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as the <span class="elsevierStyleItalic">Moirai</span> &#40;the three Fates&#41; made certain that the destiny assigned by them to every human being would take its course without obstruction&#46; In our era&#44; we dare to predict the course of diseases and the ability of our interventions to alter the fate of our patients&#46;<a class="elsevierStyleCrossRef" href="#bib0085"><span class="elsevierStyleSup">1</span></a></p><p id="par0015" class="elsevierStylePara elsevierViewall">In chronic heart failure &#40;HF&#41;&#44; an insidious and progressive syndrome&#44; patients are at high risk of death&#46;<a class="elsevierStyleCrossRef" href="#bib0090"><span class="elsevierStyleSup">2</span></a> However&#44; the availability of heart transplantation can dramatically change the prognosis of the most severe patients&#46; These patients must be identified before a major event jeopardizes their eligibility for transplantation&#46;<a class="elsevierStyleCrossRef" href="#bib0095"><span class="elsevierStyleSup">3</span></a> At present&#44; prognostication and selection of patients for heart transplantation is a complex issue&#46;<a class="elsevierStyleCrossRef" href="#bib0100"><span class="elsevierStyleSup">4</span></a> As the candidate list grows and guidelines expand candidacy&#44; heart transplantation rates remain static&#46;<a class="elsevierStyleCrossRef" href="#bib0105"><span class="elsevierStyleSup">5</span></a> This highlights the need for better candidate selection&#44; dynamic delisting and waiting list trimming&#46; Integration of multiple factors&#44; including functional capacity variables&#44; scores&#44; and clinical assessments &#40;e&#46;g&#46; frailty scores&#41;&#44; may help to select vulnerable patients for transplantation&#46;<a class="elsevierStyleCrossRef" href="#bib0110"><span class="elsevierStyleSup">6</span></a> The aging of the population and heterogeneity in clinical presentation underscore the need for a multiparametric clinical and epidemiological approach to these risk stratification systems&#46;<a class="elsevierStyleCrossRef" href="#bib0115"><span class="elsevierStyleSup">7</span></a></p><p id="par0020" class="elsevierStylePara elsevierViewall">The statistical concepts supporting risk stratification scores are complex and can be prone to bias&#46; The most commonly used parameter to assess the performance of a risk score is the concordance statistic &#40;C-statistic&#41;&#46; This reflects the probability of a patient in whom an event occurs &#40;in this case&#44; death or heart transplantation&#41; having a worse score than a patient in whom the event does not occur or occurs at a later point of time&#46;<a class="elsevierStyleCrossRef" href="#bib0120"><span class="elsevierStyleSup">8</span></a> A value of 1&#46;0 reflects perfect concordance between the prediction and outcome&#44; whereas a C-statistic of 0&#46;5 indicates random concordance&#46; Usually&#44; scores display C-statistics of 0&#46;6 to 0&#46;9&#46; HF risk stratification scores usually display values between 0&#46;75 and 0&#46;85&#44; commonly classified as good concordance&#46; Other indices can also be used in this context&#44; like the net reclassification index &#40;NRI&#41;&#44; which measures how often addition of a new variable&#44; such as VE&#47;VCO<span class="elsevierStyleInf">2</span> slope&#44; results in a change in classification that can be used to assess the effect of changes of this magnitude on the C-statistic&#46;<a class="elsevierStyleCrossRef" href="#bib0125"><span class="elsevierStyleSup">9</span></a> Importantly&#44; without an assessment of the clinical impact of the change in classification&#44; the NRI can be misleading&#44; as it overemphasizes the importance of small increases in the C-statistic&#46;</p><p id="par0025" class="elsevierStylePara elsevierViewall">Several risk stratification scores have been developed to help physicians identify the high-risk vulnerable patients who would benefit from early heart transplantation listing&#46; The most commonly used score is the Seattle Heart Failure Model &#40;SHFM&#41;&#44; which is based on 24 clinical variables&#46;<a class="elsevierStyleCrossRef" href="#bib0130"><span class="elsevierStyleSup">10</span></a> Both the SHFM and the HF Survival Score &#40;HFSS&#41;<a class="elsevierStyleCrossRef" href="#bib0135"><span class="elsevierStyleSup">11</span></a> are recommended in the latest heart transplantation guidelines&#46;<a class="elsevierStyleCrossRef" href="#bib0105"><span class="elsevierStyleSup">5</span></a> Some scores also include functional capacity variables&#44; particularly from cardiopulmonary exercise testing &#40;CPET&#41;&#59; these include the HFSS&#44; the HF-ACTION score<a class="elsevierStyleCrossRef" href="#bib0140"><span class="elsevierStyleSup">12</span></a> and the recently developed Metabolic Exercise Cardiac Kidney Indexes &#40;MECKI&#41; score&#46;<a class="elsevierStyleCrossRef" href="#bib0145"><span class="elsevierStyleSup">13</span></a> The latter score&#44; combining information on hemoglobin&#44; sodium&#44; kidney function&#44; left ventricular ejection fraction and two CPET parameters &#8211; peak oxygen consumption &#40;VO<span class="elsevierStyleInf">2</span> max&#41; in proportion to expected VO<span class="elsevierStyleInf">2</span> and the VE&#47;VCO<span class="elsevierStyleInf">2</span> slope &#8211; have recently demonstrated superiority &#40;C-statistic 0&#46;781&#41; over the commonly used SHFM &#40;0&#46;739&#41; and HFSS &#40;0&#46;723&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0110"><span class="elsevierStyleSup">6</span></a> These results were recently supported by a report by a Portuguese group that again demonstrated the superiority of the MECKI score&#44; with C-statistics between 0&#46;83 and 0&#46;87 for endpoints including heart transplantation&#44; values that indicate good agreement with predictions&#46;<a class="elsevierStyleCrossRef" href="#bib0150"><span class="elsevierStyleSup">14</span></a> The good discriminative power of the MECKI score is evident and may be related to the inclusion of both VO<span class="elsevierStyleInf">2</span> max and VE&#47;VCO<span class="elsevierStyleInf">2</span> in the score&#39;s composition&#46;</p><p id="par0030" class="elsevierStylePara elsevierViewall">Risk scores can also be subject to bias&#46; Sources of potential biases include selection bias&#44; as most scores are developed in homogeneous&#44; highly selected randomized controlled trial cohorts exposed to standard of care interventions&#46; Using real-world data&#44; as Pereira-da-Silva et al&#46;<a class="elsevierStyleCrossRef" href="#bib0155"><span class="elsevierStyleSup">15</span></a> have done in this issue of the <span class="elsevierStyleItalic">Journal</span>&#44; may help to minimize this source of bias&#46; The authors aimed to identify which prognostic factors can best discriminate HF patients who will progress to death or transplantation&#44; focusing specifically on variables obtained from CPET testing&#46; Enrolling 263 patients spanning a nine-year period&#44; the authors developed a two-step model for assessing HF risk&#46; In a highly specific first step&#44; high-risk patients are identified by a VE&#47;VCO<span class="elsevierStyleInf">2</span> slope of &#8805;39&#44; with a C-statistic of 0&#46;79&#44; without the contribution of any other variable&#46; The VE&#47;VCO<span class="elsevierStyleInf">2</span> slope is a particularly interesting parameter&#44; as it reflects ventilatory efficiency and is associated with pulmonary hypertension&#46;<a class="elsevierStyleCrossRef" href="#bib0160"><span class="elsevierStyleSup">16</span></a> It also possesses prognostic value at submaximal levels of effort&#44; increasing the usefulness of CPET in a population that is not accustomed to exercise or may be reluctant to provide maximal effort&#46; In fact&#44; VE&#47;VCO<span class="elsevierStyleInf">2</span> &#62;35 is recommended as a listing criterion in the presence of submaximal CPET &#40;respiratory exchange ratio &#60;1&#46;05&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0105"><span class="elsevierStyleSup">5</span></a> In the second step&#44; after excluding high-risk patients&#44; low-risk patients were identified by a VE&#47;VCO<span class="elsevierStyleInf">2</span> slope &#60;39&#44; in association with serum sodium &#62;136 mmol&#47;l&#44; serum creatinine &#60;1&#46;0 mg&#47;dl or variation in end-tidal carbon dioxide partial pressure of &#62;0&#46;45 kPa&#46; The presence of two or three factors had an additive effect for a better prognosis&#46; These results are clinically significant&#44; as most studies in this area have focused on identifying high-risk patients&#46; Identification of a low-risk population may help advanced HF physicians to step down care in some selected patients&#44; thereby optimizing resources and intensity of care&#46;</p><p id="par0035" class="elsevierStylePara elsevierViewall">Somewhat surprisingly&#44; no additive value was found for very strong prognostic variables&#44; such as creatinine&#44; natriuretic peptides or echocardiographic variables&#46; The lack of invasive hemodynamic data is a caveat&#44; as all patients referred for transplantation should have an invasive assessment for candidacy&#46; It would be interesting from a pathophysiological standpoint to have an invasive characterization of high-risk patients&#44; particularly to help understand which hemodynamic profiles were present in patients with VE&#47;VCO<span class="elsevierStyleInf">2</span> slopes &#62;39&#46; Also&#44; only patients able to perform CPET were enrolled&#46; Finally&#44; the score should be tested in a validation cohort&#44; as models are intervention-dependent&#46;</p><p id="par0040" class="elsevierStylePara elsevierViewall">Despite these important insights from Pereira-da-Silva et al&#46;&#8217;s study&#44; several questions remain regarding advanced HF risk assessment&#46; Are we ready to use other variables besides peak VO<span class="elsevierStyleInf">2</span> for risk stratification&#63; Is variability between serial VE&#47;VCO<span class="elsevierStyleInf">2</span> measurements a problem&#63; Do these results correlate with invasive hemodynamics&#63; Can we ignore other clinical variables that are included in other scores&#44; such as the MECKI score&#63; The guidelines recommend against relying solely on scores to select patients for heart transplantation&#46;<a class="elsevierStyleCrossRef" href="#bib0100"><span class="elsevierStyleSup">4</span></a> Heart transplantation patient selection remains a complex decision that requires a team-based approach&#44; extensive clinical experience&#44; surgical input&#44; social support and a multiparametric approach&#46;</p><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0005">Conflicts of interest</span><p id="par0050" class="elsevierStylePara elsevierViewall">The author has no conflicts of interest to declare&#46;</p></span></span>"
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Editorial comment
On the trail of the perfect prognosticator in advanced heart failure patients
À procura do melhor preditor de eventos em doentes com insuficiência cardíaca avançada
Rui Baptistaa,b
a Serviço de Cardiologia, Cardiologia A, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
b Faculty of Medicine, University of Coimbra, Coimbra, Portugal
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    "textoCompleto" => "<span class="elsevierStyleSections"><p id="par0005" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleDisplayedQuote" id="dsq0005"><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">&#8220;It appears to me a most excellent thing for the physician to cultivate Prognosis&#59; for by foreseeing and foretelling&#44; in the presence of the sick&#44; the present&#44; the past&#44; and the future&#44; and explaining the omissions which patients have been guilty of&#44; he will be the more readily believed to be acquainted with the circumstances of the sick&#59; so that men will have confidence to intrust themselves to such a physician&#46;&#8221;</p><span class="elsevierStyleSource"><span class="elsevierStyleSmallCaps">Hippocrates</span>&#44; <span class="elsevierStyleItalic">The Book of Prognostics</span></span></span></p><p id="par0010" class="elsevierStylePara elsevierViewall">In Greek mythology&#44; nothing could be done to alter one&#39;s fate&#44; as the <span class="elsevierStyleItalic">Moirai</span> &#40;the three Fates&#41; made certain that the destiny assigned by them to every human being would take its course without obstruction&#46; In our era&#44; we dare to predict the course of diseases and the ability of our interventions to alter the fate of our patients&#46;<a class="elsevierStyleCrossRef" href="#bib0085"><span class="elsevierStyleSup">1</span></a></p><p id="par0015" class="elsevierStylePara elsevierViewall">In chronic heart failure &#40;HF&#41;&#44; an insidious and progressive syndrome&#44; patients are at high risk of death&#46;<a class="elsevierStyleCrossRef" href="#bib0090"><span class="elsevierStyleSup">2</span></a> However&#44; the availability of heart transplantation can dramatically change the prognosis of the most severe patients&#46; These patients must be identified before a major event jeopardizes their eligibility for transplantation&#46;<a class="elsevierStyleCrossRef" href="#bib0095"><span class="elsevierStyleSup">3</span></a> At present&#44; prognostication and selection of patients for heart transplantation is a complex issue&#46;<a class="elsevierStyleCrossRef" href="#bib0100"><span class="elsevierStyleSup">4</span></a> As the candidate list grows and guidelines expand candidacy&#44; heart transplantation rates remain static&#46;<a class="elsevierStyleCrossRef" href="#bib0105"><span class="elsevierStyleSup">5</span></a> This highlights the need for better candidate selection&#44; dynamic delisting and waiting list trimming&#46; Integration of multiple factors&#44; including functional capacity variables&#44; scores&#44; and clinical assessments &#40;e&#46;g&#46; frailty scores&#41;&#44; may help to select vulnerable patients for transplantation&#46;<a class="elsevierStyleCrossRef" href="#bib0110"><span class="elsevierStyleSup">6</span></a> The aging of the population and heterogeneity in clinical presentation underscore the need for a multiparametric clinical and epidemiological approach to these risk stratification systems&#46;<a class="elsevierStyleCrossRef" href="#bib0115"><span class="elsevierStyleSup">7</span></a></p><p id="par0020" class="elsevierStylePara elsevierViewall">The statistical concepts supporting risk stratification scores are complex and can be prone to bias&#46; The most commonly used parameter to assess the performance of a risk score is the concordance statistic &#40;C-statistic&#41;&#46; This reflects the probability of a patient in whom an event occurs &#40;in this case&#44; death or heart transplantation&#41; having a worse score than a patient in whom the event does not occur or occurs at a later point of time&#46;<a class="elsevierStyleCrossRef" href="#bib0120"><span class="elsevierStyleSup">8</span></a> A value of 1&#46;0 reflects perfect concordance between the prediction and outcome&#44; whereas a C-statistic of 0&#46;5 indicates random concordance&#46; Usually&#44; scores display C-statistics of 0&#46;6 to 0&#46;9&#46; HF risk stratification scores usually display values between 0&#46;75 and 0&#46;85&#44; commonly classified as good concordance&#46; Other indices can also be used in this context&#44; like the net reclassification index &#40;NRI&#41;&#44; which measures how often addition of a new variable&#44; such as VE&#47;VCO<span class="elsevierStyleInf">2</span> slope&#44; results in a change in classification that can be used to assess the effect of changes of this magnitude on the C-statistic&#46;<a class="elsevierStyleCrossRef" href="#bib0125"><span class="elsevierStyleSup">9</span></a> Importantly&#44; without an assessment of the clinical impact of the change in classification&#44; the NRI can be misleading&#44; as it overemphasizes the importance of small increases in the C-statistic&#46;</p><p id="par0025" class="elsevierStylePara elsevierViewall">Several risk stratification scores have been developed to help physicians identify the high-risk vulnerable patients who would benefit from early heart transplantation listing&#46; The most commonly used score is the Seattle Heart Failure Model &#40;SHFM&#41;&#44; which is based on 24 clinical variables&#46;<a class="elsevierStyleCrossRef" href="#bib0130"><span class="elsevierStyleSup">10</span></a> Both the SHFM and the HF Survival Score &#40;HFSS&#41;<a class="elsevierStyleCrossRef" href="#bib0135"><span class="elsevierStyleSup">11</span></a> are recommended in the latest heart transplantation guidelines&#46;<a class="elsevierStyleCrossRef" href="#bib0105"><span class="elsevierStyleSup">5</span></a> Some scores also include functional capacity variables&#44; particularly from cardiopulmonary exercise testing &#40;CPET&#41;&#59; these include the HFSS&#44; the HF-ACTION score<a class="elsevierStyleCrossRef" href="#bib0140"><span class="elsevierStyleSup">12</span></a> and the recently developed Metabolic Exercise Cardiac Kidney Indexes &#40;MECKI&#41; score&#46;<a class="elsevierStyleCrossRef" href="#bib0145"><span class="elsevierStyleSup">13</span></a> The latter score&#44; combining information on hemoglobin&#44; sodium&#44; kidney function&#44; left ventricular ejection fraction and two CPET parameters &#8211; peak oxygen consumption &#40;VO<span class="elsevierStyleInf">2</span> max&#41; in proportion to expected VO<span class="elsevierStyleInf">2</span> and the VE&#47;VCO<span class="elsevierStyleInf">2</span> slope &#8211; have recently demonstrated superiority &#40;C-statistic 0&#46;781&#41; over the commonly used SHFM &#40;0&#46;739&#41; and HFSS &#40;0&#46;723&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0110"><span class="elsevierStyleSup">6</span></a> These results were recently supported by a report by a Portuguese group that again demonstrated the superiority of the MECKI score&#44; with C-statistics between 0&#46;83 and 0&#46;87 for endpoints including heart transplantation&#44; values that indicate good agreement with predictions&#46;<a class="elsevierStyleCrossRef" href="#bib0150"><span class="elsevierStyleSup">14</span></a> The good discriminative power of the MECKI score is evident and may be related to the inclusion of both VO<span class="elsevierStyleInf">2</span> max and VE&#47;VCO<span class="elsevierStyleInf">2</span> in the score&#39;s composition&#46;</p><p id="par0030" class="elsevierStylePara elsevierViewall">Risk scores can also be subject to bias&#46; Sources of potential biases include selection bias&#44; as most scores are developed in homogeneous&#44; highly selected randomized controlled trial cohorts exposed to standard of care interventions&#46; Using real-world data&#44; as Pereira-da-Silva et al&#46;<a class="elsevierStyleCrossRef" href="#bib0155"><span class="elsevierStyleSup">15</span></a> have done in this issue of the <span class="elsevierStyleItalic">Journal</span>&#44; may help to minimize this source of bias&#46; The authors aimed to identify which prognostic factors can best discriminate HF patients who will progress to death or transplantation&#44; focusing specifically on variables obtained from CPET testing&#46; Enrolling 263 patients spanning a nine-year period&#44; the authors developed a two-step model for assessing HF risk&#46; In a highly specific first step&#44; high-risk patients are identified by a VE&#47;VCO<span class="elsevierStyleInf">2</span> slope of &#8805;39&#44; with a C-statistic of 0&#46;79&#44; without the contribution of any other variable&#46; The VE&#47;VCO<span class="elsevierStyleInf">2</span> slope is a particularly interesting parameter&#44; as it reflects ventilatory efficiency and is associated with pulmonary hypertension&#46;<a class="elsevierStyleCrossRef" href="#bib0160"><span class="elsevierStyleSup">16</span></a> It also possesses prognostic value at submaximal levels of effort&#44; increasing the usefulness of CPET in a population that is not accustomed to exercise or may be reluctant to provide maximal effort&#46; In fact&#44; VE&#47;VCO<span class="elsevierStyleInf">2</span> &#62;35 is recommended as a listing criterion in the presence of submaximal CPET &#40;respiratory exchange ratio &#60;1&#46;05&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0105"><span class="elsevierStyleSup">5</span></a> In the second step&#44; after excluding high-risk patients&#44; low-risk patients were identified by a VE&#47;VCO<span class="elsevierStyleInf">2</span> slope &#60;39&#44; in association with serum sodium &#62;136 mmol&#47;l&#44; serum creatinine &#60;1&#46;0 mg&#47;dl or variation in end-tidal carbon dioxide partial pressure of &#62;0&#46;45 kPa&#46; The presence of two or three factors had an additive effect for a better prognosis&#46; These results are clinically significant&#44; as most studies in this area have focused on identifying high-risk patients&#46; Identification of a low-risk population may help advanced HF physicians to step down care in some selected patients&#44; thereby optimizing resources and intensity of care&#46;</p><p id="par0035" class="elsevierStylePara elsevierViewall">Somewhat surprisingly&#44; no additive value was found for very strong prognostic variables&#44; such as creatinine&#44; natriuretic peptides or echocardiographic variables&#46; The lack of invasive hemodynamic data is a caveat&#44; as all patients referred for transplantation should have an invasive assessment for candidacy&#46; It would be interesting from a pathophysiological standpoint to have an invasive characterization of high-risk patients&#44; particularly to help understand which hemodynamic profiles were present in patients with VE&#47;VCO<span class="elsevierStyleInf">2</span> slopes &#62;39&#46; Also&#44; only patients able to perform CPET were enrolled&#46; Finally&#44; the score should be tested in a validation cohort&#44; as models are intervention-dependent&#46;</p><p id="par0040" class="elsevierStylePara elsevierViewall">Despite these important insights from Pereira-da-Silva et al&#46;&#8217;s study&#44; several questions remain regarding advanced HF risk assessment&#46; Are we ready to use other variables besides peak VO<span class="elsevierStyleInf">2</span> for risk stratification&#63; Is variability between serial VE&#47;VCO<span class="elsevierStyleInf">2</span> measurements a problem&#63; Do these results correlate with invasive hemodynamics&#63; Can we ignore other clinical variables that are included in other scores&#44; such as the MECKI score&#63; The guidelines recommend against relying solely on scores to select patients for heart transplantation&#46;<a class="elsevierStyleCrossRef" href="#bib0100"><span class="elsevierStyleSup">4</span></a> Heart transplantation patient selection remains a complex decision that requires a team-based approach&#44; extensive clinical experience&#44; surgical input&#44; social support and a multiparametric approach&#46;</p><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0005">Conflicts of interest</span><p id="par0050" class="elsevierStylePara elsevierViewall">The author has no conflicts of interest to declare&#46;</p></span></span>"
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