Cited by 229Venous Thromboembolism Diagnosis and ManagementStatistical Methods in EpidemiologyClinical Reasoning and Diagnostic Skills

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Diagnostic and prognostic prediction models

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Hendriksen, J.M.T., Geersing, G.J., Moons, K.G.M., de Groot, J.A.H.·2013·Journal of Thrombosis and Haemostasis

DOI 10.1111/jth.12262

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Abstract

Risk prediction models can be used to estimate the probability of either having (diagnostic model) or developing a particular disease or outcome (prognostic model). In clinical practice, these models are used to inform patients and guide therapeutic management. Examples from the field of venous thrombo-embolism (VTE) include the Wells rule for patients suspected of deep venous thrombosis and pulmonary embolism, and more recently prediction rules to estimate the risk of recurrence after a first episode of unprovoked VTE. In this paper, the three phases that are recommended before a prediction model may be used in daily practice are described: development, validation, and impact assessment. In the development phase, the focus is on model development commonly using a multivariable logistic (diagnostic) or survival (prognostic) regression analysis. The performance of the developed model is expressed by discrimination, calibration and (re-) classification. In the validation phase, the developed model is tested in a new set of patients using these same performance measures. This is important, as model performance is commonly poorer in a new set of patients, e.g. due to case-mix or domain differences. Finally, in the impact phase the ability of a prediction model to actually guide patient management is evaluated. Whereas in the development and validation phase single cohort designs are preferred, this last phase asks for comparative designs, ideally randomized designs; therapeutic management and outcomes after using the prediction model is compared to a control group not using the model (e.g. usual care).

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A new diagnostic rule for deep vein thrombosis: safety and efficiency in clinically relevant subgroups
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Self-citation
Long-term health benefits and costs of measurement of carotid intima–media thickness in prevention of coronary heart disease
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(1997) · doi:10.1002/(sici)1097-0258(19970115)16:1
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Untitled work
Harrell, F., Jr. (1996) · doi:10.1002/(sici)1097-0258(19960229)15:4

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1 of the 94 references couldn’t be matched to a known paper.

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Harrell, 1996, Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors, Stat Med, 15, 361, 10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4 · DOI
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Machine learning for medical imaging: methodological failures and recommendations for the future
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A Predictive Score for Thrombosis Associated with Breast, Colorectal, Lung, or Ovarian Cancer: The Prospective COMPASS–Cancer-Associated Thrombosis Study
Gerotziafas, G., Taher, A. T., Abdel‐Razeq, H., AboElnazar, E., Spyropoulos, A. C., Shemmari, S. E., Larsen, A. K., Elalamy, I., & on behalf of the COMPASS–CAT Working Group (2017) · The Oncologist · doi:10.1634/theoncologist.2016-0414 · Cited by 272
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See all 229

Authors: Hendriksen, J. M. T., Geersing, G. J., Moons, K. G. M., & de Groot, J. A. H.
Affiliations: University Medical Center Utrecht (NL)
Corresponding author: Moons, K.G.M.
ORCID: Hendriksen, J.M.T.
Geersing, G.J.
Moons, K.G.M.
Venue: Journal of Thrombosis and Haemostasis
Publisher: Elsevier BV
Published: 2013-06
Volume: 11
Pages: 129-141
ISSN: 1538-7836 (ISSN-L)
DOI: 10.1111/jth.12262
OpenAlex: W2171370925
Cited by: 229
References: 94
Citations by year: 2026: 15 · 2025: 18 · 2024: 15 · 2023: 20 · 2022: 26 · 2021: 23 · 2020: 36 · 2019: 17 · 2018: 14 · 2017: 15 · 2016: 11 · 2015: 11 · 2014: 7 · 2013: 1
FWCI: 8.74
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (vor, 3474 d)
Locations: Journal of Thrombosis and Haemostasis (publishedVersion)
PubMed (publishedVersion)
Utrecht University Repository (Utrecht University) (submittedVersion)
Topics: Health Sciences › Medicine › Internal Medicine › Venous Thromboembolism Diagnosis and Management
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
Keywords: risk prediction model, diagnostic model, prognostic model, venous thromboembolism, deep vein thrombosis, pulmonary embolism, Wells score, model validation, discrimination (statistics), calibration, impact assessment, clinical decision support
MeSH terms: Humans, Models, Theoretical, Prognosis, Venous Thromboembolism

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