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OBJECTIVE: The COVID-19 pandemic has threatened to collapse hospital and ICU services, and it has affected the care programs for non-COVID patients. The objective was to develop a mathematical model designed to optimize predictions related to the need for hospitalization and ICU admission by COVID-19 patients. DESIGN: Prospective study. SETTING: Province of Granada (Spain). POPULATION: COVID-19 patients hospitalized, admitted to ICU, recovered and died from March 15 to September 22, 2020. STUDY VARIABLES: The number of patients infected with SARS-CoV-2 and hospitalized or admitted to ICU for COVID-19. RESULTS: The data reported by hospitals was used to develop a mathematical model that reflects the flow of the population among the different interest groups in relation to COVID-19. This tool allows to analyse different scenarios based on socio-health restriction measures, and to forecast the number of people infected, hospitalized and admitted to the ICU. CONCLUSIONS: The mathematical model is capable of providing predictions on the evolution of the COVID-19 sufficiently in advance as to anticipate the peaks of prevalence and hospital and ICU care demands, and also the appearance of periods in which the care for non-COVID patients could be intensified.
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Objective The COVID-19 pandemic has threatened to collapse hospital and ICU services, and it has affected the care programs for non-COVID patients. The objective was to develop a mathematical model designed to optimize predictions related to the need for hospitalization and ICU admission by COVID-19 patients. Design Prospective study. Setting Province of Granada (Spain). Population COVID-19 patients hospitalized, admitted to ICU, recovered and died from March 15 to September 22, 2020. Study variables The number of patients infected with SARS-CoV-2 and hospitalized or admitted to ICU for COVID-19. Results The data reported by hospitals was used to develop a mathematical model that reflects the flow of the population among the different interest groups in relation to COVID-19. This tool allows to analyse different scenarios based on socio-health restriction measures, and to forecast the number of people infected, hospitalized and admitted to the ICU. Conclusions The mathematical model is capable of providing predictions on the evolution of the COVID-19 sufficiently in advance as to anticipate the peaks of prevalence and hospital and ICU care demands, and also the appearance of periods in which the care for non-COVID patients could be intensified.
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OBJECTIVE: The COVID-19 pandemic has threatened to collapse hospital and ICU services, and it has affected the care programs for non-COVID patients. The objective was to develop a mathematical model designed to optimize predictions related to the need for hospitalization and ICU admission by COVID-19 patients. DESIGN: Prospective study. SETTING: Province of Granada (Spain). POPULATION: COVID-19 patients hospitalized, admitted to ICU, recovered and died from March 15 to September 22, 2020. STUDY VARIABLES: The number of patients infected with SARS-CoV-2 and hospitalized or admitted to ICU for COVID-19. RESULTS: The data reported by hospitals was used to develop a mathematical model that reflects the flow of the population among the different interest groups in relation to COVID-19. This tool allows to analyse different scenarios based on socio-health restriction measures, and to forecast the number of people infected, hospitalized and admitted to the ICU. CONCLUSIONS: The mathematical model is capable of providing predictions on the evolution of the COVID-19 sufficiently in advance as to anticipate the peaks of prevalence and hospital and ICU care demands, and also the appearance of periods in which the care for non-COVID patients could be intensified.
Subject(s)
COVID-19 , COVID-19/epidemiology , Delivery of Health Care , Humans , Intensive Care Units , Models, Theoretical , Pandemics , Prospective Studies , SARS-CoV-2ABSTRACT
Background: The COVID-19 pandemic has threatened to collapse hospital and Intensive Care Unit (ICU) services, and it seems to limit the care of oncologic patients. The objective was to develop a mathematical model designed to predict the hospitalization and ICU admission demands due to COVID-19 to forecast the availability of hospital resources for the scheduling of oncological surgery and medical treatment that require hospitalitation or possible use of ICU services. Methods: We have implemented a SEIR model designed to predict the number of patients requiring hospitalization and ICU admissions for COVID-19. We evaluated the model using the number of cases registered in the hospitals of the province of Granada (Spain), that altogether cover 914,678 inhabitants. Calibration was performed using data recorded between March 15 and September 22, 2020. After that, the model was validated by comparing the predictions with data registered between September 23 and November 7, 2020. Besides, we performed a predictive analysis of scenarios regarding different possible sanitary measures. Results: Using patient registered data we developed a mathematical model that reflects the flow among the different sub-groups related to COVID-19 pandemics (Table). The best algorithm that fitted the disease dynamics was Particle Swarm Optimization, that minimized the difference between model output and real data used to calibrate the model. The validation phase showed the accuracy of the predictions, especially concerning trends in hospitalizations and ICU admissions. The different scenarios modelled on November 10, 2020 allowed us to predict the evolution of the pandemic until July 1, 2021, and to detect the peaks and valleys of disease prevalence. Conclusions: The mathematical model presented provides predictions on the evolution of COVID-19, the prevalence and hospital or ICU care demands. The predictions can be used to detect periods of greater availability of hospital resources that make it possible to schedule the oncologic surgery and intensify the care for oncologic patients. Furthermore, our model can be adapted to other population by recalibrating the model according to demographic data, the local evolution of the pandemic and the health policies. (Table Presented).
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Background and importance Baricitinib has recently been used off-label for COVID-19 because of its potential role in reducing systemic inflammation, lung damage, immune response and viral endocytosis based on preclinical data. Aim and objectives To analyse the effectiveness and safety of baricitinib for severe COVID-19 in hospitalised patients. Material and methods An observational, retrospective, multidisciplinary, single centre study was conducted in patients diagnosed with COVID-19 and receiving treatment with baricitinib in a tertiary hospital between 15 March and 30 April 2020. All adult patients receiving baricitinib for 3 or more days were included. The variables collected were: sex, age, admission period, days of treatment, medication during admission, analytical parameters, overall survival (OS) and adverse events (AE). Clinical improvement was measured as the difference in values on a 1-8 scale of clinical status during admission (from 1=hospital discharge without limitation of activities to 8=death) between day +1 of starting baricitinib and day +14. Other COVID-19 treatments were allowed. Data were collected from the hospital electronic prescription programme and the electronic medical records. Statistical analysis was performed with SPSS V.25, expressing the variables as frequencies and medians (IQR), and the Wilcoxon test. Results 43 patients treated with baricitinib were included: 70% men (n=30), aged 70 years (IQR 54-79). Duration of treatment was 6 days (IQR 5-7), with a hospital stay of 12 days (IQR 9-25) from the start of baricitinib. Clinical improvement was 3 points (IQR 1-4) on the clinical scale (6 points (IQR 6-4) on day +1 vs 3 points (IQR 2-4) on day +14) with a statistically significant difference (p<0.01). At the end of the study period, the OS rate was 100% (n=43 discharge due to clinical improvement (100%)). All analytical parameters related to a poor prognosis of COVID-19 improved with statistically significant differences (p<0.05) on day +14: IL-6 -50.7 pg/mL, PCR -86.4 mg/l, ferritin -159.0 ng/mL, lymphocytes +0.41×103/mm3, platelets +51.0×103/mm3 and D-dimers -347 ng/mL. No AE of interest associated with baricitinib were found. Conclusion and relevance Patients treated with baricitinib for COVID-19 in our study presented statistically significant clinical and analytical improvement without relevant AE. The results of ongoing clinical trials will shed more light on its efficacy and safety in treating COVID-19.