Patient-centered prognosis focuses on individual patients. It is
a methodology that generates individually tailored probabilistic
predictions of a specified medical outcome that a particular
patient may experience. Its predictions are based on observable
prognostic factors. Because these predictions are both
particular-outcome-specific and individual-patient-specific,
achieving predictive accuracy poses a formidable challenge.
Nevertheless, the patient-centered methodology (PCM) appears to
produce more accurate individually tailored patient predictions
than current prognostic practice. PCM achieves its greater
predictive accuracy by exploiting several analytical devices.
1. It redesigns and retools each successive stage of the
prognostic procedure to predict the particular future outcome that
the targeted patient could experience.
2. It identifies the existence, the direction, the shape, and
the magnitude of each prognostic factor's relationship to the
particular outcome as that relationship pertains, specifically, to
patients similar to the targeted patient.
3. It relies on internal interrelationships among different
prognostic factors and the specified outcome to "fill in" missing
observations so that an individually tailored probabilistic
prediction is possible, even with incomplete patient data.
PCM is applied to 1,222 melanoma patients from the United States
and to 1,225 patients from Finland with invasive breast cancer.
Substantial improvements in prognostic accuracy are realized in
both applications compared to current prognostic practice.
Greater accuracy can lead to better treatment selection
decisions and to other improvements in patient management. Greater
prognostic accuracy can also eliminate unnecessary medical
procedures that are frequently both painful and expensive in
treating progressive diseases such as cancer.
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