The benefits of collaboration: the Nordic Arthroplasty Register Association

The Nordic Arthroplasty Register Association (NARA) was established in 2007 by arthroplasty register representatives from Sweden, Norway and Denmark with the overall aim to improve the quality of research and thereby enhance the possibility for quality improvement with arthroplasty surgery. Finland joined the NARA collaboration in 2010.NARA minimal hip, knee and shoulder datasets were created with variables that all countries can deliver. They are dynamic datasets, currently with 25 variables for hip arthroplasty, 20 for knee arthroplasty and 20 for shoulder arthroplasty.NARA has published statistical guidelines for the analysis of arthroplasty register data. The association is continuously working on the improvement of statistical methods and the application of new ones.There are 31 published peer-reviewed papers based on the NARA databases and 20 ongoing projects in different phases. Several NARA publications have significantly affected clinical practice. For example, metal-on-metal total hip arthroplasty and resurfacing arthroplasty have been abandoned due to increased revision risk based on i.a. NARA reports. Further, the use of uncemented total hip arthroplasty in elderly patients has decreased significantly, especially in Finland, based on the NARA data.The NARA collaboration has been successful because the countries were able to agree on a common dataset and variable definitions. The collaboration was also successful because the group was able to initiate a number of research projects and provide answers to clinically relevant questions. A number of specific goals, set up in 2007, have been achieved and new one has emerged in the process

Linkage of murine (T,G)-A- -L-specific idiotypic determinants to the heavy chain constant region allotypic markers

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46735/1/251_2005_Article_BF01567784.pd

Traces of trauma – a multivariate pattern analysis of childhood trauma, brain structure and clinical phenotypes

Background: Childhood trauma (CT) is a major yet elusive psychiatric risk factor, whose multidimensional conceptualization and heterogeneous effects on brain morphology might demand advanced mathematical modeling. Therefore, we present an unsupervised machine learning approach to characterize the clinical and neuroanatomical complexity of CT in a larger, transdiagnostic context. Methods: We used a multicenter European cohort of 1076 female and male individuals (discovery: n = 649; replication: n = 427) comprising young, minimally medicated patients with clinical high-risk states for psychosis; patients with recent-onset depression or psychosis; and healthy volunteers. We employed multivariate sparse partial least squares analysis to detect parsimonious associations between combinations of items from the Childhood Trauma Questionnaire and gray matter volume and tested their generalizability via nested cross-validation as well as via external validation. We investigated the associations of these CT signatures with state (functioning, depressivity, quality of life), trait (personality), and sociodemographic levels. Results: We discovered signatures of age-dependent sexual abuse and sex-dependent physical and sexual abuse, as well as emotional trauma, which projected onto gray matter volume patterns in prefronto-cerebellar, limbic, and sensory networks. These signatures were associated with predominantly impaired clinical state- and trait-level phenotypes, while pointing toward an interaction between sexual abuse, age, urbanicity, and education. We validated the clinical profiles for all three CT signatures in the replication sample. Conclusions: Our results suggest distinct multilayered associations between partially age- and sex-dependent patterns of CT, distributed neuroanatomical networks, and clinical profiles. Hence, our study highlights how machine learning approaches can shape future, more fine-grained CT research