11The use of data from early arthritis clinics for clinical research
Section snippets
Typical profiles of patients enrolled in early arthritis cohorts
The typical patient in an early arthritis cohort is one with a recent-onset joint swelling that has sought medical help because of considerable pain. Signs of inflammation are not necessarily present in this patient. The types of arthritis detected and enrolled in the cohorts clearly depend on how the clinics are situated on the basis of a supporting general practitioner (GP) network, or how the healthy population is directly made aware of the problem. If, for example, patients present directly
Where can early arthritis data help us?
Before we can discuss what early arthritis cohorts can tell us, we need to be aware that in early arthritis some immunological abnormalities occur years before symptom onset [10]. This has led to the idea that patients can never be seen before all steps in disease development occur. Thus in order to study as many sequelae in disease pathogenesis as possible, it is beneficial to get potential patients at the earliest possible time points.
But what are the delays to treatment? It is only partly
Logistic regression analysis
The traditional statistical approach to studies aiming at identifying predictors of a dichotomous outcome is the logistic regression model. In these models, the probability of occurrence of a dichotomous event is predicted by fitting independent variables to a logistic curve. The event is traditionally coded as 0/1. Logistic regression models are part of the family of generalized linear models, which can handle independent variables that are continuous or categorical. Predictors can be
Circularity of models
In using data from patients with early disease to predict rheumatoid arthritis, one central limitation is inherent to any model, namely the problem of circularity. Put simply, circularity in this context is present as soon as the status of the dependent variable (i.e. the presence or absence of the outcome) is inherently influenced by the levels of the baseline variables. A typical example is the use of RA as the dependent variable by defining it using the traditional 1987 classification
The need for ‘official’ criteria and the value of joint forces
In the past there have been several reports from individual centres providing algorithms, rules, and criteria to diagnose RA early in its course *[14], *[15], [16], *[17], *[18]. Each individual in these reports has particular merits and helps to better understand signs of a potentially persistent and erosive disease. So far, all these predictive models have suffered from the same problem of not getting widespread acceptance or application in an international community, and, with some
Pathogenetic considerations
Another major achievement from the use of data from early arthritis clinics is the growing insight into the pathogenesis of disease. A nice example is that in various early arthritis registries the association between cigarette smoking and RA has been reported [22]. Possible links between smoking and the development of RA have been demonstrated, especially in seropositive RA [23], [24]. Subsequently it has been demonstrated that the association of smoking was specific for patients with certain
Future prospects
Early arthritis clinics have helped considerably to advance the field of arthritis research at a time when it was recognized that following the pyramid approach of watchful waiting without risking therapeutic harm was no longer a respectable strategy. Nevertheless, the process of starting an early arthritis clinic is cumbersome and has taken several years for many investigators until the childhood diseases of these cohorts were subsisted. Although until now several publications have come from
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