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APPRAISING EVIDENCE ABOUT
CLINICAL PREDICTION RULES
INTRODUCTION
 Information is gathered via record review and interview
as well as through systems review and application of
diagnostic tests and clinical measures.
 The data obtained are used to:
 Identify and quality the nature and extend of the patient/
client’s problem or concern (diagnosis)
 Predict future outcome (prognosis)
 Inform development of a plan of care (intervention)
 This iterative decision making process may be facilitated
when therapist know which pieces of information are
most relevant as well as their relative contributions to
the overall picture.
CONT…..
 The traditional practice paradigm relies on knowledge,
experience and intuition often influenced by authority and
tradition to make these choices. However the potential for
inaccurate and/or costly misdirection exist in these
circumstances because individual clinical practice is
biased by exposure to patients/ client that may not be
representative of the larger population for given problem or
concern.
 Number of clinical perdition rules have been developed
that are relevant to physical therapist practice, including
but not limit to
 The Ottawa ankle rules( diagnosis)
 A rule for shoulder pain ( prognostic estimate)
 A rule for neck pain ( guidance about interventions)
STUDY CREDIBILITY
 Evidence pertaining to clinical prediction rules may
focus on derivation of the rule , validation of the
rule, or both .
CLINICAL PREDICTION RULE DERIVATION
 DID THE INVESTIGATORS OPERATIONALLY DEFINE
THE SAMPLE IN THEIR STUDY?
 Clearly articulated inclusion and exclusion criteria should be
used to ensure that subjects fit the definition of individuals
who have the potential to achieve the outcome of interest
for the clinical prediction rule.
 WERE THE SUBJECTS REPRESENTATIVE OF THE
POPULATION FROM WHICH THEY WERE DRAWN?
 The issue of representativeness pertains to the degree to
which investigators were able to capture all eligible subjects
during the time frame of the study.
 Enrolling some individuals and not others may result in
biased estimates that limit the accuracy and utility of the
prediction rule.
 DID THE INVESTIGATORS INCLUDE ALL RELEVANT
PREDICTIVE FACTORS IN DEVELOPMENT PROCESS?
 Investigators have a variety of methods for developing a list
of potential predictive factors including patient interview and
focus groups, input from relevant health care providers and
content experts, available evidence, and historical patient
records.
 WERE THE PREDICTIVE FACTORS OPERATIONALLY
DEFINED?
 This question address the validity of the measures used to
identify and quantify the predictive factor. A clear definition of
these variables is necessary to correctly identify which
subjects these characteristics and to optimise the accuracy of
the prediction rule.
 DID THE INVESTIGATORS INCLUDE A LARGE ENOUGH
SAMPLE SIZE TO ACCOMMODATE THE NUMBER OF
PREDICTIVE FACTORS USED TO DERIVE THE CLINICAL
PREDICTION RULE?
issue of sample size is twofold. First, statistical modelling methods
like regression require adequate information to predict the value of
an outcome. Even when samples are homogeneous in
composition, some individual subjects will not have all the
predictive characters of interest. Second, the precision of any
estimate is improved with more information.
 WERE THE OUTCOMES OF INTEREST OPERATIONALLY
DEFINED?
this question address the validity of the measures used to capture
the outcome(s) the rule is designed to predict.
 WERE THOSE RESPONSIBLE FOR MEASURING
OUTCOMES MASKED TO THE STATUS OF THE
PREDICTIVE FACTOR( AND VICE VERSA) ?
 tester bias can occur when those responsible for
collecting measures have knowledge of finding that
could influence their interpretation. Masking those
responsible for identifying and quantifying the predicting
factors and those responsible for collecting outcomes
reduces the opportunity for tester bias and should
improve the prediction rule’s accuracy.
 DID THE INVESTIGATORS COLLECT OUTCOME DATA
FROM ALL THE SUBJECTS ENROLLED IN THE STUDY?
 the ability to capture the outcomes in all subjects is
important because attrition for any reason may provide
a skewed representation of which outcomes occurred
and when
CLINICAL PREDICTION RULE
VALIDIATION
 DID THE INVESTIGATORS COMPARE RESULTS FROM THE
CLINICAL PREDICTION RULE TO RESULTS FROM AN
ESTABLISHED CRITERION TEST OR MEASURE OF THE
OUTCOME OF INTEREST?
 This question address the need to conform the rule is intended
to predict actually occurred. This process is similar to
confirming the validity of a diagnostic test.
 WERE ALL SUBJECTS EVALUATED WITH THE CRITERION
TEST OR MEASURE OF THE OUTCOME THE RULE IS
INTENDED TO PREDICT?
 This question clarifies the degree to which the investigators
may have introduced bias into the study by administering the
criterion test or measure only to subjects who achieved the
outcome according to the clinical prediction rule.
 WERE THE INDIVIDUAL APPLYING THE CRITERION TEST
OR MEASURE UNAWARE OF THE RESULTS OF THE
CLINICAL PREDICTION RULE?
 The criterion test or measure will be a applied by examiners
who are masked to the result of the application of the clinical
prediction rule for each subject. The concern for tester bias is
the same as described previously.
 HAS THE CLINICAL PREDICTION RULE BEEN VALIDATED
ON A POPULATION OTHER THAN THE ONE FOR WHICH IT
ORIGINALLY WAS DESIGNED?
 this question refer to efforts to expand the application of a
clinical prediction rule to other patient population.
 DID THE INVESTIGATORS CONFIRM THEIR FINDINGS WITH A
NEW SET OF SUBJECTS?
 This question alludes to the possibility that the research
findings regarding the clinical prediction rule occurred due to
unique attributes of the sample
 Repeating the study on a second set of subjects who match
the inclusion and exclusion criteria outlined for the first set
provides an opportunity to evaluate whether the same
combination of factors accurately predicts the outcome of
interest.
STUDY RESULTS
 Result from studies pertaining to clinical prediction
rules vary depending on which phase of their
evolution is being reported.
SUMMARY
 Clinical prediction rules are systematically derived
and statistically tested combinations of clinical
findings that provide meaningful predictions about
an outcome of interest.

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APPRAISING EVIDENCE ABOUT CLINICAL PREDICTION RULES.pptx

  • 2. INTRODUCTION  Information is gathered via record review and interview as well as through systems review and application of diagnostic tests and clinical measures.  The data obtained are used to:  Identify and quality the nature and extend of the patient/ client’s problem or concern (diagnosis)  Predict future outcome (prognosis)  Inform development of a plan of care (intervention)  This iterative decision making process may be facilitated when therapist know which pieces of information are most relevant as well as their relative contributions to the overall picture.
  • 3. CONT…..  The traditional practice paradigm relies on knowledge, experience and intuition often influenced by authority and tradition to make these choices. However the potential for inaccurate and/or costly misdirection exist in these circumstances because individual clinical practice is biased by exposure to patients/ client that may not be representative of the larger population for given problem or concern.  Number of clinical perdition rules have been developed that are relevant to physical therapist practice, including but not limit to  The Ottawa ankle rules( diagnosis)  A rule for shoulder pain ( prognostic estimate)  A rule for neck pain ( guidance about interventions)
  • 4. STUDY CREDIBILITY  Evidence pertaining to clinical prediction rules may focus on derivation of the rule , validation of the rule, or both .
  • 5. CLINICAL PREDICTION RULE DERIVATION  DID THE INVESTIGATORS OPERATIONALLY DEFINE THE SAMPLE IN THEIR STUDY?  Clearly articulated inclusion and exclusion criteria should be used to ensure that subjects fit the definition of individuals who have the potential to achieve the outcome of interest for the clinical prediction rule.  WERE THE SUBJECTS REPRESENTATIVE OF THE POPULATION FROM WHICH THEY WERE DRAWN?  The issue of representativeness pertains to the degree to which investigators were able to capture all eligible subjects during the time frame of the study.  Enrolling some individuals and not others may result in biased estimates that limit the accuracy and utility of the prediction rule.
  • 6.  DID THE INVESTIGATORS INCLUDE ALL RELEVANT PREDICTIVE FACTORS IN DEVELOPMENT PROCESS?  Investigators have a variety of methods for developing a list of potential predictive factors including patient interview and focus groups, input from relevant health care providers and content experts, available evidence, and historical patient records.  WERE THE PREDICTIVE FACTORS OPERATIONALLY DEFINED?  This question address the validity of the measures used to identify and quantify the predictive factor. A clear definition of these variables is necessary to correctly identify which subjects these characteristics and to optimise the accuracy of the prediction rule.
  • 7.  DID THE INVESTIGATORS INCLUDE A LARGE ENOUGH SAMPLE SIZE TO ACCOMMODATE THE NUMBER OF PREDICTIVE FACTORS USED TO DERIVE THE CLINICAL PREDICTION RULE? issue of sample size is twofold. First, statistical modelling methods like regression require adequate information to predict the value of an outcome. Even when samples are homogeneous in composition, some individual subjects will not have all the predictive characters of interest. Second, the precision of any estimate is improved with more information.  WERE THE OUTCOMES OF INTEREST OPERATIONALLY DEFINED? this question address the validity of the measures used to capture the outcome(s) the rule is designed to predict.
  • 8.  WERE THOSE RESPONSIBLE FOR MEASURING OUTCOMES MASKED TO THE STATUS OF THE PREDICTIVE FACTOR( AND VICE VERSA) ?  tester bias can occur when those responsible for collecting measures have knowledge of finding that could influence their interpretation. Masking those responsible for identifying and quantifying the predicting factors and those responsible for collecting outcomes reduces the opportunity for tester bias and should improve the prediction rule’s accuracy.  DID THE INVESTIGATORS COLLECT OUTCOME DATA FROM ALL THE SUBJECTS ENROLLED IN THE STUDY?  the ability to capture the outcomes in all subjects is important because attrition for any reason may provide a skewed representation of which outcomes occurred and when
  • 9. CLINICAL PREDICTION RULE VALIDIATION  DID THE INVESTIGATORS COMPARE RESULTS FROM THE CLINICAL PREDICTION RULE TO RESULTS FROM AN ESTABLISHED CRITERION TEST OR MEASURE OF THE OUTCOME OF INTEREST?  This question address the need to conform the rule is intended to predict actually occurred. This process is similar to confirming the validity of a diagnostic test.  WERE ALL SUBJECTS EVALUATED WITH THE CRITERION TEST OR MEASURE OF THE OUTCOME THE RULE IS INTENDED TO PREDICT?  This question clarifies the degree to which the investigators may have introduced bias into the study by administering the criterion test or measure only to subjects who achieved the outcome according to the clinical prediction rule.
  • 10.  WERE THE INDIVIDUAL APPLYING THE CRITERION TEST OR MEASURE UNAWARE OF THE RESULTS OF THE CLINICAL PREDICTION RULE?  The criterion test or measure will be a applied by examiners who are masked to the result of the application of the clinical prediction rule for each subject. The concern for tester bias is the same as described previously.  HAS THE CLINICAL PREDICTION RULE BEEN VALIDATED ON A POPULATION OTHER THAN THE ONE FOR WHICH IT ORIGINALLY WAS DESIGNED?  this question refer to efforts to expand the application of a clinical prediction rule to other patient population.
  • 11.  DID THE INVESTIGATORS CONFIRM THEIR FINDINGS WITH A NEW SET OF SUBJECTS?  This question alludes to the possibility that the research findings regarding the clinical prediction rule occurred due to unique attributes of the sample  Repeating the study on a second set of subjects who match the inclusion and exclusion criteria outlined for the first set provides an opportunity to evaluate whether the same combination of factors accurately predicts the outcome of interest.
  • 12. STUDY RESULTS  Result from studies pertaining to clinical prediction rules vary depending on which phase of their evolution is being reported.
  • 13. SUMMARY  Clinical prediction rules are systematically derived and statistically tested combinations of clinical findings that provide meaningful predictions about an outcome of interest.