Development of a predictive model and electronic health record-based probability scoring system and dashboard for postoperative respiratory failure

开发术后呼吸衰竭的预测模型和基于电子健康记录的概率评分系统和仪表板

• 批准号: 10643357
• 负责人: Jacqueline C Stocking
• 金额: $ 18.34万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10643357
• 关键词: Acute Respiratory Distress Syndrome; Acute respiratory failure; Address; Adult; Biochemical; Biochemical Markers; Biological Markers; Calibration; California; Client satisfaction; Clinical; Clinical Markers; Creatinine; Critical Care; Data; Data Collection; Development; Discrimination; Dose; Elective Surgical Procedures; Electronic Health Record; Enrollment; Environment; Epidemiology; Event; Fluid Balance; Geography; Goals; Grant; Health; Health system; Healthcare; Hospital Charges; Hospital Mortality; Hospitalization; Hour; Incidence; Inflammatory; Intervention Studies; Investments; Maps; Mechanical ventilation; Modeling; Multicenter Studies; Neurologic; Nurses; Operative Surgical Procedures; Paper; Patient-Focused Outcomes; Patients; Performance; Perioperative; Phase; Phenotype; Physicians; Positive-Pressure Respiration; Postoperative Period; Prevention strategy; Preventive; Probability; Production; Reporting; Research; Research Proposals; Resources; Respiratory Failure; Risk; Risk Adjustment; Risk Reduction; Scientist; Site; Source; Surgical complication; System; Therapeutic; Tidal Volume; Time; Universities; Ventilator; Weaning; Work; clinical predictive model; dashboard; data curation; data modeling; high risk; improved; indexing; modifiable risk; patient oriented; patient population; personalized medicine; postoperative recovery; predictive marker; predictive modeling; pressure; preventive intervention; programs; socioeconomics; treatment strategy; ventilation

PROJECT SUMMARY/ABSTRACT The objective of this research proposal project is to identify modifiable factors associated with different postoperative respiratory failure (PRF) phenotypes in adults following elective surgery and to utilize this information to develop and deploy a predictive model and electronic health record-based probability scoring system and dashboard for PRF. PRF, defined as the prolonged inability to wean from mechanical ventilation or inadequate oxygenation and/or ventilation, has an incidence of up to 7.5% and has been associated with a risk-adjusted $53,000 increase in hospital charges, 9 extra days of hospitalization, and a 22% increase in-hospital mortality. With the number of elective surgical procedures increasing annually, there is an urgent and unmet need to reduce the incidence and burden of this potentially preventable event by elucidating risk, preventive, and therapeutic factors. These factors, some of which may be modifiable, may differ between phenotypic presentations. AIM 1: To optimize and validate an automated, EHR-based, clinical prediction model for PRF. We will automate data collection and model the contributions of pre-and intra-operative factors on full model discrimination and calibration. Hypotheses: (H1.1) It is possible to automate data curation. (H1.2) A model including data from 2014-2021 and quantitative risk indices will outperform our previous model that used data from 2012-2015. AIM 2: To identify unique PRF phenotypes using clinical and biochemical markers that are readily available in the postoperative phase and determine if these markers predict PRF within 48 hours. Hypotheses: (H2.1) Readily available clinical and biochemical biomarkers (e.g., mean arterial pressure, creatinine) previously associated with hypo- and hyper-inflammatory acute respiratory distress syndrome and acute respiratory failure phenotypes are also present in PRF. (H2.2) These clinical and biochemical markers can be used to predict the probability of PRF within the next 48 hours. AIM 3: To develop and deploy a single-site, proof-of-concept, EHR-based probability scoring system, and dashboard for PRF. Hypotheses: (H3.1) Despite the benefits of the OMOP Common Data Model (CDM), data mapping into the CDM may cause information loss and decrease the predictive performance of a CDM-mapped model compared to the native, site-specific EHR model. (H3.2) The feasibility of a multisource (e.g., real-time and historic clinical and biomarker data) probability score, embedded in the EHR, will be demonstrated through successful deployment in a pre-production environment. Completing these Aims, and the five papers we foresee producing from this work will enable me to develop preliminary data for a competitive R01 proposal focused on implementing and evaluating a validated, real-time PRF predictive model in a UC-wide multi-center study. My long-term goal is to expand my existing program of research to enroll more geographically, epidemiologically, and socioeconomically diverse centers and conduct a large-scale, multisite intervention study (U grant) to validate our modeling and facilitate personalized treatment strategies to reduce the risk and burden of PRF.

项目概要/摘要 该研究计划项目的目的是确定与不同的因素相关的可修改因素 择期手术后成人术后呼吸衰竭（PRF）表型并利用该表型 用于开发和部署预测模型和基于电子健康记录的概率评分的信息 PRF 系统和仪表板。 PRF，定义为长时间无法脱离机械通气或 氧合和/或通气不足，发生率高达 7.5%，并与 风险调整后住院费用增加 53,000 美元，住院时间延长 9 天，增加 22% 院内死亡率。随着择期手术数量逐年增加，迫切需要 以及通过阐明风险来减少这种潜在可预防事件的发生率和负担的未满足的需求， 预防和治疗因素。这些因素（其中一些可能是可修改的）在不同环境下可能有所不同 表型呈现。目标 1：优化和验证基于 EHR 的自动化临床预测模型 对于 PRF。我们将自动化数据收集，并对术前和术中因素的贡献进行全面建模 模型判别和校准。假设：（H1.1）自动化数据管理是可能的。 (H1.2) A 包含 2014-2021 年数据和定量风险指数的模型将优于我们之前使用的模型 2012年至2015年的数据。目标 2：使用临床和生化标志物识别独特的 PRF 表型， 术后阶段即可轻松获得这些标记物，并确定这些标记物是否可以在 48 小时内预测 PRF。 假设：（H2.1）现成的临床和生化生物标志物（例如，平均动脉压、 肌酐）以前与低炎症和高炎症急性呼吸窘迫综合征有关， PRF 中也存在急性呼吸衰竭表型。 (H2.2) 这些临床和生化标志物 可用于预测未来 48 小时内 PRF 的概率。目标 3：开发和部署 单站点、概念验证、基于 EHR 的概率评分系统和 PRF 仪表板。假设： (H3.1) 尽管 OMOP 通用数据模型 (CDM) 有好处，但数据映射到 CDM 可能会导致 与原生模型相比，CDM 映射模型的信息丢失并降低了预测性能， 特定地点的 EHR 模型。 (H3.2) 多源的可行性（例如，实时和历史临床和 嵌入 EHR 中的生物标志物数据）概率评分将通过成功部署得到证明 在预生产环境中。完成这些目标，以及我们预计由此产生的五篇论文 工作将使我能够为一个有竞争力的 R01 提案开发初步数据，该提案的重点是实施和 在加州大学范围内的多中心研究中评估经过验证的实时 PRF 预测模型。我的长期目标是 扩大我现有的研究计划，以招募更多地理、流行病学和 社会经济多元化中心并进行大规模、多地点干预研究（U grant）以验证 我们的建模并促进个性化治疗策略，以降低 PRF 的风险和负担。