Patient Adherence to Medication within a New Multidisciplinary Thrombosis Service
Bibliographic record
Abstract
BACKGROUND Poor medication adherence puts patients who require thrombosis care at greater risk of complications. Little is known about the impact of multidisciplinary care models on medication adherence in patients requiring anticoagulation management. It is also unclear from the literature whether adherence varies in clinical practice with the advent of direct oral anticoagulants (DOACs) which have shorter half-lives and require limited or no laboratory monitoring compared to vitamin-K antagonists. We started a new multidisciplinary Adult Outpatient Thrombosis Service (TS) in October 2017 in a Canadian health authority servicing over 300 000 people. The TS is a comprehensive thrombosis and anticoagulation management program with unique, interrelated clinics providing the spectrum of care required for this patient group. The TS includes an Emergency Thrombosis Clinic for care after an acute episode of venous thromboembolism; Thrombosis Clinics addressing non-urgent thrombosis and anticoagulation questions and follow up; Anticoagulation Management Clinics for long term follow up of patients on anticoagulation; and a Perioperative Anticoagulation Management Clinic for patients on anticoagulation requiring surgery or procedures. The TS is staffed by Pharmacists, a Thrombosis Physician/Hematologist, and Clerical staff. The objective of the survey was to assess self-reported medication adherence of patients within the TS. PATIENTS/METHODS We conducted a cross-sectional survey of patients who were consulted to the TS between October 2017 and May 2019. Eligible patients were mailed an anonymous survey with a cover letter and self-addressed, return stamped envelope. Adherence to medication was assessed using the 12-item validated Adherence to Refills and Medications Scale (ARMS). The ARMS utilizes a 4 point scale (1-4), with the continuous score range of 12 to 48, and lower scores indicating better adherence. Baseline characteristics and patient satisfaction with the TS were evaluated for association with medication adherence. Linear regression analysis was used to examine the associations between patient's characteristics, their satisfaction with TS and medication adherence. RESULTS Of 1058 eligible patients, 563 responded to the survey representing a response rate of 53%. Seventeen were excluded who had more than 6 missing responses to the ARMS items. Out of remaining 546 patients with complete responses 55% (n=297) were on DOACs, 19% (n=102) on warfarin, 5.0% (n=27) on low molecular weight heparin (LMWH), 3.3% (n=18) on antiplatelet therapy and 18% (n=96) were not receiving anticoagulation therapy at the time of completing the survey. Nearly half had taken anticoagulant for 1 to 5 years (47%, n=253) while 28% (n=150) and 25% (n=137) had been on an anticoagulant for less than 1 year and more than 5 years respectively. Most patients (87%, n=475) were 50 years of age or older and half (51%, n=277) were male. About two-thirds (67%, n=360) had at least post-secondary education. The mean score for the ARMS was 13.9 (SD 2.2, range 12 to 25). Most patients (88%, n = 481) reported high adherence to medication (ARMS score = 12-16). In univariate analysis, post-secondary education (β = 0.0052, p = 0.006) and patient satisfaction with the TS (β = 0.0004; p = 0.019) significantly predicted medication adherence among participants. In multivariate analyses education (β = 0.0039, p = 0.048) and duration of anticoagulant use (β = 0.0047, p = 0.0244) were significantly associated with medication adherence. The internal consistency reliability for the ARMS tool was acceptable (Cronbach's alpha = 0.70). CONCLUSIONS Self-reported medication adherence was high (88%) in patients seen at a multidisciplinary TS. Post-secondary education and duration of anticoagulant use were important predictors of medication adherence. Disclosures Young: Sanofi Canada: Honoraria, Research Funding; Bayer: Research Funding; Pfizer: Honoraria. Chitsike:Bayer Canada: Research Funding; Sanofi Canada: Honoraria, Research Funding.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".