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class="elsevierStyleTextfn">Original Article</span>" "titulo" => "A Genome-Wide Association Study of Small Cell Lung Cancer" "tienePdf" => "en" "tieneTextoCompleto" => "en" "tieneResumen" => array:2 [ 0 => "en" 1 => "en" ] "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "645" "paginaFinal" => "650" ] ] "contieneResumen" => array:1 [ "en" => true ] "contieneTextoCompleto" => array:1 [ "en" => true ] "contienePdf" => array:1 [ "en" => true ] "resumenGrafico" => array:2 [ "original" => 1 "multimedia" => array:5 [ "identificador" => "fig0010" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => false "mostrarDisplay" => true "figura" => array:1 [ 0 => array:4 [ "imagen" => "fx1.jpeg" "Alto" => 715 "Ancho" => 1333 "Tamanyo" => 121697 ] ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "José Ramón Enjo-Barreiro, Alberto Ruano-Ravina, Silvia Diz-de-Almeida, Raquel Cruz, Inés Quintela, Julia Rey-Brandariz, Ángel Carracedo, Karl Kelsey, Mariano Provencio, Juan Barros-Dios, Leonor 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Article</span>" "titulo" => "Intermittent Hypoxia Mediates Cancer Development and Progression Through HIF-1 and miRNA Regulation" "tienePdf" => "en" "tieneTextoCompleto" => "en" "tieneResumen" => array:2 [ 0 => "en" 1 => "en" ] "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "629" "paginaFinal" => "637" ] ] "contieneResumen" => array:1 [ "en" => true ] "contieneTextoCompleto" => array:1 [ "en" => true ] "contienePdf" => array:1 [ "en" => true ] "resumenGrafico" => array:2 [ "original" => 1 "multimedia" => array:5 [ "identificador" => "fig0040" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => false "mostrarDisplay" => true "figura" => array:1 [ 0 => array:4 [ "imagen" => "fx1.jpeg" "Alto" => 938 "Ancho" => 1333 "Tamanyo" => 101677 ] ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "Giorgia Moriondo, Piera Soccio, Mélanie Minoves, Giulia Scioscia, Pasquale Tondo, Maria Pia Foschino Barbaro, Jean-Louis Pépin, Anne Briançon-Marjollet, Donato Lacedonia" "autores" => 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"https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S0300289623002144?idApp=UINPBA00003Z" "url" => "/03002896/0000005900000010/v2_202312180422/S0300289623002144/v2_202312180422/en/main.assets" ] "en" => array:20 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Original Article</span>" "titulo" => "Polysomnographic Phenotypes of Obstructive Sleep Apnea in a Real-Life Cohort: A Pathophysiological Approach" "tieneTextoCompleto" => true "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "638" "paginaFinal" => "644" ] ] "autores" => array:1 [ 0 => array:4 [ "autoresLista" => "Mercè Gasa, Neus Salord, Eva Fontanilles, Sandra Pérez Ramos, Eliseo Prado, Natalia Pallarés, Salud Santos Pérez, Carmen Monasterio" "autores" => array:8 [ 0 => array:4 [ "nombre" => "Mercè" "apellidos" => "Gasa" "email" => array:1 [ 0 => "mgasa@bellvitgehospital.cat" ] "referencia" => array:4 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] 2 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">c</span>" "identificador" => "aff0015" ] 3 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">*</span>" "identificador" => "cor0005" ] ] ] 1 => array:3 [ "nombre" => "Neus" "apellidos" => "Salord" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] ] ] 2 => array:3 [ "nombre" => "Eva" "apellidos" => "Fontanilles" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] ] ] 3 => array:3 [ "nombre" => "Sandra" "apellidos" => "Pérez Ramos" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] ] ] 4 => array:3 [ "nombre" => "Eliseo" "apellidos" => "Prado" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] ] ] 5 => array:3 [ "nombre" => "Natalia" "apellidos" => "Pallarés" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">d</span>" "identificador" => "aff0020" ] ] ] 6 => array:3 [ "nombre" => "Salud" "apellidos" => "Santos Pérez" "referencia" => array:3 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] 2 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">c</span>" "identificador" => "aff0015" ] ] ] 7 => array:4 [ "nombre" => "Carmen" "apellidos" => "Monasterio" "email" => array:1 [ 0 => "cmonasterio@bellvitgehospital.cat" ] "referencia" => array:3 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] 2 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">*</span>" "identificador" => "cor0005" ] ] ] ] "afiliaciones" => array:4 [ 0 => array:3 [ "entidad" => "Sleep Unit, Respiratory Department, Bellvitge University Hospital, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, Spain" "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:3 [ "entidad" => "Section of Respiratory Medicine, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, Spain" "etiqueta" => "b" "identificador" => "aff0010" ] 2 => array:3 [ "entidad" => "Department of Medicine, Campus Bellvitge, Universitat de Barcelona, L’Hospitalet de Llobregat, Spain" "etiqueta" => "c" "identificador" => "aff0015" ] 3 => array:3 [ "entidad" => "Biostatistics Unit, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, Spain" "etiqueta" => "d" "identificador" => "aff0020" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "Corresponding authors." ] ] ] ] "resumenGrafico" => array:2 [ "original" => 1 "multimedia" => array:5 [ "identificador" => "fig0015" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => false "mostrarDisplay" => true "figura" => array:1 [ 0 => array:4 [ "imagen" => "fx1.jpeg" "Alto" => 743 "Ancho" => 1333 "Tamanyo" => 183560 ] ] ] ] "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">Obstructive sleep apnea (OSA) is a heterogeneous and complex disease with different risk factors, pathophysiological pathways, symptoms, related comorbidities, and prognostic implications; a one-size-fits-all approach may not be worthy for all OSA patients.<a class="elsevierStyleCrossRefs" href="#bib0145"><span class="elsevierStyleSup">1,2</span></a> Nevertheless, OSA diagnosis and severity are still based on a single sleep parameter, the overall apnea–hypoapnea index (AHI) and continuous positive airway pressure (CPAP) remains the first-line therapy for the majority of patients. However, it is known that AHI alone represents a small fraction of the physiological data generated by sleep studies and it may not fully capture the polysomnographic diversity of OSA (hypoxemic burden,<a class="elsevierStyleCrossRefs" href="#bib0155"><span class="elsevierStyleSup">3,4</span></a> sleep fragmentation,<a class="elsevierStyleCrossRef" href="#bib0165"><span class="elsevierStyleSup">5</span></a> periodic limb movements,<a class="elsevierStyleCrossRef" href="#bib0170"><span class="elsevierStyleSup">6</span></a> event duration)<a class="elsevierStyleCrossRef" href="#bib0175"><span class="elsevierStyleSup">7</span></a> and the heterogeneity of the disorder. This simple categorization may explain partially the lack of response to CPAP in some clinical trials.<a class="elsevierStyleCrossRefs" href="#bib0180"><span class="elsevierStyleSup">8,9</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">Phenotyping strategies can be broadly grouped into two analytic approaches: hypothesis-driven (or supervised) and hypothesis generating (or unsupervised).<a class="elsevierStyleCrossRef" href="#bib0190"><span class="elsevierStyleSup">10</span></a> Cluster analysis (CA), a type of unsupervised learning methodology, can integrate multiple characteristics without priori groupings. The main objective of CA is to minimize the differences between two individuals within a same phenotype and maximize the differences between two individuals having distinct phenotypes. The present study plans to explore using CA additional information provided from the routine polysomnography (PSG) besides the AHI to optimize OSA categorization and consequently to move to a more precise and personalized treatment.</p><p id="par0015" class="elsevierStylePara elsevierViewall">Many previous studies indicate that additional sleep parameters may be valuable to better approximate the prognostic implications of OSA. The depth and the duration of hypoxemia<a class="elsevierStyleCrossRef" href="#bib0160"><span class="elsevierStyleSup">4</span></a> or the duration of the respiratory events<a class="elsevierStyleCrossRef" href="#bib0175"><span class="elsevierStyleSup">7</span></a> seems to improve mortality prediction over the AHI alone. Regardless identical AHI, many specific sleep phenotypes (higher hypoxemia or periodic limb movements) are associated with higher cardiovascular risk.<a class="elsevierStyleCrossRef" href="#bib0195"><span class="elsevierStyleSup">11</span></a> Many of those previous works were conducted in large community-based cohorts including mainly middle-aged men. Information on this topic in younger and female cohorts is lacking.</p><p id="par0020" class="elsevierStylePara elsevierViewall">The primary objective of the present study is to explore applying CA, whether exist specific polysomnographic phenotypes based on sleep metrics besides the overall AHI that could help improving OSA categorization from routine polysomnographic recordings in a large real-life cohort of patients referred to our sleep lab due to clinical suspected OSA. Then, to contrast clinical manifestations and comorbidities among these new sleep phenotypes and to compare this novel classification with the classical AHI categorization.</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Methods</span><p id="par0025" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleItalic">Study design and setting</span>: Ambispective cross-sectional study at the Multidisciplinary Sleep Unit of Respiratory Medicine Department of the Hospital Universitari de Bellvitge (L’Hospitalet Llobregat, Barcelona).</p><p id="par0030" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleItalic">Study population</span>: <span class="elsevierStyleUnderline">Inclusion criteria</span>: all patients aged<span class="elsevierStyleHsp" style=""></span>≥<span class="elsevierStyleHsp" style=""></span>18 years referred to the sleep unit due to suspected OSA studied by routine PSG during the period between 2016 and 2020. <span class="elsevierStyleUnderline">Exclusion criteria:</span> time of sleep<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>180<span class="elsevierStyleHsp" style=""></span>min. Total AHI<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>5<span class="elsevierStyleHsp" style=""></span>events/h. Subjects with missing data on any polysomnographic variables were excluded due to CA requirements. <span class="elsevierStyleItalic">Ethical aspects</span>: All participants gave informed written consent to use data derived from routine PSG for research purpose. This document was approved by Ethics committee of our center following Data Protection and Confidentiality Code of Current EU regulation and based on applicable state endorsement (registry number: PR206/23).</p><p id="par0035" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleItalic">Demographical and anthropometric variables</span>: At enrollment (time of PSG), demographic (age, gender) and anthropometric characteristics (body mass index, BMI; neck circumference; waist circumference), cardiovascular risk factors (systemic hypertension, HTA; diabetes; dyslipidemia; coronary heart disease, CHD; atrial fibrillation; congestive heart failure, CHF; stroke or transient ischemic attack; and other medical comorbidities (chronic pulmonary obstructive disease, COPD; depression) were collected. <span class="elsevierStyleItalic">OSA-related symptoms</span>: Each patient fulfilled a questionnaire regarding OSA-related symptoms using a 4-point scale (always, frequently, sometimes, and never); excessive daytime sleepiness was assessed using Epworth Sleepiness Scale<a class="elsevierStyleCrossRef" href="#bib0200"><span class="elsevierStyleSup">12</span></a> (ESS). <span class="elsevierStyleItalic">Sleep variables</span>: Sleep studies were done using routine healthcare polysomnography devices: Siesta (Compumedics, Melbourne, Australia) or Somté-PSG (Compumedics). Sleep<a class="elsevierStyleCrossRef" href="#bib0205"><span class="elsevierStyleSup">13</span></a> and respiratory events<a class="elsevierStyleCrossRef" href="#bib0210"><span class="elsevierStyleSup">14</span></a> were codified manually following AASM Guidelines. The following polysomnographic variables were recorded: total recording time (TRT), period between “switch off light” and “switch on light” in minutes; total sleep time (TST), the sum of all the epochs scored as sleep time in minutes; sleep latency (SL), period between “switch off lights” and the first epoch scored as sleep in minutes; sleep efficiency (SE), TST divided by TRT<span class="elsevierStyleHsp" style=""></span>×<span class="elsevierStyleHsp" style=""></span>100 in percentage. TST percentage for each sleep stage: NREM (non-rapid-eye movement) stage 1, NREM stage 2, NREM stage 3 and REM (rapid-eye movement) stage in percentage; and WASO (wake after sleep onset) calculated by the formula: WASO<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>TRT<span class="elsevierStyleHsp" style=""></span>−<span class="elsevierStyleHsp" style=""></span>SL<span class="elsevierStyleHsp" style=""></span>−<span class="elsevierStyleHsp" style=""></span>TST.</p><p id="par0040" class="elsevierStylePara elsevierViewall">Respiratory variables were: the apnea–hypoapnea index (AHI), the sum of apneas and hypoapnes per hour of sleep as events/hour and derived variables: total AHI as the AHI calculated from the overall TST, supine-AHI calculated from the TST in supine position, non-supine-AHI calculated from the TST in non-supine position, REM-AHI calculated from REM TST and non-REM-AHI calculated from NREM TST. Additionally, apnea, hypoapnea, obstructive apnea, mixed apnea and central apnea indexes were calculated. Median and maximal length of events was calculated for all events, only for apneas, and only for hypoapneas. The degree of nocturnal desaturation was assessed by the mean percentage of sleep time with arterial oxygen saturation measured by pulse-oximetry<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>90% (TC<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>90%). Five recalculated variables were obtained: REM predominance (REM-AHI divided by NREM-AHI), supine predominance (supine-AHI divided by non-supine-AHI), WASO, hypoapnea fraction (number of hypoapneas divided by [number of apneas plus number of hypoapneas]<span class="elsevierStyleHsp" style=""></span>×<span class="elsevierStyleHsp" style=""></span>100) and obstructive apnea fraction [number of obstructive apneas divided by the total number of apneas]<span class="elsevierStyleHsp" style=""></span>×<span class="elsevierStyleHsp" style=""></span>100.</p><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0055">Statistical analysis</span><p id="par0045" class="elsevierStylePara elsevierViewall">Categorical variables were presented as number of cases and percentages, while continuous variables were presented as mean and standard deviation (SD) or median and interquartile range (IQR). Seven sleep variables were suitable for the CA: hypopnea fraction, WASO, maximal length of events duration, obstructive apnea fraction, REM predominance, supine predominance, and TC<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>90%. To define final number of clusters, dissimilarity matrix was calculated with Gower's distance. Then, hierarchical divisive clustering was performed. Hierarchical divisive clustering assumes all individuals are one big cluster and divides most dissimilar ones into separate groups. Using then Elbow and Silhouette to analyze how the within sum of squares changes for the different number of clusters, the final number of clusters chosen was 3. Once the clusters were defined, standardized mean differences were used to compare variables between clusters. Subjects were distributed based on the novel phenotyping stratification and the classical AHI severity categorization (mild OSA [5<span class="elsevierStyleHsp" style=""></span>≤<span class="elsevierStyleHsp" style=""></span>AHI<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>15<span class="elsevierStyleHsp" style=""></span>events/h], moderate OSA [15<span class="elsevierStyleHsp" style=""></span>≤<span class="elsevierStyleHsp" style=""></span>AHI<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>30<span class="elsevierStyleHsp" style=""></span>events/h] or severe OSA [AHI<span class="elsevierStyleHsp" style=""></span>≥<span class="elsevierStyleHsp" style=""></span>30<span class="elsevierStyleHsp" style=""></span>events/h). All analyses were performed with a two-sided significance level of 0.05 and conducted with the use of R software version 4.1.0 (<a href="http://cran.r-project.org/">cran.r-project.org</a>).</p></span></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0060">Results</span><p id="par0050" class="elsevierStylePara elsevierViewall">The study population (<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>981) although predominantly male, the female proportion was wide representative (41%, <span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>400). The cohort had an average age of 56.0 (45.0–66.0) years old, a mean total AHI of 23.3 (12.7–42.2)<span class="elsevierStyleHsp" style=""></span>events/h and a mean BMI of 30.1 (26.9–34.5)<span class="elsevierStyleHsp" style=""></span>kg/m<span class="elsevierStyleSup">2</span>. The main characteristics of the cohort are illustrated in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>.</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><p id="par0055" class="elsevierStylePara elsevierViewall">The dendrogram of the cluster distribution is shown in <a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a> with three distinct polysomnographic phenotypes.</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><p id="par0060" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a> describes and compares the main characteristics among the clusters.</p><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><p id="par0065" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleItalic">Cluster 1: “Supine and obstructive apnea predominance cluster”</span> (44.14% of the cohort). Subjects in this cluster had a moderate median AHI value (26<span class="elsevierStyleHsp" style=""></span>events/h, IQR 17–38) and mild nocturnal hypoxia (median TC<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>90% 1.7, IQR 0.3–4.9). They have a well-structured sleep based on WASO, sleep efficacy and latency and percentage of sleep stages. Main respiratory events are hypopneas (89% of all events) and obstructive apneas (88% of all apneas) with a moderate length duration of events (maximal and median length values are 57 and 24<span class="elsevierStyleHsp" style=""></span>s, respectively). They have a significant supine predominance (median supine-AHI of 40<span class="elsevierStyleHsp" style=""></span>events/h compared with median non-supine-AHI of 13<span class="elsevierStyleHsp" style=""></span>events/h, position predominance value of 2.66) and a moderate REM predominance (median REM-AHI and median NREM-AHI of 31<span class="elsevierStyleHsp" style=""></span>events/h and 24<span class="elsevierStyleHsp" style=""></span>events/h, respectively, REM predominance of 1.22). This cluster is characterized by middle-aged subjects with an overweight or mild obesity, male predominance, higher percentage of witnessed apneas and moderate cardiovascular risk profile.</p><p id="par0070" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleItalic">Cluster 2: “Central, REM and shorter-hypopnea predominance cluster”</span> (38.12% of the cohort). Subjects in this cluster had a mild median AHI value (14<span class="elsevierStyleHsp" style=""></span>events/h, IQR 7–24<span class="elsevierStyleHsp" style=""></span>events/h) with minimal nocturnal hypoxia (median TC<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>90% 0.2, IQR 0.0–2.6). They have also a well-structured sleep based on WASO, sleep efficacy and latency and percentage of sleep stages. Main respiratory events are hypopneas (96% of all events) and from apnea fraction (4%), 87% are central apneas. Regarding length duration of events, this cluster has the shortest maximal and median values (for all respiratory events: 40<span class="elsevierStyleHsp" style=""></span>s and 21<span class="elsevierStyleHsp" style=""></span>s; specifically for apneas, maximal and median length values are 16<span class="elsevierStyleHsp" style=""></span>s and 14<span class="elsevierStyleHsp" style=""></span>s, respectively). Like cluster 1, this cluster has a significant supine predominance (median supine-AHI, median non-supine-AHI and supine predominance value are 20<span class="elsevierStyleHsp" style=""></span>events/h, 7<span class="elsevierStyleHsp" style=""></span>events/h and 2.34, respectively). They also have even a more REM predominance than cluster 1 (median REM-AHI, median NREM-AHI and REM predominance ratio value 15<span class="elsevierStyleHsp" style=""></span>events/h, 12<span class="elsevierStyleHsp" style=""></span>events/h and 1.26, respectively). Younger subjects characterize this cluster with overweight or mild obesity, higher female representation (47%), and more percentage of unrefreshing sleep complaints and less cardiovascular comorbidity.</p><p id="par0075" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleItalic">Cluster 3: “Severe hypoxemic burden and higher WASO cluster”</span> (17.74% of the cohort). Subjects in this cluster had the highest median AHI values (61<span class="elsevierStyleHsp" style=""></span>events/h, IQR 32–79<span class="elsevierStyleHsp" style=""></span>events/h) with severe nocturnal hypoxia (median TC<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>90% 38.6, IQR 21.6–66.2). They have the worse structured sleep based on WASO, sleep efficacy and latency and percentage of sleep stages. Main respiratory events are hypopneas (85% of all events) and from apnea fraction (5%), 93% are obstructive apneas. Regarding length duration of events, there is no significant difference between cluster 1 and 3 (median values: 24<span class="elsevierStyleHsp" style=""></span>s in cluster 1 vs 24<span class="elsevierStyleHsp" style=""></span>s in cluster 3; maximal values: 57<span class="elsevierStyleHsp" style=""></span>s in cluster 1 vs 61<span class="elsevierStyleHsp" style=""></span>s in cluster 3). This cluster has also supine predominance but significant lesser than cluster 1 and 2 (median supine-AHI, median non-supine-AHI and supine predominance value are 71<span class="elsevierStyleHsp" style=""></span>events/h, 29<span class="elsevierStyleHsp" style=""></span>events/h and 1.54, respectively). Unlike the other clusters, the cluster 3 has NREM predominance (median REM-AHI, median NREM-AHI and REM predominance ratio value 46<span class="elsevierStyleHsp" style=""></span>events/h, 59<span class="elsevierStyleHsp" style=""></span>events/h and 0.89, respectively). This cluster is characterized by older age with moderate to severe obesity, male predominance, classical OSA complaints, specially nocturia and the worst comorbidity profile (cardiovascular and non-cardiovascular). Excessive daytime sleepiness based on ESS did not differ between clusters.</p><p id="par0080" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a> summarizes the main sleep complaints, comorbidities, and sleep study data of patients among clusters and their potential implications in OSA management.</p><elsevierMultimedia ident="fig0010"></elsevierMultimedia><p id="par0085" class="elsevierStylePara elsevierViewall">Patient's distribution based on conventional AHI categories and based on the present polysomnographic cluster analysis is shown in <a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>. Subjects classified as severe OSA based on the AHI classification are distributed among the three-polysomnographic clusters (<50% within any cluster): 46% in cluster 1, 17% in cluster 2 and 36% in cluster 3. Many subjects categorized as moderate OSA are distributed in the “supine and obstructive apnea” cluster (57%) but more than one-third (34%) are identified in the “REM central and shorter-hypopnea” cluster and 9% in the “Severe hypoxemic burden and higher WASO” cluster. Most of patients classified as mild OSA are distributed in the “REM central and shorter-hypopnea” cluster (68%) but 28% are included in the “supine and obstructive apnea” Cluster and 4% in the “Severe hypoxemic burden and higher WASO” cluster.</p><elsevierMultimedia ident="tbl0015"></elsevierMultimedia></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0065">Discussion</span><p id="par0090" class="elsevierStylePara elsevierViewall">Using practical available data and unsupervised analytic methods, we identified three specific polysomnographic clusters in a real-life cohort studied by PSG due to suspected OSA. From a simplistic point of view, cluster 3 (“Severe hypoxemic burden and higher WASO cluster”) is closer to the severe OSA category based on the classical AHI classification, cluster 1 (“Supine and obstructive apnea cluster”) is equal to the moderate OSA category and cluster 2 (“Central, REM and shorter-hypopnea cluster”) coincide with the mild OSA category. However, many subjects considered as severe OSA are labelled in cluster 1 and few others in cluster 2. Moreover, not all patients categorized as moderate OSA are identified in cluster 1 and not all patients considered as mild OSA are included in cluster 2. The present work highlights that if we consider only the AHI to determine OSA severity, we may not collect the polysomnographic heterogenicity of the disorder and hence, consequently we could not offer the best treatment for each patient. From clinical practice, the present work pretends to generate hypothesis for future approaches to define distinct PSG patterns that offer more information than the AHI alone as the unique deterministic polysomnographic parameter. In these misclassified patients, OSA severity and hence, OSA management may be under or over-estimated according to conventional AHI classification making less benefit of CPAP therapy because it is not the best treatment for them. Nevertheless, future studies focused on longitudinal assessment as well as on response to therapy are crucial to answer this issue.</p><p id="par0095" class="elsevierStylePara elsevierViewall">There are at least four key traits of phenotypes that contribute to OSA pathogenesis: pharyngeal collapsibility, poor muscle compensation, ventilatory instability (high loop gain), and arousability from sleep (low arousal threshold).<a class="elsevierStyleCrossRef" href="#bib0220"><span class="elsevierStyleSup">16</span></a> Over the past decade and in recent years in particular, major steps towards have been focused on moving from one-size-fits-all management approach to a more precision and customized approach.<a class="elsevierStyleCrossRef" href="#bib0225"><span class="elsevierStyleSup">17</span></a> But the sleep research laboratories capable to phenotype OSA patients are available only in few centers and the procedures are time-consuming and require expertise. Moreover, this physiopathology approach will not be available in clinical routine in the near future. Therefore, there is growing concern to analyze in more detail the information provided by the routine PSG to move closer to a better understanding of polysomnographic patterns/traits that brings indirect data related to distinct OSA pathological pathways not captured by the AHI. This pragmatic approach offers the background for polycentric studies in the clinical setting.</p><p id="par0100" class="elsevierStylePara elsevierViewall">Based on this practical approach, many previous works have been published on this topic. Many of them construct PSG phenotypes of OSA and explore differences between these phenotypes on demographic, clinical manifestations, and comorbidities.<a class="elsevierStyleCrossRefs" href="#bib0230"><span class="elsevierStyleSup">18–20</span></a> Many others construct mixed phenotypes including sleep and clinical variables.<a class="elsevierStyleCrossRefs" href="#bib0245"><span class="elsevierStyleSup">21–24</span></a> Recently few large cohorts associated specific PSG phenotypes with strong outcomes<a class="elsevierStyleCrossRef" href="#bib0215"><span class="elsevierStyleSup">15</span></a> (incident global mortality associated only in “severe hypoxia” Cluster, incident diabetes in “hypopnea and hypoxia” and “PLMS” subgroups, major adverse cardiovascular events in “higher TC 90% OSA” cluster that is reduced by regular CPAP compliance). Joosten et al.<a class="elsevierStyleCrossRef" href="#bib0230"><span class="elsevierStyleSup">18</span></a> studied 1184 OSA subjects (mean AHI 15<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>7<span class="elsevierStyleHsp" style=""></span>events/h, mean age 51<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>13 years, mean BMI 30.3<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>6<span class="elsevierStyleHsp" style=""></span>kg/m<span class="elsevierStyleSup">2</span>) showing that OSA heterogenicity is based on body position and sleep stage but not on classical AHI categorization. However, this study was limited to mild–moderate OSA subjects (AHI between 5 and 30<span class="elsevierStyleHsp" style=""></span>events/h) and other additional PSG variables besides sleep stage and body position were not recorded. Lacedonia et al.<a class="elsevierStyleCrossRef" href="#bib0235"><span class="elsevierStyleSup">19</span></a> studied 198 OSA patients (AHI<span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>5<span class="elsevierStyleHsp" style=""></span>events/h) by polygraphy adding TC<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>90%, oxygen desaturation index, arterial blood gases and lung functional items to perform CA. They did not find differences in the prevalence of cardiovascular comorbidities among clusters. Authors suggested that the small number of subjects and the final available variables were limited reducing the potential statistical power. Like the present work, Nakayama et al.<a class="elsevierStyleCrossRef" href="#bib0240"><span class="elsevierStyleSup">20</span></a> identified three clusters in 210 moderate–severe OSA men with significant variations in polysomnographic variables depending on sleep stage and body position and with clear distinct apnea type among groups. Zinchuck et al.<a class="elsevierStyleCrossRef" href="#bib0195"><span class="elsevierStyleSup">11</span></a> studied 1247 PSG from a U.S. Veteran cohort (mean AHI 25<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>30<span class="elsevierStyleHsp" style=""></span>events/h, mean age 58<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>12 years, mean BMI 34.6<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>7.3<span class="elsevierStyleHsp" style=""></span>kg/m<span class="elsevierStyleSup">2</span>, 95% men) identifying seven distinct physiological phenotypes. Only three clusters (“periodic limb movement of sleep”, “hypopnea and hypoxia” and “combined severe”) were associated with higher risk of adverse cardiovascular outcomes. By contrast, this risk was not captured when subjects were categorized by conventional OSA severity classification. Most of the mentioned works<a class="elsevierStyleCrossRefs" href="#bib0195"><span class="elsevierStyleSup">11,20</span></a> included mainly male cohorts with little female representation missing the gender effect. Many others<a class="elsevierStyleCrossRefs" href="#bib0180"><span class="elsevierStyleSup">8,19</span></a> analyze few additional variables instead of all possible variables available in a routine PSG losing notable information. The present cohort, including subjects with a broad overall AHI spectrum (median 23<span class="elsevierStyleHsp" style=""></span>events/h, IQR 13–42) and with a significant female proportion (<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>400, 41%) still shows heterogenous PSG traits among clusters supporting that OSA complexity certainly is not captured by the AHI alone. From our results we hypothesized that subjects identified in cluster 3 but classified as mild OSA (AHI 5–15<span class="elsevierStyleHsp" style=""></span>events/h) may have higher global burden than presumed and subjects identified in cluster 2 but classified as severe OSA (AHI<span class="elsevierStyleHsp" style=""></span>≥<span class="elsevierStyleHsp" style=""></span>30<span class="elsevierStyleHsp" style=""></span>events/h) may have less burden than assumed.</p><p id="par0105" class="elsevierStylePara elsevierViewall">Respect strengths and limitations: (1) There is no longitudinal assessment with no data on management implications; its observational design attempt to generate new hypothesis using available signals from routine PSG to help identifying PSG patterns that may be closer to specific and plausible OSA pathological pathways. (2) Subjects included are those where PSG was indicated in clinical practice probably due to additional complex comorbidities that interfere in sleep (depression, insomnia, fibromyalgia, etc.). Thus, results could not be extrapolated to subjects where OSA diagnosis could be confirmed by validated simplified sleep tests. (3) Difficulties to assess sleep fragmentation. Sleep efficiency, latency or sleep stages percentages are a limited view of heterogenous sleep physiology. From the available data, we choose a composite variable (WASO<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>TRT<span class="elsevierStyleHsp" style=""></span>−<span class="elsevierStyleHsp" style=""></span>SL<span class="elsevierStyleHsp" style=""></span>−<span class="elsevierStyleHsp" style=""></span>TST) because although it is not able to distinguish between different patterns of sleep fragmentation, it brings more information than any other related variables alone. Furthermore, WASO is easily recorded in any PSG in real-life.</p></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Conclusions</span><p id="par0110" class="elsevierStylePara elsevierViewall">The present cluster analysis identifies three specific polysomnographic phenotypes from a large cohort referred to sleep lab due to OSA suspicious by a routine PSG. The distribution of subjects within the PSG phenotypes does not completely agree with distribution of subjects based on OSA severity categories based on classical AHI classification. This emphasized that using a simplistic AHI approach, the OSA severity is assessed by an incorrect or incomplete analysis of the heterogeneity of the disorder.</p></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Authors’ contributions</span><p id="par0115" class="elsevierStylePara elsevierViewall">All authors contributed substantially to this study. MG participated in study design, acquisition, analysis, and interpretation of data, and in the elaboration of the manuscript. MG and SP had full access to all the data in the study and take responsibility for the integrity and the accuracy of the data analysis. EF, SP and EP participated in acquisition of data. NP participated in study analysis performing cluster analysis. CM, NS and SS participated in study design, interpretation of data and in the revision of the final manuscript. All authors read and approved the final manuscript.</p></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Statement of ethics</span><p id="par0120" class="elsevierStylePara elsevierViewall">All subjects studied in our sleep unit before performing any sleep study gave informed written consent to use their clinical data derived from routine sleep studies for research purpose. This document was approved by the ethics committee of our center following the Data Protection and Confidentiality Code of the current EU regulation and based on the applicable state endorsement.</p></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Data availability statement</span><p id="par0125" class="elsevierStylePara elsevierViewall">All data generated or analyzed during this study are included in this article. Further inquiries can be addressed to the corresponding author.</p></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Funding</span><p id="par0130" class="elsevierStylePara elsevierViewall">This work was supported by the <span class="elsevierStyleGrantSponsor" id="gs1">Sociedad Española de Sueño</span> (Grant for Sleep Research 2020 to MG as principal investigator).</p></span><span id="sec0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Conflict of interest</span><p id="par0135" class="elsevierStylePara elsevierViewall">Any of the authors participating in this manuscript has conflicts of interest to declare.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:16 [ 0 => array:3 [ "identificador" => "xres2049523" "titulo" => "Graphical abstract" "secciones" => array:1 [ 0 => array:1 [ "identificador" => "abst0005" ] ] ] 1 => array:3 [ "identificador" => "xres2049524" "titulo" => "Abstract" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0010" "titulo" => "Introduction" ] 1 => array:2 [ "identificador" => "abst0015" "titulo" => "Methods" ] 2 => array:2 [ "identificador" => "abst0020" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0025" "titulo" => "Conclusions" ] ] ] 2 => array:2 [ "identificador" => "xpalclavsec1750462" "titulo" => "Keywords" ] 3 => array:2 [ "identificador" => "xpalclavsec1750463" "titulo" => "Abbreviations" ] 4 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 5 => array:3 [ "identificador" => "sec0010" "titulo" => "Methods" "secciones" => array:1 [ 0 => array:2 [ "identificador" => "sec0015" "titulo" => "Statistical analysis" ] ] ] 6 => array:2 [ "identificador" => "sec0020" "titulo" => "Results" ] 7 => array:2 [ "identificador" => "sec0025" "titulo" => "Discussion" ] 8 => array:2 [ "identificador" => "sec0030" "titulo" => "Conclusions" ] 9 => array:2 [ "identificador" => "sec0035" "titulo" => "Authors’ contributions" ] 10 => array:2 [ "identificador" => "sec0040" "titulo" => "Statement of ethics" ] 11 => array:2 [ "identificador" => "sec0045" "titulo" => "Data availability statement" ] 12 => array:2 [ "identificador" => "sec0050" "titulo" => "Funding" ] 13 => array:2 [ "identificador" => "sec0055" "titulo" => "Conflict of interest" ] 14 => array:2 [ "identificador" => "xack714044" "titulo" => "Acknowledgments" ] 15 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2023-04-05" "fechaAceptado" => "2023-07-11" "PalabrasClave" => array:1 [ "en" => array:2 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec1750462" "palabras" => array:4 [ 0 => "Cluster analysis" 1 => "Polysomnographic phenotypes" 2 => "Obstructive sleep apnea" 3 => "Apnea–hypopnea index" ] ] 1 => array:4 [ "clase" => "abr" "titulo" => "Abbreviations" "identificador" => "xpalclavsec1750463" "palabras" => array:19 [ 0 => "AHI" 1 => "BMI" 2 => "CA" 3 => "COPD" 4 => "CPAP" 5 => "DISE" 6 => "EES" 7 => "HTA" 8 => "NREM" 9 => "OSA" 10 => "PSG" 11 => "REM" 12 => "SE" 13 => "SL" 14 => "TC<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>90%" 15 => "TIA" 16 => "TRT" 17 => "TST" 18 => "WASO" ] ] ] ] "tieneResumen" => true "resumen" => array:1 [ "en" => array:3 [ "titulo" => "Abstract" "resumen" => "<span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Introduction</span><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">Obstructive sleep apnea (OSA) is heterogeneous and complex, but its severity is still based on the apnea–hypoapnea index (AHI). The present study explores using cluster analysis (CA), the additional information provided from routine polysomnography (PSG) to optimize OSA categorization.</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Methods</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">Cross-sectional study of OSA subjects diagnosed by PSG in a tertiary hospital sleep unit during 2016–2020. PSG, demographical, clinical variables, and comorbidities were recorded. Phenotypes were constructed from PSG variables using CA. Results are shown as median (interquartile range).</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Results</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">981 subjects were studied: 41% females, age 56 years (45–66), overall AHI 23<span class="elsevierStyleHsp" style=""></span>events/h (13–42) and body mass index (BMI) 30<span class="elsevierStyleHsp" style=""></span>kg/m<span class="elsevierStyleSup">2</span> (27–34). Three PSG clusters were identified: <span class="elsevierStyleItalic">Cluster 1: “Supine and obstructive apnea predominance”</span> (433 patients, 44%). <span class="elsevierStyleItalic">Cluster 2: “Central, REM and shorter-hypopnea predominance”</span> (374 patients, 38%). <span class="elsevierStyleItalic">Cluster 3: “Severe hypoxemic burden and higher wake after sleep onset”</span> (174 patients, 18%). Based on classical OSA severity classification, subjects are distributed among the PSG clusters as severe OSA patients (AHI<span class="elsevierStyleHsp" style=""></span>≥<span class="elsevierStyleHsp" style=""></span>30<span class="elsevierStyleHsp" style=""></span>events/h): 46% in cluster 1, 17% in cluster 2 and 36% in cluster 3; moderate OSA (15<span class="elsevierStyleHsp" style=""></span>≤<span class="elsevierStyleHsp" style=""></span>AHI<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>30<span class="elsevierStyleHsp" style=""></span>events/h): 57% in cluster 1, 34% in cluster 2 and 9% in cluster 3; mild OSA (5<span class="elsevierStyleHsp" style=""></span>≤<span class="elsevierStyleHsp" style=""></span>AHI<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>15<span class="elsevierStyleHsp" style=""></span>events/h): 28% in cluster 1, 68% in cluster 2 and 4% in cluster 3.</p></span> <span id="abst0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0030">Conclusions</span><p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">The CA identifies three specific PSG phenotypes that do not completely agree with classical OSA severity classification. This emphasized that using a simplistic AHI approach, the OSA severity is assessed by an incorrect or incomplete analysis of the heterogeneity of the disorder.</p></span>" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0010" "titulo" => "Introduction" ] 1 => array:2 [ "identificador" => "abst0015" "titulo" => "Methods" ] 2 => array:2 [ "identificador" => "abst0020" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0025" "titulo" => "Conclusions" ] ] ] ] "apendice" => array:1 [ 0 => array:1 [ "seccion" => array:1 [ 0 => array:4 [ "apendice" => "<p id="par1190" class="elsevierStylePara elsevierViewall">The following are the supplementary data to this article:<elsevierMultimedia ident="upi1005"></elsevierMultimedia></p>" "etiqueta" => "Appendix" "titulo" => "Supplementary data" "identificador" => "sec1070" ] ] ] ] "multimedia" => array:7 [ 0 => array:7 [ "identificador" => "fig0005" "etiqueta" => "Fig. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 1414 "Ancho" => 2341 "Tamanyo" => 288158 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">Dendrogram of cluster distribution based on polysomnographic variables. <span class="elsevierStyleItalic">Abbreviations</span>: REM, rapid-eye movement; WASO, wake after sleep onset.</p>" ] ] 1 => array:7 [ "identificador" => "fig0010" "etiqueta" => "Fig. 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 2366 "Ancho" => 2925 "Tamanyo" => 883526 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">Main sleep complaints, comorbidities, and sleep study results of patients among clusters and their potential implications in OSA management. <span class="elsevierStyleItalic">Abbreviations</span>: AHI, apnea–hypoapnea index; WASO, wake after sleep onset; REM, rapid-eye movement; TC<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>90%, the mean percentage of sleep time with arterial oxygen saturation measured by pulse-oximetry below 90%; BMI, body mass index; HTA, systemic hypertension; TIA, transient ischemic attack; COPD, chronic obstructive pulmonary disease; CPAP, continuous positive airway pressure; DISE, drug induced sleep endoscopy; CV, cardiovascular.</p>" ] ] 2 => array:8 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at1" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall"><span class="elsevierStyleItalic">Abbreviations</span>: HTA, systemic hypertension; AHI, apnea–hypoapnea index; TC<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>90%, the mean percentage of sleep time with arterial oxygen saturation measured by pulse-oximetry below 90%; REM, rapid-eye movement.</p>" "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Variables \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">N</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>981 \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Age (years) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">56 (45–66) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Gender (female) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">400 (41%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">BMI (kg/m<span class="elsevierStyleSup">2</span>) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">30.1 (26.9–34.5) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Neck circumference (cm) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">40.0 (37.0–42.0) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Waist circumference (cm) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">103 (95–113) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Witnessed apneas \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">477 (52%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Nocturia \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">661 (67%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Episodes of nocturnal asphyxia \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">381 (39%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Morning headache \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">516 (53%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Difficulty concentrating \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">577 (59%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Morning fatigue \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">518 (53%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Sleepiness Epworth scale scoring \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">11.0 (6.0–16.0) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">HTA \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">414 (42%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Diabetes \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">161 (16%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Dyslipidemia \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">341 (35%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Coronary heart disease \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">65 (7%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Atrial fibrillation \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">41 (4%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Congestive heart failure \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">12 (1%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Stroke or transient ischemic attack \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">27 (3%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Chronic pulmonary obstructive disease \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">64 (6%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Depression \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">248 (25%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Total AHI \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">23 (13–42) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">TC<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>90% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2 (0–10) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Supine-AHI \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">36 (15–65) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Non-supine-AHI \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">12 (4–26) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">REM-AHI \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">26 (10–47) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">NREM-AHI \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21 (10–43) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Hypopnea index \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">19 (11–31) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Apnea index \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (0–6) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Obstructive apnea index \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.70 (0.00–3.70) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Central apnea index \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.20 (0.00–0.80) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Mixed apnea index \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.00 (0.00–0.30) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Median length of events (seconds) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">23 (20–26) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Maximal length of events (seconds) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">51 (39–65) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Median length of apneas (seconds) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">18 (13–24 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Maximal length of apneas (seconds) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">26 (16–42) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Median length of hypopneas (seconds) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">23 (20–26) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Maximal length of hypopneas (seconds) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">52 (39–68) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Total recording time (minutes) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">434 (413–458) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Total sleep time (minutes) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">341 (292–381) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Sleep efficiency (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">80 (69–88) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Sleep latency (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">15 (8–29) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Wake after onset sleep (minutes) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">64 (38–106) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N1 stage (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">10 (7–17) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N2 stage (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">43 (36–51) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N3 stage (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">28 (20–36) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">REM stage (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">15 (10–19) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">REM predominance (ratio) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.15 (0.62–2.13) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Positional predominance (ratio) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2.35 (1.24–4.39) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Hypopnea fraction \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">92.0 (97.9–79.6) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Apnea fraction \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">7.97 (2.15–20.4) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Obstructive apnea fraction \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">70.6 (22.2–97.1) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Central apnea fraction \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">15.4 (0.00–62.5) \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3394596.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">Main characteristics of the study cohort.</p>" ] ] 3 => array:8 [ "identificador" => "tbl0010" "etiqueta" => "Table 2" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at2" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:3 [ "leyenda" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall"><span class="elsevierStyleItalic">Abbreviations</span>: ESS, Epworth sleepiness scale; HTA, systemic hypertension; TIA, transient ischemic attack; COPD, chronic obstructive pulmonary disease; AHI, apnea–hypoapnea index; TC<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>90%, the mean percentage of sleep time with arterial oxygen saturation measured by pulse-oximetry below 90%; REM, rapid-eye movement.</p>" "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Variables \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Cluster 1<span class="elsevierStyleItalic">N</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>433 \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Cluster 2<span class="elsevierStyleItalic">N</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>374 \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Cluster 3<span class="elsevierStyleItalic">N</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>174 \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">p</span><a class="elsevierStyleCrossRef" href="#tblfn0005">*</a> \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Age (years) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">57 (48–67) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">50 (42–59) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">64 (56–72)<a class="elsevierStyleCrossRef" href="#tblfn0005">*</a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Gender (female) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">165 (38%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">176 (47%)<a class="elsevierStyleCrossRef" href="#tblfn0005">*</a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">59 (34%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><0.005 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">BMI (kg/m<span class="elsevierStyleSup">2</span>) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">29.8 (26.7–33.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">29.0 (26.3–32.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">33.8 (30.2–38.0)<a class="elsevierStyleCrossRef" href="#tblfn0005">*</a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Neck circumference (cm) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">39.0 (37.0–42.0) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">39.0 (36.0–41.0) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">42.0 (39.2–45.0)<a class="elsevierStyleCrossRef" href="#tblfn0005">*</a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Waist circumference (cm) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">102 (95–111) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">100 (92.0–109) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">113 (105–122)<a class="elsevierStyleCrossRef" href="#tblfn0005">*</a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Witnessed apneas \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">235 (57%)<a class="elsevierStyleCrossRef" href="#tblfn0005">*</a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">164 (46%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">78 (49%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.007 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Nocturia \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">280 (65%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">248 (66%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">133 (76%)<a class="elsevierStyleCrossRef" href="#tblfn0005">*</a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.018 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Episodes of nocturnal asphyxia \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">164 (38%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">160 (43%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">57 (33%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.074 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Morning headache \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">222 (51%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">221 (59%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">73 (42%)<a class="elsevierStyleCrossRef" href="#tblfn0005">*</a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Difficulty concentrating \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">259 (60%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">232 (62%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">86 (50%)<a class="elsevierStyleCrossRef" href="#tblfn0005">*</a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.018 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Morning fatigue \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">241 (56%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">219 (59%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">58 (33%)<a class="elsevierStyleCrossRef" href="#tblfn0005">*</a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">ESS score \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">11 (6–15) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">12 (7–17)<a class="elsevierStyleCrossRef" href="#tblfn0005">*</a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">10 (6–15) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.039 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">HTA \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">192 (44%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">113 (30%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">109 (63%)<a class="elsevierStyleCrossRef" href="#tblfn0005">*</a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Diabetes \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">63 (14%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">47 (13%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">51 (29%)<a class="elsevierStyleCrossRef" href="#tblfn0005">*</a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Dyslipidemia \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">152 (35%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">103 (27%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">86 (49%)<a class="elsevierStyleCrossRef" href="#tblfn0005">*</a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Coronary heart disease \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">26 (6%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">18 (5%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21 (12%)<a class="elsevierStyleCrossRef" href="#tblfn0005">*</a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.005 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Atrial fibrillation \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">16 (4%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">13 (3%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">12 (7%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.141 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Congestive heart failure \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6 (1%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4 (1%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2 (1%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.929 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Stroke or TIA \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">15 (3%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n