Editorial
Advanced data analysis and intensive care medicine
Analisis avanzado de datos y medicina intensiva
a Servicio de Medicina Intensiva, Hospital Universitario del Henares, Coslada, Madrid, Spain
b Grupo de Investigación en Patología Crítica, Grado de Medicina, Universidad Francisco de Vitoria, Pozuelo de Alarcón, Madrid, Spain
c Escuela Politécnica Superior, Universidad Francisco de Vitoria, Pozuelo de Alarcón, Madrid, Spain
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The predictors appear in different branches attending to their significance in the predictive model. Values in bold letters represent the registries per branch. Values in red bold letters represent the percentage of registries with positive outcome. The variable named as “DAYS_SIMPTONS_ADMISION” is related with the number of days from first symptoms to ICU admission. The variable “linf_total”, is related to lymphocyte count per mm3. The variable named as “dosis_equiv_mpred_5d” is related with the corticosteroid dose, during the first five days of admission (mg of equivalent methylprednisolone dose). The variable named as “bbTot” is related with the total levels of bilirubin in blood. The variable names as “ldh” is related to the lactate dehydrogenase serum level. The variable DAYS_UNTIL_O2 is related to the number of days until the patient requires O2.</p>" ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "Sergio Muñoz Lezcano, Miguel Ángel Armengol de la Hoz, Alberto Corbi, Fernando López, Miguel Sánchez García, Antonio Nuñez Reiz, Tomás Fariña González, Viktor Yordanov Zlatkov" "autores" => array:8 [ 0 => array:2 [ "nombre" => "Sergio" "apellidos" => "Muñoz Lezcano" ] 1 => array:2 [ "nombre" => "Miguel Ángel" "apellidos" => "Armengol de la Hoz" ] 2 => array:2 [ "nombre" => "Alberto" "apellidos" => "Corbi" ] 3 => array:2 [ "nombre" => "Fernando" "apellidos" => "López" ] 4 => array:2 [ "nombre" => "Miguel Sánchez" "apellidos" => "García" ] 5 => array:2 [ "nombre" => "Antonio Nuñez" "apellidos" => "Reiz" ] 6 => array:2 [ "nombre" => "Tomás Fariña" "apellidos" => "González" ] 7 => array:2 [ "nombre" => "Viktor Yordanov" "apellidos" => "Zlatkov" ] ] ] ] ] "idiomaDefecto" => "en" "Traduccion" => array:1 [ "en" => array:9 [ "pii" => "S0210569123002085" "doi" => "10.1016/j.medin.2023.06.012" "estado" => "S300" "subdocumento" => "" "abierto" => array:3 [ "ES" => true "ES2" => true "LATM" => true ] "gratuito" => true "lecturas" => array:1 [ "total" => 0 ] "idiomaDefecto" => "en" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S0210569123002085?idApp=WMIE" ] ] "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S2173572723001303?idApp=WMIE" "url" => "/21735727/0000004800000001/v1_202401020427/S2173572723001303/v1_202401020427/en/main.assets" ] "en" => array:11 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Editorial</span>" "titulo" => "Advanced data analysis and intensive care medicine" "tieneTextoCompleto" => true "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "1" "paginaFinal" => "2" ] ] "autores" => array:1 [ 0 => array:4 [ "autoresLista" => "Federico Gordo Vidal, Natalia Gordo Herrera" "autores" => array:2 [ 0 => array:4 [ "nombre" => "Federico" "apellidos" => "Gordo Vidal" "email" => array:1 [ 0 => "fnatalio.gordo@salud.madrid.org" ] "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" ] ] ] 1 => array:3 [ "nombre" => "Natalia" "apellidos" => "Gordo Herrera" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">c</span>" "identificador" => "aff0015" ] ] ] ] "afiliaciones" => array:3 [ 0 => array:3 [ "entidad" => "Servicio de Medicina Intensiva, Hospital Universitario del Henares, Coslada, Madrid, Spain" "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:3 [ "entidad" => "Grupo de Investigación en Patología Crítica, Grado de Medicina, Universidad Francisco de Vitoria, Pozuelo de Alarcón, Madrid, Spain" "etiqueta" => "b" "identificador" => "aff0010" ] 2 => array:3 [ "entidad" => "Escuela Politécnica Superior, Universidad Francisco de Vitoria, Pozuelo de Alarcón, Madrid, Spain" "etiqueta" => "c" "identificador" => "aff0015" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "Corresponding author." ] ] ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Analisis avanzado de datos y medicina intensiva" ] ] "textoCompleto" => "<span class="elsevierStyleSections"><p id="par0005" class="elsevierStylePara elsevierViewall">In this issue of our journal, Muñoz Lezcano et al.<a class="elsevierStyleCrossRef" href="#bib0005"><span class="elsevierStyleSup">1</span></a> present to us the results of an analysis of a database generated by a real-time clinical information system. The overall objective of this project is to identify the most significant risk factors in patients requiring non-invasive mechanical ventilation. Additionally, they apply models to study or determine the prognosis of patients with COVID-19 using machine learning techniques.</p><p id="par0010" class="elsevierStylePara elsevierViewall">The authors not only highlight the results obtained, but also emphasize the utility of Artificial Intelligence (A.I.) techniques applied to fields such as medicine. Data analysis applied to critically ill patients can help, even in real-time, to obtain results to understand the specific characteristics of different patient groups, thus improving and personalizing care and treatment.</p><p id="par0015" class="elsevierStylePara elsevierViewall">Several studies applying these techniques have recently been published in <span class="elsevierStyleItalic">Medicina Intensiva</span>.<a class="elsevierStyleCrossRefs" href="#bib0010"><span class="elsevierStyleSup">2–4</span></a> Thanks to the technological advancements made, and the growing amount of data generated in clinical settings, the future of data analysis in critical care is promising and will likely improve the quality and outcomes of health care. Machine learning algorithms and A.I. are being used to analyze large volumes of clinical data, such as medical images, electronic health records, and laboratory test results. These algorithms can identify subtle patterns indicative of the presence of a disease or the development of complications in critically ill patients. This anticipation could make a difference in patient survival and recovery. Continuous and real-time monitoring is another area where data analysis has been improving. Medical devices are or should be more and more connected, allowing for immediate and secure data collection and transmission.</p><p id="par0020" class="elsevierStylePara elsevierViewall">Considering all the advantages that technology has to offer to the field of medicine, we should envision and facilitate an approach to data collection and storage that should not compromise patient privacy while guaranteeing data quality and trustworthiness. This challenge is not easy and requires, at least, 3 critical steps:<ul class="elsevierStyleList" id="lis0005"><li class="elsevierStyleListItem" id="lsti0005"><span class="elsevierStyleLabel">1)</span><p id="par0025" class="elsevierStylePara elsevierViewall">Creation of reliable, collaborative, and accessible databases.</p></li><li class="elsevierStyleListItem" id="lsti0010"><span class="elsevierStyleLabel">2)</span><p id="par0030" class="elsevierStylePara elsevierViewall">Integration of truly interconnected teams and systems that speak the same language and are capable of transferring information in real-time.</p></li><li class="elsevierStyleListItem" id="lsti0015"><span class="elsevierStyleLabel">3)</span><p id="par0035" class="elsevierStylePara elsevierViewall">Addition of professional profiles enabling interdisciplinary work with A.I. systems and data.</p></li></ul></p><p id="par0040" class="elsevierStylePara elsevierViewall">Although the technological world, including Artificial Intelligence, moves fast, there is still much progress to be made in the application of these resources in fields such as medicine, particularly in critical cases. If we want to continue bringing these techniques to the medical field, we should reconsider workforce planning and the addition of new professional profiles (or signing of collaboration agreements) to allow for adequate progress in this type of research to unleash its full potential.</p></span>" "pdfFichero" => "main.pdf" "tienePdf" => true "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0005" "bibliografiaReferencia" => array:4 [ 0 => array:3 [ "identificador" => "bib0005" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Predictors of mechanical ventilation and mortality in critically ill patients with COVID-19 pneumonia" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:1 [ 0 => "S. Muñoz Lezcano" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1016/j.medin.2023.06.012" "Revista" => array:2 [ "tituloSerie" => "Med Intensiva" "fecha" => "2023" ] ] ] ] ] ] 1 => array:3 [ "identificador" => "bib0010" "etiqueta" => "2" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Enhancing sepsis management through machine learning techniques: a review" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:6 [ 0 => "N. Ocampo-Quintero" 1 => "P. Vidal-Cortés" 2 => "L. del Rio Carbajo" 3 => "F. Fernández-Riverola" 4 => "M. Reboiro-Jato" 5 => "M. González-Peña" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1016/j.medin.2020.04.003" "Revista" => array:6 [ "tituloSerie" => "Med Intensiva." 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"fecha" => "2021" "volumen" => "45" "paginaInicial" => "e40" "paginaFinal" => "2" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/34717886" "web" => "Medline" ] ] ] ] ] ] ] ] 3 => array:3 [ "identificador" => "bib0020" "etiqueta" => "4" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Spanish Influenza Score (SIS): usefulness of machine learning in the development of an early mortality prediction score in severe influenza" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:1 [ 0 => "Grupo de Trabajo Gripe A Grave (GETGAG) de la Sociedad Española de Medicina Intensiva Crítica y Unidades Coronarias (SEMICYUC)" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1016/j.medin.2020.05.017" "Revista" => array:8 [ "tituloSerie" => "Med Intensiva." 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