Su Yin Myat, Haddawy Peter, Meth Panhavath, Srikaew Araya, Wavemanee Chonnikarn, Niyom Saranath Lawpoolsri, Sriraksa Kanokwan, Limpitikul Wannee, Kittirat Preedawadee, Angkasekwinai Nasikarn, Navanukroh Oranich, Mapralub Arunee, Pakdee Supansa, Kaewpuak Chotika, Tangthawornchaikul Nattaya, Malasit Prida, Avirutnan Panisadee, Mairiang Dumrong
Faculty of ICT, Mahidol University, Nakhon Pathom, Thailand.
Bremen Spatial Cognition Center, University of Bremen, Bremen, Germany.
PLoS One. 2025 Aug 4;20(8):e0327360. doi: 10.1371/journal.pone.0327360. eCollection 2025.
Dengue virus (DENV) infection is a major global health problem. While DENV infection rarely results in serious complications, the more severe illness dengue hemorrhagic fever (DHF) has a significant mortality rate due to the associated plasma leakage that may lead to hypovolemic shock. Proper care thus requires identifying patients with DHF among those with suspected dengue so that they can be provided with adequate and prompt fluid replacement. In this study we used seventeen years of pediatric patient data from a prospective cohort study in two hospitals in Thailand to develop models to predict DHF among patients with suspected dengue infection. We produced models for a general hospital setting and for a primary care unit setting lacking lab facilities. The best model using combined data from both hospitals achieved an AUC of 0.90 for the general hospital setting and 0.79 for the primary care unit setting. We then investigated the generalizability of the models by training models with data from one hospital and testing them with data from the other. For some models, we found a significant reduction in performance. Possible sources of this are differences in how attributes are defined or measured and differences in the hematological parameters of the two patient populations. We conclude that while high accuracy prediction of DHF is possible, care must be taken when applying DHF predictive models from one clinical setting to another.
登革热病毒(DENV)感染是一个重大的全球健康问题。虽然DENV感染很少导致严重并发症,但更严重的疾病登革出血热(DHF)由于相关的血浆渗漏可能导致低血容量性休克,其死亡率较高。因此,正确的护理需要在疑似登革热患者中识别出患有DHF的患者,以便为他们提供充足且及时的液体补充。在本研究中,我们使用了来自泰国两家医院一项前瞻性队列研究的17年儿科患者数据,来开发预测疑似登革热感染患者中DHF的模型。我们针对综合医院环境和缺乏实验室设施的基层医疗单位环境分别构建了模型。使用两家医院的合并数据得到的最佳模型,在综合医院环境中的AUC为0.90,在基层医疗单位环境中的AUC为0.79。然后,我们通过用一家医院的数据训练模型并使用另一家医院的数据进行测试,来研究这些模型的可推广性。对于一些模型,我们发现其性能显著下降。造成这种情况的可能原因是属性定义或测量方式的差异以及两个患者群体血液学参数的差异。我们得出结论,虽然对DHF进行高精度预测是可能的,但将DHF预测模型从一种临床环境应用到另一种临床环境时必须谨慎。