Institute for Clinical Research and Health Policy Studies

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 (PACE) Center seeks to better understand 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. Our goal is 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.

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

June C. Baglione
Senior Research Administrator

Riley Brazil
Research Fellow

Gaurav Gulati, MD
Research Fellow

Mike Hughes
Assistant Professor of Computer Science

David van Klaveren, PhD, MSc
Research Associate

Benjamin Koethe, MPH
Statistician 

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

Tufts CTSI Pilot “Understanding Algorithmic Bias and Unfairness in Healthcare”

This multidisciplinary collaboration of experts in clinical prediction, epidemiology and stakeholder engagement will develop a literature-informed map of the various concepts/measures of fairness and types of decisions relevant to the medical context; engage with expert stakeholders representing various perspectives to identify the full spectrum of relevant real-world cases; and tailor these cases for stakeholders representing diverse backgrounds, and pilot test one in a multi-ethnic patient stakeholder group.

PCORI “The Systematic, Collaborative, PFO closure Evaluation” (SCOPE) and Conference Award

The project aims to examine and provide definitive evidence as to the treatment effects of PFO closure versus medical therapy to support patient-centered decision making for patients with PFO and cryptogenic stroke. This project will organize a meeting to facilitate the collaboration of investigators with relevant trial data, the PFO DATA Consortium, with the ultimate aim of conducting a high-quality and rigorous IPD MA utilizing the 6 component databases of patients with PFO and CS.

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 and Big Data”

We aim to improve the evidence base for the prevention of stroke 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 disseminate 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.

PCORI Methods “How Well Do Clinical Prediction Models (CPMs) Validate? A Large-Scale Evaluation of Cardiovascular Clinical Prediction Models”

This project aims to conduct a large-scale validation of cardiovascular clinical prediction models (CPMs).

PCORI “Predictive Analytics and Treatment Effect Heterogeneity” (PATH)

Setting up a PCORI Predictive Analytics Resource Center (PARC) to provide various professional services including development of a portfolio of research activities in the area of predictive analytics.

Featured

• Kent DM, Paulus JK, van Klaveren D, et al. The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement. Ann Intern Med. 2020;172:35–45. [Epub ahead of print 12 November 2019]. doi: 10.7326/M18-3667.
  
  
• Kent DM, van Klaveren D, Paulus JK, et al. The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement: Explanation and Elaboration. Ann Intern Med. 2020;172:W1–W25. [Epub ahead of print 12 November 2019]. doi: 10.7326/M18-3668.
  
  
• Wessler BS, Lundquist CM, Koethe B, Park JG, Brown K, Williamson T, Ajlan M, Natto Z, Lutz JS, Paulus JK, Kent DM. Clinical Prediction Models for Valvular Heart Disease. J Am Heart Assoc. 2019 Oct 15;8(20):e011972. PubMed PMID: 31583938. doi: 10.1161/JAHA.119.011972. Epub 2019 Oct 4.
  
  
• Kent D, Paulus J, Ahmed M, and Whicher D, Editors. 2019. Caring for the Individual Patient: Understanding Heterogeneous Treatment Effects. Washington, DC: National Academy of Medicine.
  
  
• Van Klaveren D, Balan TA, Steyerberg EW, Kent DM. Models with interactions overestimated heterogeneity of treatment effects and were prone to treatment mistargeting. J Clin Epidemiol. 2019 Oct;114:72-83. Pubmed PMID: 31195109. doi: 10.1016/j.jclinepi.2019.05.029.
  
  
• Kent DM, Steyerberg E, van Klaveren D. Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects. BMJ. 2018 Dec 10;363:k4245. PubMed PMID: 30530757. doi: 10.1136/bmj.k4245.
  
  
• Van Klaveren D, Steyerberg EW, Serruys PW, Kent DM. The proposed ‘concordance-statistic for benefit’ provided a useful metric when modeling heterogeneous treatment effects. J Clin Epidemiol. 2018 Feb;94:59-68. doi: 10.1016/j.jclinepi.2017.10.021

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>

“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.

"Selecting patients for lung cancer screening by personalized risk offers limited long term gains" Tufts Medical Center. Boston, MA. February 1, 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.  

“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

Jennifer Lutz, MA
Program Coordinator II, Predictive Analytics and Comparative Effectiveness (PACE) Center
Phone: 617-636-7405
Fax: 617-636-0022
https://twitter.com/Tufts_PACE