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Clinical Prediction
Rule Estimates
Such rules are based
on analysis of a standardized set of data, including:
- Clinical
findings, and
- The final diagnosis,
for each of many patients with a diagnostic problem.
One type of clinical
prediction rule uses regression analysis to identify the best
clinical predictors and their diagnostic weights. The sum of the
diagnostic weights corresponding to a patient's findings is a
score, and the probability of disease for each patient is equivalent
to the prevalence of disease among patients with a similar score.
To create a clinical
prediction rule, follow these steps:
Cohort assembly
- Because you must have a standard data set on all patients,
it necessary to assemble the cohort prospectively. You must
decide what symptom qualifies a patient for inclusion. You need
a way to identify patients with the symptom and notify the research
assistant to obtain informed consent.
Data collection
- A standard data set is essential. Record the data on a form
that specifies the data to be obtained and the standard way
to obtain the data (standard phrasing of questions and definitions
of abnormal physical findings).
Implement protocol
- Next, implement a protocol for gathering the data necessary
to establish the diagnosis (the gold standard test). The data
may include a definitive test, careful long-term follow-up,
or a combination of the two.
Establish the
patient's diagnosis - By a means that does not use the findings
that you will use to estimate the probability of disease (predictor
variables).
Data entry -
Finally, enter the data in a file. Divide the population into
a training set (on which to define the key predictors of disease
and their relationship to one another) and a test set (on which
to test the clinical prediction rule). Use a multivariate method
(logistic regression analysis, neural net analysis, or recursive
partitioning).
An excellent reference
for investigators interested in pursuing this work is:
Wasson JH, Sox HC
Jr, Goldman L, Neff RK. Clinical prediction rules: applications
and methodologic standards. N Engl J Med. 1985;313:739-99.
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