Lu He
Graduate Trainee, University of California, Irvine
2 active projects
SPADE
Scientific Questions Being Studied
The overarching goal of this study is to improve the prediction of clinically significant adverse drug events (ADEs) by harnessing the data that is made available through the All of Us program. Although a number of ADE risk prediction tools are published in the literature, none are universally accepted and used routinely in clinical practice. Robust ADE risk prediction tools are lacking because most datasets utilized to derive ADEs are largely not generalizable. Furthermore, interindividual susceptibility to ADEs might be explainable by genetic variations, and such information is not often available in prediction models. Our specific aims are:
1. Determine the prevalence, specific types and characteristics of ADEs among participants who are receiving chronic disease medications.
2. Derive and validate a prediction model to identify characteristics that are associated with ADEs related to selected chronic disease medications.
Project Purpose(s)
- Disease Focused Research (Definitions, analyses and prediction of adverse drug reactions/events)
- Population Health
- Social / Behavioral
Scientific Approaches
Descriptive statistics will be utilized to characterize the prevalence, specific types and characteristics of ADEs for each drug. Univariate analysis with chi-square tests will be conducted to calculate odds ratios (together with 95% confidence interval) for ADEs associated with each potential risk factor, followed by multivariate logistic regression analysis using backwards selection to identify statistically significant factors and ultimately derive an ADE prediction model for each selected drug.
Anticipated Findings
Our study also intends to fill a current research gap and present findings to contribute an indispensable part of a future larger study that examines ADEs for association with both patient characteristics and pharmacogenetic information, when available, which will contribute an additional layer of information critical to the preventability of ADEs. There will be added focus on specific ethnic groups previously under-described in literature, including Hispanics and African Americans. While limitations such as inconsistency in recording of ADEs and risk factors are anticipated, an advantage of this study is that cases and controls will be selected from a large participant pool (All of Us) rather than a specific site, which will allow us to evaluate the impact of ADEs in a group that better mirrors the general patient population in the clinical setting.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Registered TierResearch Team
Owner:
- Stanley Jia - Undergraduate Student, University of California, Irvine
- Lu He - Graduate Trainee, University of California, Irvine
- Kai Zheng - Mid-career Tenured Researcher, University of California, Irvine
- Kevin Zhang - Undergraduate Student, University of California, Irvine
- Ding Quan Ng - Graduate Trainee, University of California, Irvine
- Alexandre Chan - Late Career Tenured Researcher, University of California, Irvine
Collaborators:
- Jahnavi Maddhuri - Undergraduate Student, University of California, Irvine
- Jatin Goyal - Undergraduate Student, University of California, Irvine
- Arvind Kumar - Undergraduate Student, University of California, Irvine
SPADE (v5 dataset)
Scientific Questions Being Studied
The overarching goal of this study is to improve the prediction of clinically significant adverse drug events (ADEs) by harnessing the data that is made available through the All of Us program. Although a number of ADE risk prediction tools are published in the literature, none are universally accepted and used routinely in clinical practice. Robust ADE risk prediction tools are lacking because most datasets utilized to derive ADEs are largely not generalizable. Furthermore, interindividual susceptibility to ADEs might be explainable by genetic variations, and such information is not often available in prediction models. Our specific aims are:
1. Determine the prevalence, specific types and characteristics of ADEs among participants who are receiving chronic disease medications.
2. Derive and validate a prediction model to identify characteristics that are associated with ADEs related to selected chronic disease medications.
Project Purpose(s)
- Disease Focused Research (Definitions, analyses and prediction of adverse drug reactions/events)
- Population Health
- Social / Behavioral
Scientific Approaches
Descriptive statistics will be utilized to characterize the prevalence, specific types and characteristics of ADEs for each drug. Univariate analysis with chi-square tests will be conducted to calculate odds ratios (together with 95% confidence interval) for ADEs associated with each potential risk factor, followed by multivariate logistic regression analysis using backwards selection to identify statistically significant factors and ultimately derive an ADE prediction model for each selected drug.
Anticipated Findings
Our study also intends to fill a current research gap and present findings to contribute an indispensable part of a future larger study that examines ADEs for association with both patient characteristics and pharmacogenetic information, when available, which will contribute an additional layer of information critical to the preventability of ADEs. There will be added focus on specific ethnic groups previously under-described in literature, including Hispanics and African Americans. While limitations such as inconsistency in recording of ADEs and risk factors are anticipated, an advantage of this study is that cases and controls will be selected from a large participant pool (All of Us) rather than a specific site, which will allow us to evaluate the impact of ADEs in a group that better mirrors the general patient population in the clinical setting.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Registered TierResearch Team
Owner:
- Stanley Jia - Undergraduate Student, University of California, Irvine
- Lu He - Graduate Trainee, University of California, Irvine
- Kai Zheng - Mid-career Tenured Researcher, University of California, Irvine
- Kevin Zhang - Undergraduate Student, University of California, Irvine
- Ding Quan Ng - Graduate Trainee, University of California, Irvine
- Alexandre Chan - Late Career Tenured Researcher, University of California, Irvine
Collaborators:
- Jahnavi Maddhuri - Undergraduate Student, University of California, Irvine
- Jatin Goyal - Undergraduate Student, University of California, Irvine
- Arvind Kumar - Undergraduate Student, University of California, Irvine
Request a Review of this Research Project
You can request that the All of Us Resource Access Board (RAB) review a research purpose description if you have concerns that this research project may stigmatize All of Us participants or violate the Data User Code of Conduct in some other way. To request a review, you must fill in a form, which you can access by selecting ‘request a review’ below.
