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Health Care Decisionmaking

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Prediction models may help surrogates make end-of-life treatment decisions for incompetent elderly patients

When elderly patients are incapable of making decisions about life-sustaining treatment, surrogates—often a spouse or adult child—may be given the difficult task of trying to decide what patients would have decided for themselves if they had been competent to do so. Advanced directives (ADs) allow elderly people to record their treatment preferences in advance. However, studies show that surrogates predict patients' choices with equal accuracy with and without an AD. Furthermore, patient preferences for life-sustaining care often change over time, so surrogates are trying to "hit a moving target."

According to a recent study that was supported in part by the Agency for Healthcare Research and Quality (HS08180), decision models based on the choices of a community of patients with a similar condition may supplement or perhaps provide an alternative to ADs. The 2-year study involved healthy elderly outpatients at six family practice centers and their surrogates. The participants were asked to record their preferences for four treatments in nine hypothetical illness scenarios at three different times during the study. The scenarios included current health, emphysema, Alzheimer's disease, terminal cancer with and without pain, and coma and stroke with slight or no chance of recovery. Life-sustaining treatments included artificial hydration and nutrition if unable to eat or drink and cardiopulmonary resuscitation.

Both the original and updated models were more accurate in predicting patients' preferences than were surrogates making concurrent predictions. The models reflected a trend toward refusing life-sustaining treatment for more scenarios as time progressed.

See "Predicting elderly outpatients' life-sustaining treatment preferences over time: The majority rules," by Renate M. Houts, Ph.D., William D. Smucker, M.D., Jill A. Jacobson, Ph.D., and others, in the January 2002 Medical Decision Making 22, pp. 39-52.

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