Identification of Postoperative Pulmonary Complication Risk by Phenotyping Adult Surgical Patients who Underwent General Anesthesia with Mechanical Ventilation

通过对接受机械通气全身麻醉的成人手术患者进行表型分析来识别术后肺部并发症风险

• 批准号: 10311613
• 负责人: Hideyo Tsumura
• 金额: $ 3.55万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10311613
• 关键词: Address; Admission activity; Adopted; Adult; Adult Respiratory Distress Syndrome; Age; Alveolar; Anatomy; Anesthesia procedures; Anesthesiology; Applications Grants; Area Under Curve; Big Data; Biological; Body mass index; Characteristics; Complication; Data; Data Science; Day Surgery; Effectiveness; Electronic Health Record; Fellowship; Foundations; Functional disorder; General Anesthesia; Goals; Health system; Hospital Costs; Hospitals; Incidence; Individual; Intensive Care; Intensive Care Units; Knowledge; Lead; Length; Length of Stay; Literature; Location; Logistic Regressions; Lung; Machine Learning; Mechanical ventilation; Morbidity - disease rate; National Institute of Nursing Research; Nurses; Observational Study; Odds Ratio; Operative Surgical Procedures; Oxygen; Pathology; Patient-Focused Outcomes; Patients; Phenotype; Physiological; Physiology; Pneumonia; Population Heterogeneity; Positive-Pressure Respiration; Postoperative Period; Precision Health; Problem Solving; Procedures; Research; Research Design; Research Methodology; Resources; Respiratory Signs and Symptoms; Respiratory System; Retrospective Studies; Risk; Risk Factors; Science; Scientist; Secondary to; Severities; Smoking; Statistical Methods; Subcategory; Subgroup; Surgical complication; Techniques; Tidal Volume; Time; Training; Universities; Ventilator; Ventilator-induced lung injury; advanced analytics; base; career; clinically significant; comorbidity; improved; machine learning algorithm; mortality; personalized care; personalized strategies; post-doctoral training; predictive modeling; pressure; prevent; provider adherence; recruit; reduce symptoms; sex; skills; study population; supplemental oxygen; support tools; surgery outcome; therapy development; ventilation

ABSTRACT Science: One in five patients who develop a postoperative pulmonary complication (PPC) dies within 30 days of surgery. PPCs are the second most frequent surgical complications and lead to increased admission to intensive care units, longer hospital length of stay, and high resource utilization. Ventilator induced lung injury (VILI) secondary to intraoperative mechanical ventilation is a risk for PPCs. Lung protective ventilation, which entails lower tidal volume, sufficient positive end expiratory pressure, optimal inspiratory time and an alveolar recruitment maneuver, has been adopted for intraoperative use to protect pulmonary parenchyma against VILI and ultimately reduce PPC incidence. However, we still do not know the optimal ventilator parameters to yield the lowest incidence of PPCs, because what is best varies from patient to patient. Personalized ventilator parameters are a potential solution to solve this problem. A retrospective study leveraging electronic health records (EHRs) is proposed to identify PPC risks by phenotyping adult surgical patients who underwent general anesthesia with mechanical ventilation. The specific aims of this project are to: (1) Examine the incidence of PPCs in the overall study population and phenotype patients based on nonmodifiable patient, surgical, and anesthesia characteristics; and examine the incidence of PPCs within each phenotypic subgroup; (2) Determine the optimal modifiable intraoperative ventilatory parameters associated with the lowest severity of PPCs within each phenotypic subgroup; and (3) Explore machine learning algorithms for predictive models of the incidence of PPCs on patient, surgical, and anesthesia characteristics as well as intraoperative ventilator parameters. The goal of this aim is to gain knowledge and training in machine learning to lay a foundation for postdoctoral training. Training: My long-term training goal is to become a leading nurse scientist in precision health using data science to improve patient outcomes following surgery, such as reducing PPCs. To achieve this goal, I have three short-term goals during my fellowship training: (1) gain knowledge and skills in research design to enhance precision health in anesthesiology to, (2) gain knowledge in advanced analytic techniques for conducting research using big data, and (3) gain an advanced understanding of pulmonary physiology and pathophysiology that influence anesthesia and patient surgical outcomes. This fellowship will allow me protected time to reach my training goals and to build a foundation for my long-term career goals. During the next twenty-six months as a trainee, I will obtain additional training in (1) research methods and design, (2) advanced statistical methods, (3) precision health, and (4) advanced pulmonary physiology and pathophysiology.

摘要 科学：五分之一的术后肺部并发症（PPC）患者在30天内死亡 外科手术。PPC是第二常见的手术并发症，并导致增加的入院率， 重症监护病房，住院时间较长，资源利用率高。呼吸机诱导的肺损伤 继发于术中机械通气的VILI是PPC的一种风险。肺保护性通气， 需要较低的潮气量，足够的呼气末正压，最佳吸气时间和肺泡 肺复张操作已被用于术中保护肺实质免受VILI 并最终降低PPC的发生率。然而，我们仍然不知道最佳的呼吸机参数， PPC的发生率最低，因为最好的方法因人而异。个性化呼吸机 参数是解决该问题的潜在解决方案。利用电子健康的回顾性研究 建议通过对接受手术的成人手术患者进行表型分析来识别PPC风险， 全麻机械通气。该项目的具体目标是：（1）审查 总体研究人群和基于不可改变患者的表型患者中PPC的发生率， 手术和麻醉特征;并检查每个表型亚组中PPC的发生率; (2)确定与最低严重程度相关的最佳可修改术中诊断参数 每个表型亚组内的PPC;以及（3）探索用于预测模型的机器学习算法 PPC的发生率与患者、手术和麻醉特征以及术中呼吸机有关 参数这一目标的目标是获得机器学习方面的知识和培训，为 博士后培训。 培训：我的长期培训目标是成为使用数据的精准健康领域的领先护士科学家 科学，以改善手术后的患者结局，例如减少PPC。为了实现这一目标，我 在我的奖学金培训期间，我有三个短期目标：（1）获得研究设计的知识和技能， 提高麻醉学的精确健康，（2）获得先进分析技术的知识， 使用大数据进行研究，以及（3）深入了解肺生理学， 影响麻醉和患者手术结果的病理生理学。这个奖学金会让我 保护时间来实现我的培训目标，并为我的长期职业目标奠定基础。期间 在接下来的26个月里，作为一名实习生，我将获得以下方面的额外培训：（1）研究方法和设计，（2） 先进的统计方法，（3）精确的健康，（4）先进的肺生理学和 病理生理学

项目成果

Exploring phenotype-based ventilator parameter optimization to mitigate postoperative pulmonary complications: a retrospective observational cohort study.

探索基于表型的呼吸机参数优化以减轻术后肺部并发症：一项回顾性观察队列研究。

• DOI: 10.1007/s00595-023-02785-8
• 发表时间: 2023
• 期刊: Surgery today
• 影响因子: 2.5
• 作者: Tsumura,Hideyo;Brandon,Debra;Vacchiano,Charles;Krishnamoorthy,Vijay;Bartz,Raquel;Pan,Wei
• 通讯作者: Pan,Wei