Preferences for Predictive Model Characteristics among People Living with Chronic Lung Disease: A Discrete Choice Experiment

Abstract

Background. Patients may find clinical prediction models more useful if those models accounted for preferences for false-positive and false-negative predictive errors and for other model characteristics. Methods. We conducted a discrete choice experiment to compare preferences for characteristics of a hypothetical mortality prediction model among community-dwelling patients with chronic lung disease recruited from 3 clinics in Philadelphia. This design was chosen to allow us to quantify “exchange rates” between different characteristics of a prediction model. We provided previously validated educational modules to explain model attributes of sensitivity, specificity, confidence intervals (CI), and time horizons. Patients reported their interest in using prediction models themselves or having their physicians use them. Patients then chose between 2 hypothetical prediction models each containing varying levels of the 4 attributes across 12 tasks. Results. We completed interviews with 200 patients, among whom 95% correctly chose a strictly dominant model in an internal validity check. Patients’ interest in predictive information was high for use by themselves ( n = 169, 85%) and by their physicians ( n = 184, 92%). Interest in maximizing sensitivity and specificity were similar (0.88 percentage points of specificity equivalent to 1 point of sensitivity, 95% CI 0.72 to 1.05). Patients were willing to accept a reduction of 6.10 months (95% CI 3.66 to 8.54) in the predictive time horizon for a 1% increase in specificity. Discussion. Patients with chronic lung disease can articulate their preferences for the characteristics of hypothetical mortality prediction models and are highly interested in using such models as part of their care. Just as clinical care should become more patient centered, so should the characteristics of predictive models used to guide that care.

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“…One study that did focus on the preference of chronic lung disease patients for predictive model characteristics, showed that patients were indeed highly interested in using prediction models as part of their care. 12 …”

“…More generally, there is a plethora of studies on how to communicate risks to patients, but there are relatively few studies on patient preferences for risk communication per se and their perspectives on what to communicate and what not. One study that did focus on the preference of chronic lung disease patients for predictive model characteristics, showed that patients were indeed highly interested in using prediction models as part of their care 12 …”

“…One study that did focus on the preference of chronic lung disease patients for predictive model characteristics, showed that patients were indeed highly interested in using prediction models as part of their care. 12 Prediction models are expected to improve personalized care for patients, and therefore such models need to be designed around the preferences of the people they aim to serve. In the field of PD, the perspective of persons with PD on the use of PPMs in clinical practice has not been evaluated yet.…”