Sambarey Awanti, Smith Kirk, Chung Carolina, Arora Harkirat Singh, Yang Zhenhua, Agarwal Prachi, Chandrasekaran Sriram
Biomedical Engineering, University of Michigan.
Chemical BIology, University of Michigan.
medRxiv. 2022 Jul 21:2022.07.20.22277862. doi: 10.1101/2022.07.20.22277862.
Tuberculosis (TB) afflicts over 10 million people every year and its global burden is projected to increase dramatically due to multidrug-resistant TB (MDR-TB). The Covid-19 pandemic has resulted in reduced access to TB diagnosis and treatment, reversing decades of progress in disease management globally. It is thus crucial to analyze real-world multi-domain information from patient health records to determine personalized predictors of TB treatment outcome and drug resistance. We conduct a retrospective analysis on electronic health records of 5060 TB patients spanning 10 countries with high burden of MDR-TB including Ukraine, Moldova, Belarus and India available on the NIAID-TB portals database. We analyze over 200 features across multiple host and pathogen modalities representing patient social demographics, disease presentations as seen in cChest X rays and CT scans, and genomic records with drug susceptibility features of the pathogen strain from each patient. Our machine learning model, built with diverse data modalities outperforms models built using each modality alone in predicting treatment outcomes, with an accuracy of 81% and AUC of 0.768. We determine robust predictors across countries that are associated with unsuccessful treatmentclinical outcomes, and validate our predictions on new patient data from TB Portals. Our analysis of drug regimens and drug interactions suggests that synergistic drug combinations and those containing the drugs Bedaquiline, Levofloxacin, Clofazimine and Amoxicillin see more success in treating MDR and XDR TB. Features identified via chest imaging such as percentage of abnormal volume, size of lung cavitation and bronchial obstruction are associated significantly with pathogen genomic attributes of drug resistance. Increased disease severity was also observed in patients with lower BMI and with comorbidities. Our integrated multi-modal analysis thus revealed significant associations between radiological, microbiological, therapeutic, and demographic data modalities, providing a deeper understanding of personalized responses to aid in the clinical management of TB.
结核病（TB）每年折磨着超过1000万人，由于耐多药结核病（MDR-TB），其全球负担预计将急剧增加。新冠疫情导致结核病诊断和治疗的可及性降低，逆转了全球疾病管理数十年的进展。因此，分析患者健康记录中的真实世界多领域信息，以确定结核病治疗结果和耐药性的个性化预测指标至关重要。我们对5060例结核病患者的电子健康记录进行了回顾性分析，这些患者来自10个耐多药结核病负担较高的国家，包括乌克兰、摩尔多瓦、白俄罗斯和印度，数据来自美国国立过敏与传染病研究所结核病门户网站数据库。我们分析了多种宿主和病原体模式下的200多个特征，包括患者社会人口统计学特征、胸部X光和CT扫描中可见的疾病表现，以及每个患者病原体菌株的具有药敏特征的基因组记录。我们基于多种数据模式构建的机器学习模型在预测治疗结果方面优于仅使用单一模式构建的模型，准确率为81%，曲线下面积为0.768。我们确定了与治疗失败临床结果相关的各国稳健预测指标，并在结核病门户网站的新患者数据上验证了我们的预测。我们对药物方案和药物相互作用的分析表明，协同药物组合以及含有贝达喹啉、左氧氟沙星、氯法齐明和阿莫西林的药物组合在治疗耐多药和广泛耐药结核病方面更成功。通过胸部成像确定的特征，如异常体积百分比、肺空洞大小和支气管阻塞，与病原体耐药的基因组属性显著相关。体重指数较低和患有合并症的患者也观察到疾病严重程度增加。因此，我们的综合多模式分析揭示了放射学、微生物学、治疗学和人口统计学数据模式之间的显著关联，为个性化反应提供了更深入的理解，以帮助结核病的临床管理。
