Predictive Personalized Public Health (P3H): A Novel Paradigm to Treat Infectious Disease

预测性个性化公共卫生（P3H）：治疗传染病的新范式

• 批准号: 10006784
• 负责人: STEVEN J SCHIFF
• 金额: $ 162.8万
• 依托单位: PENNSYLVANIA STATE UNIV HERSHEY MED CTR
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-05 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10006784
• 关键词: Acute; Address; Algorithms; Antigens; Assimilations; Bacterial Meningitis; Bacteriology; Big Data; Cessation of life; Characteristics; Cholera; Climate; Clinical Trials; Communicable Diseases; Complex; Country; Data; Decision Making; Developed Countries; Developing Countries; Diagnostic; Diarrhea; Dimensions; Disease; Disease Outbreaks; Economics; Engineering; Environmental Risk Factor; Epidemic; Equilibrium; Exanthema; Exercise; Fever; Frequencies; Genus staphylococcus; Geography; HIV; Hand; Health Policy; Hospitals; Hybrids; Individual; Infant; Infection; Infrastructure; Investments; Laboratories; Leadership; Leptospirosis; Machine Learning; Malaria; Measles; Medical; Medical emergency; Melioidosis; Meningitis; Methods; Microbiology; Modeling; Molecular; Morbidity - disease rate; Names; Newborn Infant; Paralysed; Patient-Focused Outcomes; Patients; Physicians; Population; Prediction of Response to Therapy; Preventive; Process; Public Health; Reaction; Resistance; Resources; Role; Sepsis; Specimen; Syndrome; System; Techniques; Technology; Testing; Time; Training; Uganda; Work; acute infection; antimicrobial; authority; climate data; cost; cost outcomes; data fusion; design; effective therapy; fighting; flu; global health; improved; individual patient; infectious disease model; infectious disease treatment; innovation; insight; microbial; mortality; neonatal infection; novel; open source; pathogen; personalized medicine; personalized predictions; point of care; predictive modeling; prospective; resistance gene; spreading factor; surveillance data

Challenge, Innovation and Impact: In recent years, we have demonstrated that it is feasible to predict epidemic disease outbreaks from retrospective seasonal and geographical case data and to show that we can take climate factors into account in our predictive models. We are moving closer to real-time prediction at the population level. But we have never used prediction at point-of-care for treating the individual patient. Presently, personalized medicine uses delayed results of laboratory testing of individuals. For infectious disease, most of such testing has targeted the pathogen in the host-pathogen interaction. The role of laboratory testing is to modify therapy after a variable period of time delay. Personalized medicine today is reactive. Complicating matters further, many infectious epidemic diseases are strongly dependent on environmental factors and climate. Lastly, we want to name the pathogens we are fighting, but we really need to know the resistance characteristics to select therapy for patients effectively. Both speciation and resistance can now be determined from molecular data, which can be integrated into point-of-care treatment predictions. We here propose a radically different approach to the treatment of infectious diseases. Our hypothesis is that the alternative to time-delayed and expensive laboratory analysis of specimens from individual patients, is to use predictive modeling to forecast point-of-care treatment. Time-delayed personalized testing can be conducted as surveillance, and that data used for real-time prediction to guide point-of-care treatment. We will introduce predictive personalized public health (P3H) policy at the individual patient level, with the potential to substantially improve patient outcomes compared with our present reactive approaches. Our key rationale is to expand population infectious disease predictive modeling in order to achieve prediction for treatment at point-of-care. Our primary insight is that we can reposition the delayed reactive personalized testing from the urgent medical decision-making process, and into a predictive modeling framework. The gaps and opportunities in technology that we will address are four-fold. First, we will employ individual case geospatial mapping at a fine scale to take into account infection spread and environmental factors. Second, our ability to perform pan-microbial analysis using molecular techniques is now feasible. Third, modeling our novel fusion of data has no simple low-dimensional solution – but machine learning technologies are now capable of handling such big data assimilation, model discovery and prediction. Fourth, our proposal is not an academic exercise. We have a partnership with the economic planners within a developing country to design and implement our new methods. We will prospectively tune and validate our algorithms in real-time. Our deliverable will be an open-source framework ready for clinical trials testing and adaptation to the public health infrastructure in any country.

挑战、创新和影响：近年来，我们已经证明，预测是可行的 从追溯的季节性和地理病例数据中发现流行病暴发，并表明我们可以 在我们的预测模型中考虑气候因素。我们正在向实时预测迈进一步 人口水平。但我们从来没有在治疗个别患者时使用过临床点的预测。 目前，个性化医疗使用延迟的个人实验室测试结果。对于传染性的 对于疾病，大多数这种检测都是针对宿主-病原体相互作用中的病原体。的作用 实验室检测是在一段可变的时间延迟后修改治疗方法。今天的个性化医疗是 被动的。更复杂的是，许多传染性流行病强烈依赖于 环境因素和气候。最后，我们想要说出我们正在抗击的病原体的名字，但我们确实需要 了解耐药特点，为有效选择治疗方案提供依据。物种形成和抗性 现在可以从分子数据中确定，这些数据可以整合到护理点治疗预测中。 我们在这里提出了一种截然不同的传染病治疗方法。我们的 假设是，替代时间延迟和昂贵的实验室分析样品来自 个别患者，是使用预测建模来预测护理点治疗。延时 个性化测试可以作为监督进行，并将数据用于实时预测以指导 护理点治疗。 我们将在个人患者层面引入预测性个性化公共卫生(P3H)政策， 与我们目前的反应性方法相比，有可能显著改善患者的预后。 我们的主要原理是扩展人口传染病预测模型，以实现预测 在疗养点接受治疗。我们的主要见解是，我们可以重新定位延迟的反应性个性化 测试从紧急的医疗决策过程，并进入预测建模框架。差距 我们将解决的技术机遇有四个方面。首先，我们将采用个别案例 在精细比例尺上绘制地理空间图，以考虑到感染传播和环境因素。第二, 我们使用分子技术进行泛微生物分析的能力现在是可行的。第三，为我们的 新的数据融合没有简单的低维解决方案，但机器学习技术现在 能够处理这样的大数据同化、模型发现和预测。第四，我们的建议不是 学术练习。我们与发展中国家的经济规划者建立了合作关系，以设计 并实施我们的新方法。我们将前瞻性地实时调整和验证我们的算法。我们的 交付成果将是一个开放源码框架，可用于临床试验、测试和适应公共卫生 任何国家的基础设施。