Background Electronic health records (EHRs) offer a wealth of observational data. Machine-learning (ML) methods are efficient at data extraction, capable of processing the information-rich free... Show moreBackground Electronic health records (EHRs) offer a wealth of observational data. Machine-learning (ML) methods are efficient at data extraction, capable of processing the information-rich free-text physician notes in EHRs. The clinical diagnosis contained therein represents physician expert opinion and is more consistently recorded than classification criteria components. Objectives To investigate the overlap and differences between rheumatoid arthritis patients as identified either from EHR free-text through the extraction of the rheumatologist diagnosis using machine-learning (ML) or through manual chart-review applying the 1987 and 2010 RA classification criteria. Methods Since EHR initiation, 17,662 patients have visited the Leiden rheumatology outpatient clinic. For ML, we used a support vector machine (SVM) model to identify those who were diagnosed with RA by their rheumatologist. We trained and validated the model on a random selection of 2000 patients, balancing PPV and sensitivity to define a cutoff, and assessed performance on a separate 1000 patients. We then deployed the model on our entire patient selection (including the 3000). Of those, 1127 patients had both a 1987 and 2010 EULAR/ACR criteria status at 1 year after inclusion into the local prospective arthritis cohort. In these 1127 patients, we compared the patient characteristics of RA cases identified with ML and those fulfilling the classification criteria. Results The ML model performed very well in the independent test set (sensitivity=0.85, specificity=0.99, PPV=0.86, NPV=0.99). In our selection of patients with both EHR and classification information, 373 were recognized as RA by ML and 357 and 426 fulfilled the 1987 or 2010 criteria, respectively. Eighty percent of the ML-identified cases fulfilled at least one of the criteria sets. Both demographic and clinical parameters did not differ between the ML extracted cases and those identified with EULAR/ACR classification criteria. Conclusions With ML methods, we enable fast patient extraction from the huge EHR resource. Our ML algorithm accurately identifies patients diagnosed with RA by their rheumatologist. This resulting group of RA patients had a strong overlap with patients identified using the 1987 or 2010 classification criteria and the baseline (disease) characteristics were comparable. ML-assisted case labeling enables high-throughput creation of inclusive patient selections for research purposes. Show less
Rheumatoid Arthritis (RA) is a chronic autoimmune disorder that affects approximately 0.5-1% of the population. RA leads to inflammation of the joints and leads to irreversible destruction of... Show moreRheumatoid Arthritis (RA) is a chronic autoimmune disorder that affects approximately 0.5-1% of the population. RA leads to inflammation of the joints and leads to irreversible destruction of joints with subsequently to significant morbidity, disability for patients. Remarkably the amount of joint destruction differs substantially between patients. Currently medication mainly aims at diminishing inflammation. Because joint destruction seems to develop even when the inflammation level is low, since RA is still not curable and finally because joint destruction is irreversible, joint destruction becomes an increasingly important feature in patients with RA. In this dissertation, different aspects of joint destruction in RA were investigated. To highlight the most important findings: first of all the heritability of joint destruction was demonstrated. In addition several genes were further studied. Two genetic findings were of most interest: IL2RA was associated with joint destruction. Since there is already medication available that aims at IL2RA, this finding creates a new treatment option for the nearby future. More intriguingly, SPAG16, an thus far with sperm association gene, appears to be relevant in joint destruction in RA as well. This put a totally new light on the gene. Naturally, most of the newly discovered associations will need further research before they can be applied in clinical practice. Show less
