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Article ; Online: Illuminating the landscape of high-level clinical trial opportunities in the All of Us Research Program.

Shyr, Cathy / Sulieman, Lina / Harris, Paul A

Journal of the American Medical Informatics Association : JAMIA

2024

Abstract: Objective: With its size and diversity, the All of Us Research Program has the potential to power and improve representation in clinical trials through ancillary studies like Nutrition for Precision Health. We sought to characterize high-level trial ... ...

Abstract	Objective: With its size and diversity, the All of Us Research Program has the potential to power and improve representation in clinical trials through ancillary studies like Nutrition for Precision Health. We sought to characterize high-level trial opportunities for the diverse participants and sponsors of future trial investment. Materials and methods: We matched All of Us participants with available trials on ClinicalTrials.gov based on medical conditions, age, sex, and geographic location. Based on the number of matched trials, we (1) developed the Trial Opportunities Compass (TOC) to help sponsors assess trial investment portfolios, (2) characterized the landscape of trial opportunities in a phenome-wide association study (PheWAS), and (3) assessed the relationship between trial opportunities and social determinants of health (SDoH) to identify potential barriers to trial participation. Results: Our study included 181 529 All of Us participants and 18 634 trials. The TOC identified opportunities for portfolio investment and gaps in currently available trials across federal, industrial, and academic sponsors. PheWAS results revealed an emphasis on mental disorder-related trials, with anxiety disorder having the highest adjusted increase in the number of matched trials (59% [95% CI, 57-62]; P < 1e-300). Participants from certain communities underrepresented in biomedical research, including self-reported racial and ethnic minorities, had more matched trials after adjusting for other factors. Living in a nonmetropolitan area was associated with up to 13.1 times fewer matched trials. Discussion and conclusion: All of Us data are a valuable resource for identifying trial opportunities to inform trial portfolio planning. Characterizing these opportunities with consideration for SDoH can provide guidance on prioritizing the most pressing barriers to trial participation.
Language	English
Publishing date	2024-04-15
Publishing country	England
Document type	Journal Article
ZDB-ID	1205156-1
ISSN	1527-974X ; 1067-5027
ISSN (online)	1527-974X
ISSN	1067-5027
DOI	10.1093/jamia/ocae062
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Article ; Online: Classification of Breast Implant Malposition.

Pacifico, Marc D / Goddard, Naveen V / Harris, Paul A

Aesthetic surgery journal

2024

Abstract: Background: Implant malposition is a well-recognized complication when using prosthetic implants in the breast for both reconstructive and aesthetic indications. However, to date, no objective classification system has been described.: Objectives: ... ...

Abstract	Background: Implant malposition is a well-recognized complication when using prosthetic implants in the breast for both reconstructive and aesthetic indications. However, to date, no objective classification system has been described. Objectives: This study presents a prospective trial of an objective and reproducible classification system for implant malposition formulated using retrospective data from a large cohort of patients with implant malposition. Methods: The authors retrospectively analyzed the degree of medial/lateral and inferior/superior implant malposition relative to their optimal position within the breast footprint in a series of 189 breasts (n = 100 patients). An objective classification system for implant malposition was devised and then applied to a prospective cohort of 53 breasts in 28 patients with implant malposition. Results: The degree of malposition in a single or combination of axes was categorised according to the distance from the ideal breast footprint and measured in centimeters (cms). The classification system incorporated the axis of malposition and distance to generate a treatment decision-making guide. Cases of Grade 1 malposition did not warrant surgical intervention, whilst surgical correction was warranted in all Grade 3 cases.In the combined patient cohort (n = 242 breasts, 128 patients), lateral, inferior, medial and superior displacement ranged between grades 1-3. There was no inter-observer variability in the grades assigned to nine out of ten patients in the prospective group. Conclusions: We have created a simple and reproducible classification system for implant malposition that allows surgeons to objectively record the extent of malposition, guides surgical decision-making and can be used to document the results of any intervention.
Language	English
Publishing date	2024-04-15
Publishing country	England
Document type	Journal Article
ZDB-ID	2087022-X
ISSN	1527-330X ; 1090-820X ; 1084-0761
ISSN (online)	1527-330X
ISSN	1090-820X ; 1084-0761
DOI	10.1093/asj/sjae084
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Article ; Online: Progress With the All of Us Research Program: Opening Access for Researchers.

Ramirez, Andrea H / Gebo, Kelly A / Harris, Paul A

JAMA

2021 Volume 325, Issue 24, Page(s) 2441–2442

MeSH term(s)	Adult ; Biomedical Research ; Cohort Studies ; Cooperative Behavior ; Data Analysis ; Genomics ; Humans ; Information Dissemination ; Research Personnel
Language	English
Publishing date	2021-06-11
Publishing country	United States
Document type	Journal Article
ZDB-ID	2958-0
ISSN	1538-3598 ; 0254-9077 ; 0002-9955 ; 0098-7484
ISSN (online)	1538-3598
ISSN	0254-9077 ; 0002-9955 ; 0098-7484
DOI	10.1001/jama.2021.7702
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Article ; Online: Physical Activity and Incident Obesity Across the Spectrum of Genetic Risk for Obesity.

Brittain, Evan L / Han, Lide / Annis, Jeffrey / Master, Hiral / Hughes, Andrew / Roden, Dan M / Harris, Paul A / Ruderfer, Douglas M

JAMA network open

2024 Volume 7, Issue 3, Page(s) e243821

Abstract: Importance: Despite consistent public health recommendations, obesity rates in the US continue to increase. Physical activity recommendations do not account for individual genetic variability, increasing risk of obesity.: Objective: To use activity, ... ...

Abstract	Importance: Despite consistent public health recommendations, obesity rates in the US continue to increase. Physical activity recommendations do not account for individual genetic variability, increasing risk of obesity. Objective: To use activity, clinical, and genetic data from the All of Us Research Program (AoURP) to explore the association of genetic risk of higher body mass index (BMI) with the level of physical activity needed to reduce incident obesity. Design, setting, and participants: In this US population-based retrospective cohort study, participants were enrolled in the AoURP between May 1, 2018, and July 1, 2022. Enrollees in the AoURP who were of European ancestry, owned a personal activity tracking device, and did not have obesity up to 6 months into activity tracking were included in the analysis. Exposure: Physical activity expressed as daily step counts and a polygenic risk score (PRS) for BMI, calculated as weight in kilograms divided by height in meters squared. Main outcome and measures: Incident obesity (BMI ≥30). Results: A total of 3124 participants met inclusion criteria. Among 3051 participants with available data, 2216 (73%) were women, and the median age was 52.7 (IQR, 36.4-62.8) years. The total cohort of 3124 participants walked a median of 8326 (IQR, 6499-10 389) steps/d over a median of 5.4 (IQR, 3.4-7.0) years of personal activity tracking. The incidence of obesity over the study period increased from 13% (101 of 781) to 43% (335 of 781) in the lowest and highest PRS quartiles, respectively (P = 1.0 × 10-20). The BMI PRS demonstrated an 81% increase in obesity risk (P = 3.57 × 10-20) while mean step count demonstrated a 43% reduction (P = 5.30 × 10-12) when comparing the 75th and 25th percentiles, respectively. Individuals with a PRS in the 75th percentile would need to walk a mean of 2280 (95% CI, 1680-3310) more steps per day (11 020 total) than those at the 50th percentile to have a comparable risk of obesity. To have a comparable risk of obesity to individuals at the 25th percentile of PRS, those at the 75th percentile with a baseline BMI of 22 would need to walk an additional 3460 steps/d; with a baseline BMI of 24, an additional 4430 steps/d; with a baseline BMI of 26, an additional 5380 steps/d; and with a baseline BMI of 28, an additional 6350 steps/d. Conclusions and relevance: In this cohort study, the association between daily step count and obesity risk across genetic background and baseline BMI were quantified. Population-based recommendations may underestimate physical activity needed to prevent obesity among those at high genetic risk.
MeSH term(s)	Female ; Humans ; Middle Aged ; Male ; Cohort Studies ; Retrospective Studies ; Population Health ; Obesity ; Exercise ; Genetic Risk Score
Language	English
Publishing date	2024-03-04
Publishing country	United States
Document type	Journal Article
ISSN	2574-3805
ISSN (online)	2574-3805
DOI	10.1001/jamanetworkopen.2024.3821
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Book ; Online: Identifying and Extracting Rare Disease Phenotypes with Large Language Models

Shyr, Cathy / Hu, Yan / Harris, Paul A. / Xu, Hua

2023

Abstract: Rare diseases (RDs) are collectively common and affect 300 million people worldwide. Accurate phenotyping is critical for informing diagnosis and treatment, but RD phenotypes are often embedded in unstructured text and time-consuming to extract manually. ...

Abstract	Rare diseases (RDs) are collectively common and affect 300 million people worldwide. Accurate phenotyping is critical for informing diagnosis and treatment, but RD phenotypes are often embedded in unstructured text and time-consuming to extract manually. While natural language processing (NLP) models can perform named entity recognition (NER) to automate extraction, a major bottleneck is the development of a large, annotated corpus for model training. Recently, prompt learning emerged as an NLP paradigm that can lead to more generalizable results without any (zero-shot) or few labeled samples (few-shot). Despite growing interest in ChatGPT, a revolutionary large language model capable of following complex human prompts and generating high-quality responses, none have studied its NER performance for RDs in the zero- and few-shot settings. To this end, we engineered novel prompts aimed at extracting RD phenotypes and, to the best of our knowledge, are the first the establish a benchmark for evaluating ChatGPT's performance in these settings. We compared its performance to the traditional fine-tuning approach and conducted an in-depth error analysis. Overall, fine-tuning BioClinicalBERT resulted in higher performance (F1 of 0.689) than ChatGPT (F1 of 0.472 and 0.591 in the zero- and few-shot settings, respectively). Despite this, ChatGPT achieved similar or higher accuracy for certain entities (i.e., rare diseases and signs) in the one-shot setting (F1 of 0.776 and 0.725). This suggests that with appropriate prompt engineering, ChatGPT has the potential to match or outperform fine-tuned language models for certain entity types with just one labeled sample. While the proliferation of large language models may provide opportunities for supporting RD diagnosis and treatment, researchers and clinicians should critically evaluate model outputs and be well-informed of their limitations.
Keywords	Computer Science - Computation and Language ; Computer Science - Artificial Intelligence
Subject code	410
Publishing date	2023-06-21
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Selecting EHR-driven recruitment strategies: An evidence-based decision guide.

Grout, Randall W / Hood, Dan / Nelson, Sarah J / Harris, Paul A / Embí, Peter J

Journal of clinical and translational science

2022 Volume 6, Issue 1, Page(s) e108

Abstract: Participant recruitment for research is a persistent bottleneck that can be improved by leveraging electronic health records (EHRs). Despite emerging evidence for various EHR-driven approaches, guidance for those attempting to select and use such ... ...

Abstract	Participant recruitment for research is a persistent bottleneck that can be improved by leveraging electronic health records (EHRs). Despite emerging evidence for various EHR-driven approaches, guidance for those attempting to select and use such approaches is limited. The national Recruitment Innovation Center established the EHR Recruitment Consult Resource (ERCR) service line to support multisite studies through implementation of EHR-driven recruitment strategies. As the ERCR, we evolved a guide through 17 consultations over 3 years with multisite studies recruiting in diverse biomedical research domains. We assessed literature and engaged domain experts to identify five key EHR-driven recruitment strategies: direct to patient messages, candidate lists for mailings/calls, direct to research alerts, point of care alerts, and participant registries. Differentiating factors were grouped into factors of study population, study protocol and recruitment workflows, and recruitment site capabilities. The decision matrix indicates acceptable or preferred strategies based on the differentiating factors. Across the ERCR consultations, candidate lists for mailing or calls were most common, participant registries were least frequently recommended, and for some studies no EHR-driven recruitment was recommended. Comparative effectiveness research is needed to refine further evidence for these and potentially new strategies to come.
Language	English
Publishing date	2022-08-08
Publishing country	England
Document type	Journal Article
ISSN	2059-8661
ISSN (online)	2059-8661
DOI	10.1017/cts.2022.439
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Article ; Online: Daily Step Counts Before and After the COVID-19 Pandemic Among All of Us Research Participants.

Desine, Stacy / Master, Hiral / Annis, Jeffrey / Hughes, Andrew / Roden, Dan M / Harris, Paul A / Brittain, Evan L

JAMA network open

2023 Volume 6, Issue 3, Page(s) e233526

MeSH term(s)	Humans ; COVID-19 ; Genetics ; Pandemics ; Population Health
Language	English
Publishing date	2023-03-01
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ISSN	2574-3805
ISSN (online)	2574-3805
DOI	10.1001/jamanetworkopen.2023.3526
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Research Electronic Data Capture (REDCap) - planning, collecting and managing data for clinical and translational research

Harris Paul A

BMC Bioinformatics, Vol 13, Iss Suppl 12, p A

2012 Volume 15

Keywords	Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
Language	English
Publishing date	2012-07-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Invited commentary.

Harris, Paul A

Journal of plastic, reconstructive & aesthetic surgery : JPRAS

2011 Volume 64, Issue 8, Page(s) 1042

MeSH term(s)	Breast Neoplasms/surgery ; Female ; Hemangiosarcoma/surgery ; Humans ; Neoplasms, Radiation-Induced/surgery ; Skin Neoplasms/surgery
Language	English
Publishing date	2011-08
Publishing country	Netherlands
Document type	Comment ; Journal Article
ZDB-ID	2217750-4
ISSN	1878-0539 ; 1748-6815 ; 0007-1226
ISSN (online)	1878-0539
ISSN	1748-6815 ; 0007-1226
DOI	10.1016/j.bjps.2011.02.019
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Article ; Online: The REDCap Mobile Application: a data collection platform for research in regions or situations with internet scarcity.

Harris, Paul A / Delacqua, Giovanni / Taylor, Robert / Pearson, Scott / Fernandez, Michelle / Duda, Stephany N

JAMIA open

2021 Volume 4, Issue 3, Page(s) ooab078

Abstract: Objectives: To share our approach for designing, developing, and deploying the Research Electronic Data Capture (REDCap) Mobile Application, details about its dissemination and support through the REDCap Consortium, and a set of lessons learned and ... ...

Abstract	Objectives: To share our approach for designing, developing, and deploying the Research Electronic Data Capture (REDCap) Mobile Application, details about its dissemination and support through the REDCap Consortium, and a set of lessons learned and guidance recommendations for others developing mobile platforms to support research in regions or situations with internet scarcity. Materials and methods: We defined minimum viable product requirements centered around Android and iOS platform availability, data capture specifications and project initiation workflow, study data synchronization, and data security. After launch, we added features based on feedback from end-users and REDCap administrators. We prioritized new features based on expected impact, difficulty, and anticipated long-term cost for sustainability. Results: We chose Apache Cordova, a combined iOS and Android development framework, based on targeted end-user technology expectations, available programmer resources, and the need to provide solutions for resource-limited settings. The REDCap Mobile Application was launched in 2015, has been enabled at over 800 REDCap Consortium partner organizations, and has supported diverse scientific studies around the world. Discussion: Apache Cordova enabled early software releases for both iOS and Android, but required ongoing optimization efforts to improve software responsiveness. Developing a robust and efficient mobile device synchronization architecture was difficult without direct access to global network infrastructures for testing. Research teams in sub-Saharan Africa helped our development team understand and simulate real-world scenarios of intermittent internet connectivity. Conclusion: Guidance recommendations based on designing, developing, deploying, and disseminating the REDCap Mobile Application may help other teams looking to develop clinical research informatics applications.
Language	English
Publishing date	2021-09-13
Publishing country	United States
Document type	Journal Article
ISSN	2574-2531
ISSN (online)	2574-2531
DOI	10.1093/jamiaopen/ooab078
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