The clinical utility curve: A proposal to improve the translation of information provided by prediction models to clinicians

被引：9

作者：

Campbell D.J. ^{[1
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机构：

[1] Department of Molecular Cardiology, St. Vincent's Institute of Medical Research, University of Melbourne, Melbourne

[2] St. Vincent's Institute of Medical Research, University of Melbourne, 41 Victoria Parade, Fitzroy, 3065, VIC

来源：

BMC Research Notes | / 9卷 / 1期

关键词：

Clinical decision-making; Prediction models;

D O I：

10.1186/s13104-016-2028-0

中图分类号：

学科分类号：

摘要：

Background: Prediction models are essential to the development of prediction rules that guide decision-making, and comparison of prediction models with and without an additional diagnostic or prognostic risk factor allows assessment of the value of the additional factor in risk prediction. However, the many different measures described to translate the information provided by a prediction model do not readily assist clinicians' decision-making. Results: The clinical utility (CU) curve is proposed as an alternative method of communication of information from a prediction model to the clinician. The CU curve is essentially a derivation of the ROC curve that has sensitivity on the y-axis and the number needed to capture one case (NNCOC) on the x-axis. It provides information about the relationship between sensitivity and false positive rate over the full range of prediction score thresholds, and it also indicates the proportion of the patient population with an absolute risk below the threshold for 100 % sensitivity that can therefore be classified as free of disease. Conclusions: The CU curve is proposed as a means to assist the translation of model information to the clinician, in the hope that it will stimulate debate and, through refinement, assist the development of prediction rules with optimal clinical utility. © 2016 Campbell.

引用

共 20 条

[1]

Moons K.G., Kengne A.P., Woodward M., Royston P., Vergouwe Y., Altman D.G., Grobbee D.E., Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio) marker, Heart, 98, pp. 683-690, (2012)

[2]

Reilly B.M., Evans A.T., Translating clinical research into clinical practice: Impact of using prediction rules to make decisions, Ann Intern Med, 144, pp. 201-209, (2006)

[3]

Khan A.A., Perlstein I., Krishna R., The use of clinical utility assessments in early clinical development, AAPS J, 11, pp. 33-38, (2009)

[4]

Steyerberg E.W., Pencina M.J., Lingsma H.F., Kattan M.W., Vickers A.J., Van Calster B., Assessing the incremental value of diagnostic and prognostic markers: A review and illustration, Eur J Clin Invest, 42, pp. 216-228, (2012)

[5]

Van Calster B., Vickers A.J., Pencina M.J., Baker S.G., Timmerman D., Steyerberg E.W., Evaluation of markers and risk prediction models: Overview of relationships between NRI and decision-analytic measures, Med Decis Making, 33, pp. 490-501, (2013)

[6]

Baker S.G., Putting risk prediction in perspective: Relative utility curves, J Natl Cancer Inst, 101, pp. 1538-1542, (2009)

[7]

Vickers A.J., Pepe M., Does the net reclassification improvement help us evaluate models and markers?, Ann Intern Med, 160, pp. 136-137, (2014)

[8]

Hilden J., On NRI, IDI, and "good-looking" statistics with nothing underneath, Epidemiology, 25, pp. 265-267, (2014)

[9]

Coppus S.F., Van Der Veen F., Opmeer B.C., Mol B.W., Bossuyt P.M., Evaluating prediction models in reproductive medicine, Hum Reprod, 24, pp. 1774-1778, (2009)

[10]

Black H.R., Crocitto M.T., Number needed to treat: Solid science or a path to pernicious rationing?, Am J Hypertens, 11, pp. 128S-134S, (1998)

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