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Do Optimal Prognostic Thresholds in Continuous Physiological Variables Really Exist? Analysis of Origin of Apparent Thresholds, with Systematic Review for Peak Oxygen Consumption, Ejection Fraction and BNP

BACKGROUND: Clinicians are sometimes advised to make decisions using thresholds in measured variables, derived from prognostic studies. OBJECTIVES: We studied why there are conflicting apparently-optimal prognostic thresholds, for example in exercise peak oxygen uptake (pVO(2)), ejection fraction (E...

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Detalles Bibliográficos
Autores principales: Giannoni, Alberto, Baruah, Resham, Leong, Tora, Rehman, Michaela B., Pastormerlo, Luigi Emilio, Harrell, Frank E., Coats, Andrew J. S., Francis, Darrel P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3903471/
https://www.ncbi.nlm.nih.gov/pubmed/24475020
http://dx.doi.org/10.1371/journal.pone.0081699
Descripción
Sumario:BACKGROUND: Clinicians are sometimes advised to make decisions using thresholds in measured variables, derived from prognostic studies. OBJECTIVES: We studied why there are conflicting apparently-optimal prognostic thresholds, for example in exercise peak oxygen uptake (pVO(2)), ejection fraction (EF), and Brain Natriuretic Peptide (BNP) in heart failure (HF). DATA SOURCES AND ELIGIBILITY CRITERIA: Studies testing pVO(2), EF or BNP prognostic thresholds in heart failure, published between 1990 and 2010, listed on Pubmed. METHODS: First, we examined studies testing pVO(2), EF or BNP prognostic thresholds. Second, we created repeated simulations of 1500 patients to identify whether an apparently-optimal prognostic threshold indicates step change in risk. RESULTS: 33 studies (8946 patients) tested a pVO(2) threshold. 18 found it prognostically significant: the actual reported threshold ranged widely (10–18 ml/kg/min) but was overwhelmingly controlled by the individual study population's mean pVO(2) (r = 0.86, p<0.00001). In contrast, the 15 negative publications were testing thresholds 199% further from their means (p = 0.0001). Likewise, of 35 EF studies (10220 patients), the thresholds in the 22 positive reports were strongly determined by study means (r = 0.90, p<0.0001). Similarly, in the 19 positives of 20 BNP studies (9725 patients): r = 0.86 (p<0.0001). Second, survival simulations always discovered a “most significant” threshold, even when there was definitely no step change in mortality. With linear increase in risk, the apparently-optimal threshold was always near the sample mean (r = 0.99, p<0.001). LIMITATIONS: This study cannot report the best threshold for any of these variables; instead it explains how common clinical research procedures routinely produce false thresholds. KEY FINDINGS: First, shifting (and/or disappearance) of an apparently-optimal prognostic threshold is strongly determined by studies' average pVO(2), EF or BNP. Second, apparently-optimal thresholds always appear, even with no step in prognosis. CONCLUSIONS: Emphatic therapeutic guidance based on thresholds from observational studies may be ill-founded. We should not assume that optimal thresholds, or any thresholds, exist.