<?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>1684-1824</journal-id>
<journal-title><![CDATA[Revista Médica Electrónica]]></journal-title>
<abbrev-journal-title><![CDATA[Rev.Med.Electrón.]]></abbrev-journal-title>
<issn>1684-1824</issn>
<publisher>
<publisher-name><![CDATA[Universidad de Ciencias Médicas de Matanzas]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1684-18242021000300601</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Modelo temporal del comportamiento de pacientes críticos con covid-19 durante su estadía en cuidados intensivos. Lombardía, Italia]]></article-title>
<article-title xml:lang="en"><![CDATA[Temporal model of the behavior of critically ill patients with COVID-19 during their staying in intensive care. Lombardy, Italy]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[García Álvarez]]></surname>
<given-names><![CDATA[Pedro Julio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Morejón Ramos]]></surname>
<given-names><![CDATA[Leodan]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Grasso Leyva]]></surname>
<given-names><![CDATA[Fernando]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Ciencias Médicas de La Habana Hospital Militar Dr. Carlos J. Finlay ]]></institution>
<addr-line><![CDATA[ La Habana]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2021</year>
</pub-date>
<volume>43</volume>
<numero>3</numero>
<fpage>601</fpage>
<lpage>615</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S1684-18242021000300601&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S1684-18242021000300601&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S1684-18242021000300601&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN  Introducción: una serie temporal es el producto de la observación de una variable en el tiempo. Es una herramienta matemática que se aplica con frecuencia en la salud. No se han elaborado modelos temporales que predigan el comportamiento de los pacientes durante su ingreso en la Unidad de Cuidados Intensivos.  Objetivos:  crear una serie temporal que permita predecir el comportamiento, durante su ingreso en la Unidad de Cuidados Intensivos, de pacientes graves producto de la covid-19 en la región de Lombardía, Italia.  Materiales y métodos:  analítico, longitudinal prospectivo con un grupo de pacientes críticos que ingresaron del 1 de abril al 1 de mayo de 2020, con diagnóstico de covid-19, en el Hospital Mayor de Crema, en la región de Lombardía, Italia. El universo estuvo constituido por 28 pacientes y se trabajó con el total de ellos.  Resultados:  composición por sexo: 48 % masculino. Media de edad: 83 años. Serie temporal: Modelo 1 que ajusta (Hold) PO2/FiO2 p = 0,251; Modelo 2 (ARIMA) SatO2/FiO2 p = 0,674 (en los dos primeros modelos el resultado se incrementó con los días, siguiendo un comportamiento predecible); Modelo 3 (ARIMA) p = 0,406 (en este caso, el resultado esperado decreció a medida que transcurrió el tiempo). Las funciones obtenidas permiten calcular el valor esperado según el día desde el ingreso.  Conclusiones:  predecir la evolución del paciente en la Unidad de Cuidados Intensivos permitió detectar tempranamente aquellos con una curva inesperada y dirigir hacia a ellos las terapéuticas más agresivas.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT  Introduction:  a time series is the product of the observation of a variable in time. It is a mathematical tool frequently applied in health. No temporal models have been developed to predict patients&#8217; behavior during their staying in the Intensive Care Unit.  Objectives:  to create a time series allowing to predict the behavior of seriously-ill patients due to COVID-19, during their staying in the Intensive Care Unit in the region of Lombardy, Italy.  Materials and methods:  analytic, longitudinal prospective study with a group of critical patients who were admitted from April 1st to May 1st, with COVID-19 diagnosis, to Ospedale Maggiore di Crema, in the Lombardy region, Italy. The universe was formed by 28 patients and all of them were worked on.  Results:  48% of patients were male. Average age: 83 years; Time series: Model 1 holding PO2/FiO2 p = 0.251; Model 2 (ARIMA) SatO2/FiO2 p = 0.674 (in the two first models the result increased with the days, following a predictable behavior=; Model 3 (ARIMA) p = 0.406 (in this case the expected result decreased as time passed). The obtained functions allow to calculate the expected value according to the day from the admission.  Conclusions:  predicting patient's evolution in the Intensive Care Unit allowed early detecting those with unexpected curves and targeting more aggressive therapies toward them.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[índice PO2/FiO2]]></kwd>
<kwd lng="es"><![CDATA[covid-19]]></kwd>
<kwd lng="es"><![CDATA[modelo predictivo]]></kwd>
<kwd lng="en"><![CDATA[PO2/FiO2 index: COVID-19]]></kwd>
<kwd lng="en"><![CDATA[predictive model]]></kwd>
</kwd-group>
</article-meta>
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