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.

项目摘要/摘要 该研究建议项目的目的是确定与不同的可修改因素 选修手术后成人的术后呼吸衰竭（PRF）表型，并利用这一点 信息以开发和部署预测模型和基于电子健康记录的概率评分 PRF的系统和仪表板。 PRF，定义为长期无法通过机械通气或 氧合和/或通风不足，发病率高达7.5％，与 风险调整后的医院费用增加了53,000美元，额外住院时间增加了9天，增长了22％ 住院死亡率。每年的选修外科手术程序的数量增加，紧急 并通过阐明风险来减轻这一可能预防事件的发病率和负担未满足 预防和治疗因素。这些因素，其中一些可能是可修改的，可能会有所不同 表型演示。目标1：优化和验证一个基于EHR的自动化，基于EHR的临床预测模型 对于prf。我们将自动数据收集自动化并建模术前和术中因素的贡献 模型歧视和校准。假设：（H1.1）可以自动化数据策划。 （H1.2） 包括2014 - 2021年数据和定量风险指数的数据的模型将胜过我们以前使用的模型 2012 - 2015年的数据。目的2：使用临床和生化标志物鉴定独特的PRF表型 在术后阶段很容易获得，并确定这些标记在48小时内是否预测PRF。 假设：（H2.1）容易获得的临床和生化生物标志物（例如，平均动脉压， 肌酐）以前与低炎性和高炎性急性呼吸窘迫综合征和 PRF中也存在急性呼吸衰竭表型。 （H2.2）这些临床和生化标记 可用于预测接下来48小时内PRF的概率。目标3：开发和部署 单位点，概念验证，基于EHR的概率评分系统和PRF的仪表板。假设： （H3.1）尽管OMOP公共数据模型（CDM）有好处，但数据映射到CDM中可能会导致 与天然相比，信息丢失并降低CDM映射模型的预测性能 特定于网站的EHR模型。 （H3.2）多元化的可行性（例如，实时和历史性的临床和历史性的临床和 EHR中嵌入的生物标志物数据）概率得分将通过成功的部署来证明 在预生产环境中。完成这些目标，以及我们预见的五篇论文 工作将使我能够为竞争性的R01提案制定初步数据，该提案旨在实施和 在UC范围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，以在地理上，流行病学上更加注册，并 社会经济多样化的中心并进行大规模的多站点干预研究（U Grant）以验证 我们的建模并促进了个性化治疗策略，以减轻PRF的风险和负担。