Sengupta Mallika, Kundu Aditya, Mandal Saikat, Chatterjee Shiv Sekhar, Ghoshal Ujjala, Banerjee Sayantan, Mukhopadhyay Kaushik
Microbiology, All India Institute of Medical Sciences, Kalyani, IND.
General Internal Medicine, All India Institute of Medical Sciences, Kalyani, IND.
Cureus. 2024 Nov 15;16(11):e73779. doi: 10.7759/cureus.73779. eCollection 2024 Nov.
Introduction Leptospirosis is a zoonotic infection caused by bacteria, which is reemerging in various regions and often poses a diagnostic challenge due to its nonspecific symptoms. While most infections are mild, severe cases occur in 5-10% of patients and are associated with high mortality, especially in areas with poor sanitation and urbanization. This study aims to investigate the association of specific parameters with leptospirosis diagnosis using a machine learning model and geographic mapping tools to identify spatial patterns and high-risk areas for the disease. Methods An observational retrospective study conducted at a tertiary care center analyzed patients clinically suspected of leptospirosis over the course of one year. The study utilized laboratory investigations, geographic mapping, and machine learning models to explore the association between various laboratory parameters and the predictive diagnosis of leptospirosis. Results The study, conducted over one year at All India Institute of Medical Sciences, Kalyani, India, included 325 patients, of whom 43 (13.2%) tested positive for leptospirosis by IgM ELISA. Geographic mapping revealed case clusters around nearby districts of West Bengal, with a few cases from Tripura and Bangladesh. The study found no significant association between individual laboratory parameters and leptospirosis diagnosis. However, machine learning models, particularly k-nearest neighbors (KNN), demonstrated moderate predictive accuracy (accuracy: 74%, area under the curve: 0.6). Conclusion Geographic mapping identified clusters of leptospirosis cases; however, no significant association was found between individual laboratory parameters and the disease diagnosis. Machine learning models, particularly KNN, demonstrated moderate predictive accuracy. The study also highlighted the overlapping clinical features of leptospirosis, dengue, and scrub typhus in West Bengal, although it noted the absence of detailed clinical data as a limitation.
引言
钩端螺旋体病是一种由细菌引起的人畜共患感染病，在各个地区再度出现，且由于其症状不具特异性，常常带来诊断挑战。虽然大多数感染症状较轻，但5% - 10%的患者会出现严重病例，且死亡率较高，尤其是在卫生条件差和城市化程度低的地区。本研究旨在使用机器学习模型和地理绘图工具，调查特定参数与钩端螺旋体病诊断之间的关联，以识别该病的空间模式和高危地区。
方法
在一家三级医疗中心进行的一项观察性回顾性研究，分析了一年内临床疑似钩端螺旋体病的患者。该研究利用实验室检查、地理绘图和机器学习模型，探讨各种实验室参数与钩端螺旋体病预测诊断之间的关联。
结果
在印度卡利亚尼的全印度医学科学研究所进行的为期一年的研究中，纳入了325名患者，其中43名（13.2%）通过IgM ELISA检测钩端螺旋体病呈阳性。地理绘图显示西孟加拉邦附近地区存在病例聚集，还有一些病例来自特里普拉邦和孟加拉国。研究发现单个实验室参数与钩端螺旋体病诊断之间无显著关联。然而，机器学习模型，特别是k近邻算法（KNN），显示出中等预测准确性（准确率：74%，曲线下面积：0.6）。
结论
地理绘图确定了钩端螺旋体病病例的聚集情况；然而，未发现单个实验室参数与疾病诊断之间存在显著关联。机器学习模型，特别是KNN，显示出中等预测准确性。该研究还强调了西孟加拉邦钩端螺旋体病、登革热和恙虫病临床特征的重叠，不过指出缺乏详细临床数据是一个局限性。
