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Risk prediction models: II. External validation, model updating, and impact assessment

Clinical prediction models are increasingly used to complement clinical reasoning and decision-making in modern medicine, in general, and in the cardiovascular domain, in particular. To these ends, developed models first and foremost need to provide accurate and (internally and externally) validated... Full description

Journal Title: Heart 2012-05, Vol.98 (9), p.691-698
Main Author: Moons, Karel G M
Other Authors: Kengne, Andre Pascal , Grobbee, Diederick E , Royston, Patrick , Vergouwe, Yvonne , Altman, Douglas G , Woodward, Mark
Format: Electronic Article Electronic Article
Language: English
Subjects:
Quelle: Alma/SFX Local Collection
Publisher: London: BMJ Publishing Group Ltd and British Cardiovascular Society
ID: ISSN: 1355-6037
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recordid: cdi_proquest_miscellaneous_1009129287
title: Risk prediction models: II. External validation, model updating, and impact assessment
format: Article
creator:
  • Moons, Karel G M
  • Kengne, Andre Pascal
  • Grobbee, Diederick E
  • Royston, Patrick
  • Vergouwe, Yvonne
  • Altman, Douglas G
  • Woodward, Mark
subjects:
  • Abridged Index Medicus
  • Analysis
  • Biological and medical sciences
  • Cardiology. Vascular system
  • Cardiovascular diseases
  • Cardiovascular Diseases - diagnosis
  • Cardiovascular Diseases - epidemiology
  • Cardiovascular Diseases - therapy
  • Care and treatment
  • clinical hypertension
  • Cost analysis
  • Decision Making
  • Delivery of Health Care - organization & administration
  • diabetes
  • epidemiology
  • general practice
  • Health risk assessment
  • Hospitals
  • Humans
  • Medical sciences
  • model impact assessment
  • model updating
  • model validation
  • Models
  • Models, Theoretical
  • obesity
  • Performance evaluation
  • Prediction model
  • prevention
  • Prognosis
  • Reproducibility of Results
  • Research validity
  • Risk Assessment - methods
  • risk prediction
  • Validation studies
ispartof: Heart, 2012-05, Vol.98 (9), p.691-698
description: Clinical prediction models are increasingly used to complement clinical reasoning and decision-making in modern medicine, in general, and in the cardiovascular domain, in particular. To these ends, developed models first and foremost need to provide accurate and (internally and externally) validated estimates of probabilities of specific health conditions or outcomes in the targeted individuals. Subsequently, the adoption of such models by professionals must guide their decision-making, and improve patient outcomes and the cost-effectiveness of care. In the first paper of this series of two companion papers, issues relating to prediction model development, their internal validation, and estimating the added value of a new (bio)marker to existing predictors were discussed. In this second paper, an overview is provided of the consecutive steps for the assessment of the model's predictive performance in new individuals (external validation studies), how to adjust or update existing models to local circumstances or with new predictors, and how to investigate the impact of the uptake of prediction models on clinical decision-making and patient outcomes (impact studies). Each step is illustrated with empirical examples from the cardiovascular field.
language: eng
source: Alma/SFX Local Collection
identifier: ISSN: 1355-6037
fulltext: fulltext
issn:
  • 1355-6037
  • 1468-201X
url: Link


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descriptionClinical prediction models are increasingly used to complement clinical reasoning and decision-making in modern medicine, in general, and in the cardiovascular domain, in particular. To these ends, developed models first and foremost need to provide accurate and (internally and externally) validated estimates of probabilities of specific health conditions or outcomes in the targeted individuals. Subsequently, the adoption of such models by professionals must guide their decision-making, and improve patient outcomes and the cost-effectiveness of care. In the first paper of this series of two companion papers, issues relating to prediction model development, their internal validation, and estimating the added value of a new (bio)marker to existing predictors were discussed. In this second paper, an overview is provided of the consecutive steps for the assessment of the model's predictive performance in new individuals (external validation studies), how to adjust or update existing models to local circumstances or with new predictors, and how to investigate the impact of the uptake of prediction models on clinical decision-making and patient outcomes (impact studies). Each step is illustrated with empirical examples from the cardiovascular field.
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publisherLondon: BMJ Publishing Group Ltd and British Cardiovascular Society
subjectAbridged Index Medicus ; Analysis ; Biological and medical sciences ; Cardiology. Vascular system ; Cardiovascular diseases ; Cardiovascular Diseases - diagnosis ; Cardiovascular Diseases - epidemiology ; Cardiovascular Diseases - therapy ; Care and treatment ; clinical hypertension ; Cost analysis ; Decision Making ; Delivery of Health Care - organization & administration ; diabetes ; epidemiology ; general practice ; Health risk assessment ; Hospitals ; Humans ; Medical sciences ; model impact assessment ; model updating ; model validation ; Models ; Models, Theoretical ; obesity ; Performance evaluation ; Prediction model ; prevention ; Prognosis ; Reproducibility of Results ; Research validity ; Risk Assessment - methods ; risk prediction ; Validation studies
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7Cardiovascular Diseases - therapy
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9clinical hypertension
10Cost analysis
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abstractClinical prediction models are increasingly used to complement clinical reasoning and decision-making in modern medicine, in general, and in the cardiovascular domain, in particular. To these ends, developed models first and foremost need to provide accurate and (internally and externally) validated estimates of probabilities of specific health conditions or outcomes in the targeted individuals. Subsequently, the adoption of such models by professionals must guide their decision-making, and improve patient outcomes and the cost-effectiveness of care. In the first paper of this series of two companion papers, issues relating to prediction model development, their internal validation, and estimating the added value of a new (bio)marker to existing predictors were discussed. In this second paper, an overview is provided of the consecutive steps for the assessment of the model's predictive performance in new individuals (external validation studies), how to adjust or update existing models to local circumstances or with new predictors, and how to investigate the impact of the uptake of prediction models on clinical decision-making and patient outcomes (impact studies). Each step is illustrated with empirical examples from the cardiovascular field.
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