Diagnostic accuracy of the INSHI consensus case definition for the diagnosis of paradoxical tuberculosis-IRIS.
journal contributionposted on 31.03.2021, 08:17 by Cari Stek, Jozefien Buyze, Joris Menten, Charlotte Schutz, Friedrich Thienemann, Lisette Blumenthal, Gary Maartens, Tom Boyles, Robert J Wilkinson, Graeme Meintjes, Lutgarde Lynen
BACKGROUND: The diagnosis of paradoxical tuberculosis-associated immune reconstitution inflammatory syndrome (TB-IRIS) relies on characteristic clinical features synthesized as the International Network for the Study of HIV-associated IRIS (INSHI) case definition. There is no confirmatory laboratory test. SETTING: Site B HIV-TB clinic in Khayelitsha, Cape Town, South Africa. METHODS: Using data of participants with HIV-associated tuberculosis starting antiretroviral treatment from a prospective trial evaluating prednisone for TB-IRIS prevention, we applied latent class analysis to model a gold standard for TB-IRIS.The model-predicted probability of TB-IRIS for each participant was used to assess the performance of the INSHI case definition and compare its diagnostic accuracy with several adapted case definitions. RESULTS: Data for this analysis were complete for 217 participants; 41% developed TB-IRIS. Our latent class model included the following parameters: respiratory symptoms, night sweats, INSHI major criteria 1, 2, and 4, maximum CRP >90 mg/l, maximum heart rate >120/min, maximum temperature >37.7 0C, and pre-ART CD4 count <50 cells/μl. The model estimated a TB-IRIS incidence of 43% and had optimal goodness of fit (Χ2=337, p=1.0). The INSHI case definition displayed a sensitivity of 0.77 and a specificity of 0.86. Replacing all the minor INSHI criteria with objectives measures (CRP elevation, fever, and/or tachycardia) resulted in a definition with better diagnostic accuracy, with a sensitivity of 0.89 and a specificity of 0.88. CONCLUSION: The INSHI case definition identifies TB-IRIS with reasonable accuracy. Amending the case definition by replacing INSHI minor criteria with objective variables improved sensitivity without loss of specificity.