# Dynamic Prediction Modeling to Improve Clinical Predictions

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2020 · $609,432

## Abstract

Project Summary
 Risk prediction is inherent to all clinical practice and public health and has been a topic of scientific research for
decades. Formal prediction models are frequently used to enhance clinicians' and researchers' ability to quantify
and communicate risk. However, a prediction model is only useful if it is accurate when applied outside of the
population within which it was developed. Unfortunately, many prediction models in use today prove inaccurate
when applied over time and to new populations, yielding not only inaccurate predictions but also a false level of
confidence about the quality of their risk assessments. This commonly occurs because models are applied to
patients with different clinical characteristics and risk of disease, to medical practices that differ from those used
to develop the model, and to methods of care that constantly change over time. The current scientific paradigm
does not readily allow models to accommodate these differences. As a result, model accuracy is often
compromised for years of clinical use, new models are slow to be developed (if at all), and these new models are
no better able to account for changing patient populations or medical practice than the original models.
 A potential solution to these problems is `Dynamic Prediction Modeling.' Rather than using existing models in
practice without accommodating their inevitable degradation in performance and, at best, infrequently developing
new models with the same limitations, dynamic prediction modeling updates an existing prediction model
continually as new data are accrued. In this approach, the updated models combine the information that is
captured in the original model with data from new patients to produce an updated model for future predictions. As
a result of this ongoing model-refinement process, dynamic prediction models have the potential to enhance and
maintain model accuracy in the presence of changing patient populations and medical practices over time.
 Our objective in this proposal is to develop and test this new paradigm for risk prediction through rigorous
statistical and applied research, to provide comprehensive guidance for the real-world use of dynamic prediction
modeling, and thus to remove critical barriers to the wider dissemination of these methods in clinical research
and practice. Specifically, this project will: (1) use formal and comprehensive simulations to develop guidelines
for implementing dynamic model recalibration, revision, and extension; (2) test and compare these dynamic
prediction modeling approaches with the traditional approach to prediction modeling in two real world and diverse
clinical settings, and then refine the methods to enhance accuracy and generalizability; and (3) formally and
prospectively test the implementation of dynamic prediction modeling in a large, multicenter population of
intensive care unit patients to demonstrate the utility, feasibility, and accuracy of dynamic prediction model...

## Key facts

- **NIH application ID:** 9904186
- **Project number:** 5R01HL141294-03
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Stephen E. Kimmel
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $609,432
- **Award type:** 5
- **Project period:** 2018-04-15 → 2020-12-21

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9904186

## Citation

> US National Institutes of Health, RePORTER application 9904186, Dynamic Prediction Modeling to Improve Clinical Predictions (5R01HL141294-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9904186. Licensed CC0.

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