Aaron Boussina

Graduate Trainee, University of California, San Diego

8 active projects

sepsis_genetics

The study aims to explore genetic and phenotypic diversity in sepsis patients, a severe condition caused by widespread inflammation due to infection. Sepsis impacts all ages and arises from various infections (bacterial, viral, fungal, or parasitic) and is therefore of…

Scientific Questions Being Studied

The study aims to explore genetic and phenotypic diversity in sepsis patients, a severe condition caused by widespread inflammation due to infection. Sepsis impacts all ages and arises from various infections (bacterial, viral, fungal, or parasitic) and is therefore of strong public health relevance. Current research lacks understanding of symptom variations and genetic factors influencing severity and mortality, especially in ancestrally diverse cohorts with detailed medical records and high-coverage genome sequencing. Utilizing AllofUs' resources, this research uses large-scale health records and genome sequencing to map genetic architecture and phenotypic heterogeneity. Ultimately, the aim is to develop predictive models that establish links between genetic and clinical risk factors and the susceptibility and outcomes of sepsis..

Project Purpose(s)

  • Disease Focused Research (Sepsis)
  • Population Health
  • Methods Development
  • Control Set
  • Ancestry

Scientific Approaches

To uncover the genetic architecture and phenotypic heterogeneity, we aim to leverage vast genome sequencing and electronic health record datasets from AllofUs. Machine learning and natural language processing will extract features from health records to characterize sepsis types and severity. A polygenic risk assessment approach will reconstruct sepsis's genetic architecture through genome-wide association studies in diverse cohorts. Combining GWAS results with clinical risk factors, we will train statistical models to predict individual susceptibility and clinical outcomes. R, Python, Anaconda, VCFtools, and PLINK will be utilized for data analysis, whole-genome sequencing, and model training.

Anticipated Findings

From genome-wide association studies using clinical sepsis outcomes and high-coverage whole-genome sequencing data, we expect to identify ancestry-specific genetic loci associated with sepsis susceptibility, various sepsis outcomes and sepsis mortality. In addition, we also expect to identify loci with gene-environment interaction effects on individual anthropometric characteristics, sepsis treatment, and even social economics factors. Upon identifying genetic loci and clinical factors of interests, we will further train a polygenic risk model to maximize the variations explained in sepsis related phenotypes. This project is hoped to yield findings that can substantiate our understanding of the genetics of sepsis in a multi-ancestral cohort including the underrepresented subjects. Furthermore, the predictive model is expected to help better guide clinical treatment and preventive medicine of sepsis.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Sex at Birth
  • Gender Identity
  • Sexual Orientation
  • Geography
  • Disability Status
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

  • Wanjun Gu - Project Personnel, University of California, San Diego
  • Aaron Boussina - Graduate Trainee, University of California, San Diego

wearables_analysis

We'd like to study what features (including social determinants of health and wearables data) contribute to readmission in patients with acute kidney injury (AKI) and sepsis. Understanding these features is important since the ability to proactively identify readmission risk could…

Scientific Questions Being Studied

We'd like to study what features (including social determinants of health and wearables data) contribute to readmission in patients with acute kidney injury (AKI) and sepsis. Understanding these features is important since the ability to proactively identify readmission risk could lead to improved consultation & follow-up for high-risk patients.

Project Purpose(s)

  • Disease Focused Research (Acute Kidney Injury & Sepsis)
  • Educational

Scientific Approaches

We'll be analyzing subsequent visits and looking at a range of features including vital signs, laboratory measurements, and survey responses. We anticipate we'll be using the PERSON, VISIT_OCCURRENCE, MEASUREMENT, and OBSERVATION tables. From these features we'll apply logistic regression, multi-scale entropy, and deep learning to build a predictive model for readmission risk. We'll identify the corresponding importance of features using odd-ratios or SHAP values.

Anticipated Findings

We anticipate that we'll be able to achieve a performant predictive model for the readmission of patients with AKI and sepsis. We hope this work will result in novel findings and publication.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Collaborators:

  • Vishal Nagarajan - Graduate Trainee, University of California, San Diego
  • Rishivardhan Krishnamoorthy - Graduate Trainee, University of California, San Diego
  • Hayden Pour - Project Personnel, University of California, San Diego

MED264_AKI_Readmission

We'd like to study what features (including social determinants of health) contribute to readmission in patients with acute kidney injury (AKI). Understanding these features is important since the ability to proactively identify readmission risk could lead to improved consultation &…

Scientific Questions Being Studied

We'd like to study what features (including social determinants of health) contribute to readmission in patients with acute kidney injury (AKI). Understanding these features is important since the ability to proactively identify readmission risk could lead to improved consultation & follow-up for high-risk patients.

Project Purpose(s)

  • Disease Focused Research (Acute Kidney Injury)
  • Educational

Scientific Approaches

We'll be analyzing subsequent visits and looking at a range of features including vital signs, laboratory measurements, and survey responses. We anticipate we'll be using the PERSON, VISIT_OCCURRENCE, MEASUREMENT, and OBSERVATION tables. From these features we'll apply logistic regression and deep learning to build a predictive model for readmission risk. We'll identify the corresponding importance of features using odd-ratios or SHAP values.

Anticipated Findings

We anticipate that we'll be able to achieve a performant predictive model for the readmission of patients with AKI. To our knowledge this is the first such study conducted on All of Us.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Collaborators:

  • Fatemeh Amrollahi - Graduate Trainee, University of California, San Diego
  • Shamim Nemati - Early Career Tenure-track Researcher, University of California, San Diego

V6 - MED264_AKI_Readmission_Group

We'd like to study what features (including social determinants of health) contribute to readmission in patients with acute kidney injury (AKI). Understanding these features is important since the ability to proactively identify readmission risk could lead to improved consultation &…

Scientific Questions Being Studied

We'd like to study what features (including social determinants of health) contribute to readmission in patients with acute kidney injury (AKI). Understanding these features is important since the ability to proactively identify readmission risk could lead to improved consultation & follow-up for high-risk patients.

Project Purpose(s)

  • Disease Focused Research (Acute Kidney Injury)
  • Educational

Scientific Approaches

We'll be analyzing subsequent visits and looking at a range of features including vital signs, laboratory measurements, and survey responses. We anticipate we'll be using the PERSON, VISIT_OCCURRENCE, MEASUREMENT, and OBSERVATION tables. From these features we'll apply logistic regression and deep learning to build a predictive model for readmission risk. We'll identify the corresponding importance of features using odd-ratios or SHAP values.

Anticipated Findings

We anticipate that we'll be able to achieve a performant predictive model for the readmission of patients with AKI. To our knowledge this is the first such study conducted on All of Us.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Collaborators:

  • Fatemeh Amrollahi - Graduate Trainee, University of California, San Diego

MED299

We intend to use this dataset in MED299 at UC San Diego in order to teach students about cloud computing and informatics.

Scientific Questions Being Studied

We intend to use this dataset in MED299 at UC San Diego in order to teach students about cloud computing and informatics.

Project Purpose(s)

  • Disease Focused Research (Sepsis )
  • Educational

Scientific Approaches

Sepsis is one of the country’s most urgent systemic health threats. Each year, more than 1.5 million people in the U.S. get sepsis. Sepsis kills a quarter-million Americans each year. Sepsis can occur when a trauma or an infection – often caused by a superbug or drug-resistant bacteria in the skin, lungs or urinary tract – triggers a chain reaction throughout the body. Without timely recognition and treatment, it can rapidly cause tissue damage, organ failure, and death; each hour treatment of sepsis is delayed mortality risk increases by an additional 4%. One in three patients who die in a hospital have sepsis. We intend to use regression analysis to study the factors that contribute to sepsis readmission.

Anticipated Findings

Successful prevention and management of sepsis, septic shock, and organ injury rely on the ability of clinicians to anticipate and estimate the risk, and administer the right life-saving treatments (e.g., antibiotics, fluids and vasopressors) at the right time.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Zaid Yousif - Research Fellow, University of California, San Diego
  • Supreeth Shashikumar - Research Fellow, University of California, San Diego
  • Shamim Nemati - Early Career Tenure-track Researcher, University of California, San Diego
  • Fatemeh Amrollahi - Graduate Trainee, University of California, San Diego
  • Aaron Boussina - Graduate Trainee, University of California, San Diego

Collaborators:

  • Arshia Nayebnazar - Other, University of California, San Diego

MED299-Spring2022

The goal is this research is to study the effect of social determinants of health on sepsis related mortality.

Scientific Questions Being Studied

The goal is this research is to study the effect of social determinants of health on sepsis related mortality.

Project Purpose(s)

  • Methods Development

Scientific Approaches

Social determinants of health will be extracted from the survey questions among hospitalized patients.

Anticipated Findings

Findings from the study may help uncover social determinants of health associated with sepsis related mortality.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Collaborators:

  • Rahul M Patil - Graduate Trainee, University of California, San Diego
  • Nancy Yuan - Graduate Trainee, University of California, San Diego
  • Haben Yhdego - Research Fellow, University of California, San Diego
  • Arshia Nayebnazar - Other, University of California, San Diego
  • Archil Srivastava - Graduate Trainee, University of California, San Diego

MED264_AKI_Readmission_Group

We'd like to study what features (including social determinants of health) contribute to readmission in patients with acute kidney injury (AKI). Understanding these features is important since the ability to proactively identify readmission risk could lead to improved consultation &…

Scientific Questions Being Studied

We'd like to study what features (including social determinants of health) contribute to readmission in patients with acute kidney injury (AKI). Understanding these features is important since the ability to proactively identify readmission risk could lead to improved consultation & follow-up for high-risk patients.

Project Purpose(s)

  • Disease Focused Research (Acute Kidney Injury)
  • Educational

Scientific Approaches

We'll be analyzing subsequent visits and looking at a range of features including vital signs, laboratory measurements, and survey responses. We anticipate we'll be using the PERSON, VISIT_OCCURRENCE, MEASUREMENT, and OBSERVATION tables. From these features we'll apply logistic regression and deep learning to build a predictive model for readmission risk. We'll identify the corresponding importance of features using odd-ratios or SHAP values.

Anticipated Findings

We anticipate that we'll be able to achieve a performant predictive model for the readmission of patients with AKI. To our knowledge this is the first such study conducted on All of Us.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Aaron Boussina - Graduate Trainee, University of California, San Diego

Collaborators:

  • Fatemeh Amrollahi - Graduate Trainee, University of California, San Diego
  • Arshia Nayebnazar - Other, University of California, San Diego

MED264_AKI_Readmission

We'd like to study what features (including social determinants of health) contribute to readmission in patients with acute kidney injury (AKI). Understanding these features is important since the ability to proactively identify readmission risk could lead to improved consultation &…

Scientific Questions Being Studied

We'd like to study what features (including social determinants of health) contribute to readmission in patients with acute kidney injury (AKI). Understanding these features is important since the ability to proactively identify readmission risk could lead to improved consultation & follow-up for high-risk patients.

Project Purpose(s)

  • Disease Focused Research (Acute Kidney Injury)
  • Educational

Scientific Approaches

We'll be analyzing subsequent visits and looking at a range of features including vital signs, laboratory measurements, and survey responses. We anticipate we'll be using the PERSON, VISIT_OCCURRENCE, MEASUREMENT, and OBSERVATION tables. From these features we'll apply logistic regression and deep learning to build a predictive model for readmission risk. We'll identify the corresponding importance of features using odd-ratios or SHAP values.

Anticipated Findings

We anticipate that we'll be able to achieve a performant predictive model for the readmission of patients with AKI. To our knowledge this is the first such study conducted on All of Us.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Aaron Boussina - Graduate Trainee, University of California, San Diego

Collaborators:

  • Fatemeh Amrollahi - Graduate Trainee, University of California, San Diego
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