AJMC

STABLE Results: Warfarin Home Monitoring Achieves Excellent INR Control

Published Online: March 18, 2014
Grace DeSantis, PhD; Jackie Hogan-Schlientz, RN, BSN; Gary Liska, BS; Shari Kipp, BS; Ramarion Sallee; Mark Wurster, MD; Kenneth Kupfer, PhD; and Jack Ansell, MD
Objectives: Point-of-care, home international normalized ratio (INR) monitoring (patient self-testing, or PST) provides an opportunity to optimize warfarin therapy as demonstrated in randomized trials. This study sought to determine the quality of warfarin therapy as determined by time in therapeutic INR range (TTR) in patients who perform home monitoring outside of a clinical trial setting.

Study Design: Retrospective analysis.

Methods: The data base of an independent diagnostic testing facility was retrospectively queried over a 2.5-year period (January 2008-June 2011) and patient TTR was analyzed based on frequency of testing, age, gender, indication for therapy, duration of therapy, and critical value occurrence.

Results: A total of 29,457 patients with multiple indications for warfarin therapy comprised the database. The mean TTR for the entire group was 69.7%, with weekly testers achieving a TTR of 74% versus 68.9% for variable testers (testing every 2-4 weeks)(P <.0001). In all categories analyzed (age, indication for anticoagulation, and referral site volume), weekly testers performed significantly better than variable testers. Older individuals had a higher TTR than younger patients. Weekly testers experienced significantly fewer critical values (INR <1.5 or >5.0) than did variable testers.

Conclusions: Point-of-care patient self-testing at home achieves high-quality warfarin therapy outside of clinical trials and compares favorably with the results achieved in randomized trials or in anticoagulation clinic settings.

Am J Manag Care. 2014;20(3):202-209
Approximately 4 million people in the United States receive oral anticoagulation therapy with the vitamin K antagonist (VKA) warfarin,1 and require frequent international normalized ratio (INR) monitoring to maintain time in the therapeutic range.2 There are several models of warfarin management designed to maintain the patients’ INR within these desired parameters.3 These include usual care (UC), which means an individual physician manages multiple patients without formal systematic monitoring policies or procedures to focus on dose management; anticoagulation clinic care (AC), which means dose management is overseen by a healthcare provider (usually a nurse or pharmacist) under physician leadership with systematic policies and procedures in place; and patient self-testing (PST) or patient self-management (PSM), which means patients perform their own INR test at home with a portable point-of-care (POC) instrument and receive dose instructions from a healthcare provider (PST) or manage their own dose (PSM). Under UC or AC, test frequency may be irregular, and is often determined by a patient’s ability to travel to a lab or clinic to obtain the INR test result, rather than INR testing frequency depending on the pharmacology and metabolism of warfarin.4

Clinical evidence has demonstrated that more frequent testing improves warfarin safety and reduces risks for thromboembolic and major bleeding events.5 The advent of POC INR devices and home monitoring has facilitated more frequent testing, provided greater consistency in testing reagents and instrumentation, and increased patient empowerment. Since 2004, the American College of Chest Physicians (ACCP) has recommended PST as a means of warfarin dose management, and according to the 2012 ACCP guidelines,6 “for patients who are motivated and can demonstrate competency, PSM is recommended over UC (Grade 2B).” This recommendation is based on the results of numerous clinical trials of PST/PSM compared with both UC and AC care. Recently, Heneghan, et al, and Bloomfield, et al, have performed independent meta-analyses of a number of clinical trials documenting the benefit of PST or PSM.7,8 Depending on how the analyses are done, each investigative group has shown greater efficacy of PST/PSM with a reduction in thromboembolism risk and/or major bleeding risk. However, there is little evidence to date, outside of randomized clinical trials (RCTs), to assess outcomes for patients who perform PST or PSM.9 We evaluated the quality of PST anticoagulation management as reflected by time in therapeutic INR range (TTR) in a large cross-section of real world (non-study) patients from the United States enrolled in a home monitoring program and sought to determine whether INR testing frequency had an impact on TTR.

METHODS

Data Source


Alere Inc, an independent diagnostic testing facility (IDTF), has a database that includes anticoagulation patient data starting in 1993 and PST data starting in 1998. It includes data from over 68,000 PSTs (>3.1 million INR results) who were referred from a variety of settings ranging from office practices (cardiology, internal medicine, family practice, hematology, oncology) to large organized clinics, and enrolled in a comprehensive PST support service.

Prescribing physicians generally select PST candidates based on whether the patient is able, willing, and reliable enough to measure their INR on a POC instrument at home.10,11 Then, physicians complete prescription forms for patient submission to the IDTF. Patients are then individually trained. Standardized protocols developed by Alere, based on human factors, support training retention and positive testing behaviors.12 All patients in this analysis were trained by experienced healthcare professionals, and immediate follow-up was provided, as needed. Following physician instructions for data and adherence management, Alere helped each patient to initiate PST, become adherent to testing, and remain adherent to therapy. Clinicians were notified of all INR results, and if patients were nonadherent.

Study Design

This Self-Testing Analysis Based on Long-term Evaluation (STABLE) is a retrospective cohort analysis of data from real-world PST assessing 2 groups: variable and weekly testing cohorts. A query was developed to collect data on all patients who were trained on or after January 1, 2008, and who completed at least 6 months of PST before June 30, 2011 (Figure 1). This window of observation was selected to capture a large cross-section of patients who qualified for PST before and after the Centers for Medicare & Medicaid Services (CMS) expanded Medicare coverage to more indications.

Selection Criteria

To eliminate potential bias as a result of individual differences in learning aptitude and time to mastery, we excluded the first 3 months of PST results after the training date, considering this to be the initiation period,13 thus offering at least 3 months of PST data to evaluate. In addition, we excluded patients with results greater than 56 days between tests (DBT) as per the Rosendaal methodology,14 patients with INR target range widths other than 1.0 (to comply with ACCP guideline ranges that are all 1.0 INR in width [eg, 2.0-3.0, 2.5-3.5]) and patients younger than 18 years (to maintain focus of this analysis on adults).

The PST prescription form requires the physician to direct the test frequency (TF), with selections that accommodate ACCP Guidelines for weekly, or options for 1 to 4 times per month (variable). Since not all patients adhere to their prescribed TF, the actual TF for each patient was established and reported. The definition adopted for this study was based on the THINRS trial that defined weekly testing as 5 to 9 days between tests (7 + 2 DBT).13 The definition of monthly testing varies in the literature. THINRS defined monthly clinic testing as 21 to 49 DBT (35 + 14 DBT), but patients showed a very low adherence rate of only 52% in that study. STABLE adopted a tighter range of 24 to 38 DBT (31 + 7 DBT).

Patient adherence to PST was used to establish study cohorts. We defined patients who reported 10 out of 12 weekly tests (83.3%) for at least a 3-month period after the initial 3-month initiation period as those who represented strong adherence. We also applied the same 83.3% adherence rate to the other TF categories for each patient over the duration of time the patient performed PST. Patients within any fixed TF who failed to meet this threshold were recategorized to the variable TF (1-4 tests/month). In summary, 4 nominal TF categories for all included patients were assigned based on the adherence model: weekly (83.3% of tests with 5-9 DBT), twice per month (83.3% of tests with 10-17 DBT), monthly (83.3% of tests with 24-38 DBT), and variable (less than 83.3% of tests in any one of the previously defined categories).

Study Measures

The TTR determined by the Rosendaal method14 was used as the primary surrogate end point for clinical outcomes. A stratified analysis was also performed, separating patients into 2 groups: low TTR and high TTR. The incidence of critical INR values (INR <1.5 or INR >5.0)15 in each group was computed as the secondary surrogate end point. These surrogate end points were categorized by actual testing frequencies. Four primary patient characteristics were evaluated: age, primary indication for warfarin, gender, and duration of PST.

Statistical Analysis

The mean TTR for each subject was calculated based on all INR test results within the observation period. The distribution of mean TTR over all subjects (and within groups of subjects) was characterized by the mean and the standard deviation (SD). The distribution of mean TTRs between subject groups was compared via the Wilcoxon rank sum test. Mean TTR was also treated as a dichotomous variable (lowmean TTR <60% vs high-mean TTR >60%). The odds ratio (OR) was used to characterize the strength of association between dichotomous variables. The significance of association between categorical variables was assessed via the χ² test. The correlation of an ordinal variable (eg, referral clinic size) with mean TTR was characterized by the Spearman correlation coefficient. The incidence of critical values (per unit time) was characterized by Kaplan-Meier cumulative probability curves and by Cox proportional hazard regression of the time between critical values (multiple critical values within each observation period were included; intervals containing no critical values were considered censored observations; repeat test results within a single day were excluded). Statistical analysis was conducted in MATLAB version 7.5 (MathWorks, Natick, Massachusetts).

Role of the Funding Source

The study was designed by clinical quality assurance and research and development teams from Alere Inc, in collaboration with outside experts in the field. All funding was provided by Alere, which conducted the query and data analysis. To maintain the privacy of all patients’ identifiable health information, and following Health Insurance Portability and Accountability Act privacy rules, only de-identified patient data were evaluated, and institutional review board approval was granted (Western IRB).

RESULTS

PST Patient Population Characteristics

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Issue: March 2014
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