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Predicting Clinical States in Individual Patients

Leonard E. Braitman, PhD; and Frank Davidoff, MD
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From Albert Einstein Healthcare Network and the American College of Physicians, Philadelphia, Pennsylvania. Acknowledgments: The authors thank Saul Axelrod, Warren Bilker, Steven Goodman, Christine Laine, Russell Localio, Michael Montgomery, and Tom Ten Have for their assistance. Requests for Reprints: Frank Davidoff, MD, American College of Physicians, Sixth Street at Race, Philadelphia, PA 19106. Current Author Addresses: Dr. Braitman: Office for Research and Technology Development, Albert Einstein Healthcare Network, Korman 100, 5501 Old York Road, Philadelphia, PA 19141.

Copyright ©2004 by the American College of Physicians

Ann Intern Med. 1996;125(5):406-412. doi:10.7326/0003-4819-125-5-199609010-00008
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A probability model expresses the relation between the presence of clinical findings (input or independent variables) and the probability that a clinical state will occur (the dependent variable); for example, it expresses the probability that a disease is present or will develop or the probability that an outcome state will be reached.Probability models are developed by using selected study groups. Although these models are most often used to make predictions for groups of patients, they can also predict clinical states for individual patients.

The following seven criteria provide a basis for the critical appraisal of probability models.In particular, physicians can use these criteria to decide when a specific probability model should be used to make a prediction in an individual patient. Five of the criteria are concerned with the applicability of a model to a particular patient: 1) the comparability of the patient and the study group used to develop the model; 2) the congruence between the clinical state of interest to patient and physician and the model's outcome; 3) the availability of all input variables where and when the prediction is to be made; 4) the usefulness of a quantitative estimate of the predicted clinical state; and 5) the degree of uncertainty in the probability estimate. The other two criteria are concerned with how well the probability model “works”: 6) the fit of probabilities calculated from the model to the outcomes actually observed and 7) the model's ability to discriminate between outcome states relative to chance and to other, more traditional, prediction methods. We illustrate the use of these criteria by applying them, in the form of questions, to a convenient, tabular version of a model that estimates a patient's chances of surviving for 10 years after having definitive surgical therapy for primary cutaneous melanoma.





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