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. 1999 Sep-Oct;6(5):412-9.
doi: 10.1136/jamia.1999.0060412.

Use of meta-analytic results to facilitate shared decision making

L A Lenert  1 , D J Cher
Affiliations

Use of meta-analytic results to facilitate shared decision making

L A Lenert et al. J Am Med Inform Assoc. 1999 Sep-Oct.

Abstract

Objectives: Describe and evaluate an Internet-based approach to patient decision support using mathematical models that predict the probability of successful treatment on the basis of meta-analytic summaries of the mean and standard deviation of symptom response.

Design: An Internet-based decision support tool was developed to help patients with benign prostatic hypertrophy (BPH) determine whether they wanted to use alpha blockers. The Internet site incorporates a meta-analytic model of the results of randomized trials of the alpha blocker terazosin. The site describes alternative treatments for BPH and potential adverse effects of alpha blockers. The site then measures patients' current symptoms and desired level of symptom reduction. In response, the site computes and displays the probability of a patient's achieving his objective by means of terazosin or placebo treatment.

Setting: Self-identified BPH patients accessing the site over the Internet.

Main outcome measures: Patients' perceptions of the usefulness of information.

Results: Over a three-month period, 191 patients who were over 50 years of age and who reported that they have BPH used the decision support tool. Respondents had a mean American Urological Association (AUA) score of 18.8 and a desired drop in symptoms of 10.1 AUA points. Patients had a 40 percent chance of achieving treatment goals with terazosin and a 20 percent chance with placebo. Patients found the information useful (93 percent), and most (71 percent) believed this type of information should be discussed before prescribing medications.

Conclusions: Interactive meta-analytic summary models of the effects of pharmacologic treatments can help patients determine whether a treatment offers sufficient benefits to offset its risks.

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Figures

Figure 1
Figure 1
Graphic representation of the probability that an individual patient will achieve his or her treatment goals. Using meta-analysis of clinical trials, the expected mean percentage change in symptom score (and standard deviation of that change) can be calculated. With this information, the probability that a patient will achieve at least the desired drop in score can be estimated using the normal distribution.
Figure 2
Figure 2
Display of individualized estimates of the probability of achieving specified treatment goals by terazosin treatment and watchful waiting. After a patient enters his current symptom score and the maximal level of symptoms he would be willing to tolerate, using the standard and a modified AUA symptom score questionnaire, the site calculates a patient-specific probability of achieving this treatment goal by either terazosin treatment or watchful waiting and displays this probability graphically.
Figure 3
Figure 3
Association between the desired level of reduction in symptom score and a patient's current symptom score (r = 0.67). A few patients were satisfied with their current level of symptoms and indicated that the maximal tolerable level of symptoms was higher than their current score. These patients have negative values for their desired reduction in symptom score.
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