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Background  The prognosis of patients with advanced schistosomiasis is poor. Pre-existing prognosis studies did not differentiate the causes of the deaths. The objectives were to evaluate the 2-year overall survival (OS) and advanced schistosomiasis-specific survival (ASS) in patients with advanced schistosomiasis after discharge through competing risk analysis and to build predictive nomograms.    Methods  Data was extracted from a previously constructed database from Hubei province. Patients were enrolled from September 2014 to January 2015, with follow up to January 2017. OS and ASS were primary outcome measures. Nomograms for estimating 2-year OS and ASS rates after discharge were established based on univariate and multivariate Cox regression model and Fine and Gray’s model. Their predictive performances were evaluated using C-index and validated in both internal and external validation cohorts.    Results  The training cohort included 1487 patients with advanced schistosomiasis. Two-year mortality rate of the training cohort was 8.27% (123/1487). Competing events accounted for 26.83% (33/123). Older age, splemomegaly clinical classification, abnormal serum DBil, AST, ALP and positive HBsAg were significantly associated with 2-year OS. Older age, splemomegaly clinical classification, abnormal serum AST, ALP and positive HBsAg were significantly associated with 2-year ASS. The established nomograms were well calibrated, and had good discriminative ability, with a C-index of 0.813 (95% CI 0.803–0.823) for 2-year OS prediction and 0.834 (95% CI 0.824–0.844) for 2-year ASS prediction. Their predictive performances were well validated in both internal and external validation cohorts.    Conclusion  The effective predictors of 2-year OS and ASS were discovered through competing risk analysis. The nomograms could be used as convenient predictive tools in clinical practice to guide follow-up and aid accurate prognostic assessment.

Nomograms to predict 2-year overall survival and advanced schistosomiasis-specific survival after discharge: a competing risk analysis