Articles: sensitivity-specificity.
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Journal of anesthesia · Mar 1995
Auditory brainstem responses after out-of-hospital cardiac arrest: Are they useful for outcome prediction?
We evaluated whether we could predict the neurologic outcome in 55 out-of-hospital cardiac arrest patients using auditory brainstem responses (ABR). ABR patterns were classified into one of 3 types by evaluation of 5 components: type 1, with all 5 components; type 2, lack of at least one response between the 2nd and 5th components; type 3, with only the first component or no response. The relation between the ABR patterns on the 3rd day following resuscitation and the neurologic outcome on hospital discharge was evaluated. ⋯ In the type-1 ABR patients, the negative predictive value that the patients were awake was 100%. In the type-3 ABR patients, the negative predictive value that the patients became brain dead was 90.9%. These results suggest that ABR on the 3rd post-resuscitation day may not be useful for predicting if patients are awake or become brain dead, although the loss of components may be a sign of morbidity, and the presence of the 2nd or later components indicates possible future prevention of brain death.
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To determine if the leukocyte esterase and bacterial nitrite rapid dipstick test for urinary tract infection (UTI) is susceptible to spectrum bias (when a diagnostic test has different sensitivities or specificities in patients with different clinical manifestations of the disease for which the test is intended). ⋯ The leukocyte esterase and bacterial nitrite dipstick test for UTI is susceptible to spectrum bias, which may be responsible for differences in the test's sensitivity reported in previous studies. As a more general principle, diagnostic tests may have different sensitivities or specificities in different parts of the clinical spectrum of the disease they purport to identify or exclude, but studies evaluating such tests rarely report sensitivity and specificity in subgroups defined by clinical symptoms. When diagnostic tests are evaluated, information about symptoms in the patients recruited for study should be included, and analyses should be done within appropriate clinical subgroups so that clinicians may decide if reported sensitivities and specificities are applicable to their patients.
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Estimates of sensitivity and specificity can be biased by the preferential referral of patients with positive test responses or ancillary clinical abnormalities (the "concomitant information vector") for diagnostic verification. When these biased estimates are analyzed by Bayes' theorem, the resultant posterior disease probabilities (positive and negative predictive accuracies) are similarly biased. Accordingly, a series of computer simulations was performed to quantify the effects of various degrees of verification bias on the calculation of predictive accuracy using Bayes' theorem. ⋯ These errors produced absolute underestimations as high as 22% in positive predictive accuracy, and as high as 14% in negative predictive accuracy, when analyzed by Bayes' theorem at a base rate of 50%. Mathematical correction for biased verification based on the test response using a previously published algorithm significantly reduced these errors by as much as 20%. These data indicate 1) that selection bias significantly distorts the determination of predictive accuracies calculated by Bayes' theorem, and 2) that these distortions can be significantly offset by a correction algorithm.