<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1405-9940</journal-id>
<journal-title><![CDATA[Archivos de cardiología de México]]></journal-title>
<abbrev-journal-title><![CDATA[Arch. Cardiol. Méx.]]></abbrev-journal-title>
<issn>1405-9940</issn>
<publisher>
<publisher-name><![CDATA[Instituto Nacional de Cardiología Ignacio Chávez]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1405-99402023001000087</article-id>
<article-id pub-id-type="doi">10.24875/acm.22000101</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Determinantes del puntaje PHQ 9 para síntomas depresivos mayores en trabajadores de la salud durante la pandemia de COVID-19 mediante técnicas de machine learning]]></article-title>
<article-title xml:lang="en"><![CDATA[Detection of determinants of PHQ 9 score for major depressive symptoms in health workers during the COVID-19 pandemic using machine learning techniques]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Battioni]]></surname>
<given-names><![CDATA[Luciano]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Scatularo]]></surname>
<given-names><![CDATA[Cristhian E.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Bellia]]></surname>
<given-names><![CDATA[Sebastián]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Lescano]]></surname>
<given-names><![CDATA[Adrián]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pereiro]]></surname>
<given-names><![CDATA[Stella M.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Giorgini]]></surname>
<given-names><![CDATA[Julio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Consejo de Insuficiencia Cardiaca e Hipertensión Pulmonar  ]]></institution>
<addr-line><![CDATA[Buenos Aires ]]></addr-line>
<country>Argentina</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Sociedad Argentina de Cardiología Consejo de Aspectos Psicosociales ]]></institution>
<addr-line><![CDATA[Buenos Aires ]]></addr-line>
<country>Argentina</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2023</year>
</pub-date>
<volume>93</volume>
<fpage>87</fpage>
<lpage>93</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-99402023001000087&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-99402023001000087&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-99402023001000087&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  Antecedentes: La pandemia por SARS-CoV2 ha generado efectos psicológicos y sociales negativos en el personal de salud, según se evidenció en la encuesta IMPPACTS-SAC20.  Objetivo: Determinar qué dominios del Patient Health Questionnaire (PHQ 9) tienen más influencia en el diagnóstico de depresión mayor e identificar subpoblaciones con elevada prevalencia de esta enfermedad.  Método: Subanálisis de la encuesta IMPPACTS-SAC20. Se utilizaron técnicas de machine learning no supervisado para realizar un análisis factorial y generar subgrupos de casos similares en cuanto a su desempeño en el puntaje PHQ 9.  Resultados: Se incluyeron 1221 encuestados que respondieron el PHQ 9. El análisis factorial demostró la presencia de dos dimensiones principales (neurastenia y autopercepción negativa) que explican el 67.2% de la varianza del cuestionario (prueba KMO 0.911; Bartlett p &lt; 0.001). La combinación de ambas dimensiones en un análisis jerárquico generó nueve clusters. Los grupos 5, 4, 2 y 1 explican el 93% de los casos de depresión mayor. Los grupos 5 y 4 presentaron valores más elevados de neurastenia, y los grupos 2 y 1 de autopercepción negativa. Los grupos 6, 7 y 8 en su conjunto presentaron una prevalencia de depresión mayor del 0.6%.  Conclusiones: La implementación de técnicas de machine learning detectó dos dimensiones dentro del puntaje PHQ 9: la neurastenia y la autopercepción negativa. Se evidenciaron subgrupos de alta prevalencia de depresión mayor, cuyas principales características clínicas fueron el sexo femenino, el consumo de alcohol, el tabaquismo y la intención suicida.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  Background: SARS-COV2 pandemic has generated deleterious psychological and social effects as reported in our survey IMPPACTS-SAC.20.  Objective: To determine which domains of the Patient Health Questionnaire (PHQ 9) have the biggest influence in the diagnosis of major depression, also, to identify subpopulations with high prevalence of this disease.  Method: IMPPACTS-SAC.20 survey analysis. Unsupervised machine learning techniques were used to perform a factorial analysis and to create groups of similar cases according to their performance in PHQ 9.  Results: 1221 participants that took the PHQ 9 questionnaire were included. Factorial analysis showed that two main dimensions (neurasthenia and negative self-perception) accounted for 67.2% of the questionnaire variance (KMO test 0.911; Bartlett p &lt; 0.001). The combination of both dimensions in hierarchical analysis generated nine clusters. Groups 5, 4, 2 and 1 explained 93% of the major depression cases. Groups 5 and 4 presented high neurasthenia values, and groups 2 and 1 high negative self-perception. Groups 6, 7 and 8 combined, presented a prevalence of major depression of 0.6%.  Conclusions: The implementation of machine learning techniques detected two dimensions within the PHQ 9 score, neurasthenia and negative self-perception. Subgroups with a high prevalence of major depression were found, whose main clinical characteristics were female sex, alcohol consumption, smoking and suicidal intention.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Depresión]]></kwd>
<kwd lng="es"><![CDATA[Cuestionario de Salud del Paciente]]></kwd>
<kwd lng="es"><![CDATA[Neurastenia]]></kwd>
<kwd lng="es"><![CDATA[Autoimagen]]></kwd>
<kwd lng="es"><![CDATA[Trabajadores de la salud]]></kwd>
<kwd lng="es"><![CDATA[COVID-19]]></kwd>
<kwd lng="en"><![CDATA[Depression]]></kwd>
<kwd lng="en"><![CDATA[Patient Health Questionnaire]]></kwd>
<kwd lng="en"><![CDATA[Neurasthenia]]></kwd>
<kwd lng="en"><![CDATA[Self-concept]]></kwd>
<kwd lng="en"><![CDATA[Healthcare workers]]></kwd>
<kwd lng="en"><![CDATA[COVID-19]]></kwd>
</kwd-group>
</article-meta>
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