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AFRICAN RESEARCH NEXUS

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medicine

N-terminal pro-B-type natriuretic peptides' prognostic utility is overestimated in meta-analyses using studyspecific optimal diagnostic thresholds

Anesthesiology, Volume 123, No. 2, Year 2015

Background: N-terminal fragment B-type natriuretic peptide (NT-proBNP) prognostic utility is commonly determined post hoc by identifying a single optimal discrimination threshold tailored to the individual study population. The authors aimed to determine how using these study-specific post hoc thresholds impacts meta-analysis results. Methods: The authors conducted a systematic review of studies reporting the ability of preoperative NT-proBNP measurements to predict the composite outcome of all-cause mortality and nonfatal myocardial infarction at 30 days after noncardiac surgery. Individual patient-level data NT-proBNP thresholds were determined using two different methodologies. First, a single combined NT-proBNP threshold was determined for the entire cohort of patients, and a meta-analysis conducted using this single threshold. Second, study-specific thresholds were determined for each individual study, with meta-analysis being conducted using these study-specific thresholds. Results: The authors obtained individual patient data from 14 studies (n = 2,196). Using a single NT-proBNP cohort threshold, the odds ratio (OR) associated with an increased NT-proBNP measurement was 3.43 (95% CI, 2.08 to 5.64). Using individual study-specific thresholds, the OR associated with an increased NT-proBNP measurement was 6.45 (95% CI, 3.98 to 10.46). In smaller studies (<100 patients) a single cohort threshold was associated with an OR of 5.4 (95% CI, 2.27 to 12.84) as compared with an OR of 14.38 (95% CI, 6.08 to 34.01) for study-specific thresholds. Conclusions: Post hoc identification of study-specific prognostic biomarker thresholds artificially maximizes biomarker predictive power, resulting in an amplification or overestimation during meta-analysis of these results. This effect is accentuated in small studies.

Statistics
Citations: 19
Authors: 19
Affiliations: 18
Identifiers
Research Areas
Health System And Policy
Study Design
Cross Sectional Study
Cohort Study
Case-Control Study
Study Approach
Systematic review