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

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.