Machine learning-based in-hospital mortality prediction of HIV/AIDS patients with Talaromyces marneffei infection in Guangxi, China.
Machine learning-based in-hospital mortality prediction of HIV/AIDS patients with Talaromyces marneffei infection in Guangxi, China.
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DOI：
10.1371/journal.pntd.0010388
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发表时间：
2022-05
影响因子：
3.8
通讯作者：
中科院分区：
文献类型：
--
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距菌病是东南亚地区的一种严重的地方性疾病。在中国，马尔尼菲塔拉霉菌(T.marneffei)感染主要集中在南方地区，尤其是广西，并导致相当大的HIV感染者住院死亡。目前，影响感染马尔尼菲毛滴虫的HIV/AIDS患者住院死亡的因素尚不完全清楚。现有的机器学习技术可以用来开发预测模型，以确定相关的预后因素来预测死亡，这似乎是降低住院死亡率的关键。我们于2012年1月至2019年6月在广西南宁市第四人民医院对HIV/AIDS合并距菌病患者进行前瞻性研究。选择临床特征，训练四种不同的机器学习模型(Logistic回归、XGBoost、KNN和支持向量机)来预测住院患者的治疗结果，并使用30%的内部验证率来评估模型的性能。机器学习模型的性能根据一系列学习指标进行评估，包括接收器操作特征曲线下的面积(AUC)。使用Shapley Additive Ex释义(Shap)工具对模型进行解释。共纳入1927例感染马尔尼菲毛滴虫的HIV/AIDS患者。2012-2019年平均住院死亡率为13.3%(256/1927)。最常见的并发症/合并感染是肺炎(68.9%)，其次是口腔念珠菌(47.5%)和肺结核(40.6%)。死亡患者较存活患者有较高的CD4/CD8比值、天冬氨酸氨基转移酶(AST)水平、肌酐水平、尿素水平、尿酸(UA)水平、乳酸脱氢酶(LDH)水平、总胆红素水平、肌酸激酶水平、白细胞计数(WBC)、中性粒细胞计数、原胶原水平和C反应蛋白(CRP)水平，而CD3+T细胞计数、CD8+T细胞计数、淋巴细胞计数、血小板(PLT)、高密度脂蛋白胆固醇(HDLc)和血红蛋白(Hb)水平低于存活组。XGBoost预测模型在训练数据集中的灵敏度为0.71，特异度为0.99，特异度为0.97，而我们的结果预测模型在测试数据集中提供了稳健的判别，显示出0.90的特异度，0.69特异度和0.96特异度。其他三款车型由于表现不佳而被排除在外。感染性休克和呼吸衰竭是最重要的预测特征，其次是尿酸、尿素、血小板和AST/ALT比值。XGBoost机器学习模型可以很好地预测感染马尔尼菲毛滴虫的HIV/AIDS患者的住院结局。该模型在距菌病人群死亡率预测和高危因素识别中具有潜在的应用价值。马尔尼菲塔拉霉菌可导致一种致命的深度播散性真菌感染--塔拉霉菌病。它广泛分布在东南亚，并在全球范围内传播，这种疾病是隐蔽的，并导致大量死亡。临床医生需要简单易用的工具来决定哪些患者在感染马尔尼菲毛滴虫后死亡风险更高。在南方中国进行的这项研究中，我们进化了XGBoost机器学习模型。使用了15项临床指标和实验室措施来估计患者因感染马尔尼菲毛虫而在医院死亡的风险。研究表明，当在患者的内部测试人群中测试时，机器学习模型具有良好的预测能力。我们预计，该模型可以帮助临床医生在入院时评估患者的死亡风险，以帮助决定可能死亡的高危患者的早期治疗时机。
Talaromycosis is a serious regional disease endemic in Southeast Asia. In China, Talaromyces marneffei (T. marneffei) infections is mainly concentrated in the southern region, especially in Guangxi, and cause considerable in-hospital mortality in HIV-infected individuals. Currently, the factors that influence in-hospital death of HIV/AIDS patients with T. marneffei infection are not completely clear. Existing machine learning techniques can be used to develop a predictive model to identify relevant prognostic factors to predict death and appears to be essential to reducing in-hospital mortality. We prospectively enrolled HIV/AIDS patients with talaromycosis in the Fourth People’s Hospital of Nanning, Guangxi, from January 2012 to June 2019. Clinical features were selected and used to train four different machine learning models (logistic regression, XGBoost, KNN, and SVM) to predict the treatment outcome of hospitalized patients, and 30% internal validation was used to evaluate the performance of models. Machine learning model performance was assessed according to a range of learning metrics, including area under the receiver operating characteristic curve (AUC). The SHapley Additive exPlanations (SHAP) tool was used to explain the model. A total of 1927 HIV/AIDS patients with T. marneffei infection were included. The average in-hospital mortality rate was 13.3% (256/1927) from 2012 to 2019. The most common complications/coinfections were pneumonia (68.9%), followed by oral candida (47.5%), and tuberculosis (40.6%). Deceased patients showed higher CD4/CD8 ratios, aspartate aminotransferase (AST) levels, creatinine levels, urea levels, uric acid (UA) levels, lactate dehydrogenase (LDH) levels, total bilirubin levels, creatine kinase levels, white blood-cell counts (WBC) counts, neutrophil counts, procaicltonin levels and C-reactive protein (CRP) levels and lower CD3+ T-cell count, CD8+ T-cell count, and lymphocyte counts, platelet (PLT), high-density lipoprotein cholesterol (HDL), hemoglobin (Hb) levels than those of surviving patients. The predictive XGBoost model exhibited 0.71 sensitivity, 0.99 specificity, and 0.97 AUC in the training dataset, and our outcome prediction model provided robust discrimination in the testing dataset, showing an AUC of 0.90 with 0.69 sensitivity and 0.96 specificity. The other three models were ruled out due to poor performance. Septic shock and respiratory failure were the most important predictive features, followed by uric acid, urea, platelets, and the AST/ALT ratios. The XGBoost machine learning model is a good predictor in the hospitalization outcome of HIV/AIDS patients with T. marneffei infection. The model may have potential application in mortality prediction and high-risk factor identification in the talaromycosis population. Talaromyces marneffei can cause a fatal deeply disseminated fungal infection- talaromycosis. It is widely distributed in Southeast Asia and spreading globally, the disease is insidious and responsible for significant deaths. Clinicians need easy-to-use tools to make decisions on which patients are at a higher risk of dying after infecting T. marneffei. In this study, conducted in Southern China, we have evolved XGBoost machine learning model. 15 clinical indicators and laboratory measures were used to estimate a patient’s risk of dying in the hospital due to the T. marneffei infection. The study showed that the machine learning model has good predictive ability when tested in an internal testing population of patients. We expect that the model could help clinicians assess a patient’s risk of death in just the time of admission to help decide on early treatment timing of high-risk patients who are likely to die.
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影响因子：
3.3
作者：
Chen, Jun;Zhang, Renfang;Lu, Hongzhou
通讯作者：
Lu, Hongzhou
影响因子：
7.3
作者：
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影响因子：
2.9
作者：
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Le T
影响因子：
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作者：
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Supparatpinyo K
DOI：
10.1111/j.1468-3083.2011.04429.x
发表时间：
2013-03-01
影响因子：
9.2
作者：
Han, J.;Lun, W. H.;Lian, S.
通讯作者：
Lian, S.