Predictive Analytics + Comparative Effectiveness (PACE) Center

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

Mabel Celestino
Research Project Coordinator

William Crown, PhD
Senior Adviser

Benjamin Koethe, MPH
Statistician

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

NIH R01 “Covert Cerebrovascular Disease Detected by Artificial Intelligence (C2D2AI) Phase 2: A Platform for Pragmatic Evidence Generation for Stroke and Dementia Prevention”

This project aims to lay the foundation for a national platform for pragmatic evidence in a population of individuals with incidentally-discovered covert cerebrovascular disease (CCD) with the goal to test a care approach for these individuals. This project is a step in that direction as we will do further epidemiological work and develop models to identify the most promising treatment for stroke and dementia risk reduction and pilot a recruitment approach for this population. These findings along with the guidance of a technical expert panel will be used to plana large clinical trial.

ADDF “Covert Cerebrovascular Disease Detected by Artificial Intelligence (C2D2AI): Pilot Investigation for Pragmatic Neuroimaging Biomarkers for Future Stroke and Dementia Risk”

Using a sample of MRI images, the team will develop deep learning models for the identification of white matter disease and covert brain infarction and the prediction of stroke and dementia. This project aims to develop a preliminary algorithm that can directly read routinely obtained neuroimaging scans and relate findings to stroke and dementia outcomes.

NIH R01 “Covert Cerebrovascular Disease Detected by Artificial Intelligence (C2D2AI): Pragmatic Neuroimaging Biomarkers for Future Stroke and Dementia Risk”

This project builds on the ADDF project and aims to develop and validate a clinically useful algorithm using deep neural networks that can directly read routinely obtained neuroimages, both MRI and head CT, at scale and relate findings to stroke and dementia outcomes.

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.

Featured

• Maas CCHM, Kent DM, Hughes MC, Dekker R, Lingsma HF, van Klaveren D. Performance metrics for models designed to predict treatment effect. BMC Med Res Methodol. 2023 Jul 8
• Leung LY, Zhou Y, Fu S, Zheng C, Luetmer PH, Kallmes DF, Liu H, Chen W, Kent DM. Risk Factors for Silent Brain Infarcts and White Matter Disease in a Real-World Cohort Identified by Natural Language Processing. Mayo Clin Proc. 2022 Jun;97(6):1114-1122. doi: 10.1016/j.mayocp.2021.11.038. Epub 2022 Apr 27.
• Kent DM, Saver JL, Kasner SE, Nelson J, Carroll JD, Chatellier G, Derumeaux G, Furlan AJ, Herrmann HC, Jüni P, Kim JS, Koethe B, Lee PH, Lefebvre B, Mattle HP, Meier B, Reisman M, Smalling RW, Soendergaard L, Song JK, Mas JL, Thaler DE. Heterogeneity of Treatment Effects in an Analysis of Pooled Individual Patient Data From Randomized Trials of Device Closure of Patent Foramen Ovale After Stroke. JAMA. 2021 Dec 14;326(22):2277-2286.
• Kent DM, Nelson J, Pittas A, Colangelo F, Koenig C, van Klaveren D, Ciemins E, Cuddeback J. An Electronic Health Record-Compatible Model to Predict Personalized Treatment Effects From the Diabetes Prevention Program: A Cross-Evidence Synthesis Approach Using Clinical Trial and Real-World Data. Mayo Clin Proc. 2021 Nov 12:S0025-6196(21)00708-4.
• Wessler BS, Nelson J, Park JG, McGinnes H, Gulati G, Brazil R, Van Calster B, van Klaveren D, Venema E, Steyerberg E, Paulus JK, Kent DM. External Validations of Cardiovascular Clinical Prediction Models: A Large-Scale Review of the Literature. Circ Cardiovasc Qual Outcomes. 2021 Aug;14(8):e007858.
• Kent DM, Leung LY, Zhou Y, Luetmer PH, Kallmes DF, Nelson J, Fu S, Zheng C, Liu H, Chen W. Association of Silent Cerebrovascular Disease Identified Using Natural Language Processing and Future Ischemic Stroke. Neurology. 2021 Sep 28;97(13):e1313-e1321.
• Paulus JK, Kent DM. Predictably Unequal: Understanding and Addressing Concerns that Algorithmic Clinical Prediction May Increase Health Disparities. Nature Digital Medicine. 2020 Jul 30;3(99).
• Kent DM, Paulus JK, van Klaveren D, D'Agostino R, Goodman S, Hayward R, Ioannidis JPA, Patrick-Lake B, Morton S, Pencina M, Raman G, Ross JS, Selker HP, Varadhan R, Vickers A, Wong JB, Steyerberg EW. The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement. Ann Intern Med. 2020 Jan 7;172:35-45.
• Kent DM, van Klaveren D, Paulus JK, D'Agostino R, Goodman S, Hayward R, Ioannidis JPA, Patrick-Lake B, Morton S, Pencina M, Raman G, Ross JS, Selker HP, Varadhan R, Vickers A, Wong JB, Steyerberg EW. The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement: Explanation and Elaboration. Ann Intern Med. 2020 Jan 7;172:W1-W25.

Individual investigators

• View Dr. Kent’s recent publications on PubMED
• View Mr. Nelson’s recent publications on PubMED
• View Dr. Paulus’ recent publications on PubMED
• View Ms. Ruthazer’s recent publications on PubMED
• View Dr. van Klaveren’s recent publications on PubMED
• View Dr. Wessler’s recent publications on PubMED

“An Overview of Bias and Fairness in Algorithmic Models” Keynote, Kaiser Permanente Symposium on Algorithmic Bias and Fairness, Oakland, CA, September 13, 2023.

“Predictive Approaches to Treatment Effect Heterogeneity” Pharmaceuticals and Outcomes and Policy Seminar, University of Florida College of Pharmacy, Gainesville, FL, April 13, 2023.

“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.

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.

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
pacecenter@tuftsmedicalcenter.org
Fax: 617.636.0022

https://twitter.com/Tufts_PACE