Challenges in the diagnosis of meningitis in low‐resource settings

doi: 10.1111/j.1365-3156.2010. 02651.xDear Sirs,We read with interest the report by Cohen et al. (2010)dealing with the diagnosis of meningitis in Malawi, inwhich cryptococcal meningitis (CM) and tuberculousmeningitis (TBM) were the most common speciﬁc diag-noses. The authors determined that these conditionscould not be reliably distinguished on the basis ofclinical and basic laboratory information alone andconcluded that better diagnostic tests are needed. Weagree with this conclusion, but wish to draw attention tothree issues in studying diagnostic strategies in low-resource settings.The ﬁrst is the importance of selecting easily measuredpredictors for analysis. Of the ﬁve variables identiﬁed inthe multivariate logistic regression model, only threewere retained in the classiﬁcation and regression tree(CART) model. The authors do not specify why theGlasgow coma score and temperature were not main-tained in the algorithm, despite the fact that temperaturewas associated with the largest s coefﬁcient (s 3.34;Odds ratio 28.01, 95% conﬁdence interval 3.1–251.69). This may have been a result of the CART methodologyitself and represents a caveat to its interpretation (Muller& Mockel 2008). Moreover, 103/263 patients with CSFabnormalities were on antiretroviral therapy (ART) atpresentation, suggesting that some cases of meningitismay have been attributable to the immune reconstitutioninﬂammatory syndrome. Unfortunately, no details aregiven about the duration of ART before presentation, orabout whether this was analysed as a potential discrim-inator between CM and TBM.Second, miscategorisation of patients affects the inter-pretation of diagnostic accuracy. Diagnosis of TBM isdifﬁcult in any setting, and it is likely that some of the75 patients with pyogenic (with a median of 81%lymphocytes in their CSF), lymphocytic, and unspeci-ﬁed meningitis actually had TBM. Previous studies insub-Saharan Africa found that the correlation betweenantemortem diagnosis and autopsy ﬁndings is poor (e.g.the clinical diagnosis of TB in adults had a sensitivity of43–80% and a speciﬁcity of only 67–76%) (Rana et al.2000; Murray et al. 2007) and that unsuspected treatableinfections are not uncommon (Lucas et al. 1994). Shortof the systematic implementation of autopsy studies inlow-resource settings, there is a need in Africa forprevalence studies that employ all relevant gold-standard investigations – not just those that are locallyavailable.Third is the problem of the validation of the diagnosticstrategy. Although it is not explicitly stated, it appears thatthe CART model was only applied to the 158 patientsmeeting the authors deﬁnition of TBM or CM, yielding asensitivity of 92% and speciﬁcity of 58% for the diagnosisof CM. However, there is no way for a clinician to tell whothese patients are at the time of clinical presentation. Themodel should be applied to the entire group of 563 patientspresenting with suspected meningitis. Had this beenperformed, the CART model would have been lessdiscriminatory still.Hence, the need for improved diagnostics. Of allavailable or projected diagnostic tests to distinguish CMand TBM, assays aimed at detection of cryptococcalantigens are probably the most discriminatory single test,assuming co-infected patients are rare. Unfortunately, thecurrent form of these assays usually requires refrigerationor freezing of reconstituted reagents, extensive sampleprocessing, which includes boiling of samples and inacti-vation of competing proteins for up to one hour, andcomplex laboratory manipulations. There is an urgentneed for simple, inexpensive and robust cryptococcalantigen detection assays, ideally using non-invasive sam-ples such as urine.Cedric P. Yansouni