Investigator Sylvia Sudat, PhD

Dr. Sudat is a Sr. Biostatistician and Scientist at the Center for Health Systems Research (CHSR). She is also a core member of the Sutter Health Institute for Advancing Health Equity (IAHE) and the Data Science team.

Over her 17-year career at Sutter Health, she has developed methods for accessing, extracting, and analyzing complex health data from multi-source electronic health records (EHRs) for clinical and observational research.

Dr. Sudat provides statistical consultation and support to Sutter researchers, and leads projects using large EHR datasets to address questions and challenges facing healthcare delivery, patient experience, and health equity.

She has 13 years’ experience in palliative care, and led the evaluation of the Sutter Health Advanced Illness Management® (AIM) program. Using findings from AIM and with EHR data, Dr. Sudat developed an algorithm to prospectively identify patients potentially eligible for palliative care, which has been implemented across Sutter Health. Her other recent work includes identification of natural subgroups of migraine using latent class analysis, and estimation of the impact of pulse oximeter inaccuracy on treatment course for Black patients with COVID-19.

Dr. Sudat currently leads projects characterizing COVID-19 treatment trajectories, evaluating practical implementation of predictive models, and improving Sutter Health’s palliative and advanced illness care identification algorithm. She is also a co-Investigator, biostatistician, and consultant on various other projects.

Dr. Sudat completed graduate training in Biostatistics at the University of California at Berkeley, where she focused on risk prediction and semi-parametric methods for estimation in the context of causal inference and observational health data.

Sutter Health Research Enterprise
2121 N. California Ave, Suite 310
Walnut Creek, CA, 94596
(925) 212-5507
sylvia.sudat@sutterhealth.org

Primary Research Interests

Publications

A population-health approach to characterizing migraine by comorbidity: Results from the Mindfulness and Migraine Cohort Study

Racial Disparities in Pulse Oximeter Device Inaccuracy and Estimated Clinical Impact on COVID-19 Treatment Course

Hyperlocalized Measures of Air Pollution and Preeclampsia in Oakland, California

Exposure to nitrogen dioxide (NO2), black carbon (BC), and ultrafine particles (UFPs) during pregnancy may increase the risk of preeclampsia

Mind the clinical-analytic gap: electronic health records and COVID-19 pandemic response

Lessons learned from the COVID-19 pandemic can result in the formation of collaborative teams: clinical operations, informatics, data analytics, and research.

Effectiveness of a group-based lifestyle change program versus usual care: an electronic health record, propensity score-matched cohort study

This study examines the effectiveness of a CDC and prevention-aligned lifestyle change program relative to usual care in clinical practice.

Recruitment, retention, and adherence in a randomized feasibility trial of mindfulness-based stress reduction for patients with migraine

Increasing evidence demonstrates effectiveness of Mindfulness-Based Stress Reduction (MBSR) for pain-related and functional disorders

The learning health system in crisis: lessons from the novel coronavirus disease pandemic

Advocating for a learning health network that promotes collaboration between health systems, community-based organizatons, and government agencies

Early weight loss and treatment response: data from a lifestyle change program in clinical practice.

Percentage weight change at 12 weeks from baseline can serve as a single indicator of nonresponse at the completion of the 12-month lifestyle change program.

Longitudinal weight outcomes from a behavioral lifestyle intervention in clinical practice.

A lifestyle change program in clinical practice is associated with modest weight loss, sustained through 24 months, in setting of cardiometabolic risk factors.

Short-term weight trajectories and long-term weight outcomes from a lifestyle intervention in real-world clinical practice.

With relationship between magnitudes of short- and long-term wt change, individual wt trajectories may be useful in identifying non-responders who need alt tx.

Conducting a pilot randomized controlled trial of community-based mindfulness-based stress reduction versus usual care for moderate-to-severe migraine: protocol for the Mindfulness and Migraine Study (M&M).

This is the first pragmatic trial in U.S. of mindfulness-based stress reduction for migraine using community classes.

Diverse transcriptional programs associated with environmental stress and hormones in the Arabidopsis receptor-like kinase gene family.

Study represents a preliminary, working model of processes and interactions in which the members of the receptor-like kinase gene family may be involved.

Using variable importance measures from causal inference to rank risk factors of schistosomiasis infection in a rural setting in China.

Results support applying analysis approaches that do not require arbitrary model pre-specification in data analysis related to water contact and schistosomiasis

Predicting all-cause risk of 30-day hospital readmission using artificial neural networks.

We demonstrate that neural networks are good candidates to capture complexity and interdependency of various EHR data fields to predict 30-day readmission risk.

Predicting need for advanced illness or palliative care in a primary care population using electronic health record data.

Applied predictive modeling to primary care population and examine the impact of biostatistical choices on model performance--predict need for palliative care.

Causal inference and prediction in health studies: environmental exposures and schistosomiasis, HIV-1 genotypic susceptibility scores and virologic suppression, and risk of hospital readmission for heart failure patients.

In this dissertation, semi-parametric methods to assess variable importance are applied to three real-world health applications.

Impact of home-based, patient-centered support for people with advanced illness in an open health system: a retrospective claims analysis of health expenditures, utilization, and quality of care at end of life.

Advanced Illness Management program has a positive impact on inpatient utilization, cost of care, hospice enrollment, and site of death.