The reliability of diagnostic coding and laboratory data to identify tuberculosis and nontuberculous mycobacterial disease among rheumatoid arthritis patients using anti-tumor necrosis factor therapy.

Purpose: Anti-tumor necrosis factor-alpha (anti-TNF) therapies are associated with severe mycobacterial infections in rheumatoid arthritis patients. We developed and validated electronic record search algorithms for these serious infections.

Methods: The study used electronic clinical, microbiologic, and pharmacy records from Kaiser Permanente Northern California (KPNC) and the Portland Veterans Affairs Medical Center (PVAMC). We identified suspect tuberculosis and nontuberculous mycobacteria (NTM) cases using inpatient and outpatient diagnostic codes, culture results, and anti-tuberculous medication dispensing. We manually reviewed records to validate our case-finding algorithms.

Results: We identified 64 tuberculosis and 367 NTM potential cases, respectively. For tuberculosis, diagnostic code positive predictive value (PPV) was 54% at KPNC and 9% at PVAMC. Adding medication dispensings improved these to 87% and 46%, respectively. Positive tuberculosis cultures had a PPV of 100% with sensitivities of 79% (KPNC) and 55% (PVAMC). For NTM, the PPV of diagnostic codes was 91% (KPNC) and 76% (PVAMC). At KPNC, ≥ 1 positive NTM culture was sensitive (100%) and specific (PPV, 74%) if non-pathogenic species were excluded; at PVAMC, ≥1 positive NTM culture identified 76% of cases with PPV of 41%. Application of the American Thoracic Society NTM microbiology criteria yielded the highest PPV (100% KPNC, 78% PVAMC).

Conclusions: The sensitivity and predictive value of electronic microbiologic data for tuberculosis and NTM infections is generally high, but varies with different facilities or models of care. Unlike NTM, tuberculosis diagnostic codes have poor PPV, and in the absence of laboratory data, should be combined with anti-tuberculous therapy dispensings for pharmacoepidemiologic research.