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    • CART models identified the most important predictors

    2018-11-07

    CART models identified the most important predictors of mortality as duration of therapy or follow-up (importance score=100%), CD4+ T cell counts (score=47%), TTP (score=38%), and age (score=21%). The identified thresholds (which partitioned patients to homogenous groups with similar mortality risk) shown in Table 2, were a follow-up or therapy duration of 5.23months, a CD4+ T cell count of 199·5, and a TTP ≤14days. CART identified an interaction between TTP and follow-up duration; patients with TTP ≤14days (i.e. high bacillary loads) had significantly higher mortality rates earlier than 5.3months. The meaning of an interaction in machine learning is that of a predictor modifying another predictor\'s effect on the outcome (i.e., mortality in this case). A TTP of ≤14days translates to ≥5.65log10CFU/mL. Notably, measures of hemodynamic instability, EPZ015666 rate, use of oral steroids, adenosine deaminase level or gender were not ranked. HIV-infection status or use of anti-retrovirus medications was also not ranked. The best CART model receiver-operating characteristic was 0·95 on the learning set and 0·68 on the test sample. This means that the probability of obtaining similar results with an independent dataset is at least 68%. CART output was examined in standard logistic regression, with results shown in Table 2. Among patients with Mtb>5.53log10CFU/mL mortality incidence was 6/197·7 person-months and the mortality incidence rate was 0·03 compared to 10/920 person-months and mortality rate of 0·01 (Fig. 4). The incidence rate difference was −0.02 (95% −0.04 to 0.01), p=0.031. Multivariable analysis based on time-to-death as an outcome on follow-up in a Cox proportional hazards model revealed a hazards-ratio of 3.57 (95% confidence interval: 1.27–10·00; p=0.016) in patients >29years and 2.91 (95% confidence interval: 1.03–8·21; p=0.044) in patients with pericardial Mtb >5.53log10CFU/mL.
    Discussion There were three main findings in this study. First, mortality rates in proven TB pericarditis were high. The fact that the overwhelming majority of deaths in this cohort occurred during directly observed therapy is troubling, and the timing of 5.3months suggests that this is more a failure of therapy rather than death from chronic complications such as constriction. Since all patients were on directly observed therapy, failure in this case may have had little to do with poor adherence. There are two possible explanations for this failure of therapy, both pharmacokinetic/pharmacodynamic (PK/PD) in nature (Shenje et al., 2015; Pasipanodya and Gumbo, 2011). First, it is most likely that there are inadequate antibiotic concentrations in TB pericardial fluid, especially of the primary sterilizing effect drugs rifampin and pyrazinamide, due to poor penetration, as we have shown elsewhere (Shenje et al., 2015). There is now sufficient hollow fiber and prospective clinical studies evidence to show that both the bactericidal activities and the sterilizing effect of antibiotics in TB is driven by high peak concentration and the area under the concentration-time curve of each drug, and that evolution-mandated pharmacokinetic variability drives many patients on recommended doses to not achieve optimal peak and area under the concentration–time curve concentrations (Shenje et al., 2015; Pasipanodya and Gumbo, 2011; Gumbo et al., 2015; Pasipanodya et al., 2013; Srivastava et al., 2011). Secondly, CART identified a CD4+ T cell count ≤199.5 as a major predictor. This happens to be the exact CD4+ T cell count cut-off point that defines severe immunosuppression. The immune system is an efficient antimicrobial agent (that is why it exists), and these patients would have been deprived of that advantage. The second main finding is that proven TB pericarditis may not be a paucibacillary disease as previously proposed; (Theron et al., 2014) indeed patients who died had pericardial bacterial burden of >5.53log10CFU/mL. This result is with the more conservative method to correlate TTP to CFU/mL of pericardial fluid. Use of the less conservative method revealed bacterial burdens in Fig. 3, much higher than even those in sputum. In our study, patients had pericardiocentesis within a week of starting anti-TB therapy. Thus, the initial bacterial burden may actually be falsely lower in some patients because of effects of therapy. However, the caveat is that these were patients chosen based on the basis of a positive Mtb culture in pericardial fluid, which could skew the bacterial burden.