of medications being taken regularly, frequency of use, and special instructions (eg, take on an empty stomach, separate levothyroxine from calcium carbonate), is challenging enough for patients after discharge from an ACS admission, but confusing or unclear instructions can further complicate matters.10 Quantification of patients’ medication regimens and modifications to drug therapy during the peri-hospitalization period (ie, from admission to 90 days post discharge) in the real-world environment versus clinical trials is important to make valid inferences in usual practice.11 The exact extent of medication regimen complexity, as defined by the number and frequency of all medications, and how this changes from admission to discharge following ACS, have yet to be described in a real-world setting.

The following descriptive study aimed to characterize the prescribing patterns of cardioprotective medications, determine the extent of the medication burden by number and frequency of medications taken per day, and compare the medication burden and frequency from admission to discharge among a cohort of patients with ACS.

METHODS

Study Design


We conducted a retrospective descriptive analysis of patients with ACS admitted to either of 2 Geisinger hospitals between January 1, 2008, and December 31, 2012.

Setting

Geisinger Health System (GHS) is an integrated healthcare delivery system offering services to residents of 44 of the state’s 67 counties in central and northeastern Pennsylvania. GHS includes the Geisinger Clinic, which provides ambulatory care to approximately 350,000 patients annually across 44 community-based practices; the Geisinger Health Plan, an insurance plan with over 450,000 covered lives; the Geisinger medical laboratory, a private lab that services all GHS facilities; 2 large tertiary care teaching hospitals; and 6 smaller community hospitals.

Data Sources

Study data were extracted from GHS’s electronic health record (EHR), EpicCare (Epic Systems Corporation; Madison, Wisconsin), which contains information for more than 2.5 million patients and has been fully operational since 2001 and 2007 in the outpatient and inpatient settings, respectively. The EHR archives information on patient demographics, medication order history, medical notes, encounters, orders, medication administration record, appointments, imaging, laboratory, and billing data every 24 hours onto duplicate servers for both clinical and nonclinical accessibility.

Study Population

Patients were included in the study cohort if they were at least 18 but younger than 90 years; admitted to either of 2 Geisinger hospitals between January 1, 2008, through December 31, 2012; and were discharged from the hospital with a principal discharge diagnosis of ACS, as identified by International Classification of Diseases, Ninth Revision (ICD-9) codes: 410.00, 410.01, 410.10, 410.11, 410.20, 410.21, 410.30,410.31, 410.40, 410.41, 410.50, 410.51, 410.60, 410.61, 410.80, 410.81; 410.70, 410.71; 410.90, 410.91; 411.1. In this analysis, the focus was on the index ACS admission, defined as the first ACS admission in the EHR during the study period with no ACS-related admissions occurring in the prior 6 months. Patients were excluded if they had a research exclusion flag in the EHR, were treated with ticagrelor due to low sample size (internal analysis revealed only 1 patient with use in 2011 and 13 patients with use in 2012), or died during hospitalization. In addition, we excluded patients who had missing or incomplete information, including any patients who did not have an encounter ID on file for the listed principal ACS admission. The lack of an encounter ID was mainly due to the initiation of the EHR within 1 hospital during the 2008 to 2009 period.

Methodological Approach

This analysis focused on describing medication use during the index ACS period. We characterized our population in terms of concomitant conditions (ie, from ICD-9 codes present on the patient’s prior to admission [PTA] problem list), inpatient laboratory tests (including cardiac troponins, lipid panel, serum creatinine concentration), length of stay, in-hospital events (including PCI), postdischarge events (including rehospitalization due to ACS), other rehospitalizations, mortality, and revascularization within 30 days and 3 months of the index admission.

Among our cohort, we evaluated medication use at 3 different time points: index admission, discharge, and post discharge up to 90 days (for those with a return visit). Admission medications were identified using the PTA medication list recorded in the EHR. PTA medications are self-verified by patients during the admission intake process from medication lists current in the EHR at the time of admission (available for 55% of patients seen by a Geisinger provider in the previous 12 months, in addition to those previously admitted to a Geisinger facility, and those with medication lists in the Geisinger system that are more than 12 months old), lists of medications filled at pharmacies that are imported into the EHR, or no previous record (in which case the PTA medication list was self-reported only) in some cases. If the PTA medication list was not available, but the patient was seen any time previously by a provider in the Geisinger Clinic, the most recent outpatient medication list was used to populate the admission medication list. PTA medication lists include prescription and nonprescription medications. Discharge medications were determined from the discharge orders. Finally, for those with a postdischarge visit, we used the outpatient reconciled medication list for the postdischarge follow-up at the latest visit from hospitalization up to 90 days post discharge.

We quantified the use of medication classes recommended for use in patients with ACS, which included aspirin, P2Y12 receptor inhibitors, statins, ACE inhibitors or ARBs, and beta-blockers. Medication complexity was quantified by 2 separate measures: total number of medications and medication frequency (defined as the number of times a day a medication is to be taken). Assessment of medication complexity was quantified for all cardiac and noncardiac traditional medications. Over-the-counter and as-needed medications (eg, ibuprofen, ranitidine, aspirin) were included, as they are often prescribed by a provider for a select indication; however, complementary and alternative medications and vitamins were excluded because our focus was on traditional medication use.

Free-text medication instructions for use (also known as signa or “sig”) in the EHR admission, and discharge and postdischarge medication lists, were mapped by a custom algorithm developed internally for mapping free-text sig instructions to a standardized set of medication frequency instructions. The time of day was not specified in the logic; only frequency on a numeric scale of times a medication was given per day. For example, a patient prescribed lisinopril 10 mg in the morning, simvastatin 20 mg in the evening, and metformin 500 mg twice daily would have a total daily medication frequency of 1 for both lisinopril and simvastatin, and 2 for metformin. After assessing all medications within a patient’s profile, the medication with the highest frequency per day was assigned as the minimum frequency a patient took medications per day. In this same example, the patient would be listed as having a minimum frequency per day of 2. This methodology represents the most conservative daily estimate of administration frequencies, biasing frequency toward the lower end.

Statistical Analysis

Treatment groups were summarized with respect to demographic and clinical characteristics. Descriptive summary statistics, including, means, medians, standard deviations, and interquartile ranges, are presented for continuous variables. Distributions of categorical variables were characterized by proportions. Comparisons of the number of medications between peri-hospitalization time points were conducted using paired t tests and the frequency of the medication was tested using χ2 tests. All analyses were performed using SAS version 9.3 software (SAS Institute; Cary, North Carolina).

RESULTS

After applying exclusion criteria, 4767 patients with a discharge diagnosis for ACS were included in the study cohort (Figure 1). Complete medication information was available for all 4767 patients during hospitalization and at discharge. A total of 4559 patients had PTA medication data that were derived from either the patient-verified list confirmed at the time of admission (3923 patients; 82.3%) or, if that list was not confirmed, from the medication list in the EHR documented prior to hospitalization (636 patients; 13.3%). The 208 patients (4.4%) with no medications within their PTA list or within the EHR were assumed to take no medications on arrival.

Table 1 describes the basic demographic and clinical characteristics of the study cohort. The average age of the cohort was 64.7 years, and the majority were male (64.6%) and white (98.5%). Most patients were current nonsmokers, with over 43% reporting a prior smoking history. Both the mean (30.7) and median (29.8) body mass index (BMI) indicate a predominantly overweight/obese population, with approximately 82% of the cohort having a BMI greater than 25. ACS breakdown revealed 33.6% had an STEMI, 42.7% had an NSTEMI, and 23.6% had unstable angina. The most common comorbidities included coronary artery disease, hypertension, hyperlipidemia, diabetes, and heart failure.

 
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