Empirical assessment of the discrimination ability of cardiovascular disease risk prediction models for mortality (Doctoral thesis)
Σιόντης, Γεώργιος/ Siontis, Georgios C. M.
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.
|Institution and School/Department of submitter:||Πανεπιστήμιο Ιωαννίνων. Σχολή Επιστημών Υγείας. Τμήμα Ιατρικής|
Καρδιαγγειακό σύστημα -- Ασθένειες
|Keywords:||Cardiovascular diseases,Predicting death|
|Appears in Collections:||Διδακτορικές Διατριβές|
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|Δ.Δ. ΣΙΟΝΤΗΣ ΓΕΩΡΓΙΟΣ 2015.pdf||7.44 MB||Adobe PDF||View/Open|
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