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"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" => "José Tomás" "apellidos" => "Gómez Sáenz" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">d</span>" "identificador" => "aff0020" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">e</span>" "identificador" => "aff0025" ] ] ] 2 => array:3 [ "nombre" => "Pilar" "apellidos" => "Escribano Subías" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">f</span>" "identificador" => "aff0030" ] ] ] ] "afiliaciones" => array:6 [ 0 => array:3 [ "entidad" => "Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain" "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:3 [ "entidad" => "Servicio de Cardiología, Instituto de Investigación Biomédica de Málaga (IBIMA), Málaga, Spain" "etiqueta" => "b" "identificador" => "aff0010" ] 2 => array:3 [ "entidad" => "Hospital Universitario Virgen de la Victoria, Universidad de Málaga (UMA), Málaga, Spain" "etiqueta" => "c" "identificador" => "aff0015" ] 3 => array:3 [ "entidad" => "Centro de Salud de Nájera, La Rioja, Spain" "etiqueta" => "d" "identificador" => "aff0020" ] 4 => array:3 [ "entidad" => "Sociedad Española de Médicos de Atención Primaria (SEMERGEN), Madrid, Spain" "etiqueta" => "e" "identificador" => "aff0025" ] 5 => array:3 [ "entidad" => "Hospital Universitario 12 de Octubre, Madrid, Spain" "etiqueta" => "f" "identificador" => "aff0030" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "Corresponding author." ] ] ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "La importancia de los datos en la hipertensión arterial pulmonar: de los registros internacionales al <span class="elsevierStyleItalic">machine learning</span>" ] ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:8 [ "identificador" => "fig0010" "etiqueta" => "Fig. 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 1256 "Ancho" => 2508 "Tamanyo" => 431679 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0020" "detalle" => "Fig. " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "<p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">Definition of artificial intelligence<span class="elsevierStyleItalic">, machine learning</span>, <span class="elsevierStyleItalic">deep learning</span> and neural networks, and the relationships between them.</p> <p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">DL: <span class="elsevierStyleItalic">deep learning</span>; AI: artificial intelligence; ML: machine learning; NR: neural networks.</p>" ] ] ] "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Importance of real-world registries in rare diseases</span><p id="par0005" class="elsevierStylePara elsevierViewall">In Europe, we define a rare disease (RD), as a disease that affects fewer than five people per 10,000 inhabitants.<a class="elsevierStyleCrossRef" href="#bib0005"><span class="elsevierStyleSup">1</span></a> But in reality, only when we look at them individually can they be understood as “rare” or uncommon. With over 6,000 known RDs today,<a class="elsevierStyleCrossRef" href="#bib0010"><span class="elsevierStyleSup">2</span></a> the overall health impact is very significant.<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">More than 80% of RDs affect less than one individual per million population.<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a> This means that, for most of them, even the most experienced doctors with a great deal of patient contact may not see a single affected person in their entire working life. Correct diagnosis of these people may be extremely difficult: according to a 2013 survey, it takes an average of more than five years, eight doctors and two to three misdiagnoses before an RD receives a proper diagnosis,<a class="elsevierStyleCrossRef" href="#bib0020"><span class="elsevierStyleSup">4</span></a> and once achieved, the challenges remain. Due to the relatively small number of affected individuals, commercial incentives to develop medicines are generally poor. In addition, the pathophysiological mechanisms underlying these RDs are often not fully understood. Ultimately, many RDs still lack adequate treatment options. Therefore, improving their diagnosis and treatment is a public health issue of utmost importance.<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a></p><p id="par0015" class="elsevierStylePara elsevierViewall">In this context, real-world registries are essential tools to achieve sufficient sample sizes to study most aspects of RDs, to guide management planning, and to promote the development and evaluation of diagnostic and therapeutic interventions. These registries make a significant contribution to evidence-based personalised medicine, as they can be used for multiple purposes, e.g. to improve case definition, improve etiopathogenic classifications, evaluate indications for treatment decisions, develop and test the utility of risk stratifications, and study the safety, efficacy, feasibility, limitations and benefits of diagnostic and therapeutic strategies under real-world conditions.<a class="elsevierStyleCrossRef" href="#bib0030"><span class="elsevierStyleSup">6</span></a></p><p id="par0020" class="elsevierStylePara elsevierViewall">Pulmonary arterial hypertension (PAH) is a clear example of this. It is an incurable RD, characterised by pulmonary vascular remodelling, progressive increase in pulmonary vascular resistance and eventually right heart failure. Despite considerable advances in the understanding of its pathophysiology and the development of targeted therapies, its prognosis remains poor.<a class="elsevierStyleCrossRef" href="#bib0035"><span class="elsevierStyleSup">7</span></a></p><p id="par0025" class="elsevierStylePara elsevierViewall">Over the last four decades, numerous local, regional, national and international PAH registries have been developed. Their value in understanding the disease from all angles is immeasurable. The main objectives of the analyses of these registries<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">8</span></a> are set out in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>.</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0030">The most relevant pulmonary arterial hypertension registries</span><p id="par0030" class="elsevierStylePara elsevierViewall">A number of registries have been developed in PAH, collecting data on a wide range of clinical and haemodynamic parameters, including prevalence and prognosis (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a> and <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>).<a class="elsevierStyleCrossRefs" href="#bib0045"><span class="elsevierStyleSup">9–11</span></a></p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><p id="par0035" class="elsevierStylePara elsevierViewall">In the early 1980s, the <span class="elsevierStyleItalic">National Institutes of Health</span> (NIH) initiated the first major national registry in the US. The aim was to obtain information on the natural history, treatment and pathogenesis of pulmonary hypertension (PH).<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">9</span></a> The first article extracted from the NIH prospectively included 187 patients with what was known as “primary PH” (now reclassified as idiopathic PAH [IPAH], which probably also included familial PAH and anorexigen-associated PAH) with a follow-up period of up to five years. The main findings were: 1) primary PH had a poor prognosis, with no specific drugs available at the time; and 2) prognosis correlated with haemodynamic severity.<a class="elsevierStyleCrossRef" href="#bib0060"><span class="elsevierStyleSup">12</span></a></p><p id="par0040" class="elsevierStylePara elsevierViewall">Two decades later, the Pulmonary Hypertension Connection Registry was initiated at three university medical centres in Chicago (IL, USA). Their study collected data from subjects diagnosed with PAH between 1982 and 2004 (retrospective cohort) and between 2004 and 2007 (prospective cohort), with the aim of providing contemporaneous survival information on a large number of PAH patients (n<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>576).<a class="elsevierStyleCrossRef" href="#bib0055"><span class="elsevierStyleSup">11</span></a></p><p id="par0045" class="elsevierStylePara elsevierViewall">Meanwhile, the French registry (<span class="elsevierStyleItalic">French Network on PAH</span>) was launched in 2002, as a prospective national registry.<a class="elsevierStyleCrossRef" href="#bib0065"><span class="elsevierStyleSup">13</span></a> Baseline characteristics and one- and three-year survival of 674 individuals were described in 2006. The researchers in this study were the first to introduce the distinction between ‘incident’ patients, who had just been diagnosed, and ‘prevalent’ patients, who had been diagnosed previously and entered the registry after a follow-up visit.</p><p id="par0050" class="elsevierStylePara elsevierViewall">The <span class="elsevierStyleItalic">Registry to Evaluate Early and Long-term PAH disease management</span> (REVEAL)<a class="elsevierStyleCrossRef" href="#bib0070"><span class="elsevierStyleSup">14</span></a> started in 2006 in the USA whose haemodynamic inclusion criteria were broadened compared to the traditional definition of PAH, with pulmonary wedge pressure (PWP) up to 18<span class="elsevierStyleHsp" style=""></span>mmHg. After a period of 18 months, 54 US sites enrolled a total of 2,967 patients.</p><p id="par0055" class="elsevierStylePara elsevierViewall">A UK registry focused on a cohort of people with incident PAH.<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">15</span></a> Between 2001 and 2009, 482 patients with these characteristics were included, which proved to be a major contribution to the knowledge of incident and previously untreated PAH cases.</p><p id="par0060" class="elsevierStylePara elsevierViewall">Very importantly, the Spanish PAH Registry (REHAP) was also born, whose first analysis included 866 subjects over a period of more than 10 years, between 1998 and 2008.<a class="elsevierStyleCrossRef" href="#bib0080"><span class="elsevierStyleSup">16</span></a> Our country registry provided specific data on the association of PAH with toxic oil syndrome (rapeseed oil). Since then, his numerous contributions have been important in the study of the different subgroups of PAH such as: congenital heart disease-related PAH,<a class="elsevierStyleCrossRef" href="#bib0085"><span class="elsevierStyleSup">17</span></a> chronic pulmonary veno-occlusive disease,<a class="elsevierStyleCrossRef" href="#bib0090"><span class="elsevierStyleSup">18</span></a> portopulmonary hypertension<a class="elsevierStyleCrossRef" href="#bib0095"><span class="elsevierStyleSup">19</span></a> and PAH secondary to systemic sclerosis.<a class="elsevierStyleCrossRef" href="#bib0100"><span class="elsevierStyleSup">20</span></a> In addition, the use, efficacy and safety of drugs such as inhaled iloprost<a class="elsevierStyleCrossRef" href="#bib0105"><span class="elsevierStyleSup">21</span></a> and selexipag<a class="elsevierStyleCrossRef" href="#bib0110"><span class="elsevierStyleSup">22</span></a> have been assessed leading to a better understanding of genetics in this field.<a class="elsevierStyleCrossRef" href="#bib0115"><span class="elsevierStyleSup">23</span></a><a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a> provides details of the publications on PAH extracted from REHAP so far.</p><p id="par0065" class="elsevierStylePara elsevierViewall">What was initially the German registry, the Comparative Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA), was started in 2007 and included people from Austria, Belgium, Germany, Italy, the Netherlands, Switzerland and the UK with any type of PH receiving PAH-specific therapy.<a class="elsevierStyleCrossRef" href="#bib0120"><span class="elsevierStyleSup">24</span></a> Similarly, the Swedish PAH Registry (SPAHR) started in 2000, with eight centres covering 90% of all PAH patients in the country.<a class="elsevierStyleCrossRef" href="#bib0050"><span class="elsevierStyleSup">10</span></a> Finally, a single centre registry: <span class="elsevierStyleItalic">Assessing de Spectrum of Pulmonary Hypertension Identified at a Referral Centre</span> (ASPIRE), the only registry to present epidemiological data from all five PH groups.<a class="elsevierStyleCrossRef" href="#bib0125"><span class="elsevierStyleSup">25</span></a></p><p id="par0070" class="elsevierStylePara elsevierViewall">In addition to general PAH registries, registries of marketed products have provided data on the potential short- and long-term unwanted effects of drugs. For example, the <span class="elsevierStyleItalic">post-marketing</span> study of bosentan (TRAX), the first approved oral PAH drug, confirmed that the significant increase in liver enzymes was 10% per year.<a class="elsevierStyleCrossRef" href="#bib0130"><span class="elsevierStyleSup">26</span></a> Registries with ambrisentan (VOLT analysis<a class="elsevierStyleCrossRef" href="#bib0135"><span class="elsevierStyleSup">27</span></a>), riociguat (EXPERT [The EXPosurE Registry RiociguaT in patients with pulmonary hypertension study]<a class="elsevierStyleCrossRef" href="#bib0140"><span class="elsevierStyleSup">28</span></a>), macitentan (OPUS study<a class="elsevierStyleCrossRef" href="#bib0145"><span class="elsevierStyleSup">29</span></a>) and selexipag (SPHERE [Supervised Pulmonary Hypertension Exercise REhabilitation] in the USA and EXPOSURE [observational cohort study of PAH patients newly treated with either Uptravi (selexipag) or any other PAH-specific therapy] in Europe) have also provided valuable indications about the use of these drugs.</p><p id="par0075" class="elsevierStylePara elsevierViewall">Also noteworthy is the prospective IPPHS case-control study, conducted in France, Belgium, the UK and the Netherlands, based on the suspicion that some appetite suppressants played a role in the risk of developing PH.<a class="elsevierStyleCrossRef" href="#bib0150"><span class="elsevierStyleSup">30</span></a> This showed a strong causal link between anorexigenic drugs and PH in 1996, which strangely did not lead the US Food and Drug Administration (FDA) to withhold approval of dexfenfluramine for the treatment of obesity.<a class="elsevierStyleCrossRef" href="#bib0155"><span class="elsevierStyleSup">31</span></a></p></span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0035">Learning from these registries. Their impact on the new 2022 European guidelines</span><p id="par0080" class="elsevierStylePara elsevierViewall">Most of the above-mentioned registries included demographic, haemodynamic and prognostic data. However, some also provide detailed information on incidence and/or prevalence, PH subgroups, symptoms and delay in diagnosis, pulmonary function tests and therapeutic practices.<a class="elsevierStyleCrossRefs" href="#bib0060"><span class="elsevierStyleSup">12–16,24,25</span></a> Only a few included data on the use of anticoagulants.<a class="elsevierStyleCrossRefs" href="#bib0070"><span class="elsevierStyleSup">14,24,32</span></a> The REVEAL study is the only one that also includes patients with a PWP between 16 and 18<span class="elsevierStyleHsp" style=""></span>mmHg. <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a> provides a quick overview of the types of data offered in the publications of each registry.</p><p id="par0085" class="elsevierStylePara elsevierViewall">For example, thanks to them today we know that:<ul class="elsevierStyleList" id="lis0005"><li class="elsevierStyleListItem" id="lsti0005"><span class="elsevierStyleLabel">a)</span><p id="par0090" class="elsevierStylePara elsevierViewall">In general, most patients are diagnosed with IPAH, hereditary PAH (HPAH) or drug-induced PAH, and data on PAH associated with other entities vary widely between studies.<a class="elsevierStyleCrossRefs" href="#bib0065"><span class="elsevierStyleSup">13,14,30,33</span></a> Pulmonary veno-occlusive disease (PVOD) is often misclassified as IPAH and therefore its true prevalence is not known, but in REHAP it was estimated at 0.16 cases per million population.<a class="elsevierStyleCrossRef" href="#bib0080"><span class="elsevierStyleSup">16</span></a></p></li><li class="elsevierStyleListItem" id="lsti0010"><span class="elsevierStyleLabel">b)</span><p id="par0095" class="elsevierStylePara elsevierViewall">The demographics of IPAH are changing, with older ages and more comorbidities.<a class="elsevierStyleCrossRefs" href="#bib0045"><span class="elsevierStyleSup">9,10,24</span></a> The different baseline characteristics of these patients with cardiopulmonary comorbidities, their poorer responses to treatment and the greater difficulty in achieving low-risk status have led the 2022 guidelines to establish a different therapeutic approach, recommending to start treatment with vasodilator monotherapy instead of the generally accepted initial dual vasodilator therapy.<a class="elsevierStyleCrossRef" href="#bib0170"><span class="elsevierStyleSup">34</span></a></p></li><li class="elsevierStyleListItem" id="lsti0015"><span class="elsevierStyleLabel">c)</span><p id="par0100" class="elsevierStylePara elsevierViewall">There is a very significant delay in the assessment of PAH. Thus, the 2022 European guidelines propose a diagnostic algorithm that could start from a first suspicion by the primary care physician in the presence of “unexplained dyspnoea”, with an adequate history taking and physical examination, as well as tests such as electrocardiogram and the NT-proBNP [N-terminal natriuretic brain peptide] laboratory parameter.</p></li><li class="elsevierStyleListItem" id="lsti0020"><span class="elsevierStyleLabel">d)</span><p id="par0105" class="elsevierStylePara elsevierViewall">They also advocate improved screening programmes in populations with PAH-associated diseases, such as scleroderma, human immunodeficiency virus (HIV) and liver cirrhosis, and in BMPR2 gene mutation carriers.<a class="elsevierStyleCrossRef" href="#bib0170"><span class="elsevierStyleSup">34</span></a></p></li><li class="elsevierStyleListItem" id="lsti0025"><span class="elsevierStyleLabel">e)</span><p id="par0110" class="elsevierStylePara elsevierViewall">The proper distinction between PAH and PH related to left heart disorders has direct therapeutic implications but can be a real diagnostic challenge.<a class="elsevierStyleCrossRef" href="#bib0175"><span class="elsevierStyleSup">35</span></a></p></li><li class="elsevierStyleListItem" id="lsti0030"><span class="elsevierStyleLabel">f)</span><p id="par0115" class="elsevierStylePara elsevierViewall">Despite current recommendations, monotherapy is still used in the majority of patients with PAH.<a class="elsevierStyleCrossRefs" href="#bib0180"><span class="elsevierStyleSup">36,37</span></a></p></li><li class="elsevierStyleListItem" id="lsti0035"><span class="elsevierStyleLabel">g)</span><p id="par0120" class="elsevierStylePara elsevierViewall">The data on oral anticoagulation are contradictory.<a class="elsevierStyleCrossRef" href="#bib0190"><span class="elsevierStyleSup">38</span></a> The 2022 European guidelines leave the prescribing decision to the clinician.<a class="elsevierStyleCrossRef" href="#bib0170"><span class="elsevierStyleSup">34</span></a></p></li><li class="elsevierStyleListItem" id="lsti0040"><span class="elsevierStyleLabel">h)</span><p id="par0125" class="elsevierStylePara elsevierViewall">Today, in an era of multiple treatments for PAH, survival has improved from the outcomes of early registries. In the 1984 NIH study, survival at one, three and five years was 68, 48 and 34%, respectively.<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">9</span></a> In more recent studies, survivals of 89–96% at one year and 73–77% at three years are reported.<a class="elsevierStyleCrossRef" href="#bib0190"><span class="elsevierStyleSup">38</span></a> However, the most recent COMPERA registry analysis shows an unexpected stagnation of this survival over the last decade. The authors attribute this phenomenon to incomplete use of initial combination therapy and the fact that most of the drugs currently in use were already in use in 2010. Also, these new therapies have shown improvements in event-free survival, but not in mortality.<a class="elsevierStyleCrossRef" href="#bib0180"><span class="elsevierStyleSup">36</span></a></p></li><li class="elsevierStyleListItem" id="lsti0045"><span class="elsevierStyleLabel">i)</span><p id="par0130" class="elsevierStylePara elsevierViewall">The risk scales developed from these registries are extremely useful. In fact, guidelines recommend their regular use with a view to reassess the patient and make therapeutic decisions based on them.<a class="elsevierStyleCrossRef" href="#bib0170"><span class="elsevierStyleSup">34</span></a></p></li></ul></p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040"><span class="elsevierStyleItalic">Big data</span> in rare diseases and pulmonary arterial hypertension: a step forward in the understanding and management of a multifaceted disease</span><p id="par0135" class="elsevierStylePara elsevierViewall">Beyond real-world registries, there are already major advances in information technology today that will soon lead to improvements in medical practice in general, and in the understanding and management of RDs in particular. From the limited number of variables selected <span class="elsevierStyleItalic">a priori</span> in classical registries, we have moved on to having huge amounts of data from a multitude of sources, usually known as <span class="elsevierStyleItalic">Big Data</span>, which could be used for scientific knowledge, but which are difficult to manage by humans. In this context, the concepts of artificial intelligence (AI) and <span class="elsevierStyleItalic">machine learning</span> (ML), have gained relevance in multiple disciplines in recent years, and RDs are no exception. <a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a> summarises these terms and the relationships between them.<a class="elsevierStyleCrossRef" href="#bib0195"><span class="elsevierStyleSup">39</span></a></p><elsevierMultimedia ident="fig0010"></elsevierMultimedia><p id="par0140" class="elsevierStylePara elsevierViewall">A 2019 review article in the <span class="elsevierStyleItalic">New England Journal of Medicine</span> by Rajkomar et al. suggested that ML could influence routine medical practice in key aspects such as prognosis, diagnosis, treatment, daily workflow and accessibility.<a class="elsevierStyleCrossRef" href="#bib0200"><span class="elsevierStyleSup">40</span></a> While more primitive AI methods have achieved “sub-human” results, newer algorithms can match (or even surpass) human capability in certain specific applications.<a class="elsevierStyleCrossRef" href="#bib0205"><span class="elsevierStyleSup">41</span></a></p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">The use of <span class="elsevierStyleItalic">machine learning</span> in rare diseases and pulmonary arterial hypertension</span><p id="par0145" class="elsevierStylePara elsevierViewall">In a 2020 systematic review of ML-based studies applied to RDs, Schaefer et al. identified a limited number of papers investigating a relatively small number of diseases compared to the huge number of known RDs.<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a> They found that a few relatively “common” (with prevalences of 1–5/10,000 population) or at least “better known” RDs, such as amyotrophic lateral sclerosis or cystic fibrosis, were the most analysed. The most commonly used algorithms were “ensemble learning”, “support vector machines” and “neural networks” methods, with data from medical images being the most commonly extracted. Limitations in applying ML with other types of data, such as unstructured text data from medical records, may be even greater than in other diseases because the terms are often not as standardised and therefore more difficult to process. On the other hand, most papers focused on the diagnosis and prognosis of RDs. But the main limitation in RDs is again the relatively small number of patients included in the studies compared to ML research in other more prevalent diseases. This is no small matter, given that the goodness of algorithms depends largely on the amount of data available for ‘training’, and highlights the need to promote inter-level (from primary care medicine to medical management to hospital specialties), inter-institutional and international collaboration to create sufficiently large datasets for ML research in RDs. Another challenge to take into account is the need to preserve privacy under current legislation.<a class="elsevierStyleCrossRefs" href="#bib0025"><span class="elsevierStyleSup">5,42</span></a> In addition, only a small proportion of studies have externally validated their algorithms or compared them with the performance of human experts, which would also be essential if they were to be used in daily practice.<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a></p><p id="par0150" class="elsevierStylePara elsevierViewall">In the specific case of PAH, there are already a good number of studies in which various ML models have resulted in algorithms that could lead to improvements in diagnosis and prognosis.</p><p id="par0155" class="elsevierStylePara elsevierViewall">Of these, most were conducted in the field of imaging. For example, Diller et al. developed an algorithm that, by automatically studying echocardiograms, achieved a sensitivity of almost 100% for detecting PAH, with several of the echocardiographic parameters predictive of mortality, and the measures being similar to those found manually.<a class="elsevierStyleCrossRef" href="#bib0215"><span class="elsevierStyleSup">43</span></a> Alabed et al. found that automatic measurements of cardiac magnetic resonance imaging (cardiac MRI), taken by an ML-based model, correlated better with right catheterisation parameters than manually obtained measurements. Right ventricular (RV) end-systolic volume, ejection fraction (EF) and mass predicted mortality in those with PAH (<span class="elsevierStyleItalic">hazard ratio</span> [HR], 1.40, 0.76, and 1.15, respectively p<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.001; n<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>920).<a class="elsevierStyleCrossRef" href="#bib0220"><span class="elsevierStyleSup">44</span></a> Even chest radiography research has resulted in models able to discriminate between subjects with normal and elevated pulmonary pressures.<a class="elsevierStyleCrossRefs" href="#bib0225"><span class="elsevierStyleSup">45,46</span></a></p><p id="par0160" class="elsevierStylePara elsevierViewall">The investigation of electrocardiograms and the strong ability of ML algorithms to detect subtle abnormalities suggestive of PH has also led to several studies. Thus, Liu et al. obtained an algorithm whose area under the curve (AUC) for detecting elevated pulmonary pressures was 0.88 (sensitivity 81.0%; specificity 79.6%). These patients with elevated pressures also had a worse prognosis at six years.<a class="elsevierStyleCrossRef" href="#bib0235"><span class="elsevierStyleSup">47</span></a></p><p id="par0165" class="elsevierStylePara elsevierViewall">Another very interesting application is the ability to detect, at a population level, those whose medical records show data (epidemiological, clinical, electrocardiographic, number of visits to the doctor, therapeutic, etc.) compatible with a diagnosis of PAH. Kiely et al published the first paper to do so and found that to diagnose 100 cases of PAH would require clinical and echocardiographic screening of 969 people initially identified as high risk by their algorithm.<a class="elsevierStyleCrossRef" href="#bib0240"><span class="elsevierStyleSup">48</span></a> In the same vein, Schuler et al. produced an algorithm with a sensitivity and specificity for the diagnosis of PAH of 0.88 and 0.93, respectively. Notably, during the internal validation process of this study, 265 cases of previously undiagnosed and unstudied PAH were identified, which should have been at least initially screened.<a class="elsevierStyleCrossRef" href="#bib0245"><span class="elsevierStyleSup">49</span></a></p><p id="par0170" class="elsevierStylePara elsevierViewall">Finally, ML techniques are highly attractive for research due to the large amount of data that can be generated by omics sciences in the field of PAH, suggesting new pathophysiological mechanisms and advancing precision medicine.<a class="elsevierStyleCrossRef" href="#bib0250"><span class="elsevierStyleSup">50</span></a> Of particular interest is the study by Sweatt et al. whose ML algorithms identified different cytokine patterns that, together with clinical features, defined four PAH subgroups with very different prognostic risks (e.g. in two “moderate risk” patients using conventional scales, the cytokine patterns successfully reclassified them as “low” and “high risk”).<a class="elsevierStyleCrossRef" href="#bib0255"><span class="elsevierStyleSup">51</span></a> Along the same lines, the algorithm of Kheyfets et al., based on variables obtained from clinical registries and a large proteomics panel, predicted the four-year risk of death or transplantation in subjects with PAH with a sensitivity and specificity of 71–100% and 81–89%, respectively.<a class="elsevierStyleCrossRef" href="#bib0260"><span class="elsevierStyleSup">52</span></a></p><p id="par0175" class="elsevierStylePara elsevierViewall">Although many of the studies mentioned above are not externally validated and therefore their applicability to daily practice is still difficult, these findings allow us to imagine a future where ML-derived algorithms will help us to diagnose PAH patients earlier, almost automatically (even in pre-clinical stages, as depicted in <a class="elsevierStyleCrossRef" href="#fig0015">Fig. 3</a>), determine their prognostic risk more accurately and choose treatment guidelines in a more personalised way.</p><elsevierMultimedia ident="fig0015"></elsevierMultimedia></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Conclusions</span><p id="par0180" class="elsevierStylePara elsevierViewall">Real-world registries have been instrumental in advancing our understanding of many RDs, including PAH. In this way, a large number of registries over the last four decades have made possible, among other things, to improve the definitions of the disease and its subgroups, to better understand its pathophysiology, to develop prognostic scores and to assess the transferability of the results of drug clinical trials to daily practice. However, at a time in history when vast amounts of data can be available from multiple sources, these registries do not adequately take them into account. For this reason, ML techniques offer a unique opportunity to improve the early diagnosis of these patients and to advance personalised medicine.</p></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0055">Funding</span><p id="par0185" class="elsevierStylePara elsevierViewall">This paper is based on the chapter created by the same authors for the PRIMORDIAL HAP (Essential PAH) course, developed and funded by the <span class="elsevierStyleGrantSponsor" id="gs0005">Spanish Society of Primary Care Physicians</span> (SEMERGEN), with the collaboration of CLOVER. Becerra-Muñoz received fees for this purpose.</p></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0060">Conflict of interest</span><p id="par0190" class="elsevierStylePara elsevierViewall">Becerra-Muñoz declares having received fees for presentations, educational events or consultancy from Janssen, MSD, Ferrer and Novartis. Escribano Subías declares having received fees for presentations, educational events or consultancy from Janssen, MSD, Ferrer and AOT. Gómez declares that he has no conflict of interest in relation to this article.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:13 [ 0 => array:3 [ "identificador" => "xres2168619" "titulo" => "Abstract" "secciones" => array:1 [ 0 => array:1 [ "identificador" => "abst0005" ] ] ] 1 => array:2 [ "identificador" => "xpalclavsec1839095" "titulo" => "Keywords" ] 2 => array:3 [ "identificador" => "xres2168618" "titulo" => "Resumen" "secciones" => array:1 [ 0 => array:1 [ "identificador" => "abst0010" ] ] ] 3 => array:2 [ "identificador" => "xpalclavsec1839094" "titulo" => "Palabras clave" ] 4 => array:2 [ "identificador" => "sec0005" "titulo" => "Importance of real-world registries in rare diseases" ] 5 => array:2 [ "identificador" => "sec0010" "titulo" => "The most relevant pulmonary arterial hypertension registries" ] 6 => array:2 [ "identificador" => "sec0015" "titulo" => "Learning from these registries. Their impact on the new 2022 European guidelines" ] 7 => array:2 [ "identificador" => "sec0020" "titulo" => "Big data in rare diseases and pulmonary arterial hypertension: a step forward in the understanding and management of a multifaceted disease" ] 8 => array:2 [ "identificador" => "sec0025" "titulo" => "The use of machine learning in rare diseases and pulmonary arterial hypertension" ] 9 => array:2 [ "identificador" => "sec0030" "titulo" => "Conclusions" ] 10 => array:2 [ "identificador" => "sec0035" "titulo" => "Funding" ] 11 => array:2 [ "identificador" => "sec0040" "titulo" => "Conflict of interest" ] 12 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2023-09-20" "fechaAceptado" => "2023-12-05" "PalabrasClave" => array:2 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec1839095" "palabras" => array:7 [ 0 => "Pulmonary Arterial Hypertension" 1 => "Pulmonary hypertension" 2 => "Rare diseases" 3 => "Registries" 4 => "Artificial intelligence" 5 => "Machine learning" 6 => "Big data" ] ] ] "es" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec1839094" "palabras" => array:7 [ 0 => "Hipertensión arterial pulmonar" 1 => "Hipertensión pulmonar" 2 => "Enfermedades raras" 3 => "Registros" 4 => "Inteligencia artificial" 5 => "<span class="elsevierStyleItalic">Machine learning</span>" 6 => "Big data" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "en" => array:2 [ "titulo" => "Abstract" "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><p id="spar0120" class="elsevierStyleSimplePara elsevierViewall">Real-world registries have been critical to building the scientific knowledge of rare diseases, including Pulmonary Arterial Hypertension (PAH). In the past 4 decades, a considerable number of registries on this condition have allowed to improve the pathology and its subgroups’ definition, to advance in the understanding of its pathophysiology, to elaborate prognostic scales and to check the transferability of the results from clinical trials to clinical practice. However, in a moment where a huge amount of data from multiple sources is available, they are not always taken into account by the registries. For that reason, Machine Learning (ML) offer a unique opportunity to manage all these data and, finally, to obtain tools that may help to get an earlier diagnose, to help to deduce the prognosis and, in the end, to advance in Personalized Medicine. Thus, we present a narrative revision with the aims of, in one hand, summing up the aspects in which data extraction is important in rare diseases -focusing on the knowledge gained from PAH real-world registries- and, on the other hand, describing some of the achievements and the potential use of the ML techniques on PAH.</p></span>" ] "es" => array:2 [ "titulo" => "Resumen" "resumen" => "<span id="abst0010" class="elsevierStyleSection elsevierViewall"><p id="spar0125" class="elsevierStyleSimplePara elsevierViewall">Los registros de vida real han resultado fundamentales para el avance en el conocimiento de numerosas Enfermedades Raras, entre las que se encuentra la Hipertensión Arterial Pulmonar (HAP). Así, en las últimas 4 décadas, un buen número de registros en este trastorno han permitido, entre otros, mejorar las definiciones de la enfermedad y sus subgrupos, conocer mejor su fisiopatología, elaborar escalas pronósticas y comprobar la transferibilidad de los resultados de los ensayos clínicos a la práctica diaria. Sin embargo, en un momento histórico en que pueden encontrarse disponibles cantidades ingentes de datos provenientes de múltiples fuentes, estos pueden no ser adecuadamente tenidos en cuenta en estos registros. Por ello, las técnicas de <span class="elsevierStyleItalic">Machine Learning</span> ofrecen una oportunidad única para obtener herramientas que mejoren el diagnóstico precoz de estos pacientes, ayuden a deducir su pronóstico, y permitan en definitiva avanzar en la Medicina Personalizada. Así, presentamos una revisión narrativa cuyos objetivos son, por un lado, resumir los aspectos para los que es importante la adquisición de datos en las enfermedades raras -poniendo el foco en el conocimiento extraído de los registros de vida real de HAP- y, por otro, describir los logros ya alcanzados y el potencial uso de la explotación de los datos mediante técnicas de inteligencia artificial en esta enfermedad.</p></span>" ] ] "multimedia" => array:5 [ 0 => array:8 [ "identificador" => "fig0005" "etiqueta" => "Fig. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 1705 "Ancho" => 3466 "Tamanyo" => 795850 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0015" "detalle" => "Fig. " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "<p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">List of pulmonary arterial hypertension registries.</p> <p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">In purple, the international real-world population registries. In orange, post-marketing drug registries. In green, the Spanish Pulmonary Arterial Hypertension Registry (REHAP) and its contributions to the medical literature.</p> <p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">ASPIRE PH: Assessing the Spectrum of Pulmonary Hypertension Identified at a Referral Centre; COMPERA: Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension; HIV: human immunodeficiency virus; IPPHS: The International Primary Pulmonary Hypertension Study; NIH: National Institutes of Health; OPUS: OPsumit® USers Registry; PAH: pulmonary arterial hypertension; PHC PAH: Pulmonary Hypertension Connection and Pulmonary Arterial Hypertension; PPH: Primary Pulmonary Hypertension; PVOD: pulmonary veno-occlusive disease; REHAP: Spanish Registry of Pulmonary Arterial Hypertension; REVEAL PAH: Registry to Evaluate Early and Long-Term Pulmonary Arterial Hypertension; SPAHR: Swedish Pulmonary Arterial Hypertension Registry; TRAX: Tracleer Excellence Post-Marketing Surveillance; UK IPAH: idiopathic pulmonary arterial hypertension in the UK; VOLT: The Volibris Tracking Study.</p>" ] ] 1 => array:8 [ "identificador" => "fig0010" "etiqueta" => "Fig. 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 1256 "Ancho" => 2508 "Tamanyo" => 431679 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0020" "detalle" => "Fig. " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "<p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">Definition of artificial intelligence<span class="elsevierStyleItalic">, machine learning</span>, <span class="elsevierStyleItalic">deep learning</span> and neural networks, and the relationships between them.</p> <p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">DL: <span class="elsevierStyleItalic">deep learning</span>; AI: artificial intelligence; ML: machine learning; NR: neural networks.</p>" ] ] 2 => array:8 [ "identificador" => "fig0015" "etiqueta" => "Fig. 3" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr3.jpeg" "Alto" => 2600 "Ancho" => 2917 "Tamanyo" => 455116 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0025" "detalle" => "Fig. " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "<p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">Typical life course of patients with pulmonary arterial hypertension, goals of machine learning-based algorithms published to date (a), and their potential impact on early diagnosis (b and c).</p> <p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">Explanation:</p> <p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">a) Patients with PAH often do not consult their GP until they experience non-specific symptoms such as dyspnoea. They usually undergo numerous additional tests and are referred to different specialists until they are referred to a specialist PAH unit, where they are adequately treated. This process can take years, during which time they may return to their doctor several times, visit the emergency department, and even be admitted to hospital. The areas where ML techniques can be applied are: 1) in the future, genetic testing may be available from birth. Algorithms will find that a confluence of several factors present from birth can predict the development of PAH, and these patients will be identified as “at risk”.</p> <p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">2) The use of <span class="elsevierStyleItalic">wearables</span>, devices that measure parameters such as heart rate, sleep architecture or oxygen saturation, is becoming increasingly common. Their abnormalities may appear earlier than symptoms. This may alert the patient to seek advice earlier.</p> <p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">3) Population-based study of medical records by ML will identify combinations of data that predict the occurrence of PAH, and these algorithms can be used to identify these patients.</p> <p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">4) As this is a RD, typical findings in ancillary tests requested by the primary care physician may go unnoticed. This can be avoided by ML-based algorithms (electrocardiographic, chest X-ray patterns).</p> <p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">5) Similarly, it may occur in tests requested by specialists (echocardiogram, CT scan, cardiac MRI).</p> <p id="spar0065" class="elsevierStyleSimplePara elsevierViewall">6) Repeated primary care and emergency department visits is one of the data used by the algorithms based on the medical records.</p> <p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">ML algorithms will create new prognostic scores and improve the choice of treatments for these patients, once they are under study in specialised PAH units.</p> <p id="spar0075" class="elsevierStyleSimplePara elsevierViewall">b) The identification of these patients at earlier stages will favour their direct referral to the Specialised Units, without consulting other specialists and reducing referral times.</p> <p id="spar0080" class="elsevierStyleSimplePara elsevierViewall">c) They could even be identified and referred to specialised units in the pre-clinical (low-risk) stages, either through alarms from wearable devices, screening techniques based on medical records, genetic findings or complementary tests such as ECG or chest X-ray (e.g. for sports competitions or pre-anaesthesia studies).</p> <p id="spar0085" class="elsevierStyleSimplePara elsevierViewall">BT: blood test; cardiac MRI: cardiac magnetic resonance imaging; ECG: electrocardiogram; RD: rare diseases; TTE: transthoracic echocardiography; PAH: pulmonary arterial hypertension; ML: <span class="elsevierStyleItalic">machine learning</span>; MRI: magnetic resonance imaging; CXR: chest X-ray.</p>" ] ] 3 => array:8 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0030" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "<p id="spar0095" class="elsevierStyleSimplePara elsevierViewall">Adapted from McGoon et al.<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">8</span></a></p><p id="spar0100" class="elsevierStyleSimplePara elsevierViewall">RCTs: randomised clinical trials; PAH: Pulmonary arterial hypertension.</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=""><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">Improve the pathophysiological understanding of PAH \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">Provide insight into the prevalence and prognosis of PAH \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">Identify risk factors for the development of PAH. \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">Outline an aetiological classification and characterise the different subtypes of PAH. \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">Generate and validate hypotheses \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">Develop well-defined strategies at diagnosis, at follow-up and together with the results of RCTs in treatment, which are at the core of the most current clinical practice guidelines. \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">Measure quality of care and adherence to clinical practice guidelines \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">Document real-world treatment and thus the transferability of findings from RCTs to daily practice \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3572239.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0090" class="elsevierStyleSimplePara elsevierViewall">Main objectives of registry analysis in pulmonary arterial hypertension.</p>" ] ] 4 => array:8 [ "identificador" => "tbl0010" "etiqueta" => "Table 2" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0035" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "<p id="spar0110" class="elsevierStyleSimplePara elsevierViewall">Adapted from Swinnen et al.<a class="elsevierStyleCrossRef" href="#bib0190"><span class="elsevierStyleSup">38</span></a></p><p id="spar0115" class="elsevierStyleSimplePara elsevierViewall">ASPIRE: Assessing the Spectrum of Pulmonary Hypertension Identified at a Referral Centre; COMPERA: Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension; NIH: National Institutes of Health; PH: pulmonary hypertension; PHC: Pulmonary Hypertension Connection; REHAP: Spanish Registry of Pulmonary Arterial Hypertension; REVEAL: Registry to Evaluate Early and Long-Term Pulmonary Arterial Hypertension; RFTs: respiratory function tests.</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"> \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">NIH1987 \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">French2006 \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">Swedish2007 \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">PHC2007 \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">Scottish2007 \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">REVEAL2010 \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">ASPIRE2012 \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">United Kingdom2012 \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">REHAP2012 \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">COMPERA2012 \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">Incidence/prevalence \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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">(Sub)Groups PH \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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">Demographics \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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">Symptoms \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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">Diagnostic delay \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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">RFTs \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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">Haemodynamic parameters \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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">Therapeutic practices \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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">Prognosis \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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">Oral anticoagulation \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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">Risk stratification \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">- \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3572238.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0105" class="elsevierStyleSimplePara elsevierViewall">Characteristics included in the studies conducted on each real-world registry.</p>" ] ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0005" "bibliografiaReferencia" => array:52 [ 0 => array:3 [ "identificador" => "bib0005" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:1 [ "referenciaCompleta" => "Commission E. 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The importance of data in Pulmonary Arterial Hypertension: From international registries to Machine Learning
La importancia de los datos en la hipertensión arterial pulmonar: de los registros internacionales al machine learning
Víctor Manuel Becerra-Muñoza,b,c,
, José Tomás Gómez Sáenzd,e, Pilar Escribano Subíasa,f
Corresponding author
a Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain
b Servicio de Cardiología, Instituto de Investigación Biomédica de Málaga (IBIMA), Málaga, Spain
c Hospital Universitario Virgen de la Victoria, Universidad de Málaga (UMA), Málaga, Spain
d Centro de Salud de Nájera, La Rioja, Spain
e Sociedad Española de Médicos de Atención Primaria (SEMERGEN), Madrid, Spain
f Hospital Universitario 12 de Octubre, Madrid, Spain