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the frequency of the combined endpoint was low &#40;n&#61;33&#41;&#46; The approaches analyzed by Paredes et al&#46; were validated by k-fold cross-validation&#44; a method for assessing the ability of a model to make generalizations on the basis of a dataset&#46; Using techniques from artificial intelligence&#44; the authors performed optimization with genetic algorithms &#40;the most popular of the larger class of evolutionary algorithms&#41;&#44; which are among the CV risk assessment tools currently recommended by US and European medical societies &#40;albeit without significant effect to date&#41;&#46;</p><p id="par0015" class="elsevierStylePara elsevierViewall">With regard to the usefulness and limitations of CV risk scores and their application in clinical practice for primary and secondary prevention&#44; it is worth examining what we know and what remains to be clarified&#46; First&#44; healthy attitudes and behavior&#44; which can be encouraged by low-cost measures both on a population-wide scale and aimed at high-risk patients&#44; can render CV risk stratification unnecessary for many&#46; However&#44; CV risk must be assessed when it comes to clinical decisions and interventions&#44; not only after CV events but also to prevent them from occurring in high-risk individuals&#46;</p><p id="par0020" class="elsevierStylePara elsevierViewall">The evolution of the Framingham Heart Study is illustrative&#46; Following its establishment more than six decades ago in 1948&#44; the project introduced the concept of risk factors in 1961&#44; developed the first formulas for predicting various CV complications and algorithms stratifying risk factors for coronary artery disease in individuals without clinical manifestations of disease in 1998&#44; and published other algorithms to estimate global CV risk and risk of CV events &#40;coronary and cerebrovascular events&#44; peripheral arterial disease and heart failure&#41; in 2008&#46;</p><p id="par0025" class="elsevierStylePara elsevierViewall">The need to adjust CV risk estimates to populations other than the original Framingham cohort &#40;5127 men and women aged between 30 and 62&#41; led to the development of other scoring systems&#44; including PROCAM&#44;<a class="elsevierStyleCrossRef" href="#bib0155"><span class="elsevierStyleSup">4</span></a> SCORE&#44;<a class="elsevierStyleCrossRef" href="#bib0160"><span class="elsevierStyleSup">5</span></a> QRISK&#44;<a class="elsevierStyleCrossRefs" href="#bib0165"><span class="elsevierStyleSup">6&#44;7</span></a> and the Reynolds risk scores for women and men&#46;<a class="elsevierStyleCrossRefs" href="#bib0175"><span class="elsevierStyleSup">8&#44;9</span></a> Differences between these systems explain the variation in their risk estimates&#44; but since 2003 the scoring system recommended by the European Society of Cardiology &#40;ESC&#41; has been SCORE&#46;<a class="elsevierStyleCrossRefs" href="#bib0160"><span class="elsevierStyleSup">5&#44;10</span></a></p><p id="par0030" class="elsevierStylePara elsevierViewall">Since that date&#44; Portugal has been classified among the low-risk countries&#46; How is a low-risk country defined&#63; In the ESC guidelines&#44; the cut-off points are based on 2008 CVD plus diabetes mortality in those aged 45&#8211;74 years &#40;220&#47;100<span class="elsevierStyleHsp" style=""></span>000 in men and 160&#47;100<span class="elsevierStyleHsp" style=""></span>000 in women&#41;&#46; However&#44; these cutoffs for classification of CV risk and therapeutic recommendations are to some extent arbitrary&#44; since risk is a continuum&#44; and some authors have found that SCORE performs less well in certain populations&#44; giving rise to some controversy&#46;<a class="elsevierStyleCrossRefs" href="#bib0190"><span class="elsevierStyleSup">11&#44;12</span></a> Risk estimation is not an exact science&#59; in the Cox proportional hazards models often used&#44; the regression coefficients are assumed to remain constant over time and in the context of different combinations of risk factors&#44; but they do in fact vary as a person ages&#44;<a class="elsevierStyleCrossRef" href="#bib0200"><span class="elsevierStyleSup">13</span></a> especially predisposing factors that aggravate independent factors&#46; Explanatory variables are considered to act multiplicatively on the hazard function&#46; At best&#44; these assumptions and suppositions&#44; and the different combinations of risk factors that interact in complex ways&#44; are difficult to model&#44; and so the models and estimates used are only approximations to &#8216;truth&#8217;&#46;<a class="elsevierStyleCrossRef" href="#bib0205"><span class="elsevierStyleSup">14</span></a></p><p id="par0035" class="elsevierStylePara elsevierViewall">The SCORE system assesses the 10-year risk of fatal cardiovascular disease &#40;mortality from myocardial infarction &#91;MI&#93;&#44; stroke&#44; aortic aneurysm or other&#41;&#46; The choice of CV mortality&#44; as opposed to fatal and nonfatal events&#44; was deliberate&#44; because nonfatal event rates are highly dependent on the definitions and detection methods used and are thus difficult to calculate accurately&#44; especially in different study cohorts with long follow-ups&#46; At the same time&#44; basing the score on mortality enables calibration to take into consideration long-term trends in CV mortality&#46; All risk estimation systems will overestimate risk in countries in which CV mortality has declined and underestimate risk if it has increased&#46; However&#44; recalibration should be undertaken if good quality&#44; up-to-date mortality and risk factor prevalence data are available&#46;<a class="elsevierStyleCrossRef" href="#bib0210"><span class="elsevierStyleSup">15</span></a> Even so&#44; the inability of the SCORE system to differentiate the 10-year risk of a fatal event due to ischemic heart disease or due to stroke in individuals aged 40&#8211;65&#44; or the risk in older individuals&#44; since the risk profile of each disease is different&#44; is a limitation&#59; for younger individuals&#44; in whom the absolute risk is low&#44; the relative risk table is applied as a way of encouraging healthier lifestyles&#46;</p><p id="par0040" class="elsevierStylePara elsevierViewall">The recently published SCORE O&#46;P&#46;<a class="elsevierStyleCrossRef" href="#bib0215"><span class="elsevierStyleSup">16</span></a> is the first CV risk assessment system developed specifically for older individuals &#40;&#8805;65 years&#41;&#44; both men and women&#46; It shows good discrimination&#44; with a low false positive rate&#44; and may reduce excessive use of medication in older people without a history of CV events&#46; The methodology excluded subjects with missing data on any of the required covariables&#44; but simulated external validation was performed using cross-validation to assess the model&#39;s ability to generalize the 10-year risk function&#46; The next step is to confirm the discriminative ability of the simulated external validation by widening the validation process using external datasets&#44; before it is made available online and included in the ESC guidelines on cardiovascular prevention&#46;</p><p id="par0045" class="elsevierStylePara elsevierViewall">In the search for ways to improve CV risk estimation for secondary prevention&#44; after various unsuccessful attempts&#44; it is unlikely that a major new risk factor will be identified that is demonstrably linked to causality&#44; or that the range of known polymorphisms will fill the gaps in risk prediction&#46; It can be a challenge to use HeartScore&#44; the online version of the SCORE risk charts &#40;available at <a href="http://www.escardio.org/"><span class="elsevierStyleUnderline">www&#46;escardio&#46;org</span></a>&#41;&#44; in the right way&#46; Alternatively&#44; the SCORE system needs to be calibrated&#44; as has been done for some countries&#44; so that the risk estimation model reliably predicts the level of absolute risk that is subsequently observed&#46;</p><p id="par0050" class="elsevierStylePara elsevierViewall">In terms of secondary prevention&#44; the number of published articles assessing risk stratification models for acute coronary syndrome &#40;ACS&#41; demonstrates the extent of interest in this subject&#46; One of the aims of clinical prediction models is to estimate the likelihood of an event after diagnosis of the disease &#40;prognosis&#41; in individual patients and to assist in clinical decision-making&#46; Some ACS need rapid diagnosis and entail critical therapeutic decisions by health professionals with different levels of knowledge and experience&#44; sometimes with limited resources&#46; To this end&#44; various risk models and scores have been developed to identify patients in emergency departments or coronary care units who are most likely to benefit from an invasive approach&#46;</p><p id="par0055" class="elsevierStylePara elsevierViewall">The best-validated risk scores were based on different populations in clinical trials &#40;TIMI<a class="elsevierStyleCrossRef" href="#bib0220"><span class="elsevierStyleSup">17</span></a> and PURSUIT<a class="elsevierStyleCrossRef" href="#bib0225"><span class="elsevierStyleSup">18</span></a>&#41; or registries &#40;GRACE<a class="elsevierStyleCrossRef" href="#bib0230"><span class="elsevierStyleSup">19</span></a> and GRACE 2&#46;0<a class="elsevierStyleCrossRef" href="#bib0235"><span class="elsevierStyleSup">20</span></a>&#41;&#46; For NSTE-ACS&#44; the GRACE score provides the best stratification of ischemic risk at hospital admission and discharge&#44;<a class="elsevierStyleCrossRefs" href="#bib0145"><span class="elsevierStyleSup">2&#44;21</span></a> and &#8211; like the GRACE 2&#46;0 score &#8211; is accordingly included in the ESC guidelines&#46;<a class="elsevierStyleCrossRefs" href="#bib0245"><span class="elsevierStyleSup">22&#44;23</span></a> An invasive strategy &#40;coronary angiography and revascularization&#41; is recommended within 24 hours for a GRACE score &#62;140 &#40;high risk&#41; and within 72 hours for a score &#62;109 and &#60;140 &#40;intermediate risk&#41;&#46; The original GRACE score&#44; based on a six-month follow-up and validated for ACS with and without ST-segment elevation&#44; was calculated on the basis of eight independent risk factors&#44; giving a maximum score of 372&#46; The PURSUIT score for unstable angina&#47;non-ST-elevation MI predicts the 30-day incidence of death and the composite of death or MI&#44; but only uses five predictive variables&#46; The TIMI score is calculated on the basis of seven variables predicting severe complications &#40;all-cause mortality&#44; new or recurrent MI&#44; or severe recurrent ischemia requiring urgent revascularization&#41; within 14 days of admission&#46; It suffers from the same limitation as others based on clinical trials in which high-risk patients were excluded&#44; but the TIMI score has shown good prognostic ability&#46; However&#44; in a multifactorial disease like atherosclerosis&#44; these scores are largely determined by the patient&#39;s age&#44; which makes it difficult for the physician to interpret an individual score&#46;<a class="elsevierStyleCrossRef" href="#bib0255"><span class="elsevierStyleSup">24</span></a> Furthermore&#44; the trials were designed with a short follow-up&#44; limiting analysis of survival and of its implications&#46; The recently updated GRACE 2&#46;0 score estimates risk of death or MI in ACS from the acute &#40;in-hospital&#41; phase to 6 months&#44; 1 year and 3 years&#46;<a class="elsevierStyleCrossRef" href="#bib0240"><span class="elsevierStyleSup">21</span></a> The size of the patient cohorts is worthy of note&#58; 1-year outcomes were derived from the dataset of 32<span class="elsevierStyleHsp" style=""></span>037 patients from the GRACE registry enrolled between January 2002 and December 2007&#44; and 3-year mortality from the UK cohort of 1274 patients&#46; The analysis&#44; which used Cox multiple regression models&#44; included the same independent predictors of outcome as the original version&#44; but also employed non-linear associations of the continuous variables of systolic blood pressure&#44; pulse&#44; age and creatinine&#44; which improved model discrimination compared to the original&#46; In addition&#44; a simplified version of the risk score was developed with substitutions for creatinine and Killip class &#40;history of renal dysfunction and diuretic usage&#44; respectively&#41;&#44; which performed almost as well&#44; enabling physicians to assess risk in a wider range of ACS patients&#46;</p><p id="par0060" class="elsevierStylePara elsevierViewall">Cost-effectiveness analyses may reveal whether an improvement in performance is sufficiently important to justify measuring another variable in clinical practice&#46; Although many potential markers have been studied in recent years&#44; no new predictors have been identified that have a sufficiently large effect to identify patients with or without the endpoint&#46; This is hardly surprising&#44; given that validation of the GRACE model revealed that the model of only eight factors conveyed more than 90&#37; of the predictive accuracy of the complete multivariable model&#46;<a class="elsevierStyleCrossRef" href="#bib0230"><span class="elsevierStyleSup">19</span></a></p><p id="par0065" class="elsevierStylePara elsevierViewall">While the value of risk scores as tools to assess prognosis is inarguable&#44; their impact on patient outcomes has not been adequately investigated&#46;<a class="elsevierStyleCrossRefs" href="#bib0260"><span class="elsevierStyleSup">25&#44;26</span></a> Risk scores are undoubtedly useful&#44; and they are simple to calculate using web-based calculators or portable apps&#46; However&#44; the literature reveals that risk scores are not applied systematically for risk management in ACS&#44; despite the evidence and the guidelines&#46; There are various reasons for this&#44; including the mistaken idea that clinical assessment and the measurement of individual markers are sufficient&#46;<a class="elsevierStyleCrossRef" href="#bib0270"><span class="elsevierStyleSup">27</span></a></p><p id="par0070" class="elsevierStylePara elsevierViewall">Gaps in knowledge for both primary and secondary prevention concerning the risk of CV events in both the short and the long term in ethnic minorities and different age-groups and genders mean that there is room for improvement&#46; Systematic collaboration between statisticians&#44; epidemiologists and clinical researchers&#44; including cardiologists and internal medicine specialists&#44; will lead to more rigorous methodologies and improve the quality of risk prediction models in CV research&#46;</p><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0005">Conflicts of interest</span><p id="par0075" class="elsevierStylePara elsevierViewall">The author has no conflicts of interest to declare&#46;</p></span></span>"
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Cardiovascular risk scores: Usefulness and limitations
Scores de risco cardiovascular: utilidade e limitações
Evangelista Rocha
Serviço de Cardiologia, Hospital das Forças Armadas – Pólo de Lisboa, Lisboa, Portugal
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    "textoCompleto" => "<span class="elsevierStyleSections"><p id="par0005" class="elsevierStylePara elsevierViewall">Prediction of cardiovascular &#40;CV&#41; risk is an aspect of CV prevention that has seen significant developments in recent years&#46; The aim is to identify the main risk factors and markers that are potential therapeutic targets and to promote the implementation of cost-effective diagnostic and prognostic strategies in primary and secondary prevention of CV disease&#46;</p><p id="par0010" class="elsevierStylePara elsevierViewall">The article by Paredes et al&#46; in this issue of the <span class="elsevierStyleItalic">Journal</span> on specific aspects of statistics and information technology is an important contribution to improving risk scores for secondary prevention&#46;<a class="elsevierStyleCrossRef" href="#bib0140"><span class="elsevierStyleSup">1</span></a> It clearly demonstrates the need for collaboration between statisticians and clinical researchers in the development and validation of risk prediction models&#46; The subjects in the study were patients in Hospital de Santa Cruz&#44; a reference center due to the quality of its interventional care&#44; especially coronary angiography and myocardial revascularization&#46; This could be a source of selection bias&#44; but the data on validation of the risk scores reveals that the study sample included the full spectrum of non-ST-elevation acute coronary syndromes &#40;NSTE-ACS&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0145"><span class="elsevierStyleSup">2</span></a> The new approaches analyzed in the study are able to cope with missing risk factors&#44; which is a way to avoid excluding cases&#44; although the authors recognize that care should be taken when extrapolating the results&#46; Another important question is the frequency of the endpoint used to determine the sample size&#44; rather than the total number of patients&#59; a simple and practical method requires at least 10 events per variable&#44; i&#46;e&#46; the number of patients with the endpoint divided by the number of predictive factors under study&#44;<a class="elsevierStyleCrossRef" href="#bib0150"><span class="elsevierStyleSup">3</span></a> and by this method&#44; the frequency of the combined endpoint was low &#40;n&#61;33&#41;&#46; The approaches analyzed by Paredes et al&#46; were validated by k-fold cross-validation&#44; a method for assessing the ability of a model to make generalizations on the basis of a dataset&#46; Using techniques from artificial intelligence&#44; the authors performed optimization with genetic algorithms &#40;the most popular of the larger class of evolutionary algorithms&#41;&#44; which are among the CV risk assessment tools currently recommended by US and European medical societies &#40;albeit without significant effect to date&#41;&#46;</p><p id="par0015" class="elsevierStylePara elsevierViewall">With regard to the usefulness and limitations of CV risk scores and their application in clinical practice for primary and secondary prevention&#44; it is worth examining what we know and what remains to be clarified&#46; First&#44; healthy attitudes and behavior&#44; which can be encouraged by low-cost measures both on a population-wide scale and aimed at high-risk patients&#44; 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led to the development of other scoring systems&#44; including PROCAM&#44;<a class="elsevierStyleCrossRef" href="#bib0155"><span class="elsevierStyleSup">4</span></a> SCORE&#44;<a class="elsevierStyleCrossRef" href="#bib0160"><span class="elsevierStyleSup">5</span></a> QRISK&#44;<a class="elsevierStyleCrossRefs" href="#bib0165"><span class="elsevierStyleSup">6&#44;7</span></a> and the Reynolds risk scores for women and men&#46;<a class="elsevierStyleCrossRefs" href="#bib0175"><span class="elsevierStyleSup">8&#44;9</span></a> Differences between these systems explain the variation in their risk estimates&#44; but since 2003 the scoring system recommended by the European Society of Cardiology &#40;ESC&#41; has been SCORE&#46;<a class="elsevierStyleCrossRefs" href="#bib0160"><span class="elsevierStyleSup">5&#44;10</span></a></p><p id="par0030" class="elsevierStylePara elsevierViewall">Since that date&#44; Portugal has been classified among the low-risk countries&#46; How is a low-risk country defined&#63; In the ESC guidelines&#44; the cut-off points are based on 2008 CVD plus diabetes mortality in those aged 45&#8211;74 years &#40;220&#47;100<span class="elsevierStyleHsp" style=""></span>000 in men and 160&#47;100<span class="elsevierStyleHsp" style=""></span>000 in women&#41;&#46; However&#44; these cutoffs for classification of CV risk and therapeutic recommendations are to some extent arbitrary&#44; since risk is a continuum&#44; and some authors have found that SCORE performs less well in certain populations&#44; giving rise to some controversy&#46;<a class="elsevierStyleCrossRefs" href="#bib0190"><span class="elsevierStyleSup">11&#44;12</span></a> Risk estimation is not an exact science&#59; in the Cox proportional hazards models often used&#44; the regression coefficients are assumed to remain constant over time and in the context of different combinations of risk factors&#44; but they do in fact vary as a person ages&#44;<a class="elsevierStyleCrossRef" href="#bib0200"><span class="elsevierStyleSup">13</span></a> especially predisposing factors that aggravate independent factors&#46; Explanatory variables are considered to act multiplicatively on the hazard function&#46; At best&#44; these assumptions and suppositions&#44; and the different combinations of risk factors that interact in complex ways&#44; are difficult to model&#44; and so the models and estimates used are only approximations to &#8216;truth&#8217;&#46;<a class="elsevierStyleCrossRef" href="#bib0205"><span class="elsevierStyleSup">14</span></a></p><p id="par0035" class="elsevierStylePara elsevierViewall">The SCORE system assesses the 10-year risk of fatal cardiovascular disease &#40;mortality from myocardial infarction &#91;MI&#93;&#44; stroke&#44; aortic aneurysm or other&#41;&#46; The choice of CV mortality&#44; as opposed to fatal and nonfatal events&#44; was deliberate&#44; because nonfatal event rates are highly dependent on the definitions and detection methods used and are thus difficult to calculate accurately&#44; especially in different study cohorts with long follow-ups&#46; At the same time&#44; basing the score on mortality enables calibration to take into consideration long-term trends in CV mortality&#46; All risk estimation systems will overestimate risk in countries in which CV mortality has declined and underestimate risk if it has increased&#46; However&#44; recalibration should be undertaken if good quality&#44; up-to-date mortality and risk factor prevalence data are available&#46;<a class="elsevierStyleCrossRef" href="#bib0210"><span class="elsevierStyleSup">15</span></a> Even so&#44; the inability of the SCORE system to differentiate the 10-year risk of a fatal event due to ischemic heart disease or due to stroke in individuals aged 40&#8211;65&#44; or the risk in older individuals&#44; since the risk profile of each disease is different&#44; is a limitation&#59; for younger individuals&#44; in whom the absolute risk is low&#44; the relative risk table is applied as a way of encouraging healthier lifestyles&#46;</p><p id="par0040" class="elsevierStylePara elsevierViewall">The recently published SCORE O&#46;P&#46;<a class="elsevierStyleCrossRef" href="#bib0215"><span class="elsevierStyleSup">16</span></a> is the first CV risk assessment system developed specifically for older individuals &#40;&#8805;65 years&#41;&#44; both men and women&#46; It shows good discrimination&#44; with a low false positive rate&#44; and may reduce excessive use of medication in older people without a history of CV events&#46; The methodology excluded subjects with missing data on any of the required covariables&#44; but simulated external validation was performed using cross-validation to assess the model&#39;s ability to generalize the 10-year risk function&#46; The next step is to confirm the discriminative ability of the simulated external validation by widening the validation process using external datasets&#44; before it is made available online and included in the ESC guidelines on cardiovascular prevention&#46;</p><p id="par0045" class="elsevierStylePara elsevierViewall">In the search for ways to improve CV risk estimation for secondary prevention&#44; after various unsuccessful attempts&#44; it is unlikely that a major new risk factor will be identified that is demonstrably linked to causality&#44; or that the range of known polymorphisms will fill the gaps in risk prediction&#46; It can be a challenge to use HeartScore&#44; the online version of the SCORE risk charts &#40;available at <a href="http://www.escardio.org/"><span class="elsevierStyleUnderline">www&#46;escardio&#46;org</span></a>&#41;&#44; in the right way&#46; Alternatively&#44; the SCORE system needs to be calibrated&#44; as has been done for some countries&#44; so that the risk estimation model reliably predicts the level of absolute risk that is subsequently observed&#46;</p><p id="par0050" class="elsevierStylePara elsevierViewall">In terms of secondary prevention&#44; the number of published articles assessing risk stratification models for acute coronary syndrome &#40;ACS&#41; demonstrates the extent of interest in this subject&#46; One of the aims of clinical prediction models is to estimate the likelihood of an event after diagnosis of the disease &#40;prognosis&#41; in individual patients and to assist in clinical decision-making&#46; Some ACS need rapid diagnosis and entail critical therapeutic decisions by health professionals with different levels of knowledge and experience&#44; sometimes with limited resources&#46; To this end&#44; various risk models and scores have been developed to identify patients in emergency departments or coronary care units who are most likely to benefit from an invasive approach&#46;</p><p id="par0055" class="elsevierStylePara elsevierViewall">The best-validated risk scores were based on different populations in clinical trials &#40;TIMI<a class="elsevierStyleCrossRef" href="#bib0220"><span class="elsevierStyleSup">17</span></a> and PURSUIT<a class="elsevierStyleCrossRef" href="#bib0225"><span class="elsevierStyleSup">18</span></a>&#41; or registries &#40;GRACE<a class="elsevierStyleCrossRef" href="#bib0230"><span class="elsevierStyleSup">19</span></a> and GRACE 2&#46;0<a class="elsevierStyleCrossRef" href="#bib0235"><span class="elsevierStyleSup">20</span></a>&#41;&#46; For NSTE-ACS&#44; the GRACE score provides the best stratification of ischemic risk at hospital admission and discharge&#44;<a class="elsevierStyleCrossRefs" href="#bib0145"><span class="elsevierStyleSup">2&#44;21</span></a> and &#8211; like the GRACE 2&#46;0 score &#8211; is accordingly included in the ESC guidelines&#46;<a class="elsevierStyleCrossRefs" href="#bib0245"><span class="elsevierStyleSup">22&#44;23</span></a> An invasive strategy &#40;coronary angiography and revascularization&#41; is recommended within 24 hours for a GRACE score &#62;140 &#40;high risk&#41; and within 72 hours for a score &#62;109 and &#60;140 &#40;intermediate risk&#41;&#46; The original GRACE score&#44; based on a six-month follow-up and validated for ACS with and without ST-segment elevation&#44; was calculated on the basis of eight independent risk factors&#44; giving a maximum score of 372&#46; The PURSUIT score for unstable angina&#47;non-ST-elevation MI predicts the 30-day incidence of death and the composite of death or MI&#44; but only uses five predictive variables&#46; The TIMI score is calculated on the basis of seven variables predicting severe complications &#40;all-cause mortality&#44; new or recurrent MI&#44; or severe recurrent ischemia requiring urgent revascularization&#41; within 14 days of admission&#46; It suffers from the same limitation as others based on clinical trials in which high-risk patients were excluded&#44; but the TIMI score has shown good prognostic ability&#46; However&#44; in a multifactorial disease like atherosclerosis&#44; these scores are largely determined by the patient&#39;s age&#44; which makes it difficult for the physician to interpret an individual score&#46;<a class="elsevierStyleCrossRef" href="#bib0255"><span class="elsevierStyleSup">24</span></a> Furthermore&#44; the trials were designed with a short follow-up&#44; limiting analysis of survival and of its implications&#46; The recently updated GRACE 2&#46;0 score estimates risk of death or MI in ACS from the acute &#40;in-hospital&#41; phase to 6 months&#44; 1 year and 3 years&#46;<a class="elsevierStyleCrossRef" href="#bib0240"><span class="elsevierStyleSup">21</span></a> The size of the patient cohorts is worthy of note&#58; 1-year outcomes were derived from the dataset of 32<span class="elsevierStyleHsp" style=""></span>037 patients from the GRACE registry enrolled between January 2002 and December 2007&#44; and 3-year mortality from the UK cohort of 1274 patients&#46; The analysis&#44; which used Cox multiple regression models&#44; included the same independent predictors of outcome as the original version&#44; but also employed non-linear associations of the continuous variables of systolic blood pressure&#44; pulse&#44; age and creatinine&#44; which improved model discrimination compared to the original&#46; In addition&#44; a simplified version of the risk score was developed with substitutions for creatinine and Killip class &#40;history of renal dysfunction and diuretic usage&#44; respectively&#41;&#44; which performed almost as well&#44; enabling physicians to assess risk in a wider range of ACS patients&#46;</p><p id="par0060" class="elsevierStylePara elsevierViewall">Cost-effectiveness analyses may reveal whether an improvement in performance is sufficiently important to justify measuring another variable in clinical practice&#46; Although many potential markers have been studied in recent years&#44; no new predictors have been identified that have a sufficiently large effect to identify patients with or without the endpoint&#46; This is hardly surprising&#44; given that validation of the GRACE model revealed that the model of only eight factors conveyed more than 90&#37; of the predictive accuracy of the complete multivariable model&#46;<a class="elsevierStyleCrossRef" href="#bib0230"><span class="elsevierStyleSup">19</span></a></p><p id="par0065" class="elsevierStylePara elsevierViewall">While the value of risk scores as tools to assess prognosis is inarguable&#44; their impact on patient outcomes has not been adequately investigated&#46;<a class="elsevierStyleCrossRefs" href="#bib0260"><span class="elsevierStyleSup">25&#44;26</span></a> Risk scores are undoubtedly useful&#44; and they are simple to calculate using web-based calculators or portable apps&#46; However&#44; the literature reveals that risk scores are not applied systematically for risk management in ACS&#44; despite the evidence and the guidelines&#46; There are various reasons for this&#44; including the mistaken idea that clinical assessment and the measurement of individual markers are sufficient&#46;<a class="elsevierStyleCrossRef" href="#bib0270"><span class="elsevierStyleSup">27</span></a></p><p id="par0070" class="elsevierStylePara elsevierViewall">Gaps in knowledge for both primary and secondary prevention concerning the risk of CV events in both the short and the long term in ethnic minorities and different age-groups and genders mean that there is room for improvement&#46; Systematic collaboration between statisticians&#44; epidemiologists and clinical researchers&#44; including cardiologists and internal medicine specialists&#44; will lead to more rigorous methodologies and improve the quality of risk prediction models in CV research&#46;</p><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0005">Conflicts of interest</span><p id="par0075" class="elsevierStylePara elsevierViewall">The author has no conflicts of interest to declare&#46;</p></span></span>"
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Revista Portuguesa de Cardiologia (English edition)
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