Schmerzregister und verwandte Datensammlungen: Eine Übersichtsarbeit

[DNVF Memorandum: Health Services Research in the Last Year of Life]

This memorandum outlines current issues concerning health services research on seriously ill and dying people in the last year of their lives as well as support available for their relatives. Patients in the last phase of life can belong to different disease groups, they may have special characteristics (e. g., people with cognitive and complex impairments, economic disadvantage or migration background) and be in certain phases of life (e. g., parents of minor children, (old) age). The need for a designated memorandum on health services research in the last year of life results from the special situation of those affected and from the special features of health services in this phase of life. With reference to these special features, this memorandum describes methodological and ethical specifics as well as current issues in health services research and how these can be adequately addressed using quantitative, qualitative and mixed methods. It has been developed by the palliative medicine section of the German Network for Health Services Research (DNVF) according to the guidelines for DNVF memoranda.

Machine-learning-based knowledge discovery in rheumatoid arthritis-related registry data to identify predictors of persistent pain

Early detection of patients with chronic diseases at risk of developing persistent pain is clinically desirable for timely initiation of multimodal therapies. Quality follow-up registries may provide the necessary clinical data; however, their design is not focused on a specific research aim, which poses challenges on the data analysis strategy. Here, machine-learning was used to identify early parameters that provide information about a future development of persistent pain in rheumatoid arthritis (RA). Data of 288 patients were queried from a registry based on the Swedish Epidemiological Investigation of RA. Unsupervised data analyses identified the following 3 distinct patient subgroups: low-, median-, and high-persistent pain intensity. Next, supervised machine-learning, implemented as random forests followed by computed ABC analysis-based item categorization, was used to select predictive parameters among 21 different demographic, patient-rated, and objective clinical factors. The selected parameters were used to train machine-learned algorithms to assign patients pain-related subgroups (1000 random resamplings, 2/3 training, and 1/3 test data). Algorithms trained with 3-month data of the patient global assessment and health assessment questionnaire provided pain group assignment at a balanced accuracy of 70%. When restricting the predictors to objective clinical parameters of disease severity, swollen joint count and tender joint count acquired at 3 months provided a balanced accuracy of RA of 59%. Results indicate that machine-learning is suited to extract knowledge from data queried from pain- and disease-related registries. Early functional parameters of RA are informative for the development and degree of persistent pain.