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

项目总结/摘要 本研究建议项目的目的是确定与不同的 成人择期手术后的术后呼吸衰竭（PRF）表型，并利用这一点 信息来开发和部署预测模型和基于电子健康记录的概率评分 系统和仪表板。PRF，定义为长期无法脱离机械通气，或 氧合和/或通气不足的发生率高达7.5%，并与 风险调整后的住院费用增加53，000美元，住院时间增加9天，增加22% 住院死亡率。随着选择性外科手术的数量逐年增加，迫切需要 以及通过阐明风险来降低这种潜在可预防事件的发生率和负担的未满足需求， 预防和治疗因素。这些因素，其中一些可能是可以修改的，可能会有所不同， 表型表现目的1：优化和验证一个自动化的，基于EHR的临床预测模型 对于PRF。我们将自动化数据收集，并对术前和术中因素的作用进行建模， 模型识别和标定。假设：（H1.1）可以自动化数据策展。（H1.2）A 包括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， 2012-2015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， 在术后阶段很容易获得，并确定这些标志物是否能在48小时内预测PRF。 假设：（H2.1）容易获得的临床和生物化学生物标志物（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相关， 急性呼吸衰竭表型也存在于PRF中。（H2.2）这些临床和生化标志物 可用于预测未来48小时内发生重复频率的概率。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 信息丢失并降低CDM映射模型的预测性能， 特定站点的EHR模型。（H3.2）多功能的可行性（例如，实时和历史临床和 生物标志物数据）概率得分，嵌入在EHR中，将通过成功部署来证明 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 我的工作将使我能够为竞争性R 01提案开发初步数据， 在加州大学范围内的多中心研究中评估经验证的实时PRF预测模型。我的长期目标是 扩大我现有的研究计划，以招募更多的地理，流行病学， 社会经济多样化的中心，并进行大规模，多地点干预研究（U赠款），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.