2017
DOI: 10.1097/dcc.0000000000000219
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Understanding Negative Predictive Value of Diagnostic Tests Used in Clinical Practice

Abstract: Nurses review, evaluate, and use diagnostic test results on a routine basis. However, the skills necessary to evaluate a particular test using statistical outcome measures is often lacking. The purpose of this article is to examine and interpret the underlying principles for use of the statistical outcomes of diagnostic screening tests (sensitivity, specificity, and positive (PPV) and negative (NPV) predictive values, with a discussion about use of SpPIn [Specificity, Positive test = rule in], and SnNOut [Sens… Show more

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Cited by 22 publications
(16 citation statements)
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“…Cxbladder had an improved NPV compared with urine cytology, which, when coupled with high sensitivity, provides reliable rule out of patients without UC. Rule-out tests with high sensitivity combined with a high NPV provide better clinical resolution for ruling out patients who have a low probability of disease [19,20] than tests with only a high NPV. These findings support previous published results, which have shown that all Cxbladder tests significantly outperformed urine cytology for identifying patients without UC [14-[ 5 _ T D $ D I F F ] 16,23].…”
Section: Discussionmentioning
confidence: 99%
“…Cxbladder had an improved NPV compared with urine cytology, which, when coupled with high sensitivity, provides reliable rule out of patients without UC. Rule-out tests with high sensitivity combined with a high NPV provide better clinical resolution for ruling out patients who have a low probability of disease [19,20] than tests with only a high NPV. These findings support previous published results, which have shown that all Cxbladder tests significantly outperformed urine cytology for identifying patients without UC [14-[ 5 _ T D $ D I F F ] 16,23].…”
Section: Discussionmentioning
confidence: 99%
“…Overall, our risk prediction model had very good discrimination and calibration and a high negative predictive value using the optimal cut-off of ≥15%. Similar to most clinical prediction rules, where a better negative predictive value is of higher utility than the positive predictive value, 15 the risk to a patient from incorrectly classifying them as not needing urgent dialysis when they do (false negative) is far greater than the risk to them if they are incorrectly classified as needing urgent dialysis when they do not (false positive). With our model, a predicted probability of <15% means there is only a 7% chance the patient will require urgent dialysis.…”
Section: Discussionmentioning
confidence: 99%
“…These results suggest that even early EAC cancers, elective parotidectomy should be considered if the tumor location is a‐EAC and/or c‐EAC. Given the importance of high sensitivity and negative predictive value in a diagnostic setting, 23 our results can be applied for clinical decision making and to determine elective parotidectomy according to tumor location.…”
Section: Discussionmentioning
confidence: 99%