Prediction models: the right tool for the right problem

被引:12
作者
Kappen, Teus H. [1 ,2 ]
Peelen, Linda M. [1 ,3 ]
机构
[1] Univ Med Ctr Utrecht, Div Anesthesiol Intens Care & Emergency Med, Utrecht, Netherlands
[2] Vanderbilt Univ, Med Ctr, Dept Anesthesiol, Nashville, TN USA
[3] Univ Med Ctr Utrecht, Julius Ctr Hlth Sci & Primary Care, Utrecht, Netherlands
关键词
calibration; discrimination; prediction model; validation; PROGNOSTIC RESEARCH; POSTOPERATIVE NAUSEA; EXTERNAL VALIDATION; PERSONALIZED MEDICINE; CLINICAL-PRACTICE; RISK ASSESSMENTS; DIAGNOSTIC-TESTS; IMPACT; RECLASSIFICATION; MARKERS;
D O I
10.1097/ACO.0000000000000386
中图分类号
R614 [麻醉学];
学科分类号
100217 ;
摘要
Purpose of review Perioperative prediction models can help to improve personalized patient care by providing individual risk predictions to both patients and providers. However, the scientific literature on prediction model development and validation can be quite technical and challenging to understand. This article aims to provide the necessary insight for clinicians to assess the value of a prediction model that they intend to use in their clinical practice. Recent findings Recent developments in prediction model research include the continuous development of new performance characteristics for prediction models, increasing insight into the limitations of old characteristics, as well as an improved understanding of the generalizability of prediction models to new populations and practices. Summary Clinicians can assess the value of a prediction model for their practice by first identifying what the usage of the model will be. Second, they can recognize which performance characteristics are relevant to their assessment of the model. Finally, they need to decide whether the available scientific evidence sufficiently matches their clinical practice to proceed with implementation.
引用
收藏
页码:717 / 726
页数:10
相关论文
共 48 条
[1]   Prognosis and prognostic research: validating a prognostic model [J].
Altman, Douglas G. ;
Vergouwe, Yvonne ;
Royston, Patrick ;
Moons, Karel G. M. .
BMJ-BRITISH MEDICAL JOURNAL, 2009, 338 :1432-1435
[2]   A simplified risk score for predicting postoperative nausea and vomiting -: Conclusions from cross-validations between two centers [J].
Apfel, CC ;
Läärä, E ;
Koivuranta, M ;
Greim, CA ;
Roewer, N .
ANESTHESIOLOGY, 1999, 91 (03) :693-700
[3]  
Austin PC, 2016, J CLIN EPIDEMIOL, DOI [10.1016/j.jclinepi.2016.05.007, DOI 10.1016/J.JCLINEPI.2016.05.007.]
[4]   Interpreting the concordance statistic of a logistic regression model: relation to the variance and odds ratio of a continuous explanatory variable [J].
Austin, Peter C. ;
Steyerberg, Ewout W. .
BMC MEDICAL RESEARCH METHODOLOGY, 2012, 12
[5]   Beyond Diagnostic Accuracy: The Clinical Utility of Diagnostic Tests [J].
Bossuyt, Patrick M. M. ;
Reitsma, Johannes B. ;
Linnet, Kristian ;
Moons, Karel G. M. .
CLINICAL CHEMISTRY, 2012, 58 (12) :1636-1643
[6]   The clinical utility curve: A proposal to improve the translation of information provided by prediction models to clinicians [J].
Campbell D.J. .
BMC Research Notes, 9 (1)
[7]  
Collins GS, 2015, ANN INTERN MED, V162, P55, DOI [10.7326/M14-0697, 10.1002/bjs.9736, 10.7326/M14-0698, 10.1016/j.jclinepi.2014.11.010, 10.1186/s12916-014-0241-z, 10.1016/j.eururo.2014.11.025, 10.1038/bjc.2014.639, 10.1136/bmj.g7594]
[8]  
Cook NR, 2008, STAT MED, V27, P191, DOI 10.1002/sim.2987
[9]   A new framework to enhance the interpretation of external validation studies of clinical prediction models [J].
Debray, Thomas P. A. ;
Vergouwe, Yvonne ;
Koffijberg, Hendrik ;
Nieboer, Daan ;
Steyerberg, Ewout W. ;
Moons, Karel G. M. .
JOURNAL OF CLINICAL EPIDEMIOLOGY, 2015, 68 (03) :280-289
[10]   Aligning incentives to fulfil the promise of personalised medicine [J].
Dzau, Victor J. ;
Ginsburg, Geoffrey S. ;
Van Nuys, Karen ;
Agus, David ;
Goldman, Dana .
LANCET, 2015, 385 (9982) :2118-2119