An analytical approach to characterize morbidity profile dissimilarity between distinct cohorts using electronic medical records

AbstractWe describe a two-stage analytical approach for characterizing morbidity profile dissimilarity among patient cohorts using electronic medical records. We capture morbidities using the International Statistical Classification of Diseases and Related Health Problems (ICD-9) codes. In the first stage of the approach separate logistic regression analyses for ICD-9 sections (e.g., “hypertensive disease” or “appendicitis”) are conducted, and the odds ratios that describe adjusted differences in prevalence between two cohorts are displayed graphically. In the second stage, the results from ICD-9 section analyses are combined into a general morbidity dissimilarity index (MDI). For illustration, we examine nine cohorts of patients representing six phenotypes (or controls) derived from five institutions, each a participant in the electronic MEdical REcords and GEnomics (eMERGE) network. The phenotypes studied include type II diabetes and type II diabetes controls, peripheral arterial disease and peripheral arterial disease controls, normal cardiac conduction as measured by electrocardiography, and senile cataracts

De-black-boxing health AI: demonstrating reproducible machine learning computable phenotypes using the N3C-RECOVER Long COVID model in the All of Us data repository

Machine learning (ML)-driven computable phenotypes are among the most challenging to share and reproduce. Despite this difficulty, the urgent public health considerations around Long COVID make it especially important to ensure the rigor and reproducibility of Long COVID phenotyping algorithms such that they can be made available to a broad audience of researchers. As part of the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative, researchers with the National COVID Cohort Collaborative (N3C) devised and trained an ML-based phenotype to identify patients highly probable to have Long COVID. Supported by RECOVER, N3C and NIH’s All of Us study partnered to reproduce the output of N3C’s trained model in the All of Us data enclave, demonstrating model extensibility in multiple environments. This case study in ML-based phenotype reuse illustrates how open-source software best practices and cross-site collaboration can de-black-box phenotyping algorithms, prevent unnecessary rework, and promote open science in informatics

Electronic Health Record Based Algorithm to Identify Patients with Autism Spectrum Disorder

Objective: Cohort selection is challenging for large-scale electronic health record (EHR) analyses, as International Classification of Diseases 9th edition (ICD-9) diagnostic codes are notoriously unreliable disease predictors. Our objective was to develop, evaluate, and validate an automated algorithm for determining an Autism Spectrum Disorder (ASD) patient cohort from EHR. We demonstrate its utility via the largest investigation to date of the co-occurrence patterns of medical comorbidities in ASD. Methods: We extracted ICD-9 codes and concepts derived from the clinical notes. A gold standard patient set was labeled by clinicians at Boston Children’s Hospital (BCH) (N = 150) and Cincinnati Children’s Hospital and Medical Center (CCHMC) (N = 152). Two algorithms were created: (1) rule-based implementing the ASD criteria from Diagnostic and Statistical Manual of Mental Diseases 4th edition, (2) predictive classifier. The positive predictive values (PPV) achieved by these algorithms were compared to an ICD-9 code baseline. We clustered the patients based on grouped ICD-9 codes and evaluated subgroups. Results: The rule-based algorithm produced the best PPV: (a) BCH: 0.885 vs. 0.273 (baseline); (b) CCHMC: 0.840 vs. 0.645 (baseline); (c) combined: 0.864 vs. 0.460 (baseline). A validation at Children’s Hospital of Philadelphia yielded 0.848 (PPV). Clustering analyses of comorbidities on the three-site large cohort (N = 20,658 ASD patients) identified psychiatric, developmental, and seizure disorder clusters. Conclusions: In a large cross-institutional cohort, co-occurrence patterns of comorbidities in ASDs provide further hypothetical evidence for distinct courses in ASD. The proposed automated algorithms for cohort selection open avenues for other large-scale EHR studies and individualized treatment of ASD

Admixture mapping and subsequent fine-mapping suggests a biologically relevant and novel association on chromosome 11 for type 2 diabetes in African Americans.

Type 2 diabetes (T2D) is a complex metabolic disease that disproportionately affects African Americans. Genome-wide association studies (GWAS) have identified several loci that contribute to T2D in European Americans, but few studies have been performed in admixed populations. We first performed a GWAS of 1,563 African Americans from the Vanderbilt Genome-Electronic Records Project and Northwestern University NUgene Project as part of the electronic Medical Records and Genomics (eMERGE) network. We successfully replicate an association in TCF7L2, previously identified by GWAS in this African American dataset. We were unable to identify novel associations at p<5.0×10(-8) by GWAS. Using admixture mapping as an alternative method for discovery, we performed a genome-wide admixture scan that suggests multiple candidate genes associated with T2D. One finding, TCIRG1, is a T-cell immune regulator expressed in the pancreas and liver that has not been previously implicated for T2D. We performed subsequent fine-mapping to further assess the association between TCIRG1 and T2D in >5,000 African Americans. We identified 13 independent associations between TCIRG1, CHKA, and ALDH3B1 genes on chromosome 11 and T2D. Our results suggest a novel region on chromosome 11 identified by admixture mapping is associated with T2D in African Americans

Demonstrating paths for unlocking the value of cloud genomics through cross cohort analysis

Abstract Recently, large scale genomic projects such as All of Us and the UK Biobank have introduced a new research paradigm where data are stored centrally in cloud-based Trusted Research Environments (TREs). To characterize the advantages and drawbacks of different TRE attributes in facilitating cross-cohort analysis, we conduct a Genome-Wide Association Study of standard lipid measures using two approaches: meta-analysis and pooled analysis. Comparison of full summary data from both approaches with an external study shows strong correlation of known loci with lipid levels (R2 ~ 83–97%). Importantly, 90 variants meet the significance threshold only in the meta-analysis and 64 variants are significant only in pooled analysis, with approximately 20% of variants in each of those groups being most prevalent in non-European, non-Asian ancestry individuals. These findings have important implications, as technical and policy choices lead to cross-cohort analyses generating similar, but not identical results, particularly for non-European ancestral populations

Candidate region targeted for fine-mapping.

<p>Using Seattle SNPs genome browser, the candidate genes located within 100<i>TCIRG1</i> gene, their orientation, and gene structure are displayed. SNPs annotated for these genes are located at the top of the figure denoted by hash marks. (Image generated from <a href="http://pga.gs.washington.edu/" target="_blank">http://pga.gs.washington.edu/</a>).</p