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    "textoCompleto" => "<span class="elsevierStyleSections"><p id="par0005" class="elsevierStylePara elsevierViewall">Sleep apnea&#8211;hypopnea syndrome &#40;SAHS&#41; is a complex&#44; heterogeneous&#44; multifactorial disorder with a variable clinical presentation&#46;<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">1</span></a> Continuous positive airway pressure &#40;CPAP&#41; treatment is effective in symptomatic patients&#46; However&#44; while there is strong evidence for the association between severe SAHS&#44; cardiovascular disease &#40;CVD&#41;&#44; and mortality in observational studies&#44; randomized clinical trials have shown that CPAP does not reduce the risk of these outcomes&#46;<a class="elsevierStyleCrossRef" href="#bib0080"><span class="elsevierStyleSup">2</span></a> One possible explanation is that&#44; at present&#44; the management of the disease is based mainly on the apnea&#8211;hypopnea index &#40;AHI&#41;&#44; despite the fact that it is difficult for a single parameter to capture the entire heterogeneity of the disease&#46; This situation underlines the need for precision medicine in SAHS&#46; Just as phenotypes have been described in other chronic diseases such as asthma and COPD&#44; attempts have been made in the last decade to identify SAHS phenotypes that can characterize patients with a higher risk of morbidity and mortality and a higher likelihood of responding to treatment&#46; A series of statistical techniques have been used to analyze multifactorial data&#44; one of the most important being cluster analysis&#44; a multivariate statistical technique that seeks to group elements &#40;or variables&#41; in the attempt to achieve the maximum homogeneity within groups and the greatest difference between groups&#46;<a class="elsevierStyleCrossRef" href="#bib0085"><span class="elsevierStyleSup">3</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">The validity of the identification of phenotypes will depend on the homogeneity of the variables analyzed&#44; their progress over time&#44; and their reproducibility&#46;</p><p id="par0015" class="elsevierStylePara elsevierViewall">One of the first cluster analyses in SAHS was the Iceland Cohort &#40;ISAC&#41; conducted in 2014&#46;<a class="elsevierStyleCrossRef" href="#bib0090"><span class="elsevierStyleSup">4</span></a> Since then&#44; other studies have been published&#44; many of which have focused on moderate-severe SAHS&#44; although some include mild manifestations&#46;<a class="elsevierStyleCrossRefs" href="#bib0095"><span class="elsevierStyleSup">5&#8211;8</span></a> However these studies differ widely in terms of variables studied&#44; cluster methods applied&#44; and the duration of follow-up&#46; Therefore&#44; many different phenotypes have been identified and few have been associated with relevant clinical outcomes&#46;</p><p id="par0020" class="elsevierStylePara elsevierViewall">The ISAC study identified 3 phenotypes&#44; classified as patients with &#8220;disturbed sleep&#8221; &#40;32&#46;7&#37;&#41;&#44; &#8220;minimally symptomatic&#8221; patients &#40;24&#46;7&#37;&#41; and patients with &#8220;excessive daytime sleepiness&#8221; &#40;42&#46;6&#37;&#41;&#46; One of the aims of this study was to determine if there was a higher likelihood of comorbidities &#40;hypertension &#91;HT&#93;&#44; diabetes&#44; CVD&#41; in the &#8220;minimally symptomatic&#8221; group&#46; Vavougios et al&#46;<a class="elsevierStyleCrossRef" href="#bib0100"><span class="elsevierStyleSup">6</span></a> identified 6 groups&#44; of which 2 had severe SAHS and 2 had moderate SAHS&#44; differentiated by the presence of comorbidities&#46; In patients with a similar AHI&#44; the variables associated with &#8220;sick&#8221; phenotypes were age&#44; body mass index &#40;BMI&#41;&#44; presence of HT&#44; and lower daytime oxygen saturation&#46; Similarly&#44; Lacedonia et al&#46;<a class="elsevierStyleCrossRef" href="#bib0115"><span class="elsevierStyleSup">9</span></a> identified a phenotype with more comorbidities that was associated with higher nocturnal and daytime hypoxemia and a higher BMI&#46; Turino et al&#46;&#44;<a class="elsevierStyleCrossRef" href="#bib0120"><span class="elsevierStyleSup">10</span></a> in a cohort of patients receiving CPAP&#44; found 2 phenotypes associated with higher mortality&#58; the &#8220;neoplastic&#8221; group &#40;with a high prevalence of malignant neoplasms&#41; and &#8220;oldest &#40;mean age 72 years&#41; and cardiac disease patients&#8221;&#46;</p><p id="par0025" class="elsevierStylePara elsevierViewall">Another limitation of most of these studies&#44; apart from the variability of the parameters analyzed&#44; is that their cross-sectional design rules out any prediction of progress of the phenotypes over time with respect to relevant clinical findings&#46; The first study to analyze the clinical course of phenotypes was that of Zinchuk et al&#46;<a class="elsevierStyleCrossRef" href="#bib0110"><span class="elsevierStyleSup">8</span></a>&#58; this group identified 7 phenotypes according to 65 polysomnographic variables&#46; The groups that were significantly associated with the primary outcome &#40;a combination of stroke&#44; transient ischemic attack&#44; acute coronary syndrome&#44; or death&#41; at 4&#46;9 years presented &#8220;restless leg syndrome&#8221;&#44; &#8220;hypopnea and hypoxia&#8221;&#44; and &#8220;combined severe&#8221;&#46; No association with AHI was identified&#46; A second study of the original ISAC cohort described the 2-year follow-up of the original phenotypes in subjects treated with CPAP&#59; improved symptoms were observed in all three groups&#46; Although CPAP adherence and symptomatic improvement were greater in the group with excessive daytime sleepiness&#44; there were no differences in the prevalence of comorbidities&#46;<a class="elsevierStyleCrossRef" href="#bib0125"><span class="elsevierStyleSup">11</span></a></p><p id="par0030" class="elsevierStylePara elsevierViewall">ISAC is the only study that other cohorts have attempted to reproduce&#44; but besides finding some differences in the number of clusters and their characteristics&#44; the association with comorbidities of these studies also proved different&#58;<ul class="elsevierStyleList" id="lis0005"><li class="elsevierStyleListItem" id="lsti0005"><span class="elsevierStyleLabel">&#8226;</span><p id="par0035" class="elsevierStylePara elsevierViewall">Kim et al&#46;<a class="elsevierStyleCrossRef" href="#bib0130"><span class="elsevierStyleSup">12</span></a> identified 3 clusters similar to those described in ISAC&#58; in this case&#44; the &#8220;minimally symptomatic&#8221; group was the most prevalent and no phenotype was found to be associated with more comorbidities&#44; except for a higher prevalence of HT in patients with &#8220;disturbed sleep&#8221;&#46; Unlike ISAC&#44; the cohort was a general Korean population&#44; and cultural differences may account for the different manifestation of symptoms and partly justify the different outcomes&#46;</p></li><li class="elsevierStyleListItem" id="lsti0010"><span class="elsevierStyleLabel">&#8226;</span><p id="par0040" class="elsevierStylePara elsevierViewall">Keenan et al&#46;<a class="elsevierStyleCrossRef" href="#bib0135"><span class="elsevierStyleSup">13</span></a> reproduced ISAC in a new clinical cohort in Iceland and in another multi-ethnic cohort&#46; They found 5 clusters &#40;80&#37; of patients were in the original 3 clusters&#41; with similar prevalences to ISAC&#44; but the &#8220;disturbed sleep&#8221; group was the one with the highest prevalence of comorbidities&#46;</p></li><li class="elsevierStyleListItem" id="lsti0015"><span class="elsevierStyleLabel">&#8226;</span><p id="par0045" class="elsevierStylePara elsevierViewall">The Sleep Heart Health Study cohort went a step further by following patients for up to 11&#46;8 years and analyzing the incidence of CVD&#46; In addition to the three ISAC groups&#44; they identified a new group&#58; &#8220;moderate sleepiness&#8221;&#46; In the &#8220;excessive daytime sleepiness&#8221; group &#40;16&#46;7&#37;&#41;&#44; a higher prevalence and incidence of CVD was observed&#44; but surprisingly&#44; the greater AHI score of this group is not discussed&#46;<a class="elsevierStyleCrossRef" href="#bib0140"><span class="elsevierStyleSup">14</span></a></p></li></ul></p><p id="par0050" class="elsevierStylePara elsevierViewall">The somewhat reproducible finding of the 3 clinical ISAC phenotypes in different cohorts is interesting&#46; However&#44; the phenotype most associated with comorbidities varies &#40;&#8220;minimally symptomatic&#8221; in the original&#44; &#8220;disturbed sleep&#8221; in the Kim et al&#46;<a class="elsevierStyleCrossRef" href="#bib0130"><span class="elsevierStyleSup">12</span></a> study&#44; none in Keenan et al&#46;&#8217;s Iceland cohort<a class="elsevierStyleCrossRef" href="#bib0135"><span class="elsevierStyleSup">13</span></a>&#41;&#44; prompting us to ask if they are really identifying the same phenotypes&#46;</p><p id="par0055" class="elsevierStylePara elsevierViewall">To date&#44; cluster studies in SAHS reflect biases derived from variables previously established in the analysis&#46; Thus&#44; the generalization of the described phenotypes is limited by methodological differences&#44; and further studies using a multifactorial approach &#40;clinical&#44; physiological&#44; biological&#44; polysomnographic parameters&#44; clinical consequences&#41; and a consensus definition of the variables analyzed will be needed to evaluate the most appropriate grouping techniques&#46; Incorporating large quantities of data &#40;big data&#41; from electronic medical records is a promising strategy for advancing precision medicine in sleep apnea&#46;</p></span>"
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Editorial
Cluster Analysis to Identify Apnea–Hypopnea Syndrome Phenotypes: Where Are We Heading?
Análisis de clúster para identificar fenotipos de síndrome de apneas-hipopneas del sueño: ¿hacia dónde vamos?
María Guadalupe Silveiraa,
Corresponding author
mgsilveira@gmail.com

Corresponding author.
, Patricia Lloberesb
a Servicio de Neumología, Parc Sanitari Sant Joan de Déu, Sant Boi de Llobregat, Barcelona, Spain
b Servicio de Neumología, Hospital General Universitario Vall d’Hebron. Ciber Enfermedades Respiratorias (CIBERES), Barcelona, Spain
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        "titulo" => "An&#225;lisis de cl&#250;ster para identificar fenotipos de s&#237;ndrome de apneas-hipopneas del sue&#241;o&#58; &#191;hacia d&#243;nde vamos&#63;"
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    "textoCompleto" => "<span class="elsevierStyleSections"><p id="par0005" class="elsevierStylePara elsevierViewall">Sleep apnea&#8211;hypopnea syndrome &#40;SAHS&#41; is a complex&#44; heterogeneous&#44; multifactorial disorder with a variable clinical presentation&#46;<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">1</span></a> Continuous positive airway pressure &#40;CPAP&#41; treatment is effective in symptomatic patients&#46; However&#44; while there is strong evidence for the association between severe SAHS&#44; cardiovascular disease &#40;CVD&#41;&#44; and mortality in observational studies&#44; randomized clinical trials have shown that CPAP does not reduce the risk of these outcomes&#46;<a class="elsevierStyleCrossRef" href="#bib0080"><span class="elsevierStyleSup">2</span></a> One possible explanation is that&#44; at present&#44; the management of the disease is based mainly on the apnea&#8211;hypopnea index &#40;AHI&#41;&#44; despite the fact that it is difficult for a single parameter to capture the entire heterogeneity of the disease&#46; This situation underlines the need for precision medicine in SAHS&#46; Just as phenotypes have been described in other chronic diseases such as asthma and COPD&#44; attempts have been made in the last decade to identify SAHS phenotypes that can characterize patients with a higher risk of morbidity and mortality and a higher likelihood of responding to treatment&#46; A series of statistical techniques have been used to analyze multifactorial data&#44; one of the most important being cluster analysis&#44; a multivariate statistical technique that seeks to group elements &#40;or variables&#41; in the attempt to achieve the maximum homogeneity within groups and the greatest difference between groups&#46;<a class="elsevierStyleCrossRef" href="#bib0085"><span class="elsevierStyleSup">3</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">The validity of the identification of phenotypes will depend on the homogeneity of the variables analyzed&#44; their progress over time&#44; and their reproducibility&#46;</p><p id="par0015" class="elsevierStylePara elsevierViewall">One of the first cluster analyses in SAHS was the Iceland Cohort &#40;ISAC&#41; conducted in 2014&#46;<a class="elsevierStyleCrossRef" href="#bib0090"><span class="elsevierStyleSup">4</span></a> Since then&#44; other studies have been published&#44; many of which have focused on moderate-severe SAHS&#44; although some include mild manifestations&#46;<a class="elsevierStyleCrossRefs" href="#bib0095"><span class="elsevierStyleSup">5&#8211;8</span></a> However these studies differ widely in terms of variables studied&#44; cluster methods applied&#44; and the duration of follow-up&#46; Therefore&#44; many different phenotypes have been identified and few have been associated with relevant clinical outcomes&#46;</p><p id="par0020" class="elsevierStylePara elsevierViewall">The ISAC study identified 3 phenotypes&#44; classified as patients with &#8220;disturbed sleep&#8221; &#40;32&#46;7&#37;&#41;&#44; &#8220;minimally symptomatic&#8221; patients &#40;24&#46;7&#37;&#41; and patients with &#8220;excessive daytime sleepiness&#8221; &#40;42&#46;6&#37;&#41;&#46; One of the aims of this study was to determine if there was a higher likelihood of comorbidities &#40;hypertension &#91;HT&#93;&#44; diabetes&#44; CVD&#41; in the &#8220;minimally symptomatic&#8221; group&#46; Vavougios et al&#46;<a class="elsevierStyleCrossRef" href="#bib0100"><span class="elsevierStyleSup">6</span></a> identified 6 groups&#44; of which 2 had severe SAHS and 2 had moderate SAHS&#44; differentiated by the presence of comorbidities&#46; In patients with a similar AHI&#44; the variables associated with &#8220;sick&#8221; phenotypes were age&#44; body mass index &#40;BMI&#41;&#44; presence of HT&#44; and lower daytime oxygen saturation&#46; Similarly&#44; Lacedonia et al&#46;<a class="elsevierStyleCrossRef" href="#bib0115"><span class="elsevierStyleSup">9</span></a> identified a phenotype with more comorbidities that was associated with higher nocturnal and daytime hypoxemia and a higher BMI&#46; Turino et al&#46;&#44;<a class="elsevierStyleCrossRef" href="#bib0120"><span class="elsevierStyleSup">10</span></a> in a cohort of patients receiving CPAP&#44; found 2 phenotypes associated with higher mortality&#58; the &#8220;neoplastic&#8221; group &#40;with a high prevalence of malignant neoplasms&#41; and &#8220;oldest &#40;mean age 72 years&#41; and cardiac disease patients&#8221;&#46;</p><p id="par0025" class="elsevierStylePara elsevierViewall">Another limitation of most of these studies&#44; apart from the variability of the parameters analyzed&#44; is that their cross-sectional design rules out any prediction of progress of the phenotypes over time with respect to relevant clinical findings&#46; The first study to analyze the clinical course of phenotypes was that of Zinchuk et al&#46;<a class="elsevierStyleCrossRef" href="#bib0110"><span class="elsevierStyleSup">8</span></a>&#58; this group identified 7 phenotypes according to 65 polysomnographic variables&#46; The groups that were significantly associated with the primary outcome &#40;a combination of stroke&#44; transient ischemic attack&#44; acute coronary syndrome&#44; or death&#41; at 4&#46;9 years presented &#8220;restless leg syndrome&#8221;&#44; &#8220;hypopnea and hypoxia&#8221;&#44; and &#8220;combined severe&#8221;&#46; No association with AHI was identified&#46; A second study of the original ISAC cohort described the 2-year follow-up of the original phenotypes in subjects treated with CPAP&#59; improved symptoms were observed in all three groups&#46; Although CPAP adherence and symptomatic improvement were greater in the group with excessive daytime sleepiness&#44; there were no differences in the prevalence of comorbidities&#46;<a class="elsevierStyleCrossRef" href="#bib0125"><span class="elsevierStyleSup">11</span></a></p><p id="par0030" class="elsevierStylePara elsevierViewall">ISAC is the only study that other cohorts have attempted to reproduce&#44; but besides finding some differences in the number of clusters and their characteristics&#44; the association with comorbidities of these studies also proved different&#58;<ul class="elsevierStyleList" id="lis0005"><li class="elsevierStyleListItem" id="lsti0005"><span class="elsevierStyleLabel">&#8226;</span><p id="par0035" class="elsevierStylePara elsevierViewall">Kim et al&#46;<a class="elsevierStyleCrossRef" href="#bib0130"><span class="elsevierStyleSup">12</span></a> identified 3 clusters similar to those described in ISAC&#58; in this case&#44; the &#8220;minimally symptomatic&#8221; group was the most prevalent and no phenotype was found to be associated with more comorbidities&#44; except for a higher prevalence of HT in patients with &#8220;disturbed sleep&#8221;&#46; Unlike ISAC&#44; the cohort was a general Korean population&#44; and cultural differences may account for the different manifestation of symptoms and partly justify the different outcomes&#46;</p></li><li class="elsevierStyleListItem" id="lsti0010"><span class="elsevierStyleLabel">&#8226;</span><p id="par0040" class="elsevierStylePara elsevierViewall">Keenan et al&#46;<a class="elsevierStyleCrossRef" href="#bib0135"><span class="elsevierStyleSup">13</span></a> reproduced ISAC in a new clinical cohort in Iceland and in another multi-ethnic cohort&#46; They found 5 clusters &#40;80&#37; of patients were in the original 3 clusters&#41; with similar prevalences to ISAC&#44; but the &#8220;disturbed sleep&#8221; group was the one with the highest prevalence of comorbidities&#46;</p></li><li class="elsevierStyleListItem" id="lsti0015"><span class="elsevierStyleLabel">&#8226;</span><p id="par0045" class="elsevierStylePara elsevierViewall">The Sleep Heart Health Study cohort went a step further by following patients for up to 11&#46;8 years and analyzing the incidence of CVD&#46; In addition to the three ISAC groups&#44; they identified a new group&#58; &#8220;moderate sleepiness&#8221;&#46; In the &#8220;excessive daytime sleepiness&#8221; group &#40;16&#46;7&#37;&#41;&#44; a higher prevalence and incidence of CVD was observed&#44; but surprisingly&#44; the greater AHI score of this group is not discussed&#46;<a class="elsevierStyleCrossRef" href="#bib0140"><span class="elsevierStyleSup">14</span></a></p></li></ul></p><p id="par0050" class="elsevierStylePara elsevierViewall">The somewhat reproducible finding of the 3 clinical ISAC phenotypes in different cohorts is interesting&#46; However&#44; the phenotype most associated with comorbidities varies &#40;&#8220;minimally symptomatic&#8221; in the original&#44; &#8220;disturbed sleep&#8221; in the Kim et al&#46;<a class="elsevierStyleCrossRef" href="#bib0130"><span class="elsevierStyleSup">12</span></a> study&#44; none in Keenan et al&#46;&#8217;s Iceland cohort<a class="elsevierStyleCrossRef" href="#bib0135"><span class="elsevierStyleSup">13</span></a>&#41;&#44; prompting us to ask if they are really identifying the same phenotypes&#46;</p><p id="par0055" class="elsevierStylePara elsevierViewall">To date&#44; cluster studies in SAHS reflect biases derived from variables previously established in the analysis&#46; Thus&#44; the generalization of the described phenotypes is limited by methodological differences&#44; and further studies using a multifactorial approach &#40;clinical&#44; physiological&#44; biological&#44; polysomnographic parameters&#44; clinical consequences&#41; and a consensus definition of the variables analyzed will be needed to evaluate the most appropriate grouping techniques&#46; Incorporating large quantities of data &#40;big data&#41; from electronic medical records is a promising strategy for advancing precision medicine in sleep apnea&#46;</p></span>"
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        "nota" => "<p class="elsevierStyleNotepara" id="npar0005">Please cite this article as&#58; Silveira MG&#44; Lloberes P&#46; An&#225;lisis de cl&#250;ster para identificar fenotipos de s&#237;ndrome de apneas-hipopneas del sue&#241;o&#58; &#191;hacia d&#243;nde vamos&#63; Arch Bronconeumol&#46; 2020&#59;56&#58;689&#8211;690&#46;</p>"
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Article information
ISSN: 15792129
Original language: English
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