BackgroundBig data research is important for studying uncommon diseases in real-world settings. Most big data studies in axial spondyloarthritis (axSpA) have been limited to populations identified with billing codes for ankylosing spondylitis (AS). axSpA is a more inclusive concept, and reliance on AS codes does not produce a comprehensive axSpA study population. The first objective was to describe our process for establishing an appropriate sample of patients with and without axSpA for developing accurate axSpA identification methods. The second objective was to determine the classification performance of AS billing codes against the chart-reviewed axSpA reference standard.MethodsVeteran Health Affairs clinical and administrative data, between January 2005 and June 2015, were used to randomly select patients with clinical phenotypes that represented high, moderate, and low likelihoods of an axSpA diagnosis. With chart review, the sampled patients were classified as Yes axSpA, No axSpA or Uncertain axSpA, and these classification assignments were used as the reference standard for determining the positive predictive value (PPV) and sensitivity of AS ICD-9 codes for axSpA.ResultsSix hundred patients were classified as Yes axSpA (26.8%), No axSpA (68.3%), or Uncertain axSpA (4.8%). The PPV and sensitivity of an AS ICD-9 code for axSpA were 83.3% and 57.3%, respectively.ConclusionsStandard methods of identifying axSpA patients in a large dataset lacked sensitivity. An appropriate sample of patients with and without axSpA was established and characterized for developing novel axSpA identification methods that are anticipated to enable previously impractical big data research.

Objective.Observational axial spondyloarthritis (axSpA) research in large datasets has been limited by a lack of adequate methods for identifying patients with axSpA, because there are no billing codes in the United States for most subtypes of axSpA. The objective of this study was to develop methods to accurately identify patients with axSpA in a large dataset.Methods.The study population included 600 chart-reviewed veterans, with and without axSpA, in the Veterans Health Administration between January 1, 2005, and June 30, 2015. AxSpA identification algorithms were developed with variables anticipated by clinical experts to be predictive of an axSpA diagnosis [demographics, billing codes, healthcare use, medications, laboratory results, and natural language processing (NLP) for key SpA features]. Random Forest and 5-fold cross validation were used for algorithm development and testing in the training subset (n = 451). The algorithms were additionally tested in an independent testing subset (n = 149).Results.Three algorithms were developed: Full algorithm, High Feasibility algorithm, and Spond NLP algorithm. In the testing subset, the areas under the curve with the receiver-operating characteristic analysis were 0.96, 0.94, and 0.86, for the Full algorithm, High Feasibility algorithm, and Spond NLP algorithm, respectively. Algorithm sensitivities ranged from 85.0% to 95.0%, specificities from 78.0% to 93.6%, and accuracies from 82.6% to 91.3%.Conclusion.Novel axSpA identification algorithms performed well in classifying patients with axSpA. These algorithms offer a range of performance and feasibility attributes that may be appropriate for a broad array of axSpA studies. Additional research is required to validate the algorithms in other cohorts.

A 56-year-old female was referred to us for evaluation of a prolonged partial thromboplastin time (PTT) without correction in a 1:1 mix (evidence of an inhibitor), markedly low factor IX activity (1% or less on multiple occasions), and factor IX inhibitor (ranging from 19 to 40 Bethesda units on multiple occasions). Her prothrombin time (PT) was normal. No other laboratory results were provided at the time of her referral. Her physician had treated her with cyclophosphamide and prednisone for a presumed acquired factor IX antibody in preparation for orthopedic surgery.The differential diagnosis of a PTT-based inhibitor typically includes heparin, a specific factor inhibitor directed against a clotting factor in the intrinsic pathway, or a lupus anticoagulant (LA). Heparin was excluded because she had no known exposure to therapeutic heparin, and the abnormalities had been noted in multiple samples drawn at different times, making sample contamination unlikely.Despite laboratory results suggestive of a coagulopathy, the patient had a negative bleeding history. Pertinent history included hypothyroidism, diabetes, and abnormal autoimmune serologic tests suggestive, but not diagnostic of systemic lupus erythematosis. Physical examination revealed obesity, but no organomegaly, adenopathy, or evidence of bleeding.Additional labwork (PT, PTT, thrombin time, lupus anticoagulant testing, and factor assays for factors VIII, IX, and XI) was performed to further evaluate the possibilities of a factor inhibitor versus a lupus anticoagulant. Although the previous testing appeared consistent with an acquired factor IX inhibitor, this was felt to be quite unlikely as a positive bleeding history would be expected in someone with such low factor IX activity.Testing was performed on platelet-poor plasma obtained by venipuncture and prepared according to standard practice. All testing was performed on Diagnostica Stago instruments (Asnieres, France).Lupus anticoagulant testing was performed according to published guidelines [1,2]. Screening tests included an LAsensitive PTT (PTT-LA, Diagnostica Stago, Asnieres, France) and dilute Russell Viper Venom time (dRVVT, CRYOcheck LA Check, Precision Biologics. Dartmouth, Nova Scotia). 1:1 mixing studies used equal parts of patient plasma and pooled normal plasma (CRYOcheck Pooled Normal Plasma, Precision Biologic, Dartmouth, Nova Scotia). Phospholipid-dependence was confirmed with the phospholipid neutralization procedure for PTT-based testing (CRYOcheck Platelet Lysate, Precision Biologic, Dartmouth, Nova Scotia) and the dRVVT confirmation test for dRVVTbased testing (CRYOcheck LA Sure, Precision Biologic, Dartmouth, Nova Scotia). Cutoffs for positivity were established by our laboratory.Routine factor assays for factors VIII, IX, and XI were performed using PTT-based (STA-PTT-A, Diagnostica Stago, Asnieres, France) one-stage clotting methods. Patient factor activity was determined by adding patient plasma to congenitally factor-deficient plasma (HRF, Raleigh, NC), performing a PTT on the...

Use of rheumatology care and DMARD was low for veterans with IA. DMARD exposure was strongly associated with rheumatology care use. Veterans in the general IA population living far from rheumatology sites accessed rheumatology care and bDMARD less frequently than veterans living close to rheumatology sites.

Sequential anterior ischaemic optic neuropathy was observed in a patient treated with a tumour necrosis factor a (TNF) inhibitor, adalimumab, for ankylosing spondylitis. He developed decreased visual acuity in the right eye after 17 months of treatment. Findings showed right optic disc oedema with haemorrhages and visual field defect. Adalimumab was discontinued and vision stabilised. After restarting adalimumab, he developed optic neuropathy in the left eye. Findings showed optic disc oedema, with haemorrhages and visual field changes in the left eye. Adalimumab may be associated with optic neuropathy; providers prescribing TNF inhibitors should be aware of optic neuropathy as a potential complication.