Sistema de Alerta Temprana Covid-19 en la historia electrónica de pacientes hospitalizados

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Abstract

Resumen Objetivo Diseñar una alerta temprana con signos y síntomas de la enfermedad COVID-19 para detectar los distintos niveles de riesgo de padecer dicha enfermedad, establecer el nivel de vigilancia y activar los protocolos adecuados. Método Estudio descriptivo en tres fases: Revisión bibliográfica; Consenso entre expertos; Pilotaje de la alerta temprana Resultados Las variables predictoras de severidad para construir el algoritmo fueron: tos seca, fiebre, saturación de oxígeno, disnea y PCR. Se diseñó el algoritmo y se implementó en el aplicativo electrónico de gestión de Cuidados de Línea Abierta (GACELA Care)®. Conclusiones Con la implantación de la alerta temprana se espera una mejor gestión de los cuidados en los pacientes hospitalizados susceptibles de padecer COVI-19.

Translated abstract

Abstract Objective To design an early warning system with signs and symptoms of COVID-19 disease in order to detect the levels of risk on suffering from this disease, to establish the level of vigilance and activate the appropriate protocols. Method A descriptive study in three phases: Bibliographic review; Consensus by experts and Piloting of early warning. Results Variables considered as predictors of severity to create the early warning algorithm were: dry cough, fever, oxygen saturation, dyspnea and PCR. The algorithm was designed and implemented in the Open Line Care Management electronic application (GACELA Care) ®. Conclusions With the implementation of early warning system, better care management is expected in inpatients that are susceptible to COVI-19.

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Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study

Annemarie Docherty, Ewen Harrison, Christopher A Green … (2020)

Abstract Objective To characterise the clinical features of patients admitted to hospital with coronavirus disease 2019 (covid-19) in the United Kingdom during the growth phase of the first wave of this outbreak who were enrolled in the International Severe Acute Respiratory and emerging Infections Consortium (ISARIC) World Health Organization (WHO) Clinical Characterisation Protocol UK (CCP-UK) study, and to explore risk factors associated with mortality in hospital. Design Prospective observational cohort study with rapid data gathering and near real time analysis. Setting 208 acute care hospitals in England, Wales, and Scotland between 6 February and 19 April 2020. A case report form developed by ISARIC and WHO was used to collect clinical data. A minimal follow-up time of two weeks (to 3 May 2020) allowed most patients to complete their hospital admission. Participants 20 133 hospital inpatients with covid-19. Main outcome measures Admission to critical care (high dependency unit or intensive care unit) and mortality in hospital. Results The median age of patients admitted to hospital with covid-19, or with a diagnosis of covid-19 made in hospital, was 73 years (interquartile range 58-82, range 0-104). More men were admitted than women (men 60%, n=12 068; women 40%, n=8065). The median duration of symptoms before admission was 4 days (interquartile range 1-8). The commonest comorbidities were chronic cardiac disease (31%, 5469/17 702), uncomplicated diabetes (21%, 3650/17 599), non-asthmatic chronic pulmonary disease (18%, 3128/17 634), and chronic kidney disease (16%, 2830/17 506); 23% (4161/18 525) had no reported major comorbidity. Overall, 41% (8199/20 133) of patients were discharged alive, 26% (5165/20 133) died, and 34% (6769/20 133) continued to receive care at the reporting date. 17% (3001/18 183) required admission to high dependency or intensive care units; of these, 28% (826/3001) were discharged alive, 32% (958/3001) died, and 41% (1217/3001) continued to receive care at the reporting date. Of those receiving mechanical ventilation, 17% (276/1658) were discharged alive, 37% (618/1658) died, and 46% (764/1658) remained in hospital. Increasing age, male sex, and comorbidities including chronic cardiac disease, non-asthmatic chronic pulmonary disease, chronic kidney disease, liver disease and obesity were associated with higher mortality in hospital. Conclusions ISARIC WHO CCP-UK is a large prospective cohort study of patients in hospital with covid-19. The study continues to enrol at the time of this report. In study participants, mortality was high, independent risk factors were increasing age, male sex, and chronic comorbidity, including obesity. This study has shown the importance of pandemic preparedness and the need to maintain readiness to launch research studies in response to outbreaks. Study registration ISRCTN66726260.

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Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal

Laure Wynants, Ben Van Calster, Marc M. J. Bonten … (2020)

Abstract Objective To review and critically appraise published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at risk of being admitted to hospital for covid-19 pneumonia. Design Rapid systematic review and critical appraisal. Data sources PubMed and Embase through Ovid, Arxiv, medRxiv, and bioRxiv up to 24 March 2020. Study selection Studies that developed or validated a multivariable covid-19 related prediction model. Data extraction At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). Results 2696 titles were screened, and 27 studies describing 31 prediction models were included. Three models were identified for predicting hospital admission from pneumonia and other events (as proxy outcomes for covid-19 pneumonia) in the general population; 18 diagnostic models for detecting covid-19 infection (13 were machine learning based on computed tomography scans); and 10 prognostic models for predicting mortality risk, progression to severe disease, or length of hospital stay. Only one study used patient data from outside of China. The most reported predictors of presence of covid-19 in patients with suspected disease included age, body temperature, and signs and symptoms. The most reported predictors of severe prognosis in patients with covid-19 included age, sex, features derived from computed tomography scans, C reactive protein, lactic dehydrogenase, and lymphocyte count. C index estimates ranged from 0.73 to 0.81 in prediction models for the general population (reported for all three models), from 0.81 to more than 0.99 in diagnostic models (reported for 13 of the 18 models), and from 0.85 to 0.98 in prognostic models (reported for six of the 10 models). All studies were rated at high risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, and high risk of model overfitting. Reporting quality varied substantially between studies. Most reports did not include a description of the study population or intended use of the models, and calibration of predictions was rarely assessed. Conclusion Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that proposed models are poorly reported, at high risk of bias, and their reported performance is probably optimistic. Immediate sharing of well documented individual participant data from covid-19 studies is needed for collaborative efforts to develop more rigorous prediction models and validate existing ones. The predictors identified in included studies could be considered as candidate predictors for new models. Methodological guidance should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, studies should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. Systematic review registration Protocol https://osf.io/ehc47/, registration https://osf.io/wy245.

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Breakthrough: Chloroquine phosphate has shown apparent efficacy in treatment of COVID-19 associated pneumonia in clinical studies

Jianjun Gao, Zhenxue Tian, Xu Yang (2020)

The coronavirus disease 2019 (COVID-19) virus is spreading rapidly, and scientists are endeavoring to discover drugs for its efficacious treatment in China. Chloroquine phosphate, an old drug for treatment of malaria, is shown to have apparent efficacy and acceptable safety against COVID-19 associated pneumonia in multicenter clinical trials conducted in China. The drug is recommended to be included in the next version of the Guidelines for the Prevention, Diagnosis, and Treatment of Pneumonia Caused by COVID-19 issued by the National Health Commission of the People's Republic of China for treatment of COVID-19 infection in larger populations in the future.

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Journal

Journal ID (publisher-id): ene

Title: Ene

Abbreviated Title: Ene.

Publisher: Martín Rodríguez Álvaro (Santa Cruz de La Palma, La Palma, Spain )

ISSN (Electronic): 1988-348X

Publication date (Print and electronic): 2020

Volume: 14

Issue: 3

Electronic Location Identifier: e14312

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[2] Castilla y León orgnameGerencia Regional de Salud de Castilla y León orgdiv1Servicio de Sistemas de Información y Resultados de Salud España

[1] Valladolid orgnameHospital Clínico Universitario de Valladolid España

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Publisher ID: S1988-348X2020000300012 Publisher ID: S1988-348X(20)01400300012

SO-VID: 091c7d9a-3117-4580-8dc5-e6cafc4d1c4c

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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

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Date accepted : 01 October 2020

Date received : 01 June 2020

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Figures: 0, Tables: 0, Equations: 0, References: 20, Pages: 0

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Sistema de Alerta Temprana Covid-19 en la historia electrónica de pacientes hospitalizados Translated title: Early Warning System Covid-19 in the electronic care records of inpatients

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Novel Coronavirus Disease COVID-19

Most cited references 20

Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study

Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal

Breakthrough: Chloroquine phosphate has shown apparent efficacy in treatment of COVID-19 associated pneumonia in clinical studies

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