Using Big Data to Understand Sepsis in an Immunocompromised Population

使用大数据了解免疫功能低下人群的脓毒症

• 批准号: 10064529
• 负责人: Margaret Lind
• 金额: $ 4.09万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-05 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10064529
• 关键词: Address; Admission activity; Adopted; Adult; Affect; Algorithms; Allogenic; American; Antibiotic Therapy; Antibiotics; Bacteremia; Big Data; Cancer Patient; Clinical; Clinical Data; Code; Collection; Complex; Data; Detection; Development; Diagnosis; Diagnostic; Disease; Early Diagnosis; Early treatment; Event; Frequencies; General Population; Goals; Health; Hospital Mortality; Hospitals; Immune response; Immunocompetent; Immunocompromised Host; Infection; Inflammatory Response; Intensive Care Units; Life; Liquid substance; Machine Learning; Mediating; Medical Records; Methodology; Methods; Modeling; Morbidity - disease rate; Organ failure; Outcome; Paper; Patients; Population; Predictive Value; Procedures; Publishing; Research; Research Personnel; Risk; Sensitivity and Specificity; Sepsis; Speed; Staphylococcus aureus; Streptococcus; System; Systemic Inflammatory Response Syndrome; Techniques; Testing; Time; Transplant Recipients; Transplantation; United States; Validation; Work; accurate diagnosis; base; cancer care; clinical practice; data cleaning; hematopoietic cell transplantation; high risk; improved; machine learning method; markov model; mathematical model; microbiota; mortality; mortality risk; neural network; novel; outcome prediction; patient population; predictive modeling; predictive tools; random forest; tool; treatment guidelines

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.

项目摘要/摘要 通过该建议，我将开发和评估针对同种异体造血的败血症预测工具 细胞移植（HCT）受体。同种异体HCT接受者是免疫功能低下的人群 受败血症的影响不成比例，这是一种威胁生命的对感染的免疫学反应。 虽然已经很好地确定，用液体和广谱抗生素对败血症进行早期检测和治疗 降低死亡率的风险，最近的数据表明在同种异体HCT中早期广谱抗生素使用 接受者可能对发病率和死亡率有微生物群介导的有害影响。由于风险 与同种异体HCT接受者之间的错过和错误鉴定的败血症有关，早期和 准确的败血症诊断至关重要。但是，败血症通常具有挑战性的诊断，并且被造成更多 同种异体HCT接受者复杂的事实是，移植后败血症的表现不同 如败血症所存在的移植程序的常见并发症。在以前的工作中，我们证明了 当前的败血症临床标准在同种异体HCT受体中的预测价值较低，并得出结论认为 需要特定于人口的预测工具。最近开发了单个算法，机器学习败血症 预测工具在一般情况下显示出令人鼓舞的结果。但是，很少有研究 已经测试了机器学习工作流以高风险，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，当前的败血症预测工具依赖于假设 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。首先是开发同种异体的自动败血症预测工具 HCT接收者使用最先进的集合机器学习工作流程（超级学习者） 放松当前败血症预测工具的单个算法假设。第二个是估计 与当前可用的工具相比，该工具以传统的（准确方法）和新颖的方式进行 （健康结果的数学建模）。这两个目标将完成，以改进的最终目标 同种异体HCT接受者之间的败血症预测，从而降低了败血症相关的死亡率和 在这种难以诊断的人群中，不适当的抗生素使用。此外，这项研究将推动 关于机器学习预测工具在临床实践和 使用合奏建模来预测罕见的高案例死亡疾病。这样的进步有潜力 改善败血症以外的健康结果的预测，并减轻可治疗疾病的负担 免疫功能低下的人群。

项目成果

Are hematopoietic cell transplant recipients with Gram-negative bacteremia spending more time outpatient while on intravenous antibiotics? Addressing trends over 10 years at a single center.

• DOI: 10.1002/iid3.486
• 发表时间: 2021-12
• 期刊: Immunity, inflammation and disease
• 作者: Lind ML;Roncaioli S;Liu C;Bryan A;Sweet A;Tverdek F;Sorror M;Phipps AI *;Pergam SA *
• 通讯作者: Pergam SA *