<?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>1029-3019</journal-id>
<journal-title><![CDATA[MEDISAN]]></journal-title>
<abbrev-journal-title><![CDATA[MEDISAN]]></abbrev-journal-title>
<issn>1029-3019</issn>
<publisher>
<publisher-name><![CDATA[Centro Provincial de Información de Ciencias Médicas]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1029-30192022000600001</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Modelo predictivo de riesgo para el diagnóstico temprano de la diabetes mellitus de tipo 2]]></article-title>
<article-title xml:lang="en"><![CDATA[Predictive model of risk for the early diagnosis of type II diabetes mellitus]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[González Hernández]]></surname>
<given-names><![CDATA[Maurio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Hospital Militar de Ejército Fermín Valdés Domínguez  ]]></institution>
<addr-line><![CDATA[Holguín ]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<volume>26</volume>
<numero>6</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S1029-30192022000600001&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S1029-30192022000600001&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S1029-30192022000600001&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN  Introducción:  El diagnóstico temprano de la diabetes mellitus de tipo 2 permite al personal de salud implementar estrategias para evitar las complicaciones crónicas que pudieran derivarse. A tales efectos, en las últimas dos décadas se han desarrollado modelos predictivos que incluyen cada día más variables.  Objetivo:  Elaborar un modelo predictivo para el diagnóstico temprano de la diabetes mellitus de tipo 2 en una población holguinera.  Métodos: Se realizó un estudio de cohorte que incluyó a todos los pacientes atendidos en las consultas de endocrinología del área de salud Pedro Díaz Coello y del Hospital Militar Fermín Valdés Domínguez de la provincia de Holguín, para lo cual se tomaron 2 cohortes: una de análisis y otra de validación. Para el procesamiento estadístico se efectuó el análisis univariado y el multivariado; en tanto se determinó la asociación entre variables dependientes e independientes.  Resultados:  En la serie predominaron el sexo femenino, los pacientes sin antecedentes de diabetes mellitus e hipertensión arterial, así como los que presentaban hipotiroidismo, enfermedad periodontal y normopeso, entre otros; asimismo, el modelo resultó significativo estadísticamente (X2=31,1 y p=0,000) y explicó 80,9 % de la variable de salida, validada por las variables de análisis. La sensibilidad fue de 96,9 % y la especificidad de 86,6 %; mientras que el área bajo la curva tuvo un rango de 0,725 a 0,833.  Conclusiones:  El modelo predictivo elaborado es una herramienta muy útil para el diagnóstico de pacientes con riesgo de presentar diabetes mellitus de tipo 2.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT  Introduction:  The early diagnosis of the type II diabetes mellitus allows the health staff to implement strategies in order to avoid the chronic complications that could be derived. To such effects, in the last two decades predictive models have been developed that include more variables every day.  Objective:  To elaborate a predictive model for the early diagnosis of type II diabetes mellitus in a population from Holguín.  Methods:  A cohort study was carried out that included all the patients assisted in the endocrinology services of Pedro Díaz Coello health area and Fermín Valdés Domínguez Military Hospital in Holguín province, for which 2 cohorts were taken: one of analysis and another of validation. For the statistical processing the univaried and multivaried analysis were carried out; as long as the association between dependent and independent variables was determined.  Results: In the series there was a prevalence of the female sex, patients without history of diabetes mellitus and hypertension, as well as those that presented hypothyroidism, periodontal disease and normal weight, among others; also, the pattern was statistically significant (X2=31.1 and p=0.000) and explained 80.9 % of the logout variable validated by the analysis variables. The sensibility was of 96.9 % and the specificity of 86.6 %; while the area under the curve had a range from 0.725 to 0.833.  Conclusions:  The predictive model elaborated is a very useful tool for the diagnosis of patients with risk of type II diabetes mellitus.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[diabetes mellitus de tipo 2]]></kwd>
<kwd lng="es"><![CDATA[diagnóstico precoz]]></kwd>
<kwd lng="es"><![CDATA[modelo predictivo]]></kwd>
<kwd lng="en"><![CDATA[type II diabetes mellitus]]></kwd>
<kwd lng="en"><![CDATA[early diagnosis]]></kwd>
<kwd lng="en"><![CDATA[predictive model]]></kwd>
</kwd-group>
</article-meta>
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<surname><![CDATA[Ugel]]></surname>
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<article-title xml:lang=""><![CDATA[External validation of the Finnish diabetes risk score in Venezuela using a national sample: The EVESCAM]]></article-title>
<source><![CDATA[Prim Care Diabetes]]></source>
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