Centro de Pesquisas Gonçalo Moniz, OCRUZ, Bahia, Brazil.
Malar J. 2010 May 6;9:117. doi: 10.1186/1475-2875-9-117.
Accurate malaria diagnosis is mandatory for the treatment and management of severe cases. Moreover, individuals with asymptomatic malaria are not usually screened by health care facilities, which further complicates disease control efforts. The present study compared the performances of a malaria rapid diagnosis test (RDT), the thick blood smear method and nested PCR for the diagnosis of symptomatic malaria in the Brazilian Amazon. In addition, an innovative computational approach was tested for the diagnosis of asymptomatic malaria.
The study was divided in two parts. For the first part, passive case detection was performed in 311 individuals with malaria-related symptoms from a recently urbanized community in the Brazilian Amazon. A cross-sectional investigation compared the diagnostic performance of the RDT Optimal-IT, nested PCR and light microscopy. The second part of the study involved active case detection of asymptomatic malaria in 380 individuals from riverine communities in Rondônia, Brazil. The performances of microscopy, nested PCR and an expert computational system based on artificial neural networks (MalDANN) using epidemiological data were compared.
Nested PCR was shown to be the gold standard for diagnosis of both symptomatic and asymptomatic malaria because it detected the major number of cases and presented the maximum specificity. Surprisingly, the RDT was superior to microscopy in the diagnosis of cases with low parasitaemia. Nevertheless, RDT could not discriminate the Plasmodium species in 12 cases of mixed infections (Plasmodium vivax + Plasmodium falciparum). Moreover, the microscopy presented low performance in the detection of asymptomatic cases (61.25% of correct diagnoses). The MalDANN system using epidemiological data was worse that the light microscopy (56% of correct diagnoses). However, when information regarding plasma levels of interleukin-10 and interferon-gamma were inputted, the MalDANN performance sensibly increased (80% correct diagnoses).
An RDT for malaria diagnosis may find a promising use in the Brazilian Amazon integrating a rational diagnostic approach. Despite the low performance of the MalDANN test using solely epidemiological data, an approach based on neural networks may be feasible in cases where simpler methods for discriminating individuals below and above threshold cytokine levels are available.
准确的疟疾诊断对于重症病例的治疗和管理至关重要。此外，无症状的疟疾患者通常不会在医疗机构接受筛查，这进一步增加了疾病控制的难度。本研究比较了疟疾快速诊断检测（RDT）、厚血涂片法和巢式 PCR 对巴西亚马逊地区有症状疟疾的诊断性能。此外，还测试了一种创新的计算方法用于无症状疟疾的诊断。
该研究分为两部分。第一部分，在巴西亚马逊一个新城市化社区中，对 311 名有疟疾相关症状的个体进行了被动病例检测。一项横断面调查比较了 RDT Optimal-IT、巢式 PCR 和光学显微镜的诊断性能。研究的第二部分涉及在巴西朗多尼亚的河流社区中对 380 名无症状疟疾患者进行主动病例检测。比较了显微镜、巢式 PCR 和基于人工神经网络（MalDANN）的专家计算系统的性能，该系统使用流行病学数据。
巢式 PCR 被证明是诊断有症状和无症状疟疾的金标准，因为它检测到了大多数病例，并具有最高的特异性。令人惊讶的是，在诊断低疟原虫血症病例时，RDT 优于显微镜。然而，RDT 无法区分 12 例混合感染（间日疟原虫+恶性疟原虫）的疟原虫种类。此外，显微镜在检测无症状病例时表现不佳（正确诊断的病例为 61.25%）。使用流行病学数据的 MalDANN 系统的表现不如光学显微镜（正确诊断的病例为 56%）。然而，当输入白细胞介素-10 和干扰素-γ的血浆水平信息时，MalDANN 的性能明显提高（正确诊断的病例为 80%）。
疟疾诊断的 RDT 可能在巴西亚马逊地区找到一种有前途的应用，整合一种合理的诊断方法。尽管 MalDANN 测试仅使用流行病学数据的性能较低，但在有更简单的方法可用于区分低于和高于细胞因子阈值的个体的情况下，基于神经网络的方法可能是可行的。
