Cutpoints in Clinical Chemistry: Time for Fundamental Reassessment

Vickers, Andrew J.; Lilja, Hans

doi:10.1373/clinchem.2008.114694

Cited by 28 publications

(14 citation statements)

References 8 publications

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“…As noted by Morrow and Antman (18 ), the increased sensitivity of these newer assays requires that we refine what we consider "normal." Alternatively, one could start using risk prediction in place of cut values (19 ).…”

Section: Discussionmentioning

confidence: 99%

Short- and Long-Term Risk Stratification Using a Next-Generation, High-Sensitivity Research Cardiac Troponin I (hs-cTnI) Assay in an Emergency Department Chest Pain Population

Kavsak

Wang

et al. 2009

Clinical Chemistry

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BACKGROUND:The next-generation, high-sensitivity cardiac troponin assays can measure quantifiable concentrations of cTn in a majority of individuals, but there are few studies assessing these assays for risk stratification. The present study was undertaken to determine if a research hs-cTnI assay can be useful for predicting death/myocardial infarction (MI), both short-and long-term, in an emergency department acute coronary syndrome (ACS) population.

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Section: Discussionmentioning

confidence: 99%

Short- and Long-Term Risk Stratification Using a Next-Generation, High-Sensitivity Research Cardiac Troponin I (hs-cTnI) Assay in an Emergency Department Chest Pain Population

Kavsak

Wang

et al. 2009

Clinical Chemistry

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“…However, dichotomization has many drawbacks and is widely criticized by statisticians. [10][11][12][13][14][15] What is necessary or sensible in clinical and therapeutic settings is not relevant to how research data should best be analyzed. Indeed, in the clinical research context, such simplicity is gained at a high cost and may well create problems rather than solve them.…”

Section: Categorizing Continuous Variablesmentioning

confidence: 99%

Analysis by Categorizing or Dichotomizing Continuous Variables Is Inadvisable: An Example from the Natural History of Unruptured Aneurysms

Naggara¹,

Raymond²,

Guilbert³

et al. 2011

AJNR Am J Neuroradiol

243

153

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SUMMARY:In medical research analyses, continuous variables are often converted into categoric variables by grouping values into Ն2 categories. The simplicity achieved by creating Ն2 artificial groups has a cost: Grouping may create rather than avoid problems. In particular, dichotomization leads to a considerable loss of power and incomplete correction for confounding factors. The use of data-derived "optimal" cut-points can lead to serious bias and should at least be tested on independent observations to assess their validity. Both problems are illustrated by the way the results of a registry on unruptured intracranial aneurysms are commonly used. Extreme caution should restrict the application of such results to clinical decision-making. Categorization of continuous data, especially dichotomization, is unnecessary for statistical analysis. Continuous explanatory variables should be left alone in statistical models.ABBREVIATIONS: ACA ϭ anterior cerebral artery; CHUM ϭ Centre hospitalier de l'Université de Montré al; ICA ϭ internal carotid artery; ISUIA ϭ International Study of Unruptured Intracranial Aneurysms; MCA ϭ middle cerebral artery; Pcirc ϭ posterior circulation; PcomA ϭ posterior communicating artery; SAH ϭ subarachnoid hemorrhage; UIA ϭ unruptured intracranial aneurysms U IAs are common (approximately 2% of the adult population), but they most often remain silent until a rupture occurs (incidence, 2-20/10,000/year).1 No one is sure what to do with them, but with the increasing accessibility of noninvasive imaging of the brain, the problem is growing rapidly. 2A common and yet controversial approach to decisionmaking is to compare the natural history of the disease and the risks of treatment.3,4 One prominent risk factor for rupture of UIAs is size. In 1998, a landmark study on this subject, the ISUIA, estimated from retrospectively obtained data that the risk of rupture of aneurysms smaller than 10 mm was extremely low.5 Subsequent guidelines published in 2000 discouraged the treatment of aneurysms smaller than that size. 6In a 2003 study, the same group, confronted with different results when data were collected prospectively, claimed that aneurysms Ͻ7 mm in a special subgroup of patients (defined by the absence of a history of rupture of another lesion, having an aneurysm located in the anterior circulation, and selected for observation) were at zero risk of rupture, but only when some carotid aneurysms were excluded (PcomA aneurysms). 7Despite these specifications, a threshold of 7 mm is now used by many as a normative criterion for clinical decisions 8 or in cost-effectiveness analyses. 9Size of UIAs can serve to illustrate the problems associated with the categorization of continuous variables, in particular dichotomization. Our aim is to consider how continuous variables should be treated and analyzed when we suspect that risk increases or decreases in proportion to the variable in question. We address the following questions: What are the advantages and disadvantages of categorization? If we de...

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“…Hirschfeld Page 4 cutpoints all together (Vickers & Lilja, 2009) we believe that many of these problems can be averted once the variability of cutpoints is taken into account.…”

Section: Preprintsmentioning

confidence: 99%

Quantifying the variability of optimal cutpoints and reference values for diagnostic measures

Hirschfeld

Zernikow

2014

Preprint

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Aim: Empirical studies in medicine -and most other fields of study -yield results that are uncertain to a certain degree. Medical research on interventions has made tremendous leaps forward by quantifying and reporting this uncertainty using p-values and confidence intervals. In contrast to this, most diagnostic studies that determine optimal cutpoints or reference values for diagnostic measures ignore that their outcomes, i.e. the specific cutpoints or normal ranges they recommend, are subject to chance variability. Methods:In this paper we use a simple simulation approach to quantify the variability of optimal cutpoints for two published studies. The first determined an optimal cutpoint for Becks Depression Inventory (BDI) in adults. The second determined reference values for Quantitative Sensory Testing (QST) in children. Results: We find that frequently employed cutpoints to interpret BDI scores and QST results are highly variable. For the BDI we find that replication of this study may identify values between 14 and 21 as optimal cutpoints.The lower cutpoint results in a misclassification of 15% of the healthy adults as depressed, the upper cutpoint results in a misclassification rate of 2%. For the QST we find that the upper end of the normal range HPT varies between 46.9 and 50.2 degrees Celsius. Conclusions:Based on our results we argue that researchers should be required to estimate and report the variability of reference values and optimal cutpoints for diagnostic tools. This may improve the harmonization of findings across studies and provides a rationale for planning future studies.PeerJ PrePrints | http://dx.doi.org/10.7287/peerj.preprints.635v1 | CC-BY 4.0 Open Access |

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Cutpoints in Clinical Chemistry: Time for Fundamental Reassessment

Cited by 28 publications

References 8 publications

Short- and Long-Term Risk Stratification Using a Next-Generation, High-Sensitivity Research Cardiac Troponin I (hs-cTnI) Assay in an Emergency Department Chest Pain Population

Short- and Long-Term Risk Stratification Using a Next-Generation, High-Sensitivity Research Cardiac Troponin I (hs-cTnI) Assay in an Emergency Department Chest Pain Population

Analysis by Categorizing or Dichotomizing Continuous Variables Is Inadvisable: An Example from the Natural History of Unruptured Aneurysms

Quantifying the variability of optimal cutpoints and reference values for diagnostic measures

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