Chest pain, the main symptom of acute myocardial infarction (AMI), accounts for ~5% of all emergency department (ED) visits. In the absence of ECG abnormalities, diagnostic gold standard for AMI relies on serial troponin (cTn) measurements which are inconclusive in 20-40% of patients, requiring additional testing and prolonged observation in the ED. A missed diagnosis of AMI without proper treatment is life threatening and thus rule-out diagnosis requires very high sensitivity. Hence, a rapid point of care (POC) test utilized as an adjunct to cTn with enhanced diagnostic performance would be revolutionary for risk stratification and timely and safe triaging of patients with suspected MI in ED. Inflammatix is a molecular diagnostics company focused on developing and bringing to market best in class, immune response based, data-driven testing. We developed a point-of-care instrument, MyRNA, capable of quantitating up to 64 mRNAs in under 30 minutes (with <2 minutes operator time), directly from patients' blood, in a fully disposable cartridge. We specialize in use of state-of-art multi-cohort analysis and machine learning (ML) to identify and validate robust biomarkers that generalize across real-world data heterogeneity, in diverse clinical contexts. Previous work demonstrated the potential of blood gene expression as a biomarker for MI, however a clinical test based on immune response in blood gene expression is yet to be developed. We applied our analytical framework to 6 publicly available datasets and identified a multi-gene AMI signature in peripheral blood that allows us to differentiate patients with AMI from clinically relevant controls with AUC ~ 0.95. In this project, we propose to take the AMI signature from preliminary results through research and initial development stages, up to formal clinical diagnostic development. We will generate a significant amount of independent data, leverage Inflammatix ML capabilities to further refine the mRNA signature and deliver a robust classifier ready for validation in prospective studies. In Specific Aim 1, we will generate, process, and analyze RNA-seq data for 900 blood samples from retrospective cohorts closely representing the target test population. In Specific Aim 2, we will first refine, optimize, and validate the mRNA signature; and then develop a prototype ML classifier. Specifically, we will 1) integrate expression data from all cohorts while minimizing bias; 2) apply Bayesian multi-cohort framework for final gene set selection with 300 new samples; 3) develop and evaluate discriminatory performance of AMI classifier prototype; and 4) validate the AMI classifier prototype on 600 unseen samples. These steps will produce: i) a validated set of genes for AMI; ii) an integrated dataset; and iii) a classifier prototype (AUC > 0.90), ready for clinical validation via prospective studies in Phase 2 research. This test, when developed as a cartridge on Inflammatix's POC instrument, MyRNA, will facilitate clinician's triaging decisions of patients with suspected MI, improve patient outcomes, and reduce healthcare costs.
Public Health Relevance Statement: NARRATIVE This project addresses the significant clinical need to develop a rapid, clinically useful point of care (POC) test with enhanced diagnostic precision for patients presenting with suspected myocardial infarction (MI). Harnessing meta-analysis and machine learning, and embracing heterogeneity found in multiple cohort and real-world applications, this project will discover and validate a discernable blood gene expression response to MI. Success would be revolutionary in risk stratification and safe triaging of patients with suspected MI, positively affecting patient management and outcomes, and optimizing healthcare spending and Emergency Room occupancy.
Project Terms: data integration; blood infection; bloodstream infection; Sepsis; Outcome; blind; Population; transcriptomics; clinical relevance; clinically relevant; prototype; real world application; combat; bio-markers; biologic marker; biomarker; Biological Markers; product development; point-of-care diagnostics; RNA Seq; RNA sequencing; RNAseq; transcriptomic sequencing; transcriptome sequencing; Patient Triage; ED patient; ER patient; Emergency Room patient; Emergency Department patient; ED visit; ER visit; Emergency care visit; Emergency hospital visit; Emergency room visit; Emergency department visit; personalized diagnosis; precise diagnostics; precision diagnostics; personalized diagnostics; clinically actionable; gene signatures; genetic signature; molecular diagnostics; acute symptom; Retrospective cohort; stratify risk; risk stratification; medical diagnostic; clinical diagnostics; acute infection; multilayer perceptron; analysis pipeline; data heterogeneity; data set heterogeneity; dataset heterogeneity; heterogeneous data; heterogeneous data sets; heterogeneous datasets; heterogenous data sets; heterogenous datasets; heterogenous data; bio-informatics pipeline; bioinformatics pipeline; support vector machine; machine learning based method; machine learning methodologies; machine learning method; Rapid diagnostics; point of care testing; machine learning pipeline; machine learning based pipeline; machine learning model; machine learning based model; machine learning classifier; machine learning based classifier; Affect; Blood; Blood Reticuloendothelial System; Chest Pain; Diagnosis; Electrocardiogram; ECG; EKG; Electrocardiography; Engineering; Gene Expression; Genes; Geography; Goals; Grant; Heterogeneity; Libraries; Marketing; Methods; Myocardial Infarction; Cardiac infarction; Myocardial Infarct; cardiac infarct; coronary attack; coronary infarct; coronary infarction; heart attack; heart infarct; heart infarction; Patients; Phenotype; Prospective Studies; Research; RNA; Non-Polyadenylated RNA; RNA Gene Products; Ribonucleic Acid; Messenger RNA; mRNA; Sensitivity and Specificity; Target Populations; Testing; Time; Translating; Triage; Troponin; Work; Generations; Measures; Health Costs; Healthcare Costs; Health Care Costs; Cohort Analyses; Cohort Analysis; Diagnostic tests; health care; Healthcare; Data Set; Acute myocardial infarct; Acute myocardial infarction; Blood Sample; Blood specimen; improved; Clinical; Phase; Logistic Regressions; Training; peripheral blood; Bayesian Methodology; Bayesian Statistical Method; Bayesian approaches; Bayesian classification method; Bayesian classification procedure; Bayesian posterior distribution; Bayesian Method; Measurement; Collaborations; Letters; Immunological response; host response; immune system response; immunoresponse; Immune response; instrument; Diagnostic; Machine Learning; machine based learning; Life; System; Accident and Emergency department; Emergency Department; Emergency room; Performance; success; cohort; Modeling; Sampling; response; Meta-Analysis; Address; Data; Clinical Sensitivity; research clinical testing; Clinical Evaluation; Clinical Testing; clinical test; Patient-Focused Outcomes; Patient outcome; Patient-Centered Outcomes; patient oriented outcomes; Small Business Innovation Research Grant; SBIR; Small Business Innovation Research; Validation; validations; Preparation; preparations; Process; Development; developmental; point of care
