Identifying patient subgroups and processes of care that cause outcome differences following ICU vs. ward triage among patients with acute respiratory failure and sepsis

确定急性呼吸衰竭和脓毒症患者在 ICU 与病房分诊后导致结局差异的患者亚组和护理流程

• 批准号: 10734357
• 负责人: Scott D Halpern
• 金额: $ 75.18万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10734357
• 关键词: Accident and Emergency department; Accounting; Acute; Acute Renal Failure with Renal Papillary Necrosis; Acute Respiratory Distress Syndrome; Acute respiratory failure; Admission activity; Affect; Algorithms; American; Antibiotics; COVID-19; COVID-19 impact; California; Caring; Cessation of life; Chronic Obstructive Pulmonary Disease; Classification; Clinical; Collaborations; Complex; Complication; Consultations; Data; Data Set; Delirium; Development; Diagnostic; Electronic Health Record; Elements; Goals; Guidelines; Health Care Costs; Health system; Heart failure; Heterogeneity; Hospitalization; Hospitals; Hour; Iatrogenesis; Infection; Inpatients; Intensive Care Units; Knowledge; Length of Stay; Life; Liquid substance; Location; Machine Learning; Measures; Mediation; Mediator; Methodology; Methods; Modeling; Outcome; Palliative Care; Patient Admission; Patient Care; Patient-Focused Outcomes; Patients; Pattern; Pennsylvania; Phenotype; Population Heterogeneity; Probability; Process; Public Health; Recommendation; Resistance; Sepsis; Steroids; Subgroup; Syndrome; Testing; Triage; Universities; Update; Urinary tract infection; Work; acute care; cohort; coronavirus disease; current pandemic; end of life care; experience; future pandemic; high risk; improved; improved outcome; indexing; innovation; insight; meetings; mortality; mortality risk; multidisciplinary; novel; pandemic disease; patient subsets; preservation; septic patients; treatment pattern; unsupervised learning; ward

PROJECT SUMMARY Decisions to admit patients with acute respiratory failure (ARF) and sepsis (the most common and lethal cause of the acute respiratory distress syndrome) to intensive care units (ICUs) are highly variable across the US. And, yet, these triage decisions have a substantial impact on patient outcomes. In our prior work, we used detailed electronic health record (EHR) data from 9.2 million hospitalizations and found that decisions to admit ARF patients to wards were associated with a 3.8% absolute increase in mortality. In contrast, choices to admit sepsis patients to ICUs resulted in considerably longer length of stay and a 5.1% absolute increase in death. The nationwide impact of such discretionary triage would be exponentially greater. Our findings highlight tremendous opportunities to improve ARF and sepsis outcomes by identifying the patient subgroups and processes of care that most strongly contribute to the benefits and harms of ICU- versus ward-based care. This application proposes to update our ARF and sepsis cohort such that it includes all admissions from 2013 through 2022 across 29 hospitals in the Kaiser Permanente Northern California and University of Pennsylvania health systems, and incorporate more than 100 more data fields per patient. This curation of highly granular EHR data will enable us to identify the: (1) distinct patient subgroups and phenotypes among those meeting the syndromic criteria of `ARF' and `sepsis;' and the (2) processes of care and (3) inpatient complications that causally explain the observed associations of ICU vs. ward triage with patient outcomes. Our multidisciplinary team will apply diverse expertise in instrumental variable regression, mediation analyses, machine learning, complex EHR data, and probabilistic phenotyping to complete three aims that promote our long-term goal of improving care, and hence outcomes, for patients with ARF and sepsis regardless of where they are treated. Several methodological innovations will enable us to achieve these goals, and, in turn, to not only surmount key limitations of prior studies that sought to determine which acutely ill patients benefit from ICU admission, but identify the mechanisms underlying such triage effects. These data will also allow us to quantify the impact of COVID-19 on ICU and ward triage patterns, care processes, and outcomes among ARF and sepsis patients, thereby modernizing our results and enabling their applicability to pandemic eras. Completing the aims of this study will improve public health by identifying ways in which emergency departments, ICUs, and wards can improve outcomes for the more than 4 million Americans hospitalized each year with ARF and/or sepsis. Such results will enable development and testing of personalized triage algorithms, and guide optimal care for patients without always requiring ICU admission, thereby improving patient outcomes, reducing health care costs, and preserving ICU capacity for patients who truly need it.

项目概要 决定收治患有急性呼吸衰竭 (ARF) 和败血症（最常见和致命的原因）的患者 在美国，从急性呼吸窘迫综合征到重症监护病房 (ICU) 的情况差异很大。 然而，这些分诊决策对患者的治疗结果具有重大影响。在我们之前的工作中，我们使用了 920 万名住院患者的详细电子健康记录 (EHR) 数据发现，入院决定 ARF 患者入住病房与死亡率绝对增加 3.8% 相关。相比之下，选择承认 脓毒症患者入住 ICU 导致住院时间显着延长，死亡率绝对增加 5.1%。 这种酌情分类的全国性影响将呈指数级增长。我们的研究结果强调 通过识别患者亚组和改善 ARF 和败血症结果的巨大机会 与病房护理相比，对 ICU 护理的益处和危害影响最大的护理流程。 此应用程序建议更新我们的 ARF 和脓毒症队列，使其包含 2013 年以来的所有入院病例 到 2022 年，北加州凯撒医疗机构和宾夕法尼亚大学的 29 家医院将开展这项工作 健康系统，并为每个患者纳入 100 多个数据字段。这种高度精细的策划 EHR 数据将使我们能够识别：(1) 不同的患者亚组和表型 “ARF”和“败血症”的综合征标准；以及 (2) 护理过程和 (3) 住院并发症 因果关系解释了观察到的 ICU 与病房分诊与患者结果之间的关联。我们的多学科 团队将运用工具变量回归、中介分析、机器学习等方面的不同专业知识， 复杂的 EHR 数据和概率表型来完成三个目标，促进我们的长期目标 改善 ARF 和脓毒症患者的护理，从而改善预后，无论他们在哪里接受治疗。 多项方法创新将使我们能够实现这些目标，进而不仅能够超越 之前的研究试图确定哪些急症患者能从 ICU 入院中受益，但其主要局限性在于， 但要确定这种分类效应背后的机制。这些数据还将使我们能够量化影响 COVID-19 对 ARF 和脓毒症患者的 ICU 和病房分诊模式、护理流程和结果的影响， 从而使我们的成果现代化并使其适用于大流行时代。 完成本研究的目标将通过确定紧急情况的处理方式来改善公共卫生 科室、重症监护室和病房可以改善超过 400 万住院美国人的治疗结果 患有 ARF 和/或败血症的一年。这些结果将有助于个性化分诊的开发和测试 算法，并指导患者的最佳护理，而无需总是需要入住 ICU，从而改善 患者治疗效果，降低医疗保健成本，并为真正需要的患者保留 ICU 容量。