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

项目摘要/摘要 本研究提案项目的目标是确定与不同的 成人择期手术后呼吸衰竭(PRF)表型及其应用 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的系统和控制面板。PRF，定义为长期无法脱离机械通风或 氧合和/或换气不足，发病率高达7.5%，并与 经风险调整后，医院费用增加53,000美元，额外住院9天，增加22% 住院死亡率。随着择期手术的数量每年都在增加，迫切需要 以及未得到满足的需要，通过阐明风险来减少这一潜在可预防事件的发生率和负担， 预防和治疗因素。这些因素(其中一些可能是可修改的)可能不同 表型陈述。目标1：优化和验证基于电子病历的自动化临床预测模型 对于PRF。我们将自动收集数据，并对术前和术中因素的作用进行全面建模 模型识别和标定。假设：(H1.1)使数据管理自动化是可能的。(H1.2)A 包含2014-2021年数据和量化风险指数的模型将优于我们之前使用的模型 2012-2015年的数据。目的2：使用临床和生化标记物鉴定独特的PRF表型 在术后阶段随时可用，并确定这些标记物是否在48小时内预测PRF。 假设：(H2.1)现成的临床和生化标记物(例如，平均动脉压， 肌酐)以前与低炎性和高炎症性急性呼吸窘迫综合征有关 急性呼吸衰竭的表型也出现在PRF中。(H2.2)这些临床和生化标志物 可用于预测未来48小时内发生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公共数据模型(CDM)带来了好处，但将数据映射到CDM可能会导致 信息丢失并降低CDM映射模型的预测性能， 现场特定的EHR模型。(H3.2)多来源的可行性(例如，实时和历史的临床和 生物标记物数据)概率分数嵌入到电子病历中，将通过成功的部署进行演示 在生产前的环境中。完成这些目标，以及我们预计由此产生的五篇论文 这项工作将使我能够为具有竞争力的R01提案开发初步数据，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 扩展我现有的研究计划，以招收更多的地域、流行病学和 并进行大规模、多地点干预研究(U GRANT)以验证 我们的建模和促进个性化的治疗策略，以减少PRF的风险和负担。

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.