Empirical assessment of the discrimination ability of cardiovascular disease risk prediction models for mortality
Φόρτωση...
Ημερομηνία
Συγγραφείς
Σιόντης, Γεώργιος
Siontis, Georgios C. M.
Τίτλος Εφημερίδας
Περιοδικό ISSN
Τίτλος τόμου
Εκδότης
Πανεπιστήμιο Ιωαννίνων. Σχολή Επιστημών Υγείας. Τμήμα Ιατρικής
Περίληψη
Τύπος
Είδος δημοσίευσης σε συνέδριο
Είδος περιοδικού
Είδος εκπαιδευτικού υλικού
Όνομα συνεδρίου
Όνομα περιοδικού
Όνομα βιβλίου
Σειρά βιβλίου
Έκδοση βιβλίου
Συμπληρωματικός/δευτερεύων τίτλος
Περιγραφή
More people now live with disease and conditions that impair health than at any
other time in history; prognosis research provides crucial evidence for translating
findings from the laboratory to humans, and from clinical research to clinical
practice. Prognosis research seeks to understand and improve future outcomes in
people with a given disease or health condition.
Although typically in medical terms prognosis refers to the most likely
clinical course of a diseased patient, the term is also applied to the prediction of
future risk in a normal population. Except in rare instances, both of these settings
include a stochastic element, one that is subject to chance.294 Prognostication and
prediction involve estimating risk, or the probability of a future event or state.
The outcome not only is unknown, but does not yet exist, distinguishing this task
from diagnosis. Therefore, prognostic models, the core-tool of prognostication,
add the element of time.293 Clinically, prognostic models are often used for risk
stratification, or for assigning levels of risk, such as high, intermediate, or low,
which may then form the basis of treatment decisions.
Models for prognostic risk prediction have been widely used in the
cardiovascular field to predict risk of future events or to stratify apparently
healthy individuals into risk categories. Appropriate model assessment is critical
to the determination of clinical impact and to guideline development. Prediction
tools are useful only when they are easily accessible at the point of care, which is
why for most of them there is also designed an online calculator. Such calculators
are implemented in electronic patient records, electronic order entry systems, or
smartphone or tablet applications. Overall, prediction models that include age,
sex, symptoms, and risk factors allow for accurate estimation of the probability of
coronary artery disease in low prevalence populations. The addition of single
predictors in previously established models requires specific statistical approach and verification. Implementation of such updated models can improve clinical
outcomes, but need further evaluation in individual models’ level.
The country-specific predictions for estimated 10 year cardiovascular
disease burden are striking, particularly areas with large proportion of high-risk
individuals. A next step would be to quantify the positive effects on a population
level if these prediction models and subsequent risk based preventative
management were used in these countries. By use of so-called population-level
linked-evidence models, estimates of country-specific 10 year cardiovascular
disease-risk groups can be combined with known effect sizes from randomised
trials of various treatments (eg, lipid-lowering and blood-pressure-lowering
drugs), supplemented with treatment adherence figures, to quantify the expected
decrease in cardiovascular disease burden per country within 10 years. These
predictions might further help, and indeed convince, decision-makers across the
world to decide on wide-scale introduction of risk-based management for
cardiovascular disease.
Prognostic tools should be evaluated in several sequential stages: initial
model performance (model development), prospective validation in independent
cohorts (external validation of a model), impact on patient management and
outcome and cost-effectiveness. However, even for established and widely used
prognostic tools, many of these steps suffer from methodological limitations and
in many cases are missing. Moreover, it is imporantant to highlight the paucity of
evidence around their impact on patient management and clinical outcomes.
Such important evidence would ideally come from randomized control trials
(RCTs), which compare the outcomes of patients whose management is guided by
the proposed prognostic tool with the outcomes of patients who are managed
without it. However, there are so many prognostic tools, that it is impossible to
evaluate all of them in RCTs. Efforts should focus around those with most
promising results. In selecting which models to test in randomized trials, one
may wish to consider not only satisfactory, validated discriminating ability, but
also what is the respective change in disease management that can be
anticipated; how effective are the available preventive or treatment interventions
for the disease and how much room exists for improvement; what is the expected cost to get the information required for building the model, and to implement it
in practice; and how likely it is that the model can be used widely by non-expert
health practitioners. Going through such a checklist is likely to eliminate the large
majority of proposed prognostic models. Nevertheless, there are currently no
randomized trials assessing the implementation of any cardiovascular prediction
models. Such studies should be encouraged. A more through and systematic
research agenda would be useful to build surrounding late implementation
issues, including ease of use, and impact on resources in diverse settings.
The bottom line is that the best test of a prediction model is not accuracy
but improved clinical outcomes. Compared with clinician judgment, a prediction
model might improve diagnostic accuracy, reduce costs and harms, and lead to
improved health outcomes. Documenting this benefit requires RCTs in which
providers are randomized to use the proposed prediction model or not, and the
outcome is improved health. Very few models have been tested in this way.46,98
Prediction of risk is not enough—we need evidence that prediction can lead to
actions that reduce risk beyond what would occur without the prediction rule.
Περιγραφή
Λέξεις-κλειδιά
Cardiovascular diseases, Predicting death
Θεματική κατηγορία
Prognosis, Καρδιαγγειακό σύστημα -- Ασθένειες
Παραπομπή
Σύνδεσμος
Γλώσσα
en
Εκδίδον τμήμα/τομέας
Πανεπιστήμιο Ιωαννίνων. Σχολή Επιστημών Υγείας. Τμήμα Ιατρικής
Όνομα επιβλέποντος
Ιωαννίδης, Ιωάννης
Εξεταστική επιτροπή
Ίωαννίδης, Ιωάννης
Windecker, Stephan
Γουδέβενος, Ιωάννης
Σαλαντή, Γεωργία
Τατσιώνη, Αθηνά
Τσιλίδης, Κωνσταντίνος
Μαυρίδης, Δημήτριος
Windecker, Stephan
Γουδέβενος, Ιωάννης
Σαλαντή, Γεωργία
Τατσιώνη, Αθηνά
Τσιλίδης, Κωνσταντίνος
Μαυρίδης, Δημήτριος
Γενική Περιγραφή / Σχόλια
Ίδρυμα και Σχολή/Τμήμα του υποβάλλοντος
Πανεπιστήμιο Ιωαννίνων. Σχολή Επιστημών Υγείας. Τμήμα Ιατρικής
Πίνακας περιεχομένων
Χορηγός
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Βιβλιογραφία : σ. 215-231
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