A Bayesian analysis of the association between Leukotriene A4 Hydrolase genotype and survival in tuberculous meningitis
Tuberculous meningitis ( TBM ) is the most severe form of tuberculosis . Despite effective antimicrobial therapy , it results in 20–25% mortality in HIV-negative individuals and ~40% mortality in HIV-positive individuals ( Stadelman et al . , 2020; Thwaites et al . , 2013 ) . A long-standing hypothesis that an excessive intracerebral inflammatory response underlies TBM mortality ( Shane and Riley , 1953 ) led to multiple trials of adjunctive anti-inflammatory treatment with corticosteroids ( e . g . dexamethasone ) ( Prasad et al . , 2016; Wilkinson et al . , 2017 ) . Findings from a randomised controlled trial ( RCT ) in Vietnam that adjunctive dexamethasone improved survival by ~30% led to it becoming standard of care treatment ( Thwaites et al . , 2004 ) . However , the modest benefit of adjunctive dexamethasone treatment suggested a heterogeneity in glucocorticoid-responsiveness ( Donald and Schoeman , 2004; Schoeman and Donald , 2013 ) . Findings in a zebrafish model of TB provided a testable hypothesis for a mechanism underlying this heterogeneity ( Thwaites et al . , 2004; Tobin et al . , 2012; Tobin et al . , 2010 ) . The zebrafish findings suggested that either deficiency or excess of leukotriene A4 hydrolase ( LTA4H ) , a key enzyme that regulates the balance of pro- and anti-inflammatory eicosanoids , increase susceptibility to TBM for opposite reasons - too little or too much inflammation ( Tobin et al . , 2012; Tobin et al . , 2010 ) . It became possible to test the prediction when a common human functional LTA4H promoter variant ( rs17525495 ) was identified comprising a C/T transition that controlled LTA4H expression , with the T allele causing increased expression ( Tobin et al . , 2012 ) . A retrospective analysis of patient LTA4H rs17525495 genotypes in the Vietnam dexamethasone RCT cohort confirmed the prediction ( Thwaites et al . , 2004; Tobin et al . , 2012 ) . Among HIV-negative patients , the survival benefit of dexamethasone was restricted to patients with the hyper-inflammatory TT genotype , with CC patients potentially harmed by this treatment ( Tobin et al . , 2012 ) . These findings supported the model that mortality from TBM was due to two distinct inflammatory states , and that LTA4H genotype might be a critical determinant of inflammation and consequently of the response to adjunctive anti-inflammatory treatment . If true , then personalized genotype-directed adjunctive glucocorticoid treatment would be warranted , with the drug given only to TT patients . This would be particularly important given the possible harm to the hypo-inflammatory CC group , as well as the adverse effects of long-term high dose treatment with a broadly acting immunosuppressant . To further these findings , two new studies of the association of LTA4H genotype with TBM survival in HIV-negative patients were performed in Vietnam and Indonesia , respectively ( Thuong et al . , 2017; van Laarhoven et al . , 2017 ) . Because glucocorticoid adjunctive therapy had become standard of care owing to the benefit observed in the randomised controlled trial ( Thwaites et al . , 2004 ) , all patients received it in both studies . Therefore , the prediction that could be tested was that TT mortality is less than CC+CT mortality . Whereas the Vietnam study confirmed this prediction , the Indonesia study did not . The Vietnam cohort had an overall mortality of 18 . 8% , similar to that reported in the literature ( Stadelman et al . , 2020 ) . A striking feature of the Indonesia cohort was its more than two-fold increased mortality in comparison with the Vietnam cohort . Moreover , most of the Indonesia cohort deaths occurred early with a median time to death of eight days versus 50 days in Vietnam ( Table 1 ) . This high early mortality raised the possibility that the impact of the LTA4H variant differs by disease severity , and may not be relevant in more severe disease ( Fava and Schurr , 2017 ) . If so , then the effects of the LTA4H genotype were being masked by the preponderance of extremely severe cases in the Indonesia cohort ( Fava and Schurr , 2017 ) . Both studies used , as the primary metric of significance testing , Cox regression modelling , an approach that assumes that the ratio of hazard rates between groups is constant throughout the observed period ( Bradburn et al . , 2003; Greenland et al . , 2016 ) . Therefore , this analytical method could miss important differences in these studies of TBM , a disease which can present acutely yet have a prolonged time-course with vastly differing mortality risks over time ( Thuong et al . , 2017; Thwaites et al . , 2013; van Laarhoven et al . , 2017 ) . Moreover , testing the hypothesis that LTA4H effects are limited to specific disease severity grades requires subgroup analysis . The use of frequentist statistics would limit the ability to perform such subgroup analyses because the penalties it sets for multiple comparisons do not reflect real-world situations ( Gelman and Loken , 2013; Smith and Ebrahim , 2002 ) . Bayesian analysis is ideally suited to simultaneously estimate treatment effects in multiple subgroups because it results in different interactions with the number of results obtained which are much less problematic than those arising in frequentist analysis ( Box 1 and Appendix 1 ) . Therefore , we used a Bayesian approach to analyse data from the two cohorts ( Gelman and Loken , 2013; MacKay , 2003; Zampieri et al . , 2020 ) ( Appendix 2 ) . Bayesian analysis also enables the detection of significant differences that might be limited to just a part of the time-course and therefore would allow analysis to be independent of the kinetics of death in the Vietnam and Indonesia cohorts . Medical management decisions are guided by an assessment of the probabilities of outcome . In TBM , the question faced by the clinician is how likely is glucocorticoid therapy going to help or harm a patient . Bayesian paradigms , unlike frequentist ones , understand probability in a real-world way , using it to indicate the plausibility of a particular conclusion ( MacKay , 2003; Zampieri et al . , 2020 ) . Finally , relevant to this re-analysis of completed clinical studies , Bayesian paradigms have less potential for bias arising from post-hoc analysis ( Appendix 1 ) . The severity grade-specific analyses , coupled with temporal analyses made possible by Bayesian methods , reveal that the LTA4H TT genotype is associated with survival in both cohorts .
The age range of patients was similar in the Vietnam and Indonesia cohorts with Indonesia patients tending to be younger ( Table 1 ) . We compared the cohorts for disease severity on presentation using both measures used in the studies , the Glasgow Coma Score ( GCS ) and the modified British Medical Research Council TBM grade ( TBM grade ) ( Box 2 ) . The Indonesia cohort had more severe disease on presentation by both measures ( Table 1 and Figure 1 ) . We used the TBM grade for further analyses because it divides patients into just three severity groups , making comparisons more feasible . Importantly , it also provides clinically relevant separation of GCS 15 patients , the most highly represented in both cohorts ( Figure 1 ) , into those with and without focal neurological signs . Because the Indonesia cohort was skewed towards more severe disease on presentation , one explanation for the lack of an LTA4H genotype association with survival in Indonesia was that the underlying association is overridden by severe disease , a strong independent correlate of mortality ( Fava and Schurr , 2017; Wang et al . , 2019 ) . Indeed , a detailed comparison of the Vietnam and Indonesia cohorts showed that 76% of Indonesia patients presented in Grade 2 versus 47% of the Vietnam patients ( Table 1 ) . This increase was driven entirely by a shift from Grade 1 ( 9% vs 37% in Vietnam ) . The cohorts had nearly equal proportions of Grade 3 patients ( 15% each ) . Therefore , the ~2 fold-increased overall mortality in Indonesia could be largely accounted for by a corresponding increase of Grade 2 patients ( 1 . 6 fold higher than Vietnam ) . Disease severity as assessed by BMRC Grade or GCS at presentation is also a strong predictor of earlier death ( Fava and Schurr , 2017; Wang et al . , 2019 ) , and the Indonesia patients died sooner ( median time to death 8 days versus 50 days in Vietnam ) ( Table 1 ) . If LTA4H genotype associates with survival most strongly in mild disease , then the association seen for the entire Vietnam cohort ( Thuong et al . , 2017 ) should be strongest in Grade 1 patients . We tested this prediction with Bayesian analysis using a prior that was intentionally uninformative and very wide , while still being centred on clinician-expected survival curves and hazard rates . We included additional parameters to allow for the possibility that not all patient deaths would be linked to the same mode of death . Importantly , the model and priors used allowed us to incorporate our pre-existing knowledge that mortality risk to a population of TBM patients varies smoothly with time , rather than occurring at a number of discrete times common to all patients as is implied by the maximum likelihood solution illustrated by a Kaplan-Meier plot . The details of the model and the priors are in Appendix 2 . The definitions of terms and abbreviations used throughout the paper are in Box 3 . In the original Vietnam study , the TT genotype was associated with survival and the CC and CT genotypes had similarly increased mortality over TT ( Thuong et al . , 2017 ) , so we compared TT survival to that of CT and CC combined ( non-TT ) . We first confirmed that the TT variant distribution did not differ by grade on presentation ( Table 2 ) . Bayesian analysis confirmed that the TT genotype was associated with a survival advantage in the overall cohort ( Figure 2A ) . Moreover , the analysis revealed that this survival advantage manifested early and persisted over most of the observation period ( Figure 2A ) . The Bayesian method enables a more detailed evaluation of mortality risk over time through a hazard rate analysis . This analysis reinforced the significantly higher hazard rate for non-TT starting at 4 days and persisting through 120 days ( Figure 2A , inset ) . As predicted by the earlier analysis , the survival probability between CC and CT was not significantly different at any time during the 270 day observation period ( data not shown ) . When we stratified the Vietnam patients by grade and LTA4H genotype , we got a surprising result . The LTA4H TT association with survival was barely present in Grade 1 , a bit more in Grade 2 , and strongest in Grade 3 where it reached significance ( Figure 2B–D ) . Similar to the overall cohort , the Grade 3 increased survival probability for TT also manifested early and persisted throughout ( Figure 2D ) . Hazard rate analysis again showed that non-TT patients had a greatly increased risk of mortality very early ( Figure 2D , inset ) . The non-TT over TT hazard rate ratio peaked at 16 on day 3 ( Figure 2D , inset ) . This early high peak dropped only gradually over time; it was 5 at day 100 and remained >1 throughout ( data not shown ) . Again survival of CC and CT patients was not significantly different at any time during the 270 day observation period ( data not shown ) . In sum , our analysis revealed that in Vietnam , LTA4H TT was associated with survival , not in mild disease as suggested earlier ( Fava and Schurr , 2017 ) , but rather in the most severe disease grade . In fact , the bulk of the overall association was being driven by Grade 3 patients who constituted only 15 . 9% of the cohort ( Table 1 ) . Non-TT patients were at greatest risk of dying within days of admission , a risk that diminished with time but remained greater than the TT patients throughout . Bayesian analysis found that , in the overall Indonesia cohort , survival of the TT patient group was higher than non-TT though falling short of significance ( maximum probability 0 . 92 ) ( Figure 2E ) . Moreover , this analysis detected that the hazard rate for non-TT patients was higher than TT patients from day 1 to day 13; the non-TT over TT ratio reached significance on days 2 and 3 , at which time the non-TT hazard rate was twice that of TT . Thus , while the TT beneficial effect was weaker than in Vietnam ( compare Figure 2E–A ) , hazard rate analysis showed that as in Vietnam , TT benefit manifested early ( Figure 2E inset , compare to Figure 2A inset ) . For the grade-stratified cohorts , the analysis of Grade 1 patients was uninformative as there was only one TT patient in this group who survived throughout ( Table 2 and Figure 2F; also see Appendix 2 , section 4 ) . In Grade 2 , the TT survival effect was significant , in contrast to Grade 2 Vietnam ( compare Figure 2G–C ) . Rather , the pattern of the Grade 2 association was similar to Vietnam Grade 3 with a significant early TT survival benefit . As in Vietnam , the TT survival benefit started within days with an early hazard rate peak for the non-TT group . In Grade 3 , the LTA4H TT effect was again absent ( Figure 2H ) . As in Vietnam , survival of CC and CT patients was not significantly different at any time during the analysis for the overall cohort and for each of the three grades ( data not shown ) . Since Grade 2 patients constitute the bulk of the Indonesia cohort ( 75 . 5% ) , why was the LTA4H genotype effect in this grade not reflected in the overall cohort analysis ? This was particularly curious given that in Vietnam the overall significant effect was being driven very substantially by Grade 3 patients who constituted only 15 . 9% of the cohort . We saw that the LTA4H TT benefit was weaker and less prolonged in Indonesia Grade 2 than in Vietnam Grade 3 ( compare Figure 2G to D ) . Non-TT patients had similar mortality in Indonesia Grade 2 and Vietnam Grade 3 ( compare Figure 2G to D ) . Thus , Bayesian analysis revealed an LTA4H TT survival association in Indonesia as well . The association being only in Grade 2 and not in Grade 3 patients suggested an upper limit of disease severity for its efficacy . Why might LTA4H effects stop after Grade 2 in Indonesia ? A closer comparison of the overall survival between the two sites suggested that there were mortality differences between the two cohorts for all grades combined and in grade-for-grade comparisons that were LTA4H-independent ( Figure 2 ) . For instance , Indonesia Grade 2 non-TT patients had a mortality risk similar to that of Vietnam Grade 3 non-TT patients ( compare Figures 2D and 1G ) . We confirmed by Bayesian analysis that within each cohort , mortality risk increased with grade severity ( Thuong et al . , 2017; van Laarhoven et al . , 2017; Figure 3 ) . From early on , Grade 1 survival was significantly greater than Grade 2 , which was significantly greater than Grade 3 ( Figure 3A and B ) . The hazard rate ratios highlighted that while the risk of increased mortality with higher grade was highest early , it was sustained long-term ( Figure 3C and D ) . Importantly , both the survival and hazard rate analyses again pointed to an increased grade-for-grade risk of mortality in Indonesia over Vietnam . Next , we directly compared grade-for-grade mortality between the cohorts . Vietnam survival probability was higher in all grades , and significantly so for Grades 2 and 3 with survival gaps of 18% and 24% , respectively ( Figure 4A , C and E ) . Similarly , hazard rates did not differ significantly between the cohorts in Grade 1 ( Figure 4B ) , but there was a significant and substantial increase in early hazard rates for Indonesia Grades 2 and 3 ( Figure 4D and F ) . We ruled out the possibility that this increased mortality was because the Indonesia patients had more severe disease within each grade as reflected in the GCS . While they had somewhat more severe disease in Grade 2 , in Grade 3 where the difference was the most pronounced , they had milder disease ( Figure 1 ) . In sum , these analyses show that the inherent higher mortality associated with more severe disease on presentation was sharply accentuated in Indonesia . Indonesia grade 2 patients experienced similar mortality risk as Vietnam Grade 3 patients with the Indonesia Grade 3 patients experiencing far greater mortality . This higher mortality could potentially explain the finding that the LTA4H TT survival advantage did not extend to Indonesia Grade 3 patients . It may be that the TT genotype advantage , in response to corticosteroid treatment , is overridden by other factors that cause extreme mortality .
The finding in two independent cohorts in Vietnam collected from 2001 to 2004 and 2011–2015 that a common functional human variant was associated with responsiveness to adjunctive glucocorticoid treatment in TBM represented an ideal example of pharmacogenomics , coming as it did from mechanistic understanding of the underlying reason ( Sadée and Dai , 2005; Thuong et al . , 2017; Tobin et al . , 2012 ) . The potential importance of these findings was heightened with subsequent pathway analyses in the zebrafish ( where the LTA4H link to disease severity was discovered ) identifying more specific , inexpensive , steroid-sparing drugs that might circumvent LTA4H-mediated pathology ( Roca and Ramakrishnan , 2013; Roca et al . , 2019 ) . It was disappointing and puzzling when a similar study of TBM patients in Indonesia failed to find a significant association ( van Laarhoven et al . , 2017 ) . In a commentary published alongside the Indonesia and second Vietnam studies , Fava and Schurr suggested that background genetic differences between the cohorts might account for the lack of an LTA4H association ( Fava and Schurr , 2017 ) . However , noting that the Indonesia cohort presented with more severe disease and died earlier , they postulated that rather than invoking an unknown genetic phenomenon , it was more likely that the beneficial effects of dexamethasone for the LTA4H TT genotype were nullified in more severe disease . This concept of the disease being too severe for outcomes to be influenced by intervention has precedent . The beneficial effect of fluoroquinolones in TBM is present in Grade1 or 2 disease , but lost in Grade 3 disease ( Thwaites et al . , 2011 ) . To test if increased grade severity on presentation in Indonesia was sufficient to account for the loss of the LTA4H TT survival effect , a grade-for-grade analysis within and between the two cohorts was necessary , while taking into account the temporal changes in mortality risk over the several months-long observation period . Moreover , both the magnitude and the time to mortality seemed to vary dramatically between the cohorts ( Table 1 ) . We realized that Bayesian methods were ideal for these complex and intrinsically multivariate comparisons and possibly the only path to a biologically and clinically relevant understanding . When we analysed the Vietnam cohort separated by grade severity , we saw that there was indeed a relationship between LTA4H effect and grade severity . However , it was opposite to what had been predicted ( Fava and Schurr , 2017 ) . The TT survival benefit became stronger not weaker with increasing grade . The analysis also suggested the reason for this . Patients with mild disease on presentation did well regardless of LTA4H genotype , so that the added benefit of the TT genotype was small . In Indonesia , the LTA4H TT effect was present in Grade 2 and completely absent in Grade 3 , a finding that was perplexing until we analysed the LTA4H-independent mortality risk of the two cohorts . The Indonesia cohort did not just contain a greater number of patients presenting at a more severe grade as had been noted earlier ( Fava and Schurr , 2017 ) , but patients in this cohort had substantially higher early mortality than their Vietnam counterparts even grade-for-grade ( Table 1 ) . Indonesia patients had nearly twice the mortality risk in Grade 2 and ~50% higher risk in Grade 3 . Tellingly , Grade 2 Indonesia overall mortality risk was virtually identical to Grade 3 Vietnam ( 38% versus 37 . 9% ) , suggesting that this level of overall mortality represents the boundary of the beneficial effect of LTA4H TT . Thus , LTA4H TT efficacy was limited by other factors that cause mortality . These factors appear independent of severity grade on presentation , and if they exceed a threshold ( represented by about ~40% mortality ) then the beneficial effect of LTA4H TT is lost . A limitation of this study is that we cannot definitively determine the causes of this LTA4H genotype-independent excess Grade 3 mortality in Indonesia because the patient populations , hospital care protocols and the availability of intensive care differed between each study site . One possibility is that better ancillary care was possible in Vietnam where all patients were enrolled into a clinical trial versus only 17% in Indonesia ( Thuong et al . , 2017; van Laarhoven et al . , 2017 ) . The excess Grade 3 Indonesia mortality peaked within days , and optimized respiratory support , in particular , would be essential to keep patients alive through the early high risk stage in order allow for anti-inflammatory effects of corticosteroids to benefit the TT patients . Thus the beneficial effect of dexamethasone to the LTA4H TT group may simply not have had time to come into play in Grade 3 Indonesia patients . A second possibility is that Indonesia Grade 3 patients presented with a greater degree of dysregulated inflammation in a manner not revealed by standard metrics of judging disease severity . If this were the case then corticosteroids might no longer be beneficial . Since both TT and non-TT patients suffered identical excess mortality risk , its cause would have been LTA4H-independent . Indonesia patients tended to be younger than Vietnam patients in all grades ( Table 1 ) , and perhaps were more prone to develop such a response . Such a previously unrecognized form of dysregulated inflammation might be caused by other genetic variants uniquely present in the Indonesia patients and would work through an inflammatory pathway that is less responsive to dexamethasone . Why was the LTA4H TT effect thought to be limited to the least severe patients in both cohorts ( Fava and Schurr , 2017 ) ? Two reasons might explain this . First , it was not appreciated that apart from having fewer Grade 1 patients , Indonesia patients also suffered higher grade-for-grade mortality risk ( Fava and Schurr , 2017 ) . Second , because of a paucity of less severe patients , a subgroup analysis was performed in an attempt to tease out an effect in this group . Patients were divided into GCS 14–15 ( less severe ) versus <13 ( more severe ) , and a nonsignificant TT survival benefit was found in the GCS 14–15 group only , supporting the idea that TT effects , if any , were limited to the less severe group ( Figure 5A and B ) . However , our analysis now shows the two reasons why this grouping resulted in the LTA4H TT effect going unrecognized: ( 1 ) GCS 15 does not separate patients by whether or not they have neurological signs , so it includes all BMRC Grade 1 and some BMRC Grade 2 patients ( Box 2 ) ; ( 2 ) the grouping used resulted in the Grade 2 patients being split so that some were grouped with Grade 3 and the remaining with Grade 1; ( 3 ) of the 11 TT patients in the GCS 14–15 grouping only 1 TT patient was in Grade 1 , with the remaining 10 in Grade 2 , so the effect seen in their GCS 14–15 was entirely from Grade 2 patients ( compare Figure 5B–C ) . In any case this subgroup TT effect would have been impossible to detect with the frequentist analyses used . The use of Bayesian methods has identified subgroups of TT patients that gain the most survival benefit from dexamethasone treatment . Our findings set the stage for using Bayesian methods for subgroup analyses of the ongoing trial with the goal of comparing the survival of CC and CT patients randomised to dexamethasone or placebo , and also comparing the survival of each of these groups individually and combined to TT patients who are all getting dexamethasone ( Donovan et al . , 2018; Donovan and Phu , 2020 ) . Finally , in addition to providing guidance for TBM pharmacogenomic approaches , we hope that our analyses highlight the unique value of Bayesian methods for providing guidance for other complex diseases with difficult treatment decisions . The vital importance of defining the patient populations and subgroups which will benefit the most from specialised interventions and treatments is increasingly appreciated ( Sadée and Dai , 2005 ) . This is not only to target such treatments to those who will benefit , but to avoid their adverse effects in those individuals who have little chance of experiencing a clinically relevant benefit from them . Our finding that the LTA4H TT genotype’s salutary role is incumbent on the optimization of other factors that maximize patient survival has broad implications for pharmacogenomic approaches .
The anonymized patient cohort data used here has been previously described in detail ( Thuong et al . , 2017; van Laarhoven et al . , 2017 ) . Our analysis included all 439 HIV-negative patients from the Vietnam study and 376 of the 515 HIV-negative patients from the Indonesia study . The remaining 139 Indonesian patients were excluded for lack of information on LTA4H rs17525495 genotype ( 87 ) , baseline disease severity ( 44 ) or outcome ( 8 ) ( Supplementary file 1 ) . All Vietnam patients were admitted to one of two tertiary care referral hospitals in Ho Chi Minh City , Vietnam: Pham Ngoc Thach Hospital for Tuberculosis and Lung Disease ( designated Hospital 1 ) and the Hospital for Tropical Diseases ( designated Hospital 2 ) ( Heemskerk et al . , 2016 ) . All patients were treated with adjunctive dexamethasone for the first 6–8 weeks with the regimen adjusted to disease severity on presentation ( Thwaites et al . , 2004 ) . Patient cohorts were compared overall as well as stratified into disease severity groups based on the TBM grade and by LTA4H genotypes , into the TT group ( previously linked to response to steroids ) and the CC , CT , and combined CC and CT genotypes ( non-TT group ) . The Bayesian analysis methods to compare survival probabilities are detailed in Appendix 2 . We limited analysis to the first 9 months of the one-year observation period in Indonesia to be compatible with the 9 month observation period in Vietnam . For the data in Tables 1 and 2 , Bayesian posterior probabilities for the comparisons of genotype and GCS frequencies , for each bin separately and for the mean of the distribution , were based on a flat Dirichlet prior on the bin probabilities . Comparisons of age and time to median mortality were done using comparisons of arithmetic means of distributions , allowing Bayesian model choice between the following distribution families: Gaussian , log-Gaussian , Student , log-Student , Gamma , inverse-Gamma , Gamma-power . For age , log-Gaussian had overall the highest posterior probability; for time to median mortality , inverse-Gamma was preferred .
Tuberculous meningitis has high mortality , linked to excessive inflammation . However , adjunctive anti-inflammatory corticosteroids reduce mortality by only 30% , suggesting that inflammatory pathophysiology causes only a subset of deaths . In Vietnam , the survival benefit of anti-inflammatory corticosteroids was most pronounced in patients with a C/T promoter variant in the leukotriene A4 hydrolase ( LTA4H ) gene encoding an enzyme that regulates inflammatory eicosanoids . LTA4H TT patients with increased expression had increased survival , consistent with corticosteroids benefiting individuals with hyper-inflammatory responses . However , an Indonesia study did not find an LTA4H TT genotype survival benefit . Here using Bayesian methods to analyse both studies , we find that LTA4H TT genotype confers survival benefit that begins early and continues long-term in both populations . This benefit is nullified in the most severe cases with high early mortality . LTA4H genotyping together with disease severity assessment may target glucocorticoid therapy to patients most likely to benefit from it .
Tuberculous meningitis is a serious infection of the lining of the brain , which affects over 100 , 000 people a year . Without treatment , it is always fatal: even with proper antibiotics , about a quarter of patients do not survive and many will have permanent brain damage . Overactive inflammation is thought to contribute to this process . Corticosteroid drugs , which dampen the inflammatory process , are therefore often used during treatment . However , they merely reduce mortality by 30% , suggesting that only some people benefit from them . Two recent studies have linked the genetic makeup of individuals who have tuberculous meningitis to how they respond to corticosteroids . There were , in particular , differences in the LTA4H gene that codes for an inflammation-causing protein . According to these results , only individuals carrying high-inflammation versions of the LTA4H gene would benefit from the treatment . Yet a third study did not find any effect of the genetic background of patients . All three papers used frequentist statistics to draw their conclusions , only examining the percentage of people who survived in each group . Yet , this type of analysis can miss important details . It also does not work well when the number of patients is small , or when the effectiveness of a drug varies during the course of an illness . Another method , called Bayesian statistics , can perform better under these limitations . In particular , it takes into account the probability of an event based on prior knowledge – for instance , that the risk of dying varies smoothly with time . Here , Whitworth et al . used Bayesian statistics to reanalyse the data from these studies , demonstrating that death rates were correlated with the type of LTA4H gene carried by patients . In particular , corticosteroid treatment worked best for people with the high inflammation versions of the gene . However , regardless of genetic background , corticosteroids were not effective if patients were extremely sick before being treated . The work by Whitworth et al . demonstrates the importance of using Bayesian statistics to examine the effectiveness of medical treatments . It could help to design better protocols for tuberculous meningitis treatment , tailored to the genetic makeup of patients .