Development of a new travellers' diarrhoea clinical severity classification and its utility in confirming rifamycin-SV efficacy.

Background: travellers' diarrhoea (TD) is frequently reported with incidence up to 40% in high-risk destinations. Previous studies showed that the number of loose stools alone is inadequate to holistically predict the severity of TD. To improve the prediction of prognosis and to optimize treatments, a simple risk-based clinical severity classification has been developed.
Methods: pooled baseline data of signs and symptoms and number of loose stools from 1098 subjects enrolled in two double-blind Phase 3 trials of rifamycin-SV were analyzed with correlation, multiple correspondence analyses, prognostic factor criteria, and Contal and O'Quigley method to generate a TD severity classification (mild, moderate and severe). The relative importance of this classification on resolution of TD was assessed by Cox proportional model hazard model on the time to last unformed stool (TLUS).
Results: the analysis showed that TLUS were longer for the severe [hazard ratio (HR) 0.24; P < 0.001; n = 173] and moderate (HR 0.54; P = 0.0272; n = 912) vs mild. Additionally, when the treatment assigned in the studies was investigated in the severity classification, the results yielded that rifamycin-SV significantly shortened TLUS vs placebo for all subjects (HR 1.9; P = 0.0006), severe (HR 5.9; P = 0.0232) and moderate (HR 1.7; P = 0.0078) groups and was as equally efficacious as ciprofloxacin for all subjects, moderate and severe groups (HRs: 0.962, 0.9, 1.2; all P = NS, respectively). When reassessed by this classification, rifamycin-SV showed consistent efficacy with the Phase 3 studies.
Conclusions: this newly developed TD clinical severity classification demonstrated strong prognostic value and clinical utility by combining patients' multiple signs and symptoms of enteric infection and number of loose stools to provide a holistic assessment of TD. By expanding on the current classification by incorporating patient reported outcomes in addition to TLUS, a classification like the one developed, may help optimize patient selection for future clinical studies.
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