Abstract

The PCORI mission is to address questions about health care from the patients’ perspective, such as “What is my health status and its trajectory?” and “What are my treatment options and the expected benefits and harms of each?” The purpose of this PCORI-funded project is to make it easier for clinicians and patients to find valid answers to these and other clinical questions by using modern digital tools that support (1) learning from the experience of prior patients, and (2) translating what is learned to inform the decision at hand, taking into account each patient’s unique circumstances. For this project, we developed and implemented statistical methods called bayesian hierarchical models that combine existing data on past clinical experience from a reference population with new measurements for the individual. Clinicians currently use such methods when screening patients for disease. Modern technologies make it possible for this proven approach to extend far beyond its current use. The recent revolution in information technology has unleashed new types of health data, from DNA sequences to functional images of the brain to patient-reported outcomes. Furthermore, the electronic health record captures every patient’s sequence of health measurements, diagnoses, and treatments. The bayesian methods developed and reported on here combine even complex data to produce predictions about an individual patient’s health status, trajectory, and likely benefits and harms of interventions.

In addition to developing novel methods, we facilitated their use by creating and locally disseminating a software package, OSLER inHealth, that will allow other researchers to apply this methodology. The software repository is open-source and includes the methodology developed as part of this research as well as other existing methods that facilitate individualized health prediction.

We have tested the proposed methods and software on 3 case studies to (1) estimate the frequency with which various pathogens cause children’s pneumonia and predict which pathogen is likely to be causing a particular child’s pneumonia given her or his clinical data, potentially reducing unnecessary use of antibiotics; (2) infer whether a prostate cancer is indolent or aggressive for a patient under active surveillance; and (3) characterize the variation in multiple, time-varying symptoms of major mental disorders, including schizophrenia and depression, and then use this knowledge to provide patient-specific estimates of past and, likely, future trajectories.

With this project, we have developed and demonstrated the value of combining even complex measurements on a population of patients, then translating this experience into more valid assessments of a new patient’s health status and trajectory. The model also supports inferences about the likely benefits and harms associated with available interventions.

To cite this document, please use:

Zeger SL, Wu Z, Coley Y, et al. (2020). Using a Bayesian Approach to Predict Patients’ Health and Response to Treatment. Patient-Centered Outcomes Research Institute (PCORI). https://doi.org/10.25302/09.2020.ME.140820318