Using Big Data to Understand Sepsis in an Immunocompromised Population

项目总结/摘要 通过这个提议，我将开发和评估一种针对同种异体造血干细胞的脓毒症预测工具， 细胞移植（HCT）受者。同种异体HCT接受者是免疫功能低下的人群， 不成比例地受到脓毒症的影响，脓毒症是对感染的一种危及生命的失调的免疫反应。 虽然已经确定，早期发现和治疗脓毒症的液体和广谱抗生素， 降低死亡率的风险，最近的数据表明，早期广谱抗生素使用同种异体HCT 接受者可能对发病率和死亡率具有微生物群介导的有害影响。因为有风险 与同种异体HCT接受者中漏诊和错误识别的脓毒症事件相关， 准确的脓毒症诊断至关重要。然而，脓毒症通常具有诊断挑战性， 在同种异体HCT接受者中，由于移植后脓毒症表现不同， 移植手术的常见并发症如败血症。在以前的工作中，我们证明了， 目前的败血症临床标准在同种异体HCT接受者中的预测价值较低，并得出结论， 需要特定人群的预测工具。最近开发的单一算法，机器学习败血症 预测工具已经在一般的免疫活性群体中显示出有希望的结果。然而，很少有研究 已经测试了机器学习工作流程预测高风险，免疫功能低下的败血症的能力， 患者，例如同种异体HCT接受者。此外，目前的脓毒症预测工具依赖于以下假设： 预测因子和结果之间的真实关系包含在单个算法中。这 拟议的工作有两个主要目标。第一个是开发一种自动化的脓毒症预测工具， HCT接受者使用最先进的基于集成的机器学习工作流程（超级学习者）， 放松了当前脓毒症预测工具的单一算法假设。第二个是估计的效用 该工具与传统（精确度方法）和新方法中的现有工具相比 （健康结果的数学模型）。这两个目标都将完成，最终目标是提高 同种异体HCT接受者中的脓毒症预测，并且在这种情况下，降低脓毒症相关的死亡率， 在这一难以诊断的人群中使用抗生素不当。此外，这项研究将推动 围绕机器学习预测工具在临床实践中的有用性以及 使用集成模型预测罕见的高病死率疾病。这些进步有可能 改善对脓毒症以外的健康结果的预测，减少可治疗疾病的负担， 免疫力低下的人群。

PROJECT SUMMARY/ABSTRACT Through this proposal, I will develop and evaluate a sepsis prediction tool targeted to allogeneic hematopoietic cell transplant (HCT) recipients. Allogeneic HCT recipients are an immunocompromised population that is disproportionately affected by sepsis, a life-threatening dysregulated immunologic response to an infection. While it is well established that early detection and treatment of sepsis with fluids and broad-spectrum antibiotics reduce the risk of mortality, recent data suggests early broad-spectrum antibiotic use in allogeneic HCT recipients may have microbiota-mediated detrimental effects on morbidity and mortality. Because of the risks associated with both missed and falsely identified sepsis events among allogeneic HCT recipients, early and accurate sepsis diagnosis is crucial. However, sepsis is generally challenging to diagnose and is made more complicated in allogeneic HCT recipients by the fact that sepsis presents differently following transplantation and common complications of the transplant procedure present like sepsis. In previous work, we demonstrated that current sepsis clinical criteria have low predictive value among allogeneic HCT recipients and concluded that population specific prediction tools are needed. Recently developed single algorithm, machine learning sepsis prediction tools have shown promising results in general, immuno-competent populations. However, few studies have tested the ability of machine learning workflows to predict sepsis in high-risk, immunocompromised patients, such as allogeneic HCT recipients. Additionally, current sepsis prediction tools rely on the assumption that the true relationship between the predictors and the outcome is contained within a single algorithm. This proposed work has two main objectives. The first is to develop an automated sepsis prediction tool for allogeneic HCT recipients using a state-of-the-art ensemble-based machine learning workflow (the super learner) that relaxes the single algorithm assumption of current sepsis prediction tools. The second is to estimate the utility of this tool in comparison to currently available tools in both traditional (accuracy methods) and novel ways (mathematical modeling of health outcomes). Both aims will be completed with the ultimate goal of improving sepsis prediction among allogeneic HCT recipients and in such, reducing sepsis related mortality and inappropriate antibiotic use among this hard to diagnose population. Further, this research will advance the methodological discussion around the usefulness of machine learning prediction tools in clinical practice and the use of ensemble modeling for prediction of rare, high-case fatality diseases. Such advances have the potential to improve the prediction of health outcomes beyond sepsis and reduce the burden of treatable diseases among immunocompromised populations.