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Open Access Research article

Extensions to Regret-based Decision Curve Analysis: An application to hospice referral for terminal patients

Athanasios Tsalatsanis1*, Laura E Barnes1, Iztok Hozo2 and Benjamin Djulbegovic13

Author Affiliations

1 Center for Evidence-based Medicine and Health Outcomes Research, University of South Florida, Tampa, FL, USA

2 Department of Mathematics, Indiana University Northwest, Gary, IN, USA

3 H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA

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BMC Medical Informatics and Decision Making 2011, 11:77  doi:10.1186/1472-6947-11-77

Published: 23 December 2011

Abstract

Background

Despite the well documented advantages of hospice care, most terminally ill patients do not reap the maximum benefit from hospice services, with the majority of them receiving hospice care either prematurely or delayed. Decision systems to improve the hospice referral process are sorely needed.

Methods

We present a novel theoretical framework that is based on well-established methodologies of prognostication and decision analysis to assist with the hospice referral process for terminally ill patients. We linked the SUPPORT statistical model, widely regarded as one of the most accurate models for prognostication of terminally ill patients, with the recently developed regret based decision curve analysis (regret DCA). We extend the regret DCA methodology to consider harms associated with the prognostication test as well as harms and effects of the management strategies. In order to enable patients and physicians in making these complex decisions in real-time, we developed an easily accessible web-based decision support system available at the point of care.

Results

The web-based decision support system facilitates the hospice referral process in three steps. First, the patient or surrogate is interviewed to elicit his/her personal preferences regarding the continuation of life-sustaining treatment vs. palliative care. Then, regret DCA is employed to identify the best strategy for the particular patient in terms of threshold probability at which he/she is indifferent between continuation of treatment and of hospice referral. Finally, if necessary, the probabilities of survival and death for the particular patient are computed based on the SUPPORT prognostication model and contrasted with the patient's threshold probability. The web-based design of the CDSS enables patients, physicians, and family members to participate in the decision process from anywhere internet access is available.

Conclusions

We present a theoretical framework to facilitate the hospice referral process. Further rigorous clinical evaluation including testing in a prospective randomized controlled trial is required and planned.