Lien Frank, Lin Huang-Shen, Wu You-Ting, Chiueh Tzong-Shi
Department of Internal Medicine, Chang Gung Memorial Hospital, Chiayi, Taiwan.
Department of Infectious Diseases, Chang Gung Memorial Hospital, Chiayi, Taiwan.
BMC Infect Dis. 2022 Mar 26;22(1):287. doi: 10.1186/s12879-022-07223-7.
Biomarkers, such as leukocyte count, C-reactive protein (CRP), and procalcitonin (PCT), have been commonly used to predict the occurrence of life-threatening bacteremia and provide prognostic information, given the need for prompt intervention. However, such diagnosis methods require much time and money. Therefore, we propose a method with a high prediction capability using machine learning (ML) models based on complete blood count (CBC) and differential leukocyte count (DC) and compare its performance with traditional CRP or PCT biomarker methods and those of models incorporating CRP or PCT biomarkers.
We collected 366,586 daily blood culture (BC) results, of which 350,775 (93.2%), 308,803 (82.1%), and 23,912 (6.4%) cases were issued CBC/DC (CBC/DC group), CRP with CBC/DC (CRP&CBC/DC group), and PCT with CBC/DC (PCT&CBC/DC group), respectively. For the ML methods, conventional logistic regression and random forest models were selected, trained, applied, and validated for each group. Fivefold validation and prediction capability were also evaluated and reported.
Overall, the ML methods, such as the random forest model, demonstrated promising performances. When trained with CBC/DC data, it achieved an area under the ROC curve (AUC) of 0.802, which is superior to the prediction conventionally made with CRP/PCT levels (0.699/0.731). Upon evaluating the performance enhanced by incorporating CRP or PCT biomarkers, it reported no substantial AUC increase with the addition of either CRP or PCT to CBC/DC data, which suggests the predicting power and applicability of using only CBC/DC data. Moreover, it showed competitive prognostic capability compared to the PCT test with similar all-cause in-hospital mortality (45.10% vs. 47.40%) and overall median survival time (27 vs. 25 days).
The ML models using only CBC/DC data yielded more accurate bacteremia predictions compared to those by methods using CRP and PCT data and reached similar prognostic performance as by PCT data. Thus, such models are potentially complementary and competitive with traditional CRP and PCT biomarkers for conducting and guiding antibiotic usage.
鉴于需要及时干预，生物标志物，如白细胞计数、C反应蛋白（CRP）和降钙素原（PCT），已被广泛用于预测危及生命的菌血症的发生并提供预后信息。然而，此类诊断方法需要大量时间和金钱。因此，我们提出一种基于全血细胞计数（CBC）和白细胞分类计数（DC）的机器学习（ML）模型的高预测能力方法，并将其性能与传统的CRP或PCT生物标志物方法以及纳入CRP或PCT生物标志物的模型进行比较。
我们收集了366,586份每日血培养（BC）结果，其中350,775例（93.2%）、308,803例（82.1%）和23,912例（6.4%）分别为CBC/DC组（CBC/DC group）、CRP联合CBC/DC组（CRP&CBC/DC group）和PCT联合CBC/DC组（PCT&CBC/DC group）。对于ML方法，为每个组选择、训练、应用和验证了传统逻辑回归和随机森林模型。还评估并报告了五重验证和预测能力。
总体而言，随机森林模型等ML方法表现出良好的性能。当使用CBC/DC数据进行训练时，其受试者工作特征曲线下面积（AUC）达到0.802，优于传统的基于CRP/PCT水平的预测（0.699/0.731）。在评估纳入CRP或PCT生物标志物后性能的提升时，结果表明在CBC/DC数据中添加CRP或PCT后AUC没有显著增加，这表明仅使用CBC/DC数据的预测能力和适用性。此外，与PCT检测相比，它在全因院内死亡率（45.10%对47.40%）和总体中位生存时间（27天对25天）相似的情况下，显示出具有竞争力的预后能力。
与使用CRP和PCT数据的方法相比，仅使用CBC/DC数据的ML模型对菌血症的预测更准确，并且达到了与PCT数据相似的预后性能。因此，此类模型在指导抗生素使用方面可能与传统的CRP和PCT生物标志物具有互补性和竞争力。
