Real-World Computable Phenotypes of Patient-Reported Disability in Multiple Sclerosis
Common tools to measure multiple sclerosis (MS) disability are rarely available in the real-world clinical setting. Leveraging electronic health records (EHR) data and disability outcomes from two independent EHR-linked MS research registries, we aimed to develop test and validate computable phenotypes of patient-reported MS disability status. After multiple model iterations, random forest model containing ±6 months of codified EHR data alone reaches potentially clinically actionable accuracy and concordance index while still being the most pragmatic for clinical deployment. Our pragmatic computable phenotypes of patient-reported disability could improve MS patient monitoring at the point of care enable large-scale clinical investigations, and may have clinical applications beyond MS.
Learning Objectives
- Develop, test, and validate computable phenotypes for patient-reported MS disability status using EHR data from multi-center, clinic-based MS cohorts.
- Assess the accuracy and clinical relevance of machine learning models trained on various EHR feature sets to ensure their applicability in real-world clinical settings.
- Explore the broader application of machine learning with multi-center EHR-registry data to generate computable phenotypes with the potential for clinical deployment.
Speaker
- Wen Zhu, M.D. (University of Pittsburgh)
Harnessing Diverse Populations to Advance Multiple Sclerosis Research
To advance MS research we created a demographically diverse multiple sclerosis (MS) cohort from All of Us using an unsupervised approach (2,030 MS cases, 30% non-White). MS polygenic risk score based on existing MS genomic map predicts MS well for European ancestry but poorly for African ancestry. Known non-genetic MS risk factors (obesity, smoking, vitamin D deficiency) showed consistent association across racial/ethnic groups. This recourse helps increase knowledge of MS risk in diverse populations.
Learning Objectives
- Understand the importance of including demographically diverse populations (e.g., All of Us Research Program) in multiple sclerosis (MS) research for validating risk factors and advancing precision medicine.
- Learn the application of a novel unsupervised phenotyping method to identify MS cases more accurately than traditional rule-based methods Evaluate the effectiveness of genetic (e.g., polygenic risk score) and non-genetic (e.g., obesity, smoking, Vitamin D deficiency) predictors of MS susceptibility across different racial and ethnic groups.
Speaker
- Chen Hu, M.S. (University of Pittsburgh)
Development and Implementation of Electronic Phenotyping Algorithms for Precision Medicine: A Framework for EHR-Based Clinical Trial Recruitment
Germline genetic testing is increasingly recommended for conditions with genetic etiologies that influence medical management. However, it's underutilized due to barriers at system, patient, and clinician levels. This study will use a hybrid cluster randomized trial to test nudges, informed by behavioral economics, aimed at increasing genetic testing uptake. Rapid cycle optimization will ensure effective implementation in diverse healthcare settings.
Learning Objectives
- Recognize barriers to genetic testing at system, patient, and clinician levels. Assess behavioral nudges to increase genetic testing uptake using a randomized trial.
Speaker
- Anurag Verma (University of Pennsylvania)
A Phenotype Algorithm for Classification of Single Ventricle Physiology using Electronic Health Records
We developed a phenotyping algorithm for identifying individuals with single ventricle physiology based on data from the electronic health record. Our algorithm was developed using features extracted from a cohort of 1,020 patients with ferumoxytol-enhanced MRI scans seen at our institution. When evaluated on a separate, broader cohort of 2,500 patients with clinically-adjudicated congenital heart disease, our algorithm demonstrated an accuracy of 99.2% and sensitivity of 97.5%, exceeding the performance of existing published methods.
Learning Objectives
- Understand the challenges and complexities associated with diagnosing Single Ventricle Physiology (SVP) in congenital heart disease (CHD) patients.
- Comprehend the role of a phenotype algorithm in improving the classification and diagnosis of SVP using electronic health records (EHRs). Recognize how structured and unstructured data from EHRs can be leveraged to enhance diagnostic accuracy for rare and complex conditions like SVP.
Speaker
- Hang Xu, Ph.D (UCLA)
A Generalized Tool to Assess Algorithmic Fairness in Disease Phenotype Definitions
For evidence from observational studies to be reliable, researchers must ensure that the patient populations of interest are accurately defined. However, disease definitions can be extremely difficult to standardize and implement accurately across different datasets and study requirements. Furthermore, in this context, they must also ensure that populations are represented fairly to accurately reflect populations’ various demographic dynamics and to not overgeneralize across non-applicable populations. In this work, we present a generalized tool to assess the fairness of disease definitions by evaluating their implementation across common fairness metrics. Our approach calculates fairness metrics and provides a robust method to examine coarse and strongly intersecting populations across many characteristics. We highlight workflows when working with disease definitions, provide an example analysis using an OMOP CDM patient database, and discuss potential directions for future improvement and research.
Learning Objectives
- Understand what fairness within phenotype definitions involve, what potential fairness metrics are available, and ways to assess fairness and equity within observational health research settings.
Speaker
- Jacob Zelko, B.S. (Northeastern University Roux Institute)
Continuing Education Credit
Physicians
The American Medical Informatics Association is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.
The American Medical Informatics Association designates this online enduring material for 1.5 AMA PRA Category 1™ credits. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
Claim credit no later than March 10, 2028 or within two years of your purchase date, whichever is sooner. No credit will be issued after March 10, 2028.
ACHIPsTM
AMIA Health Informatics Certified ProfessionalsTM (ACHIPsTM) can earn 1 professional development unit (PDU) per contact hour.
ACHIPsTM may use CME/CNE certificates or the ACHIPsTM Recertification Log to report 2025 Summit sessions attended for ACHIPsTM Recertification.
Claim credit no later than within three years of the release date or within two years of your purchase date, whichever is sooner.