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Predictive Analytics + Comparative Effectiveness (PACE) Center


Medical care is delivered one patient at a time, but the evidence for practicing is derived by aggregating many patients—typically thousands or tens of thousands of patients—into groups. This group-derived evidence would be highly informative for medical practice if all patients were identical. The dissimilarity of individual patients, however, potentially undermines clinical research as a scientific basis for the practice of medicine.

The Predictive Analytics and Comparative Effectiveness Center (PACE), led by Dr. David Kent, seeks to understand better and address the limitations of using group-derived evidence as the basis for decision-making in individual patients. Our approach is based on the close integration of clinical and statistical reasoning. We aim to provide clinicians and patients with evidence better tailored to their particular circumstances; we have expertise in clinical medicine, risk modeling, individual patient meta-analysis, and observational comparative effectiveness studies.

The PACE Center provides clinicians and patients with evidence better tailored to their particular circumstances through risk modeling. Among other projects, Dr. Kent is the Principal Investigator of several federally-funded research grants related to these issues, including several methods grants from the Patient-Centered Outcomes Research Institute (PCORI) and grants from the National Institutes of Health (NIH)/National Institute of Neurological Disorders and Stroke (NINDS) focused on cerebrovascular disease. Dr. Kent works with five additional faculty investigators and statisticians.

More information

Researchers + staff
PACE Retreat 2018

David Kent, MD, CM, MSc
Director and Professor of Medicine

Mabel Celestino
Research Project Coordinator

William Crown, PhD
Senior Adviser

Benjamin Koethe, MPH

Keren Ladin, PhD, MSc
Associate Professor, Depts of Occupational Therapy and Community Health, Tufts University; Director, Research on Ethics, Aging, and Community Health (REACH Lab)

Lester Y. Leung, MD, MSc
Director, Comprehensive Stroke Center; Director, Stroke and Young Adults (SAYA) Program

Jennifer Lutz, MA
Research Assistant

C.H.M. (Carolien) Maas, MSc
Student Researcher

Jinny Park, MPH
Research Project Coordinator

Valery (Val) Sukovatitsyn
Senior Research Administrator

Jenica Upshaw, MD
Medical Director, Cardio-Oncology Program; Attending Physician, Advanced Heart Failure

David van Klaveren, PhD

Ellen Vickery, MS
Clinical Studies Manager

Benjamin S. Wessler, MD
Associate Director; Staff Cardiologist and Assistant Professor of Medicine


Greenwall Foundation "A Rubric for Predictive Analytics in Healthcare"

This multidisciplinary collaboration of experts will develop a rubric to guide algorithmic bias and fairness concerns within clinical prediction modeling. This project draws on the expertise of numerous professionals in fields such as machine learning, prediction modeling, health informatics, medical ethics, diversity and inclusion, as well as patient stakeholder engagement to explore algorithmic fairness within healthcare settings.

PCORI "The Systematic, Collaborative, PFO Closure Evaluation" (SCOPE)

The project aims to examine and provide definitive evidence of the treatment effects of PFO closure versus medical therapy to support patient-centered decision-making for patients with PFO and cryptogenic stroke. This project conducted a high-quality and rigorous IPD MA utilizing the six component databases of patients with PFO and CS.

W.L. Gore & Associates "How Well Is PFO Closure Likely To Work in Patients Over 60 Years of Age? An Analysis of the Risk of Paradoxical Embolism Database"

The project aims to examine and provide definitive evidence of the treatment effects of PFO closure versus medical therapy to support patient-centered decision-making for patients with PFO and cryptogenic stroke who are over 60 years of age, using data from multiple databases.

Tufts Collaborates "Estimating Individual Treatment Effects From Randomized Clinical Trials Using Machine Learning"

This project will provide a benchmark comparison of state-of-the-art machine learning approaches for treatment effect prediction on 18 large clinical trials.

NIH K23 "Palliative Care for High-Risk TAVR Patients: The Impact of Multimorbidity"

This proposal addresses knowledge gaps related to the significance of multiple chronic conditions (MCC), the nature of unmet care needs, and the use of early palliative care for high-risk patients treated with TAVR who are unlikely to do well.

NIH R01 "Enabling Comparative Effectiveness Research in Silent Brain Infarction Through Natural Language Processing + Big Data"

We aim to improve the evidence base for stroke prevention in patients with silent brain infarct (SBI). We will develop a natural language processing algorithm that can accurately identify cases of SBI and estimate the comparative effectiveness of medications on the risk of future stroke and dementia.

PCORI Dissemination + Implementation "Improving Diabetes Prevention With Benefit-Based Tailored Treatment

Disseminating Patient-Centered Estimates of Benefit": The goals of this project are to give a clear and clinically actionable understanding of the variation in patient-centered estimates of benefits of diabetes prevention interventions based on our PCORI-funded prediction model derived directly from the Diabetes Prevention Program (DPP) trial. The Tufts PACE DPP Risk model was developed for use in different electronic health record systems to provide information on a patient's individualized risk for developing diabetes and how well preventive treatment—an intensive lifestyle program or taking Metformin—was likely to reduce risk for the patient.

Project Materials:

  • Risk Model and SMART App Overview
  • SMART on FHIR Application Summary for Clinicians
  • DM Risk Patient Instructions
  • PCORI Annual Meeting 2018 poster
  • OptumLabs presentation
  • PCORI Annual Meeting 2019 presentation
  • PCORI Annual Meeting 2021 poster

PCORI Methods Covid Enhancement "Generalizable Prognostic Models for Patient-Centered Decisions in COVID-19"

This project seeks to develop and validate clinical prediction models in two large hospital systems (including approximately 20,000 hospitalized COVID-19 patients) to predict the need for mechanical ventilation and mortality in patients hospitalized with COVID-19. These tools will be piloted in at least one setting.



Individual investigators

PACE news + media

“Anatomic Features Key for Selecting Patients Best Suited to PFO Closure.” Yael L. Maxwell. TCTMD, produced by the Cardiovascular Research Foundation (CRF). Oct 14, 2022.

"New Formula Predicts With High Accuracy Which Patients With Recurring Stroke Would Benefit From PFO Closure" Press Release, Tufts Medical Center, Boston, MA December 14, 2021

“Heterogeneity of treatment effect and risk-stratified approach for reporting/ implementing clinical trial results.” 16th Global Cardiovascular Clinical Trialists (CVCT) Forum. Washington, DC. December 7, 2019.

“Prediabetes Predictive Model to Personalize Diabetes Risk.” OptumLabs Research and Translation Forum. Boston, MA. November 20, 2019.

“Improving Diabetes Prevention Based on Predicted Benefits of Treatment.” What’s Right For Me? Practical Approaches to Personalized Medicine. PCORI Annual Meeting. Washington, DC. September 18, 2019.

“Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects in randomized trials.” Society for Clinical Trials Annual Meeting. New Orleans, LA. May 19, 2019.

“Point-Hemoglobin A1c Goals: Is Lower Better?” Tufts Medical Center Point-Counterpoint Grand Rounds. Boston, MA. October 12, 2018.

“Evidence and the Individual Patient: Understanding Heterogeneous Treatment Effects for Patient-Centered Care.” National Academy of Medicine. Washington, DC. May 31, 2018.

“Personalized Risk Information in Cost Effectiveness Studies (PRICES).” Chapter 14. Health Economics Common Fund Research Symposium. Bethesda, MD. September 25, 2017.

“Moving Beyond Averages” Patient-Centered Outcomes Research Institute. January 2017

“PACE Symposium: Using Group Data to Treat Individuals” Tufts Medical Center, Boston, MA. June 4, 2015.

“Getting it Right the First Time: Can We Predict Who is Likely to Respond?” The Myth of Average: Why Individual Patient Differences Matter Conference. Omni Shoreham Hotel, Washington DC, Nov 30, 2012.

"Risk Modeling for Targeting Therapies to those who can Benefit: The Example of PFO Closure in Cryptogenic Stroke.” Center for Clinical and Translational Sciences (CCaTS) Grand Rounds. Mayo Clinic, Rochester, MN, September 27, 2013.

“An index to identify stroke-related versus incidental patent foramen ovale in cryptogenic stroke.” Neurology Podcast. August 13 2013 Issue.

CPM registry

To better understand the extent of Clinical Prediction Model (CPM) development and to help researchers and clinicians, we have created the Tufts PACE CPM Registry, a field synopsis of over 1,000 CPMs that predict clinical outcomes for patients with and at risk for cardiovascular disease.

Contact us

Predictive Analytics and Comparative Effectiveness (PACE) Center
The Institute for Clinical Research and Health Policy Studies
Tufts Medical Center
800 Washington St., Box 63
Boston, MA 02111

Direct inquiries to:
Mabel Celestino and/or Jinny Park
Research Project Coordinator, Predictive Analytics and Comparative Effectiveness (PACE) Center
Fax: 617.636.0022

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