Performance of Ambulatory Electrocardiographic Data for Prediction of Stroke and Heart Failure Events.

Background: Despite clear associations between arrhythmia burden and cardiovascular risk, clinical risk scores that predict cardiovascular events do not incorporate individual-level arrhythmia characteristics from long-term continuous monitoring (LTCM).

Objectives: This study evaluated the performance of risk models that use data from LTCM and patient claims for prediction of heart failure (HF) and ischemic stroke.

Methods: We retrospectively analyzed features extracted from up to 14 days of LTCM electrocardiogram (ECG) data linked to patient-level claims data for 320,974 Medicare beneficiaries who underwent ZioXT ambulatory monitoring.

Reproducible Reporting of the Collection and Evaluation of Annotations for Artificial Intelligence Models.

This work puts forth and demonstrates the utility of a reporting framework for collecting and evaluating annotations of medical images used for training and testing artificial intelligence (AI) models in assisting detection and diagnosis. AI has unique reporting requirements, as shown by the AI extensions to the Consolidated Standards of Reporting Trials (CONSORT) and Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) checklists and the proposed AI extensions to the Standards for Reporting Diagnostic Accuracy (STARD) and Transparent Reporting of a Multivariable Prediction model for Individual Prognosis or Diagnosis (TRIPOD) checklists. AI for detection and/or diagnostic image analysis requires complete, reproducible, and transparent reporting of the annotations and metadata used in training and testing data sets.

Image-based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno-oncology Biomarker Working Group on Breast Cancer.

Recent advances in the field of immuno-oncology have brought transformative changes in the management of cancer patients. The immune profile of tumours has been found to have key value in predicting disease prognosis and treatment response in various cancers. Multiplex immunohistochemistry and immunofluorescence have emerged as potent tools for the simultaneous detection of multiple protein biomarkers in a single tissue section, thereby expanding opportunities for molecular and immune profiling while preserving tissue samples.

Prediction of atrial fibrillation from at-home single-lead ECG signals without arrhythmias.

Early identification of atrial fibrillation (AF) can reduce the risk of stroke, heart failure, and other serious cardiovascular outcomes. However, paroxysmal AF may not be detected even after a two-week continuous monitoring period. We developed a model to quantify the risk of near-term AF in a two-week period, based on AF-free ECG intervals of up to 24 h from 459,889 patch-based ambulatory single-lead ECG (modified lead II) recordings of up to 14 days.

Cost-Effectiveness of AF Screening With 2-Week Patch Monitors: The mSToPS Study.

Background: The mSToPS study (mHealth Screening to Prevent Strokes) reported screening older Americans at risk for atrial fibrillation (AF) and stroke using 2-week patch monitors was associated with increased rates of AF diagnosis and anticoagulant prescription within 1 year and improved clinical outcomes at 3 years relative to matched controls. Cost-effectiveness of this AF screening approach has not been explored.

Methods: We conducted a US-based health economic analysis of AF screening using patient-level data from mSToPS.

Initial interactions with the FDA on developing a validation dataset as a medical device development tool.

Quantifying tumor-infiltrating lymphocytes (TILs) in breast cancer tumors is a challenging task for pathologists. With the advent of whole slide imaging that digitizes glass slides, it is possible to apply computational models to quantify TILs for pathologists. Development of computational models requires significant time, expertise, consensus, and investment.

Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer.

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results.

Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer.

Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector-based).

Pilot study to evaluate tools to collect pathologist annotations for validating machine learning algorithms.

Validation of artificial intelligence (AI) algorithms in digital pathology with a reference standard is necessary before widespread clinical use, but few examples focus on creating a reference standard based on pathologist annotations. This work assesses the results of a pilot study that collects density estimates of stromal tumor-infiltrating lymphocytes (sTILs) in breast cancer biopsy specimens. This work will inform the creation of a validation dataset for the evaluation of AI algorithms fit for a regulatory purpose.

A Pathologist-Annotated Dataset for Validating Artificial Intelligence: A Project Description and Pilot Study.

Purpose: Validating artificial intelligence algorithms for clinical use in medical images is a challenging endeavor due to a lack of standard reference data (ground truth). This topic typically occupies a small portion of the discussion in research papers since most of the efforts are focused on developing novel algorithms. In this work, we present a collaboration to create a validation dataset of pathologist annotations for algorithms that process whole slide images.

Clinical and Analytical Validation of a Novel Urine-Based Test for the Detection of Allograft Rejection in Renal Transplant Patients.

In this clinical validation study, we developed and validated a urinary Q-Score generated from the quantitative test QSant, formerly known as QiSant, for the detection of biopsy-confirmed acute rejection in kidney transplants. Using a cohort of 223 distinct urine samples collected from three independent sites and from both adult and pediatric renal transplant patients, we examined the diagnostic utility of the urinary Q-Score for detection of acute rejection in renal allografts. Statistical models based upon the measurements of the six QSant biomarkers (cell-free DNA, methylated-cell-free DNA, clusterin, CXCL10, creatinine, and total protein) generated a renal transplant Q-Score that reliably differentiated stable allografts from acute rejections in both adult and pediatric renal transplant patients.

Analytical Validation of Relative Average Telomere Length Measurement in a Clinical Laboratory Environment.

Background: Average telomere length in whole blood has become a biomarker of aging, disease, and mortality risk across a broad range of clinical conditions. The most common method of telomere length measurement for large patient sample sets is based on quantitative PCR (qPCR). For laboratory-developed tests to be performed on clinical samples, they must undergo a rigorous analytical validation, currently regulated under CLIA.

Cost effectiveness analysis of a next generation risk assessment score for cardiovascular disease.

Objectives: The goal of this study is to determine the cost-effectiveness of MIRISK VP, a next generation coronary heart disease risk assessment score, in correctly reclassifying and appropriately treating asymptomatic, intermediate risk patients.

Study Design: A Markov model was employed with simulated subjects based on the Multi-Ethnic Study of Atherosclerosis (MESA). This study evaluated three treatment strategies: (i) practice at MESA enrollment, (ii) current guidelines, and (iii) MIRISK VP in MESA.

Clinical utility of a novel coronary heart disease risk-assessment test to further classify intermediate-risk patients.

Background: Current coronary heart disease (CHD) risk assessments inadequately assess intermediate-risk patients, leaving many undertreated and vulnerable to heart attacks. A novel CHD risk-assessment (CHDRA) tool was developed for intermediate-risk stratification using biomarkers and established risk factors to significantly improve CHD risk discrimination.

Hypothesis: Physicians will change their treatment plan in response to more information about a patient's CHD risk level provided by the CHDRA test.

Circulating angiopoietins-1 and -2, angiopoietin receptor Tie-2 and vascular endothelial growth factor-A as biomarkers of acute myocardial infarction: a prospective nested case-control study.

Background: Angiogenesis is up-regulated in myocardial ischemia. However, limited data exist assessing the value of circulating angiogenic biomarkers in predicting future incidence of acute myocardial infarction (AMI). Our aim was to examine the association between circulating levels of markers of angiogenesis with risk of incident acute myocardial infarction (AMI) in men and women.

Circulating chemokines accurately identify individuals with clinically significant atherosclerotic heart disease.

Serum inflammatory markers correlate with outcome and response to therapy in subjects with cardiovascular disease. However, current individual markers lack specificity for the diagnosis of coronary artery disease (CAD). We hypothesize that a multimarker proteomic approach measuring serum levels of vascular derived inflammatory biomarkers could reveal a "signature of disease" that can serve as a highly accurate method to assess for the presence of coronary atherosclerosis.

Characterization of compound mechanisms and secondary activities by BioMAP analysis.

Introduction: Unexpected drug activities account for many of the failures of new chemical entities in clinical trials. These activities can be target-dependent, resulting from feedback mechanisms downstream of the primary target, or they can occur as a result of unanticipated secondary target(s). Methods that would provide rapid and efficient characterization of compounds with respect to a broad range of biological pathways and mechanisms relevant to human disease have the potential to improve preclinical and clinical success rates.

Approaches to the analysis of cell signaling networks and their application in drug discovery.

The ability to predict the safety and efficacy of novel drugs prior to clinical testing is a key goal in pharmaceutical drug discovery. Gaining a mechanistic understanding of the complex cell signaling networks (CSNs) underlying disease processes promises to help reduce the number of clinical failures by identifying points of intervention as well as redundancies and feedback mechanisms that contribute to toxicities, lack of efficacy and unexpected biological activities. Experimental and computational approaches to analyzing and modeling CSNs are currently being validated using simple organisms and cell lines.

Rapid structure-activity and selectivity analysis of kinase inhibitors by BioMAP analysis in complex human primary cell-based models.

Rapid, quantitative methods for characterizing the biological activities of kinase inhibitors in complex human cell systems could allow the biological consequences of differential target selectivity to be monitored early in development, improving the selection of drug candidates. We have previously shown that Biologically Multiplexed Activity Profiling (BioMAP) permits rapid characterization of drug function based on statistical analysis of protein expression data sets from complex primary human cellbased models of disease biology. Here, using four such model systems containing primary human endothelial cells and peripheral blood mononuclear cells in which multiple signaling pathways relevant to inflammation and immune responses are simultaneously activated, we demonstrate that BioMAP analysis can detect and distinguish a wide range of inhibitors directed against different kinase targets.

An integrative biology approach for analysis of drug action in models of human vascular inflammation.

Unexpected drug activities discovered during clinical testing establish the need for better characterization of compounds in human disease-relevant conditions early in the discovery process. Here, we describe an approach to characterize drug function based on statistical analysis of protein expression datasets from multiple primary human cell-based models of inflammatory disease. This approach, termed Biologically Multiplexed Activity Profiling (BioMAP), provides rapid characterization of drug function, including mechanism of action, secondary or off-target activities, and insights into clinical phenomena.

Method for analyzing signaling networks in complex cellular systems.

Now that the human genome has been sequenced, the challenge of assigning function to human genes has become acute. Existing approaches using microarrays or proteomics frequently generate very large volumes of data not directly related to biological function, making interpretation difficult. Here, we describe a technique for integrative systems biology in which: (i) primary cells are cultured under biologically meaningful conditions; (ii) a limited number of biologically meaningful readouts are measured; and (iii) the results obtained under several different conditions are combined for analysis.