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Clinical Utility Index Calculator
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https://tinyurl.com/3uf36y49
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Quickly find the Qualitative and Quantitative Accuracy & Utility of Diagnostic and Screening Tests
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https://t.co/zxxXw6ctFj
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Instructions: enter your data from diagnostic/screening test results in yellow cells and all others are automatically calculated // help? see www.clinicalutility.co.uk
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Data Entry
Data Table
Reverse LookUp
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0Very Poor
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0.36Poor
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Enter Total # Cases:
24CASESNON-CASESIf prevalence is0.050.49Fair
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total by criterion standard
cases0.64Good
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# Cases Correctly Detected:
18
>>>>
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TEST POSITIVE
18119>>>PPV= 0.947
and sensitivity is
0.70.81Excellent
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number of true positives
true positives
False negatives
true positives
false positives
total positives
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Enter Total # Non-Cases:
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TEST NEGATIVE
606>>>NPV= 0
and specificity is
0.985
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non-cases (aka controls) by criterion standard
non-cases
false negatives
true negatives
total negatives
0Tiny
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# Non-Cases Correctly Detected:
0
>>>>
124125and sample is10000113Very small
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number of true negatives
true negatives
False positives
total cases
total non-cases
total tested...............then287Small
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Cases5003477Moderate
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......therefore you had a tested population of
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Sensitivity= 0.75
Specificity= 0
Prevalence =0.96
TP =35042888Large
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4.000Non-cases9500519877Very large
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TN = 9357.5
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Quantitative Results
allow 5s to calculate.........
Qualitative Interpretation
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Lower CI
Upper CI
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Sensitivity =>
75.00%57.68%92.32%
sensitivity (useful if you already know the number of cases)
The SENSITIVITY of the test is
LOW…..meaning a positive test does not often occur in those with the condition
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TP / TP+TN
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Specificity =>
0.00%0.00%0.00%
specificity (useful if you already know the number of non-cases)
The SPECIFICITY of the test is
EXTREMELY LOW…..meaning a negative very rarely occurs in those without the condition, limiting its value
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TN / TN+FP
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Positive Predictive Value = >
94.74%84.70%100.00%
positive predictive value (useful when you know the number of positive results)
The PPV of the test is
HIGH…..meaning false positives are uncommon in those who screen positive
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TP / TP+FP
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Negative Predictive Value = >
0.00%0.00%0.00%
negative predictive value (useful when you know the number of negative results)
The NPV of the test is
EXTREMELY LOW…..meaning false negatives are too common in those who screen negatives, negating screening value
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TN / TN+FN
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CUI Positive
0.7110.5250.896
case-finding value (ruling-in true positives)
CLINICAL UTILITY of the test for case-finding (confirmation) is
FAIR for case-finding
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SN*PPV
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CUI Negative
0.0000.0000.000
screening value (ruling out true negatives)
CLINICAL UTILITY of the test for screening (ruling out) is
VERY POOR for screening (ie confirming a non-case)
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SP*NNP
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Prevalence = >
0.960n/an/a
The PREVALENCE of the condition is
EXTREMELY COMMON…..hence will favour case-finding but be very cautious about false negatives
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CASES/POPULATION
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Likelihood Ratio +ve =>
0.7500.600.94
The total SAMPLE SIZE is
Very small
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Sensitivity / 1-specificity
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Likelihood Ratio -ve =>
#DIV/0!#DIV/0!#DIV/0!
Notes and cautions
LOW total sample size, be aware of all confidence intervals
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1-specificity / sensitivity
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Test Score (Fraction Correct) % =>
72.0054.4089.60
Value of this single test for *BOTH* screening and case-finding is
VERY POOR
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TP+TN/POPULATION
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Added NPV Value
Added PPV Value
Take home message
This test is moderate for case-finding (needs improvement)
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Graphical100.0%-4.0%-1.3%5.3%
and its best feature is its
positive predictive value (useful when you know the number of positive results)
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We would rate this test as
3.8/10
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Normalized Results (per 100 people)
Live Visual (tab #2 to view)
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CASES
NON-CASES
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TEST POSITIVE
72476
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true positives
false positives
total positives
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TEST NEGATIVE
24024
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false negatives
true negatives
total negatives
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964100
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total cases
total non-cases
adjusted
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Normalized Results (per 1000 people)
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CASES
NON-CASES
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TEST POSITIVE
72040760
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true positives
false positives
total positives
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TEST NEGATIVE
2400240
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false negatives
true negatives
total negatives
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960401000
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total cases
total non-cases
adjusted
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Open Graphic
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Everday description
In a sample of 1000, where 960 are cases, this test would correctly identify 720 of960 true cases but it would miss 240 cases (ie false negatives).
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Of the 40 non-cases, this test would correctly identify 0 non-cases but it would falsy identify 40 non-cases as false positives.
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After testing 1000 times, 760 tests would be positive, but of these 40 would be false positives, which is 5.3%
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