Measuring disease activity in patients with early rheumatoid arthritis

Current treatment strategies for rheumatoid arthritis (RA) aim to suppress the patient’s disease activity as early as possible. This requires valid and reliable measurements of disease activity. The DAS28 (Disease Activity Score for 28 joints) is an index measure that combines a 28‐tender joint count, a 28‐swollen joint count, a laboratory measure of inflammation (either the ESR or the CRP), and a patient‐reported feeling of general health (VAS-GH) into a single measure of disease activity. Though the DAS28 is frequently used, several concerns have been expressed. First, disease activity in omitted joints. Since assessing all joints is unfeasible, 28 joint counts (of the hands, wrists, elbows, shoulders, and knees) have been proposed. Although our studies showed that the inclusion of forefoot joints did not significantly improve the measurement range nor the measurement precision of the joint counts, these joints were frequently affected. This suggests that the assessment of omitted joints can be important when monitoring the disease trajectory of individual patients. Second, the interchangeable use of the ESR and CRP. Acute phase reactants are commonly used to quantify the severity of inflammation in RA. However, elevated concentrations of these reactants can be due to both the rheumatic disease and external factors like a patient’s age and sex. Hence, these external influences should be taken into account. We also showed that the DAS28‐CRP tends to yield lower scores than the DAS28‐ESR. Therefore, their scores cannot be used interchangeably. Third, the inclusion of the VAS-GH. Our analyses showed that the patient-reported VAS-GH has a poor reliability. Furthermore, its relatively low weighting within the DAS28 was decreased even further after weight optimization. Therefore, alternative, more reliable, patient‐reported outcome measures should be explored to incorporate the patient perspective on disease activity within the DAS28. This thesis shows that the DAS28 is a fairly reliable measure. While it gives good estimations of disease activity on a population level, inconsistencies can occur on an individual level. It is important to interpret DAS28 scores within their context, including both disease related and non‐disease related factors. Even though the DAS28 can guide the treatment process in clinical practice, a thorough inquiry of clinical and patient‐reported symptoms remains important

What does big data mean for personalized medicine?

Background The rapid and ongoing digitalization of society leads to an exponential growth of both structured and unstructured data, so-called Big Data. This wealth of information opens the door to the development of more sophisticated personalized health technologies. The analysis of log data from such applications and wearables provide the opportunity to personalize and to improve their persuasiveness and long-term use. However, aren’t there any boundaries when using this data as input for data-driven patient-centered feedback systems? If not technologically, then perhaps ethically? Can we simply gather and connect all the information we can find on the Internet and the patients’ health records without question, in order to increase the match between the system, its users and the context? Methods In our current research, we use big data sets from digital platforms and wearable technologies to support self-care, used by patients with chronic diseases like diabetes, heart failure, COPD and mental health problems and their caregivers. We apply machine learning techniques (algorithms) to identify patterns and user-profiles in the log data sets from real-time use of technologies across Europe. To better understand the implications of big data for our healthcare system we will investigate stakeholders’ perceptions regarding factors that are crucial for using and managing big data to support personalized medicine. We will perform focus groups and Q-sort studies to get a broader picture of how to use and interpret data from large and complex datasets in an effective, efficient, secure and safe way to design real-time, accurate, persuasive and personalized feedback systems. Results Monitoring the use of health technologies so far provided us insight in how patients can benefit from IT, for example, how users explore new applications or which elements of a website are (hardly) used. We learned from our research that it is a challenge to find a balance between data utility (personalizing feedback) and data security (how to store, share and use anonymized data in such a way that individual patients can benefit from it?). At this moment, advanced machine learning analysis are conducted to identify usage patterns and predictors for return on the long term. First results of these analyses are expected in May 2015. To better understand how big data impacts society, safety, healthcare and business, and what the critical factors are for using algorithms (machine learning) to personalize healthcare we plan to perform meta-level, boundary crossing research via focus groups and Q-sort studies. First results are expected in July 2015. Conclusion Data about IT usage and patient profiles provide new knowledge about how large and unstructured data sets can be used to improve the usability and persuasiveness of technologies and to personalize coaching of patients. Current findings in research indicate there is a gap between collecting big data and “interpreting and translating” this data into user-friendly, safe, unobtrusive and sense making feedback for patients. To estimate the relevance of the outcomes of data-analysis, a better understanding is needed of models that drive the algorithms to analyze big data

Exploring Fatigue Trajectories in Early Symptomatic Knee and Hip Osteoarthritis: 6-year Results from the CHECK Study

Objective: To examine whether different groups of fatigue trajectories can be identified among patients with early symptomatic osteoarthritis (OA) of the knee or hip, to describe the level of fatigue severity within each of these fatigue groups, and to investigate the involvement of age, sex, use of medication, comorbidity, and OA severity in relation to group membership. Methods: Six years of followup data on fatigue (Medical Outcomes Study Short Form-36 Vitality scale) came from the Cohort Hip and Cohort Knee (CHECK) cohort. Growth mixture modeling was applied to identify distinct fatigue trajectories as well as to take into account the effects of the patient characteristics. Results: Three fatigue trajectories were identified: low fatigue, low-to-high fatigue, and high fatigue. Latter trajectories showed considerable overlap from years 2 to 6, but differed in some patient characteristics in comparison with each other and in comparison with the low fatigue group. Comorbidity, medication use, and sex were significantly associated with the identified trajectories. Women, individuals with a comorbid disease, and those who used medication were more likely to follow a high fatigue trajectory. Conclusion: These findings suggest heterogeneous development of fatigue in the early OA population associated with varying patient characteristics. Further, this study shows that a considerable number of patients with OA already experience elevated levels of fatigue at an early stage of OA. While these findings need to be replicated, the identification of these trajectories with differing patient characteristics may warrant tailored psychosocial interventions for patients with elevated levels of fatigue

Technology to support integrated Antimicrobial Stewardship Programs: a user centered and stakeholder driven development approach

The rise of antimicrobial resistance (AMR) is a severe global health problem. Tackling this problem requires the prudent prescribing of antimicrobials. This is promoted through Antimicrobial Stewardship Programs (ASPs). In this position paper we describe i) how a socio-technical multidisciplinary approach (based on the CeHRes Roadmap) can be applied in the development and implementation of Antimicrobial Stewardship technologies and ii) how this approach can be of value to support Antimicrobial Stewardship in practice. The CeHRes Roadmap entails five different phases to explore and test how an eHealth technology can be tailored to the target group and successfully implemented in practice: i) contextual inquiry, ii) value specification, iii) design, iv) operationalization, v) evaluation. In this position paper we describe the lessons learned from research and practice to guide future developments of technology based ASP interventions. Since AMR is a huge wicked problem on a global level, it requires innovative methods and models to empower general public and professionals to be proactive rather than reactive in a digitalized world. We highlight how to combat the dangerous rise of antimicrobial resistance in the future

De Mythes en Missers over MRSA

Voor de preventie en controle van de verspreiding van MRSA is de medewerking van patiënten en in toenemende mate die van het algemene publiek van groot belang. Om beide partijen in staat te stellen hieraan een positieve bijdrage te leveren, moeten zij enige basiskennis hebben over MRSA. Maar wat weet men eigenlijk over MRSA? Over de gezondheidsrisico’s van een MRSA-besmetting en de mogelijkheden om die te verkleinen. Wat denkt men zelf te kunnen doen? Hoe zit het met MRSA en dieren? Om hier inzicht in te krijgen heeft het Centre for eHealth & Wellbeing Research recent onder 1590 mensen uit het algemeen publiek een vragenlijst uitgezet. Het bleek dat de kennis over MRSA over het algemeen zeer gering was en dat er een aantal misconcepties bestaan die ontkracht moeten worden om de verspreiding te kunnen beperken

How age and sex affect the erythrocyte sedimentation rate and C-reactive protein in early rheumatoid arthritis

BACKGROUND: The erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) are two commonly used measures of inflammation in rheumatoid arthritis (RA). As current RA treatment guidelines strongly emphasize early and aggressive treatment aiming at fast remission, optimal measurement of inflammation becomes increasingly important. Dependencies with age, sex, and body mass index have been shown for both inflammatory markers, yet it remains unclear which inflammatory marker is affected least by these effects in patients with early RA. METHODS: Baseline data from 589 patients from the DREAM registry were used for analyses. Associations between the inflammatory markers and age, sex, and BMI were evaluated first using univariate linear regression analyses. Next, it was tested whether these associations were independent of a patient's current disease activity as well as of each other using multiple linear regression analyses with backward elimination. The strengths of the associations were compared using standardized beta (beta) coefficients. The multivariate analyses were repeated after 1 year. RESULTS: At baseline, both the ESR and CRP were univariately associated with age, sex, and BMI, although the association with BMI disappeared in multivariate analyses. ESR and CRP levels significantly increased with age (beta-ESR = 0.017, p < 0.001 and beta-CRP = 0.009, p = 0.006), independent of the number of tender and swollen joints, general health, and sex. For each decade of aging, ESR and CRP levels became 1.19 and 1.09 times higher, respectively. Furthermore, women demonstrated average ESR levels that were 1.22 times higher than that of men (beta = 0.198, p = 0.007), whereas men had 1.20 times higher CRP levels (beta = -0.182, p = 0.048). Effects were strongest on the ESR. BMI became significantly associated with both inflammatory markers after 1 year, showing higher levels with increasing weight. Age continued to be significantly associated, whereas sex remained only associated with the ESR level. CONCLUSIONS: Age and sex are independently associated with the levels of both acute phase reactants in early RA, emphasizing the need to take these external factors into account when interpreting disease activity measures. BMI appears to become more relevant at later stages of the disease