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Principal Investigator: GREGORY F. COOPER
Organization: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
Fiscal Year: 2024
Award: $575,480
Funding agency: National Heart Lung and Blood Institute
Abstract
More than 790,000 patients undergo mechanical ventilation for acute respiratory failure (ARF) in the United
States each year at a cost of $27 billion. The in-hospital mortality for these patients is nearly 35%, and for
patients with critical illness, such as acute respiratory distress syndrome (ARDS), mortality can approach 50%.
In some patients, guideline-appropriate care with lung-protective ventilation or prone positioning will save lives,
yet in many others, an individualized treatment is elusive. There is a need for advances in leveraging
opportunities in data science to improve outcomes from respiratory failure. The primary method for generating
new evidence is the randomized clinical trial (RCT). Yet they are often costly, take many years, and can be
slow to accelerate learning and implementation at the bedside. In addition, RCTs usually enroll a moderate
number of patients at high cost (100 to 1000s) and measure a limited range of covariates (10 to 100s). Thus,
they do not lead to prediction of highly individualized treatment effects, as called for by the NHLBI Working
Group on Research Priorities.
In contrast, real-world evidence from electronic health records (EHRs) includes many patients (often millions)
and covariates (often 1000s). They are inherently generalizable, less costly, and less timely to acquire than
conducting RCTs. However, the estimation of treatment effects from EHR data is often biased due to
confounding, which occurs when a treatment and its effect(s) are both causally influenced by one or more
events. This project uses two Specific Aims to solve these challenges. Aim 1 proposes to develop and evaluate
a new method for making individualized predictions of treatment effects using data from RCTs and EHRs. It
uses “embedded” RCTs in which the clinical trial occurs within the context of usual care of a health system.
The embedded RCT data are applied to control for confounding when using EHR data to predict treatment
effects. Aim 2 will apply these methods to two embedded RCTs at UPMC that are studying treatments that
may help prevent ARF. The OPTIMISE C-19 trial is studying monoclonal antibody therapy for non-hospitalized
patients with SARS-CoV-2 infection. The PeriOp trial will be studying perioperative interventions to improve
post-operative outcomes after major surgery. The hypothesis to be investigated is that the proposed new
methods will predict the effects of treatment on acute respiratory failure and other outcomes more accurately
than will using the clinical trial or the EHR data alone. Such results would provide support that these methods
yield individualized predictions of treatment effects that can inform clinical care to help prevent ARF.
Terms: <ARDS><Acceleration><Acute Respiratory Distress><Acute Respiratory Distress Syndrome><Acute respiratory failure><Adult ARDS><Adult RDS><Adult Respiratory Distress Syndrome><Airway failure><Award><Bayesian Method><Bayesian Methodology><Bayesian Statistical Method><Bayesian approaches><Bayesian classification method><Bayesian classification procedure><Bayesian posterior distribution><Big Data><BigData><COVID infected patient><COVID patient><COVID positive patient><COVID-19 infected patient><COVID-19 infection><COVID-19 patient><COVID-19 positive patient><COVID-19 virus infection><COVID19 infection><COVID19 patient><COVID19 positive patient><Caring><Clinical Trials><Companions><Conduct Clinical Trials><Critical Illness><Critically Ill><Da Nang Lung><Data><Data Science><Data Set><Deterioration><EHR system><Electronic Health Record><Enrollment><Event><Funding><Generations><Guidelines><Health system><Hospital Mortality><In-house Mortalities><Individual><Inhospital Mortality><Intervention><Intervention Strategies><Learning><Lung><Lung Respiratory System><Maps><Measures><Mechanical ventilation><Methods><Modeling><Monoclonal Antibody Therapy><NHLBI><NIH><National Heart, Lung, and Blood Institute><National Institutes of Health><Operative Procedures><Operative Surgical Procedures><Outcome><Patients><Perioperative><Population><Post-Operative><Postoperative><Postoperative Period><Prediction of Response to Therapy><Prone Position><Public Health><Randomized><Research Priority><Respiratory Failure><SARS-CoV-2 infected patient><SARS-CoV-2 infection><SARS-CoV-2 patient><SARS-CoV-2 positive patient><SARS-CoV2 infection><Selection for Treatments><Severe acute respiratory syndrome coronavirus 2 infection><Shock Lung><Sight><Statistical Methods><Stiff lung><Strategic vision><Surgical><Surgical Interventions><Surgical Procedure><Training><Translational Research><Translational Science><Treatment outcome><United States><United States National Institutes of Health><Vision><care as usual><clinical care><clinical trial enrollment><coronavirus disease 2019 infected patient><coronavirus disease 2019 infection><coronavirus disease 2019 patient><coronavirus disease 2019 positive patient><coronavirus disease infected patient><coronavirus disease patient><coronavirus disease positive patient><coronavirus disease-19 patient><coronavirus patient><cost><customized therapy><customized treatment><design><designing><electronic health care record><electronic health medical record><electronic health plan record><electronic health record system><electronic health registry><electronic medical health record><enroll><high risk><high risk group><high risk individual><high risk people><high risk population><improved><improved outcome><in silico><individualized medicine><individualized patient treatment><individualized predictions><individualized therapeutic strategy><individualized therapy><individualized treatment><infected with COVID-19><infected with COVID19><infected with SARS-CoV-2><infected with SARS-CoV2><infected with coronavirus disease 2019><infected with severe acute respiratory syndrome coronavirus 2><innovate><innovation><innovative><interventional strategy><mAB-based therapy><mAb therapy><mAb-based therapeutics><mechanical respiratory assist><mechanically ventilated><mortality><novel><patient infected with COVID><patient infected with COVID-19><patient infected with SARS-CoV-2><patient infected with coronavirus disease><patient infected with coronavirus disease 2019><patient infected with severe acute respiratory syndrome coronavirus 2><patient specific therapies><patient specific treatment><patient with COVID><patient with COVID-19><patient with COVID19><patient with SARS-CoV-2><patient with coronavirus disease><patient with coronavirus disease 2019><patient with severe acute respiratory distress syndrome coronavirus 2><personalization of treatment><personalized medicine><personalized predictions><personalized therapy><personalized treatment><predict therapeutic response><predict therapy response><prevent><preventing><pulmonary><randomisation><randomization><randomized, clinical trials><randomly assigned><response><response to therapy><response to treatment><selection of treatment><severe acute respiratory syndrome coronavirus 2 infected patient><severe acute respiratory syndrome coronavirus 2 patient><severe acute respiratory syndrome coronavirus 2 positive patient><statistic methods><surgery><tailored medical treatment><tailored therapy><tailored treatment><therapeutic response><therapy prediction><therapy response><therapy selection><translation research><translational investigation><treatment as usual><treatment effect><treatment prediction><treatment response><treatment response prediction><treatment responsiveness><treatment selection><unique treatment><usual care><ventilation><visual function><wet lung><work group><working group>