Hu Lufeng, Li Huaizhong, Cai Zhennao, Lin Feiyan, Hong Guangliang, Chen Huiling, Lu Zhongqiu
Department of Pharmacy, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Department of Computing, Lishui University, Lishui, Zhejiang, P. R. China.
PLoS One. 2017 Oct 19;12(10):e0186427. doi: 10.1371/journal.pone.0186427. eCollection 2017.
The prognosis of paraquat (PQ) poisoning is highly correlated to plasma PQ concentration, which has been identified as the most important index in PQ poisoning. This study investigated the predictive value of coagulation, liver, and kidney indices in prognosticating PQ-poisoning patients, when aligned with plasma PQ concentrations. Coagulation, liver, and kidney indices were first analyzed by variance analysis, receiver operating characteristic curves, and Fisher discriminant analysis. Then, a new, intelligent, machine learning-based system was established to effectively provide prognostic analysis of PQ-poisoning patients based on a combination of the aforementioned indices. In the proposed system, an enhanced extreme learning machine wrapped with a grey wolf-optimization strategy was developed to predict the risk status from a pool of 103 patients (56 males and 47 females); of these, 52 subjects were deceased and 51 alive. The proposed method was rigorously evaluated against this real-life dataset, in terms of accuracy, Matthews correlation coefficients, sensitivity, and specificity. Additionally, the feature selection was investigated to identify correlating factors for risk status. The results demonstrated that there were significant differences in the coagulation, liver, and kidney indices between deceased and surviving subjects (p<0.05). Aspartate aminotransferase, prothrombin time, prothrombin activity, total bilirubin, direct bilirubin, indirect bilirubin, alanine aminotransferase, urea nitrogen, and creatinine were the most highly correlated indices in PQ poisoning and showed statistical significance (p<0.05) in predicting PQ-poisoning prognoses. According to the feature selection, the most important correlated indices were found to be associated with aspartate aminotransferase, the aspartate aminotransferase to alanine ratio, creatinine, prothrombin time, and prothrombin activity. The method proposed here showed excellent results that were better than that produced based on blood-PQ concentration alone. These promising results indicated that the combination of these indices can provide a new avenue for prognosticating the outcome of PQ poisoning.
百草枯（PQ）中毒的预后与血浆PQ浓度高度相关，血浆PQ浓度已被确定为PQ中毒最重要的指标。本研究探讨了凝血、肝脏和肾脏指标与血浆PQ浓度相结合时，对预测PQ中毒患者预后的价值。首先通过方差分析、受试者工作特征曲线和Fisher判别分析对凝血、肝脏和肾脏指标进行分析。然后，建立了一个基于机器学习的新型智能系统，以基于上述指标的组合有效地对PQ中毒患者进行预后分析。在所提出的系统中，开发了一种采用灰狼优化策略的增强型极限学习机，用于预测103例患者（56例男性和47例女性）的风险状态；其中，52例受试者死亡，51例存活。针对这个真实数据集，从准确性、马修斯相关系数、敏感性和特异性方面对所提出的方法进行了严格评估。此外，还进行了特征选择以识别风险状态的相关因素。结果表明，死亡和存活受试者的凝血、肝脏和肾脏指标存在显著差异（p<0.05）。天冬氨酸转氨酶、凝血酶原时间、凝血酶原活性、总胆红素、直接胆红素、间接胆红素、丙氨酸转氨酶、尿素氮和肌酐是PQ中毒中相关性最高的指标，在预测PQ中毒预后方面具有统计学意义（p<0.05）。根据特征选择，发现最重要的相关指标与天冬氨酸转氨酶、天冬氨酸转氨酶与丙氨酸转氨酶的比值、肌酐、凝血酶原时间和凝血酶原活性有关。本文提出的方法显示出优异的结果，优于仅基于血液PQ浓度得出的结果。这些有前景的结果表明，这些指标的组合可为预测PQ中毒的结局提供一条新途径。
