Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, Hubei 430030, China.
College of Mathematics and Statistics, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, Hubei 430074, China.
EBioMedicine. 2019 Sep;47:309-318. doi: 10.1016/j.ebiom.2019.08.028. Epub 2019 Aug 23.
To date, no risk prediction tools have been developed to identify high mortality risk of patients with advanced schistosomiasis within 2 years after discharge. We aim to derive and validate a risk prediction model to be applied in clinical practice. The risk prediction model was derived from 1487 patients from Jingzhou and externally validated by 723 patients of Huangshi, two prefecture-level cities in Hubei province, China (from September 2014 to January 2015, with follow-up to January 2017). The baseline variables were collected. The mean age [SD] was 62.89 [10.38] years for the derivation cohort and 62.95 [12.22] years for the external validation cohort. The females accounted for 36.3% and 43.7% of the derivation and validation cohorts, respectively. 8.27% patients (123/1487) in the derivation cohort and 7.75% patients (56/723) in the external validation cohort died within 2 years after discharge. We constructed 4 models based on the 7 selected variables: age, clinical classification, serum direct bilirubin (DBil), aspartate aminotransferase (AST), alkaline phosphatase (ALP), hepatitis B surface antigen (HBsAg), alpha fetoprotein (AFP) at admission. In the external validation cohort, the multivariate model including 7 variables had a C statistic of 0.717 (95% CI, 0.646-0.788) and improved integrated discrimination improvement (IDI) value and net reclassification improvement (NRI) value compared to the other reduced models. Therefore, a multivariate model was developed to predict the 2-year mortality risk for patients with advanced schistosomiasis after discharge. It could also help guide follow-up, aid prognostic assessment and inform resource allocation.
迄今为止,尚无风险预测工具可用于识别晚期血吸虫病患者出院后 2 年内的高死亡率风险。我们旨在开发并验证一种可应用于临床实践的风险预测模型。该风险预测模型源自中国湖北省两个地级市荆州和黄石的 1487 名患者(2014 年 9 月至 2015 年 1 月,随访至 2017 年 1 月),并通过另外 723 名患者进行了外部验证。收集了基线变量。推导队列的平均年龄[SD]为 62.89 [10.38]岁,验证队列的平均年龄[SD]为 62.95 [12.22]岁。推导队列中女性占 36.3%,验证队列中女性占 43.7%。推导队列中有 8.27%的患者(123/1487)和验证队列中有 7.75%的患者(56/723)在出院后 2 年内死亡。我们根据 7 个选定变量构建了 4 个模型:年龄、临床分类、血清直接胆红素(DBil)、天门冬氨酸氨基转移酶(AST)、碱性磷酸酶(ALP)、乙型肝炎表面抗原(HBsAg)、甲胎蛋白(AFP)入院时。在外部验证队列中,与其他简化模型相比,包含 7 个变量的多变量模型的 C 统计量为 0.717(95%CI,0.646-0.788),并提高了综合判别改善(IDI)值和净重新分类改善(NRI)值。因此,建立了一个多变量模型来预测晚期血吸虫病患者出院后 2 年内的死亡风险。它还可以帮助指导随访、辅助预后评估和资源分配。