Extensions to regret-based decision curve analysis: an application to hospice referral for terminal patients

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.

Figure 1

Decision tree for hospice referral. In this figure, p is the probability that a patient's LE is less than or equal to 6 months; 1-p is the probability that a patient's LE is greater than 6 months; U_i: are the utilities associated with each outcome; Rg_i is the regret associated with each outcome.

Figure 2

DVAS (Dual Visual Analogue Scales). The DVAS are used for the elicitation of the decision maker's threshold probability.

Figure 3

Decision tree describing the overall hospice referral process. In this figure p = P (D +): probability the patient's LE is less than or equal to 6 months; 1 - p = P (D -): probability the patient's LE is greater than 6 months; U_i, i ∈ [1,4]: the utilities corresponding to each of the decision model outcomes; Hosp: hospice referral; Rx treatment continuation; Rg: regret associated with an action; H_Rx : utility losses due to harms of treatment; H_Hosp: utility losses due to harms of hospice; H_te: utility losses due to harms of the prognostic test (SUPPORT).

Figure 4

Decision curves for hospice referral. In this figure, RRR_Hosp = 0, RRR_Rx = 0, H_Rx = H_Hosp = H_te = 0. At threshold probability equal to 10%, the optimal decision is refer the patient to hospice; at 40% the optimal decision is to use the SUPPORT model.

Figure 5

Decision curves as a function of RRR_RX. In this figure, RRR_Hosp = 0, RRR_Rx = 2 to 8%, H_Rx = H_Hosp = H_te = 0. As the effect of treatment increases, the regret associated with treating all patients and with the SUPPORT model slightly decreases. The strategy of using the SUPPORT model to refer a patient to hospice is the action with the least amount of regret for the wider range of threshold probabilities.

Figure 6

Decision curves as a function of RRR_Hosp. In this figure, RRR_Hosp = 2% to 8%, RRR_Rx = 0, H_Rx = H_Hosp = H_te = 0. As the effect of hospice care increases, the regret associated with hospice and with the SUPPORT model slightly decreases. As previously, the strategy of using the SUPPORT model to refer a patient to hospice is the action with the least amount of regret for a wide range of threshold probabilities.

Figure 7

Decision curves as a function of H_te. In this figure, RRR_Hosp = 0, RRR_Rx = 0, H_Rx = H_Hosp = 0 and H_te = 0% and 10%. As the harms associated to the prediction test increase, so does the expected regret of utilizing the SUPPORT model for hospice referral. Increasing the harms due to treatment or due to hospice care does not have an effect on the decision curves.

Figure 8

Block diagram outlining the operation of the DSS.

Figure 9

Elicitation of threshold probability. The user (patient/surrogate/physician) weighs the two alternative management strategies in terms of regret.

Figure 10

SUPPORT user interface. The user enters all information regarding the particular patient to compute the probability of death and survival within the next 6 months. LE results are presented to the patient through the decision justification module after the patient's request.

Figure 11

Justification of the hospice referral recommendation. The particular patient depicted has 29% threshold probability at which the optimal strategy is derived by the SUPPORT model. The patient has 85% probability of death in the next 180 days. Therefore, the optimal decision is to be referred to hopsice.

Figure 12

Block diagram summarizing the decision process. The bold route corresponds to the patient simulated in figures 10, 11 and 12.