Pulmonary Embolism (PE) is a potentially life-threatening condition that affects adults of all ages yet can present with a myriad of symptoms, ranging from chest pain to shortness of breath, syncope, or seizure. Currently, physicians can assess for this condition with a blood test (D-dimer) which has high sensitivity but very poor specificity, thus resulting in a larger number of false positives. Alternatively, or in the case of a positive D-dimer, computed tomography pulmonary angiography (CTPA) or ventilation-perfusion (VQ) scan can be used, both of which are expensive and expose the patient to a significant amount of ionizing radiation. To develop a more specific blood test for PE, Biocogniv will apply state-of-the-art artificial intelligence (AI) to aggregate analysis of existing blood biomarkers measured on two commercially available multiplex immunoassay platforms, one of which is currently used in hospital labs. A sufficiently accurate, rapid and cost-effective test could be broadly applied (i.e., like troponin-I for myocardial infarction) to reduce overuse of CT, simplify ED decision making, and reduce the number of deaths from unrecognized PE. In this proposed Phase I single center study, Biocogniv will collaborate with the University of Vermont Medical Center (UVMMC) to demonstrate proof-of-concept for diagnosing PE in emergency department (ED) patients for whom there is sufficient concern for PE to warrant a D-dimer test as part of routine clinical care. Specific Aim I is to collect blood from 225 emergency department (ED) patients at UVMMC who were suspected of having PE (including 75 patients that are confirmed to have PE by CTPA), and analyze the blood plasma with quantitative immunoassays to create training and validation datasets for AI analysis. Immunoassays will be comprised of a set of 6 rapid FDA-cleared chemiluminescent immunoassays performed in parallel, and a 20-plex bead-based immunoassay panel targeting known cardiovascular and inflammatory markers associated with acute PE. Specific Aim II is to develop AI data models for two pretest populationsall D-dimer tested patients and just D-dimer positive patientsfor bead-based and point-of-care immunoassay datasets (analyzed separately), then evaluate the performance of each data model on a subset of blood plasma data withheld for validation. As part of the evaluation of AI data model performance, the potential impact of study size on data model accuracy will be simulated by plotting specificity as a function of training sample number to show that adding more samples can improve test results. The performance of each approach (i.e., AI methodology applied to a given immunoassay panel for a given pretest population) will be compared and used to plan a Phase II multi-center study and to identify and attract a suitable instrumentation partner. Biocognivs end goal is to develop a rapid, highly specific and sensitive FDA cleared test for PE that will become the standard of care for initial diagnosis and triage of patients with potential pulmonary embolism, disrupting the $2B/year D-Dimer market and improving ED patient care.
Public Health Relevance Statement: Pulmonary embolism (PE) is a potentially fatal condition that presents with non-specific symptoms (including chest pain, shortness of breath, and fainting) and is notoriously difficult to diagnose: as many as 1 in 3 Emergency Department (ED) patients ultimately diagnosed with PE are initially mis-diagnosed, and up to 1 in 3 PE deaths are only discovered upon autopsy. Biocogniv seeks to develop a new blood test for PE that is just as sensitive as the existing D-Dimer test, but that also has the higher specificity needed to screen and stratify all emergency department patients with PE-like symptoms. The expected impact of such a test would be to increase patient safety by reducing the overuse of radiative imaging, standardize clinical care processes, reduce costs and save lives.
Project Terms: Accident and Emergency department; Acute; Address; Adult; Affect; Age; Angiography; Area; Artificial Intelligence; Autopsy; base; Biological Assay; Biological Markers; Blood; Blood coagulation; Blood Proteins; Blood Tests; Cardiovascular Diseases; Cardiovascular system; Cessation of life; Chest Pain; clinical care; cost; cost effective; Creatine Kinase MB Isoenzyme; Data; data modeling; Data Set; Decision Making; Deep Vein Thrombosis; Development; Diagnosis; Diagnostic; Diagnostic tests; Dyspnea; Emergency Department patient; Evaluation; Fibrin fragment D; Goals; Gold; Hospitals; Image; Immunoassay; improved; inflammatory marker; instrumentation; Intravenous; Ionizing radiation; Life; Lower Extremity; Lung; Machine Learning; Measures; Medical center; Methodology; Methods; Modeling; molecular marker; Multicenter Studies; Myocardial Infarction; Myoglobin; novel; novel diagnostics; Patient Care; patient safety; Patient Triage; Patients; Performance; performance tests; Perfusion; Phase; Physicians; Plasma; point of care; Population; Predictive Value; Process; Pulmonary Embolism; Radiation; Risk; ROC Curve; Sampling; Scanning; Seizures; Sensitivity and Specificity; Shortness of Breath; Specificity; standard of care; Standardization; Symptoms; Syncope; targeted biomarker; Test Result; Testing; Training; Travel; Troponin I; Universities; Validation; Venous system; ventilation; Vermont; X-Ray Computed Tomography
