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.
<h4>Background</h4>Clinicians are sometimes advised to make decisions using thresholds in measured variables, derived from prognostic studies.<h4>Objectives</h4>We studied why there are conflicting apparently-optimal prognostic thresholds, for example in exercise peak oxygen...
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Public Library of Science (PLoS)
2014-01-01
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| author | Alberto Giannoni Resham Baruah Tora Leong Michaela B Rehman Luigi Emilio Pastormerlo Frank E Harrell Andrew J S Coats Darrel P Francis |
| author_facet | Alberto Giannoni Resham Baruah Tora Leong Michaela B Rehman Luigi Emilio Pastormerlo Frank E Harrell Andrew J S Coats Darrel P Francis |
| author_sort | Alberto Giannoni |
| collection | DOAJ |
| description | <h4>Background</h4>Clinicians are sometimes advised to make decisions using thresholds in measured variables, derived from prognostic studies.<h4>Objectives</h4>We studied why there are conflicting apparently-optimal prognostic thresholds, for example in exercise peak oxygen uptake (pVO2), ejection fraction (EF), and Brain Natriuretic Peptide (BNP) in heart failure (HF).<h4>Data sources and eligibility criteria</h4>Studies testing pVO2, EF or BNP prognostic thresholds in heart failure, published between 1990 and 2010, listed on Pubmed.<h4>Methods</h4>First, we examined studies testing pVO2, 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.<h4>Results</h4>33 studies (8946 patients) tested a pVO2 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 pVO2 (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).<h4>Limitations</h4>This study cannot report the best threshold for any of these variables; instead it explains how common clinical research procedures routinely produce false thresholds.<h4>Key findings</h4>First, shifting (and/or disappearance) of an apparently-optimal prognostic threshold is strongly determined by studies' average pVO2, EF or BNP. Second, apparently-optimal thresholds always appear, even with no step in prognosis.<h4>Conclusions</h4>Emphatic therapeutic guidance based on thresholds from observational studies may be ill-founded. We should not assume that optimal thresholds, or any thresholds, exist. |
| format | Article |
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| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2014-01-01 |
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| spelling | doaj-art-9c8f933669774ea79af86e861819728c2025-08-20T03:34:36ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-0191e8169910.1371/journal.pone.0081699Do 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.Alberto GiannoniResham BaruahTora LeongMichaela B RehmanLuigi Emilio PastormerloFrank E HarrellAndrew J S CoatsDarrel P Francis<h4>Background</h4>Clinicians are sometimes advised to make decisions using thresholds in measured variables, derived from prognostic studies.<h4>Objectives</h4>We studied why there are conflicting apparently-optimal prognostic thresholds, for example in exercise peak oxygen uptake (pVO2), ejection fraction (EF), and Brain Natriuretic Peptide (BNP) in heart failure (HF).<h4>Data sources and eligibility criteria</h4>Studies testing pVO2, EF or BNP prognostic thresholds in heart failure, published between 1990 and 2010, listed on Pubmed.<h4>Methods</h4>First, we examined studies testing pVO2, 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.<h4>Results</h4>33 studies (8946 patients) tested a pVO2 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 pVO2 (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).<h4>Limitations</h4>This study cannot report the best threshold for any of these variables; instead it explains how common clinical research procedures routinely produce false thresholds.<h4>Key findings</h4>First, shifting (and/or disappearance) of an apparently-optimal prognostic threshold is strongly determined by studies' average pVO2, EF or BNP. Second, apparently-optimal thresholds always appear, even with no step in prognosis.<h4>Conclusions</h4>Emphatic therapeutic guidance based on thresholds from observational studies may be ill-founded. We should not assume that optimal thresholds, or any thresholds, exist.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0081699&type=printable |
| spellingShingle | Alberto Giannoni Resham Baruah Tora Leong Michaela B Rehman Luigi Emilio Pastormerlo Frank E Harrell Andrew J S Coats Darrel P Francis 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. PLoS ONE |
| title | 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. |
| title_full | 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. |
| title_fullStr | 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. |
| title_full_unstemmed | 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. |
| title_short | 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. |
| title_sort | 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 |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0081699&type=printable |
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