Publications

BACKGROUND

Treat-to-target is the recommended strategy for the management of rheumatoid arthritis (RA) and involves regular assessment of disease activity using validated measures and subsequent adjustment of medical therapy if patients are not in remission or low disease activity. Recommendations published in 2012 detailed the preferred disease activity measures but there have been few publications on implementation of disease activity measures in a real-world clinic setting.

METHODS

Plan-Do-Study-Act (PDSA) methodology was used over two cycles with a goal of increasing provider measurement of disease activity during all RA patient visits. In PDSA cycle 1, we implemented a paper-based form to help providers assess disease activity in RA patients. PDSA cycle 2 included the creation of separate patient and physician forms for collection of information, identification of patients prior to their clinic visit and incorporation of medical assistants into the workflow.

RESULTS

The first PDSA cycle improved the number of RA patients with documented disease activity measures from 24 % over a 4-week period, to an average of 44 % over an 8-week period. The second PDSA cycle showed a sustained and dramatic improvement, with 85 % of patients having a disease activity measure recorded over a 27-week period.

CONCLUSIONS

Implementation of disease activity measurement in a typical academic rheumatology clinic can be achieved by standardizing workflow using a simple paper form.

Patient-reported health status is highly valued as a key measure of health care quality, yet little is known about the extent to which it is determined by subjective perception compared with objective measures of disease severity. We sought to compare the associations of depressive symptoms and objective measures of cardiac disease severity with perceived functional status in patients with stable coronary artery disease. We assessed depressive symptoms, severity of cardiovascular disease, and perceived functional status in a cross-sectional study of 1,023 patients with stable coronary artery disease. We compared the extent to which patient-reported functional status was influenced by depressive symptoms versus objective measures of disease severity. We then evaluated perceived functional status as a predictor of subsequent cardiovascular hospitalizations during 8.8 years of follow-up. Patients with depressive symptoms were more likely to report poor functional status than those without depressive symptoms (44% vs 17%; p <0.001). After adjustment for traditional risk factors and co-morbid conditions, independent predictors of poor functional status were depressive symptoms (odds ratio [OR] 2.68, 95% confidence interval [CI] 1.89 to 3.79), poor exercise capacity (OR 2.30, 95% CI 1.65 to 3.19), and history of heart failure (OR 1.61, 95% CI 1.12 to 2.29). Compared with patients who had class I functional status, those with class II functional status had a 96% greater rate (hazard ratio 1.96, 95% CI 1.15 to 3.34) and those with class III or IV functional status had a 104% greater rate (hazard ratio 2.04, 95% CI 1.12 to 3.73) of hospitalization for HF, adjusted for baseline demographic characteristics, co-morbidities, cardiac disease severity, and depressive symptoms. In conclusion, depressive symptoms and cardiac disease severity were independently associated with patient-reported functional status. This suggests that perceived functional status may be as strongly influenced by depressive symptoms as it is by cardiovascular disease severity.

Predicting in vivo pharmacokinetic parameters such as clearance from in vitro data is a crucial part of the drug-development process. There is a commonly cited trend that drugs that are highly protein-bound and are substrates for hepatic uptake transporters often yield the worst predictions. Given this information, 11 different data sets using human microsomes and hepatocytes were evaluated to search for trends in accuracy, extent of protein binding, and drug classification based on the Biopharmaceutics Drug Disposition Classification System (BDDCS), which makes predictions about transporter effects. As previously reported, both in vitro systems (microsomes and hepatocytes) gave a large number of inaccurate results, defined as predictions falling more than 2-fold outside of in vivo values. The weighted average of the percentage of inaccuracy was 66.5%. BDDCS class 2 drugs, which are subject to transporter effects in vivo unlike class 1 compounds, had a higher percentage of inaccurate predictions and often had slightly larger bias. However, since the weighted average of the percentage of inaccuracy was still high in both classes (81.9% for class 2 and 62.3% for class 1), it may be currently hard to use BDDCS class to predict potential accuracy. The results of this study emphasize the need for improved in vitro to in vivo extrapolation experimental methods, as using physiologically based scaling is still not accurate, and BDDCS cannot currently help predict accurate results.