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.

项目摘要 急性呼吸衰竭（ARF）和脓毒症（最常见和致命的原因）患者的入院决定 急性呼吸窘迫综合征）到重症监护室（ICU）的风险在美国各地差异很大。 然而，这些分流决定对患者的结果有重大影响。在我们之前的工作中，我们使用 来自920万住院患者的详细电子健康记录（EHR）数据，并发现 ARF患者进入病房与死亡率绝对增加3.8%相关。相反，选择承认 脓毒症患者到ICU导致住院时间显著延长，死亡绝对增加5.1%。 这种自由裁量的分类在全国范围内的影响将成倍增加。我们的研究结果强调 通过识别患者亚组，改善ARF和脓毒症结局的巨大机会， 护理过程中，最强烈的贡献的好处和危害的ICU与病房为基础的护理。 本申请建议更新我们的ARF和脓毒症队列，以便纳入2013年以来的所有入院病例 到2022年，在北方加州和宾夕法尼亚大学的29家医院进行 卫生系统，并纳入了100多个数据字段每个病人。这种高度颗粒化的 EHR数据将使我们能够识别：（1）不同的患者亚组和表型之间的会议 “ARF”和“败血症”的综合征标准;（2）护理过程和（3）住院并发症， 因果解释观察到的ICU与病房分诊与患者结局的相关性。我们的多学科 团队将在工具变量回归，中介分析，机器学习， 复杂的EHR数据和概率表型，以完成三个目标，促进我们的长期目标， 改善ARF和脓毒症患者的护理，从而改善结局，无论他们在哪里接受治疗。 一些方法上的创新将使我们能够实现这些目标，反过来，不仅超越 先前研究试图确定哪些急性病患者从ICU住院中受益， 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 COVID-19对ARF和败血症患者的ICU和病房分诊模式、护理流程和结局的影响， 从而使我们的结果现代化，并使其适用于大流行时期。 完成这项研究的目的将通过确定紧急情况下， 各科室、ICU和病房可以改善400多万住院美国人的治疗效果， ARF和/或脓毒症。这样的结果将使开发和测试个性化分流 算法，并指导患者的最佳护理，而不总是需要ICU入院，从而提高 患者的治疗效果，降低医疗保健成本，并为真正需要的患者保留ICU容量。