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  1. Article ; Online: The PANDEMYC Score. An Easily Applicable and Interpretable Model for Predicting Mortality Associated With COVID-19

    Juan Torres-Macho / Pablo Ryan / Jorge Valencia / Mario Pérez-Butragueño / Eva Jiménez / Mario Fontán-Vela / Elsa Izquierdo-García / Inés Fernandez-Jimenez / Elena Álvaro-Alonso / Andrea Lazaro / Marta Alvarado / Helena Notario / Salvador Resino / Daniel Velez-Serrano / Alejandro Meca

    Journal of Clinical Medicine, Vol 9, Iss 3066, p

    2020  Volume 3066

    Abstract: This study aimed to build an easily applicable prognostic model based on routine clinical, radiological, and laboratory data available at admission, to predict mortality in coronavirus 19 disease (COVID-19) hospitalized patients. Methods: We ... ...

    Abstract This study aimed to build an easily applicable prognostic model based on routine clinical, radiological, and laboratory data available at admission, to predict mortality in coronavirus 19 disease (COVID-19) hospitalized patients. Methods: We retrospectively collected clinical information from 1968 patients admitted to a hospital. We built a predictive score based on a logistic regression model in which explicative variables were discretized using classification trees that facilitated the identification of the optimal sections in order to predict inpatient mortality in patients admitted with COVID-19. These sections were translated into a score indicating the probability of a patient’s death, thus making the results easy to interpret. Results. Median age was 67 years, 1104 patients (56.4%) were male, and 325 (16.5%) died during hospitalization. Our final model identified nine key features: age, oxygen saturation, smoking, serum creatinine, lymphocytes, hemoglobin, platelets, C-reactive protein, and sodium at admission. The discrimination of the model was excellent in the training, validation, and test samples (AUC: 0.865, 0.808, and 0.883, respectively). We constructed a prognostic scale to determine the probability of death associated with each score. Conclusions: We designed an easily applicable predictive model for early identification of patients at high risk of death due to COVID-19 during hospitalization.
    Keywords SARS-CoV-2 ; COVID-19 ; prediction score ; mortality ; Medicine ; R
    Subject code 310
    Language English
    Publishing date 2020-09-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Characteristics, complications and outcomes among 1549 patients hospitalised with COVID-19 in a secondary hospital in Madrid, Spain

    Ane Abad-motos / Jesús Troya / Eva Jiménez / Mario Fontán-Vela / Jorge Valencia / Ines Fernandez-Jimenez / Elena Alba Álvaro-Alonso / Elsa Izquierdo-García / Andrea Lazaro Cebas / Elisa Gallego Ruiz-Elvira / Ana Josefa Tebar-Martinez / Belén Garcia-Marina / Gabriela Peña-Lillo / Laura Macaya / Mario Pérez-Butragueño / Blanca Herrero Mendoza / Natividad Plazas Andreu / Liliana Vázquez Bol / Sandra María Gadin López /
    Álvaro Martínez Alcalá / Sonia Pérez Gonzalez / Amalia Martínez de la Gándara / Alejandro Zuazua Gonzalez / Javier Montoya Adarraga / Miguel Ángel Lara Álvarez / Enrique Calvo Aranda / Rosa M Lorente Ramos / María Alcantud / Roberto Alcázar Arroyo / Verónica Suberviola Sánchez-Caballero / Ariela Candelario Cáceres / Juan Churruca Sarasqueta / Juan Torres Macho / Ismael Escobar Rodríguez / Raquel Ruíz Páez / Alberto Esquivel López / Pablo de la Cueva Dobao / Natalia Blanca López / José García Hinojosa / Cristina Sevillano Collantes / Alfonso Camacho Aroca / Tamar Talaván Zañón / María Ángeles Sánchez Úriz / Carlos Bibiano Guillén / Miguel Ángel López Arenas / Margarita Gimeno Arández / Francisco Javier Quintero Gutiérrez del Álamo

    BMJ Open, Vol 10, Iss

    a retrospective case series study

    2020  Volume 11

    Abstract: Objectives To describe demographic, clinical, radiological and laboratory characteristics, as well as outcomes, of patients admitted for COVID-19 in a secondary hospital.Design and setting Retrospective case series of sequentially hospitalised patients ... ...

    Abstract Objectives To describe demographic, clinical, radiological and laboratory characteristics, as well as outcomes, of patients admitted for COVID-19 in a secondary hospital.Design and setting Retrospective case series of sequentially hospitalised patients with confirmed SARS-CoV-2, at Infanta Leonor University Hospital (ILUH) in Madrid, Spain.Participants All patients attended at ILUH testing positive to reverse transcriptase-PCR on nasopharyngeal swabs and diagnosed with COVID-19 between 1 March 2020 and 28 May 2020.Results A total of 1549 COVID-19 cases were included (median age 69 years (IQR 55.0–81.0), 57.5% men). 78.2% had at least one underlying comorbidity, the most frequent was hypertension (55.8%). Most frequent symptoms at presentation were fever (75.3%), cough (65.7%) and dyspnoea (58.1%). 81 (5.8%) patients were admitted to the intensive care unit (ICU) (median age 62 years (IQR 51–71); 74.1% men; median length of stay 9 days (IQR 5–19)) 82.7% of them needed invasive ventilation support. 1393 patients had an outcome at the end of the study period (case fatality ratio: 21.2% (296/1393)). The independent factors associated with fatality (OR; 95% CI): age (1.07; 1.06 to 1.09), male sex (2.86; 1.85 to 4.50), neurological disease (1.93; 1.19 to 3.13), chronic kidney disease (2.83; 1.40 to 5.71) and neoplasia (4.29; 2.40 to 7.67). The percentage of hospital beds occupied with COVID-19 almost doubled (702/361), with the number of patients in ICU quadrupling its capacity (32/8). Median length of stay was 9 days (IQR 6–14).Conclusions This study provides clinical characteristics, complications and outcomes of patients with COVID-19 admitted to a European secondary hospital. Fatal outcomes were similar to those reported by hospitals with a higher level of complexity.
    Keywords Medicine ; R
    Language English
    Publishing date 2020-11-01T00:00:00Z
    Publisher BMJ Publishing Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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