Résumé
Pour orienter sa démarche diagnostique ou thérapeutique, le clinicien doit estimer, le plus souvent de manière implicite, la probabilité du diagnostic suspecté ou le pronostic de la pathologie en cause. Des modèles de prédiction clinique ont été dérivés et validés de manière rigoureuse pour l’assister face à des situations cliniques variées rencontrées en médecine d’urgence [1–3]. Ces modèles de prédiction clinique assignent à chaque patient une probabilitéen fonction de la valeur de différents prédicteurs recueillis par l’interrogatoire, l’examen clinique ou des tests simples [4].
Preview
Unable to display preview. Download preview PDF.
Références
Aujesky D, Obrosky DS, Stone RA, et al. (2005) Derivation and validation of a prognostic model for pulmonary embolism. Am J Respir Crit Care Med 172: 1041–1046
Fine MJ, Auble TE, Yealy DM, et al. (1997) A prediction rule to identify low-risk patients with community-acquired pneumonia. N Engl J Med 336: 243–250
Wells PS, Anderson DR, Bormanis J, et al. (1997) Value of assessment of pretest probability of deep-vein thrombosis in clinical management. Lancet 350: 1795–1798
Steyerberg EW (2010) Clinical prediction models. New York: Springer
Pencina MJ, D’Agostino RB, Vasan RS (2010) Statistical methods for assessment of added usefulness of new biomarkers. Clin Chem Lab Med 48: 1703–1711
Pencina MJ, D’Agostino RB, Sr., D’Agostino RB, Jr., Vasan RS (2008) Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 27: 157–172
Ware JH (2006) The limitations of risk factors as prognostic tools. N Engl J Med 355: 2615–2617
Pepe MS, Janes H, Longton G, et al. (2004) Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. Am J Epidemiol 159: 882–890
Harreil FE (2001) Regression modeling strategies. With applications to linear models, logistic regression, and survival analysis. New York: Springer
Cook NR, Paynter NP (2011) Performance of reclassification statistics in comparing risk prediction models. Biom J 53: 237–258
Pepe MS, Feng Z, Gu JW (2008) Comments on ‘Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond’ by M. J. Pencina et al., Statistics in Medicine (DOI: 10.1002/sim.2929). Stat Med 27: 173–181
Janes H, Pepe MS, Gu W (2008) Assessing the value of risk predictions by using risk stratification tables. Ann Intern Med 149: 751–760
Scherz N, Labarère J, Mean M, et al. (2010) Prognostic importance of hyponatremia in patients with acute pulmonary embolism. Am J Respir Crit Care Med 182: 1178–1183
Pencina MJ, D’Agostino RB, Sr., Steyerberg EW (2011) Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med 30: 11–21
Tzoulaki I, Liberopoulos G, Ioannidis JP (2011) Use of reclassification for assessment of improved prediction: an empirical evaluation. Int J Epidemiol 40: 1094–1105.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag France, Paris
About this chapter
Cite this chapter
Labarère, J., Raskovalova, T. (2012). Quantification de la valeur ajoutée d’un biomarqueur par l’indice de Net Reclassification Improvement . In: Claessens, YÉ., Ray, P. (eds) Les biomarqueurs en médecine d’urgence. Références en médecine d’urgence. Collection de la SFMU. Springer, Paris. https://doi.org/10.1007/978-2-8178-0297-8_5
Download citation
DOI: https://doi.org/10.1007/978-2-8178-0297-8_5
Publisher Name: Springer, Paris
Print ISBN: 978-2-8178-0296-1
Online ISBN: 978-2-8178-0297-8
eBook Packages: MedicineMedicine (R0)