von Wedel Cäcilie, Matthies Lars Christian, Dierks Clemens, Tober-Lau Pinkus, Bardtke Lara, Lingscheid Tilman, Nordmann Tamara, Jochum Johannes, Huebl Lena, Tappe Dennis, Zoller Thomas, Pechmann Klara, Okwu Dearie Glory, Malinga Emma Gladis, Ralser Markus, Sander Leif Erik, Zoleko-Manego Rella, Ramharter Michael, Mombo-Ngoma Ghyslain, Kurth Florian, Mischlinger Johannes
Department of Clinical Research, Centre de Recherches Médicales de Lambaréné, Lambaréné, Gabon.
Department of Infectious Diseases and Critical Care Medicine, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany.
Open Forum Infect Dis. 2025 Jul 17;12(8):ofaf421. doi: 10.1093/ofid/ofaf421. eCollection 2025 Aug.
The clinical presentation of individuals infected with is exceptionally diverse, ranging from asymptomatic parasitemia to life-threatening disease. Frequent previous exposure to spp results in partial protection from severe disease; however, this protection wanes in individuals emigrating from holoendemic regions, and there are currently no reliable biomarkers that accurately indicate this semi-immunity.
Data were analyzed from 1392 adults infected with in Gabon and Germany. Full blood count parameters and ratios were evaluated individually and as a combined ensemble-based machine learning classifier to predict disease severity, ranging from asymptomatic infection to severe malaria. As a secondary objective, the influence of previous exposure to spp was assessed.
Comparing asymptomatic parasitemia with uncomplicated malaria in Gabonese and comparing uncomplicated with severe malaria in German patients revealed significantly lower platelet counts (218 vs 150 ×10/µL, < .0001; 85 vs 40 ×10/µL, < .0001, respectively) and higher neutrophil counts (2.32 vs 2.57 ×10/µL, = .0037; 3.08 vs 4.49 ×10/µL, < .0001) in those with greater infection severity. The machine learning classifier outperformed single parameters in differentiating infection severity in both comparisons (area under the receiver operating characteristic curve, 0.94 and 0.84). Lymphocyte and monocyte counts showed a pattern that follows the level of previous malaria exposure, with lower cell counts in naive vs previously exposed patients, regardless of infection severity.
The value of simple full blood count parameters for classification of infection severity and previous exposure is considerable. The accuracy can be increased by integrating individual parameters into a joint machine learning model.
感染疟原虫的个体临床表现极为多样，从无症状寄生虫血症到危及生命的疾病不等。既往频繁接触疟原虫可对严重疾病产生部分保护作用；然而，这种保护作用在从高度流行地区移民的个体中会减弱，目前尚无可靠的生物标志物能准确指示这种半免疫状态。
对加蓬和德国1392例感染疟原虫的成年人的数据进行分析。分别评估全血细胞计数参数和比率，并将其作为基于组合整体的机器学习分类器，以预测疾病严重程度，范围从无症状感染到重症疟疾。作为次要目标，评估既往接触疟原虫的影响。
比较加蓬无症状寄生虫血症与非复杂性疟疾患者，以及德国非复杂性疟疾与重症疟疾患者，结果显示感染严重程度较高者的血小板计数显著更低（分别为218对150×10⁹/µL，P<0.0001；85对40×10⁹/µL，P<0.0001），中性粒细胞计数更高（2.32对2.57×10⁹/µL，P = 0.0037；3.08对4.49×10⁹/µL，P<0.0001）。在两项比较中，机器学习分类器在区分感染严重程度方面均优于单个参数（受试者工作特征曲线下面积分别为0.94和0.84）。淋巴细胞和单核细胞计数呈现出与既往疟疾接触水平相关的模式，无论感染严重程度如何，未接触过疟疾的患者与既往接触过疟疾的患者相比，细胞计数更低。
简单的全血细胞计数参数在疟原虫感染严重程度分类和既往接触情况评估方面具有重要价值。将个体参数整合到联合机器学习模型中可提高准确性。
