ROC curve analysis: a useful statistic multi-tool in the research of nephrology.
Area under the curve
Diagnostic test,
Discriminatory ability
Receiver operator characteristic curve
Sensitivity
Specificity
Journal
International urology and nephrology
ISSN: 1573-2584
Titre abrégé: Int Urol Nephrol
Pays: Netherlands
ID NLM: 0262521
Informations de publication
Date de publication:
26 Mar 2024
26 Mar 2024
Historique:
received:
15
01
2024
accepted:
04
03
2024
medline:
26
3
2024
pubmed:
26
3
2024
entrez:
26
3
2024
Statut:
aheadofprint
Résumé
In the past decade, scientific research in the area of Nephrology has focused on evaluating the clinical utility and performance of various biomarkers for diagnosis, risk stratification and prognosis. Before implementing a biomarker in everyday clinical practice for screening a specific disease context, specific statistic measures are necessary to evaluate the diagnostic accuracy and performance of this biomarker. Receiver Operating Characteristic (ROC) Curve analysis is an important statistical method used to estimate the discriminatory performance of a novel diagnostic test, identify the optimal cut-off value for a test that maximizes sensitivity and specificity, and evaluate the predictive value of a certain biomarker or risk, prediction score. Herein, through practical examples, we aim to present a simple methodological approach to explain in detail the principles and applications of ROC curve analysis in the field of nephrology pertaining diagnosis and prognosis.
Identifiants
pubmed: 38530584
doi: 10.1007/s11255-024-04022-8
pii: 10.1007/s11255-024-04022-8
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. The Author(s).
Références
Obuchowski NA, Bullen JA (2018) Receiver operating characteristic (ROC) curves: review of methods with applications in diagnostic medicine. Phys Med Biol 63(7):07TR1
doi: 10.1088/1361-6560/aab4b1
Cook NR (2008) Statistical evaluation of prognostic versus diagnostic models: beyond the ROC curve. Clin Chem 54(1):17–23
doi: 10.1373/clinchem.2007.096529
pubmed: 18024533
Kamarudin AN, Cox T, Kolamunnage-Dona R (2017) Time-dependent ROC curve analysis in medical research: current methods and applications. BMC Med Res Methodol 17(1):1–19
doi: 10.1186/s12874-017-0332-6
Zhou J, Yuan W, Guo Y, Wang Y, Dai Y, Shen Y et al (2023) Asprosin is positively associated with metabolic syndrome in hemodialysis patients: a cross-sectional study. Ren Fail 45(1):2220425
doi: 10.1080/0886022X.2023.2220425
pubmed: 37317534
pmcid: 10274553
Rodrigues HCN, Silva ML, dos Santos MM, da Silva JM, Domingues MFP, Tanni SÉ et al (2023) Higher urea-to-albumin ratio is associated with mortality risk in critically ill COVID-19 patients. Clin Nutr ESPEN 56:9–12
doi: 10.1016/j.clnesp.2023.04.017
pubmed: 37344090
pmcid: 10129333
You X, Huang YY, Wang Y, Yu MX, Li XY, Xu L et al (2022) Prediction model for cardiovascular disease risk in hemodialysis patients. Int Urol and Nephrol 54:1–8
doi: 10.1007/s11255-021-02984-7
Roumeliotis S, Liakopoulos V, Roumeliotis A, Stamou A, Panagoutsos S, D’Arrigo G et al (2021) Prognostic factors of fatal and nonfatal cardiovascular events in patients with type 2 diabetes: the role of renal function biomarkers. Clin Diabetes 39(2):188–196
doi: 10.2337/cd20-0067
pubmed: 33986571
pmcid: 8061536
Tikhonoff V, Casiglia E, Virdis A, Grassi G, Angeli F, Arca M et al (2024) Prognostic value and relative cutoffs of triglycerides predicting cardiovascular outcome in a large regional-based italian database. J Am Heart Assoc 13(3):e030319
doi: 10.1161/JAHA.123.030319
pubmed: 38293920