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Purpose The aim of this study was to develop and validate case-finding algorithms for granulomatosis with polyangiitis (Wegener's, GPA), microscopic polyangiitis (MPA), and eosinophilic GPA (Churg–Strauss, EGPA). Methods Two hundred fifty patients per disease were randomly selected from two large healthcare systems using the International Classification of Diseases version 9 (ICD9) codes for GPA/EGPA (446.4) and MPA (446.0). Sixteen case-finding algorithms were constructed using a combination of ICD9 code, encounter type (inpatient or outpatient), physician specialty, use of immunosuppressive medications, and the anti-neutrophil cytoplasmic antibody type. Algorithms with the highest average positive predictive value (PPV) were validated in a third healthcare system. Results An algorithm excluding patients with eosinophilia or asthma and including the encounter type and physician specialty had the highest PPV for GPA (92.4%). An algorithm including patients with eosinophilia and asthma and the physician specialty had the highest PPV for EGPA (100%). An algorithm including patients with one of the diagnoses (alveolar hemorrhage, interstitial lung disease, glomerulonephritis, and acute or chronic kidney disease), encounter type, physician specialty, and immunosuppressive medications had the highest PPV for MPA (76.2%). When validated in a third healthcare system, these algorithms had high PPV (85.9% for GPA, 85.7% for EGPA, and 61.5% for MPA). Adding the anti-neutrophil cytoplasmic antibody type increased the PPV to 94.4%, 100%, and 81.2% for GPA, EGPA, and MPA, respectively. Conclusion Case-finding algorithms accurately identify patients with GPA, EGPA, and MPA in administrative databases. These algorithms can be used to assemble population-based cohorts and facilitate future research in epidemiology, drug safety, and comparative effectiveness. Copyright © 2016 John Wiley & Sons, Ltd.

Objective This report describes the case management of musculoskeletal disorders for an employee in a college work environment using both chiropractic care and applied ergonomics. Clinical Findings A 54-year-old male office worker presented with decreased motor function in both wrists; intermittent moderate-to-severe headaches; and pain or discomfort in the neck, both shoulders, left hand and wrist, and lumbosacral region resulting from injuries sustained during recreational soccer and from excessive forces and awkward postures when interacting with his home and office computer workstations. Intervention and Results Ergonomic training, surveillance, retrofitted equipment with new furniture, and an emphasis on adopting healthy work-style behaviors were applied in combination with regular chiropractic care. Baseline ergonomic job task analysis identified risk factors and delineated appropriate control measures to improve the subject's interface with his office workstation. Serial reevaluations at 3-month, 1-year, and 2-year periods recorded changes to the participant's pain, discomfort, and work-style behaviors. At end of study and relative to baseline, pain scale improved from 4/10 to 2/10; general disability improved from 4 to 0; and hand grip strength (pounds) increased from 20 to 105 (left) and 45 to 100 (right). Healthy work habits and postures adopted in the 3-month to 1-year period regressed to baseline exposures for 3 of 6 risk priorities identified in the ergonomic job task analysis. Conclusion The patient responded positively to the intervention of chiropractic care and applied ergonomics.

The aim of this study was to develop and validate case-finding algorithms for granulomatosis with polyangiitis (Wegener's, GPA), microscopic polyangiitis (MPA), and eosinophilic GPA (Churg-Strauss, EGPA).Two hundred fifty patients per disease were randomly selected from two large healthcare systems using the International Classification of Diseases version 9 (ICD9) codes for GPA/EGPA (446.4) and MPA (446.0). Sixteen case-finding algorithms were constructed using a combination of ICD9 code, encounter type (inpatient or outpatient), physician specialty, use of immunosuppressive medications, and the anti-neutrophil cytoplasmic antibody type. Algorithms with the highest average positive predictive value (PPV) were validated in a third healthcare system.An algorithm excluding patients with eosinophilia or asthma and including the encounter type and physician specialty had the highest PPV for GPA (92.4%). An algorithm including patients with eosinophilia and asthma and the physician specialty had the highest PPV for EGPA (100%). An algorithm including patients with one of the diagnoses (alveolar hemorrhage, interstitial lung disease, glomerulonephritis, and acute or chronic kidney disease), encounter type, physician specialty, and immunosuppressive medications had the highest PPV for MPA (76.2%). When validated in a third healthcare system, these algorithms had high PPV (85.9% for GPA, 85.7% for EGPA, and 61.5% for MPA). Adding the anti-neutrophil cytoplasmic antibody type increased the PPV to 94.4%, 100%, and 81.2% for GPA, EGPA, and MPA, respectively.Case-finding algorithms accurately identify patients with GPA, EGPA, and MPA in administrative databases. These algorithms can be used to assemble population-based cohorts and facilitate future research in epidemiology, drug safety, and comparative effectiveness. Copyright © 2016 John WileySons, Ltd.