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OBJECTIVES: Evaluating whether meteorological and geographical variables could be associated with the severity of COVID-19 in Spain. METHODS: An ecological study was performed to analyze the influence of meteorological and geographical factors in hospital admissions and deaths due to COVID-19 in the 52 provinces of Spain (24 coastal and 28 inland regions), during the first three pandemic waves. Medical and mortality data were collected from the CarlosIII Health Institute (ISCIII) and meteorological variables were requested to the Spanish State Meteorological Agency (AEMET). RESULTS: Regarding the diagnosed cases it is remarkable that the percentage of patients hospitalized for COVID-19 was lower in the coastal provinces than in the inland ones (8.7±2.6% vs. 11.5±2.6%; P=9.9×10-5). Furthermore, coastal regions registered a lower percentage of mortality than inland regions (2.0±0.6% vs. 3.1±0.8%; P=1.7×10-5). Mean air temperature was inversely correlated both with COVID-19 hospitalizations (Rho: -0.59; P=3.0×10-6) and mortality (Rho: -0.70; P=5.3×10-9). In those provinces with a mean air temperature <10°C mortality by COVID-19 was twice that of those with >16°C. Finally, we found an association between mortality and the location of the province (coastal/inland), altitude, patient age and the average air temperature; the latter was inversely and independently correlated with mortality (non-standardized ß coeff.: -0.24; 95%CI: -0.31 to -0.16; P=2.38×10-8). CONCLUSIONS: The average air temperature was inversely associated with COVID-19 mortality in our country during the first three waves of the pandemic.
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Objectives Evaluating whether meteorological and geographical variables could be associated with the severity of COVID-19 in Spain. Methods An ecological study was performed to analyze the influence of meteorological and geographical factors in hospital admissions and deaths due to COVID-19 in the 52 provinces of Spain (24 coastal and 28 inland regions), during the first three pandemic waves. Medical and mortality data were collected from the Carlos III Health Institute (ISCIII) and meteorological variables were requested to the Spanish State Meteorological Agency (AEMET). Results Regarding the diagnosed cases it is remarkable that the percentage of patients hospitalized for COVID-19 was lower in the coastal provinces than in the inland ones (8.7 ± 2.6% vs. 11.5 ± 2.6%;p = 9.9 × 10−5). Furthermore, coastal regions registered a lower percentage of mortality than inland regions (2.0 ± 0.6% vs. 3.1 ± 0.8%;p = 1.7 × 10−5). Mean air temperature was inversely correlated both with COVID-19 hospitalizations (Rho: −0.59;p = 3.0 × 10-6) and mortality (Rho: −0.70;p = 5.3 × 10−9). In those provinces with a mean air temperature <10 °C mortality by COVID-19 was twice that of those with >16 °C. Finally, we found an association between mortality and the location of the province (coastal/inland), altitude, patient age and the average air temperature;the latter was inversely and independently correlated with mortality (non standardised B coeff.: −0.24;IC 95%: −0.31 to −0.16;p = 2.38 × 10−8). Conclusions The average air temperature was inversely associated with COVID-19 mortality in our country during the first three waves of the pandemic.
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Objectives: Evaluating whether meteorological and geographical variables could be associated with the severity of COVID-19 in Spain. Methods: An ecological study was performed to analyze the influence of meteorological and geographical factors in hospital admissions and deaths due to COVID-19 in the 52 provinces of Spain (24 coastal and 28 inland regions), during the first three pandemic waves. Medical and mortality data were collected from the Carlos III Health Institute (ISCIII) and meteorological variables were requested to the Spanish State Meteorological Agency (AEMET). Results: Regarding the diagnosed cases it is remarkable that the percentage of patients hospitalized for COVID-19 was lower in the coastal provinces than in the inland ones (8.7 ± 2.6% vs. 11.5 ± 2.6%; p = 9.9 × 10-5). Furthermore, coastal regions registered a lower percentage of mortality than inland regions (2.0 ± 0.6% vs. 3.1 ± 0.8%; p = 1.7 × 10-5). Mean air temperature was inversely correlated both with COVID-19 hospitalizations (Rho: -0.59; p = 3.0 × 10-6) and mortality (Rho: -0.70; p = 5.3 × 10-9). In those provinces with a mean air temperature <10 °C mortality by COVID-19 was twice that of those with >16 °C. Finally, we found an association between mortality and the location of the province (coastal/inland), altitude, patient age and the average air temperature; the latter was inversely and independently correlated with mortality (non standardised B coeff.: -0.24; IC 95%: -0.31 to -0.16; p = 2.38 × 10-8). Conclusions: The average air temperature was inversely associated with COVID-19 mortality in our country during the first three waves of the pandemic.
Objetivos: Evaluar si factores meteorológicos y geográficos pudieron relacionarse con la gravedad de la COVID-19 en España. Métodos: Estudio ecológico, a escala provincial, que analiza la influencia de factores meteorológicos y geográficos en la hospitalización y mortalidad por COVID-19 en las 52 provincias españolas (24 costeras y 28 del interior), durante las tres primeras olas. Los datos de hospitalizaciones y mortalidad se obtuvieron del Instituto de Salud Carlos III (ISCIII). Los datos epidemiológicos del Instituto Nacional Estadística (INE) y la Red Nacional de Vigilancia Epidemiológica (RENAVE). Las variables meteorológicas de la Agencia estatal de meteorología (AEMET). Resultados: El porcentaje de pacientes hospitalizados por COVID-19, del total de personas infectadas, fue inferior en las provincias costeras que en las del interior peninsular (8,7 ± 2,6% vs. 11,5 ± 2,6%; p = 9,9 × 10−5). De igual manera la costa registró menor porcentaje de mortalidad que el interior peninsular (2,0 ± 0,6% vs. 3,1 ± 0,8%; p = 1,7 × 10−5). La temperatura media correlacionó negativamente con la hospitalización (Rho: −0,59; p = 3,0 × 10−6) y la mortalidad por COVID-19 (Rho: −0,70; p = 5,3 × 10−9). Las provincias con una temperatura media <10 °C duplicaron la mortalidad por COVID respecto a las de >16 °C. La mortalidad se relacionó con la localización provincial (costa/interior), la altitud, la edad de la población y la temperatura media, siendo esta última la variable asociada de manera independiente (Coef. B no estandarizado: −0,24; IC 95%: −0,31 a −0,16; p = 2,38 × 10−8). Conclusiones: La mortalidad por COVID-19 durante las tres primeras olas de la pandemia en nuestro país se asoció inversamente con la temperatura media.
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INTRODUCTION: Povidone-iodine and hydrogen peroxide could be effective in against SARS-CoV-2. METHODS: A "non-interventional trial" in 88 patients (43±17 yrs., 55% men) with SARS-CoV-2 in nasopharyngeal swabs (RT-PCR). 31 received mouth rinses/gargling with povidone-iodine (every 8 hours, two consecutive days), 17 with mouth rinses/gargling of hydrogen peroxide, and 40 controls. Were repeated PCR in 3, 11 and 17 days. RESULTS: After intervention the viral load (Log10 copies/ml) remained similar in povidone-iodine (4.3±2.7 copies/ml), hydrogen peroxide (4.6±2.9 copies/ml; p=0.40) and controls (4.4±3.0 copies/ml). The percentage of patients with a negative result in the second PCR was 27% in povidone-iodine group, 23% in hydrogen peroxide and 32% in controls; in the third PCR, 62%, 54% y 58% respectively; and in the fourth PCR, 81%, 75% y 81%. CONCLUSION: Our results do not support the clinical usefulness of mouth rinses/gargling with povidone-iodine or hydrogen peroxide in patients with COVID-19.
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INTRODUCTION: Povidone-iodine and hydrogen peroxide could be effective in against SARS-CoV-2. METHODS: A "non-interventional trial" in 88 patients (43±17 yrs., 55% men) with SARS-CoV-2 in nasopharyngeal swabs (RT-PCR). 31 received mouth rinses/gargling with povidone-iodine (every 8h, two consecutive days), 17 with mouth rinses/gargling of hydrogen peroxide, and 40 controls. Were repeated PCR in 3, 11 and 17 days. RESULTS: After intervention the viral load (Log10 copies/ml) remained similar in povidone-iodine (4.3±2.7 copies/ml), hydrogen peroxide (4.6±2.9 copies/ml; p=0.40) and controls (4.4±3.0 copies/ml). The percentage of patients with a negative result in the second PCR was 27% in povidone-iodine group, 23% in hydrogen peroxide and 32% in controls; in the third PCR, 62%, 54% y 58% respectively; and in the fourth PCR, 81%, 75% y 81%. CONCLUSION: Our results do not support the clinical usefulness of mouth rinses/gargling with povidone-iodine or hydrogen peroxide in patients with COVID-19.
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Objetivos: Evaluar si factores meteorológicos y geográficos pudieron relacionarse con la gravedad de la COVID-19 en España. Métodos: Estudio ecológico, a escala provincial, que analiza la influencia de factores meteorológicos y geográficos en la hospitalización y mortalidad por COVID-19 en las 52 provincias españolas (24 costeras y 28 del interior), durante las tres primeras olas. Los datos de hospitalizaciones y mortalidad se obtuvieron del Instituto de Salud Carlos III (ISCIII). Los datos epidemiológicos del Instituto Nacional Estadística (INE) y la Red Nacional de Vigilancia Epidemiológica (RENAVE). Las variables meteorológicas de la Agencia estatal de meteorología (AEMET). Resultados: El porcentaje de pacientes hospitalizados por COVID-19, del total de personas infectadas, fue inferior en las provincias costeras que en las del interior peninsular (8,7±2,6% vs. 11,5 ±2,6%;p=9,9x10-5). De igual manera la costa registró menor porcentaje de mortalidad que el interior peninsular (2,0±0,6% vs. 3,1±0,8%;p=1,7x10-5). La temperatura media correlacionó negativamente con la hospitalización (Rho: -0,59;p=3,0x10-6) y la mortalidad por COVID-19 (Rho: -0,70;p=5,3x10-9). Las provincias con una temperatura media <10ºC duplicaron la mortalidad por COVID respecto a las de >16ºC. La mortalidad se relacionó con la localización provincial (costa/interior), la altitud, la edad de la población y la temperatura media, siendo esta última la variable asociada de manera independiente (Coef. B no estandarizado: -0,24;IC 95%: -0,31 a -0,16;p=2,38x10-8). Conclusiones: La mortalidad por COVID-19 durante las tres primeras olas de la pandemia en nuestro país se asoció inversamente con la temperatura media.
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Introduction Several studies have analyzed the influence of meteorological and geographical factors on the incidence of COVID-19. Seasonality could be important in the transmission of SARS-CoV-2. This study aims to evaluate the geographical pattern of COVID-19 in Spain and its relationship with different meteorological variables. Methods A provincial ecological study analyzing the influence of meteorological and geographical factors on the cumulative incidence of COVID-19 in the 52 (24 coastal and 28 inland) Spanish provinces during the first three waves was carried out. The cumulative incidence was calculated with data from the National Statistical Institute (INE) and the National Epidemiological Surveillance Network (RENAVE), while the meteorological variables were obtained from the Spanish Meteorological Agency (AEMET). Results The total cumulative incidence, in all three waves, was lower in the coastal provinces than in the inland ones (566 ± 181 vs. 782 ± 154;P = 2.5 × 10−5). The cumulative incidence correlated negatively with mean air temperature (r = −0.49;P = 2.2 × 10−4) and rainfall (r = −0.33;P = .01), and positively with altitude (r = 0.56;P = 1.4 × 10−5). The Spanish provinces with an average temperature <10 °C had almost twice the cumulative incidence than the provinces with temperatures >16 °C. The mean air temperature and rainfall were associated with the cumulative incidence of COVID-19, regardless of other factors (Beta Coefficient of −0.62;P = 3.7 × 10−7 and −0.47;P = 4.2 × 10−5 respectively) Conclusions Meteorological and geographical factors could influence the evolution of the pandemic in Spain. Knowledge regarding the seasonality of the virus would help to predict new waves of COVID-19 infections
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Introduction Povidone-iodine and hydrogen peroxide could be effective in against SARS-CoV-2. Methods A “non-interventional trial” in 88 patients (43 ± 17 yrs., 55% men) with SARS-CoV-2 in nasopharyngeal swabs (RT-PCR). 31 received mouth rinses/gargling with povidone-iodine (every 8 h, two consecutive days), 17 with mouth rinses/gargling of hydrogen peroxide, and 40 controls. Were repeated PCR in 3, 11 and 17 days. Results After intervention the viral load (Log10 copies/ml) remained similar in povidone-iodine (4.3 ± 2.7 copies/ml), hydrogen peroxide (4.6 ± 2.9 copies/ml;p = 0.40) and controls (4.4 ± 3.0 copies/ml). The percentage of patients with a negative result in the second PCR was 27% in povidone-iodine group, 23% in hydrogen peroxide and 32% in controls;in the third PCR, 62%, 54% y 58% respectively;and in the fourth PCR, 81%, 75% y 81%. Conclusion Our results do not support the clinical usefulness of mouth rinses/gargling with povidone-iodine or hydrogen peroxide in patients with COVID-19.
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Introduction: Several studies have analyzed the influence of meteorological and geographical factors on the incidence of COVID-19. Seasonality could be important in the transmission of SARS-CoV-2. This study aims to evaluate the geographical pattern of COVID-19 in Spain and its relationship with different meteorological variables. Methods: A provincial ecological study analyzing the influence of meteorological and geographical factors on the cumulative incidence of COVID-19 in the 52 (24 coastal and 28 inland) Spanish provinces during the first three waves was carried out. The cumulative incidence was calculated with data from the National Statistical Institute (INE) and the National Epidemiological Surveillance Network (RENAVE), while the meteorological variables were obtained from the Spanish Meteorological Agency (AEMET). Results: The total cumulative incidence, in all three waves, was lower in the coastal provinces than in the inland ones (566 ± 181 vs. 782 ± 154; P = 2.5 × 10-5). The cumulative incidence correlated negatively with mean air temperature (r = -0.49; P = 2.2 × 10-4) and rainfall (r = -0.33; P = .01), and positively with altitude (r = 0.56; P = 1.4 × 10-5). The Spanish provinces with an average temperature <10 °C had almost twice the cumulative incidence than the provinces with temperatures >16 °C. The mean air temperature and rainfall were associated with the cumulative incidence of COVID-19, regardless of other factors (Beta Coefficient of -0.62; P = 3.7 × 10-7 and -0.47; P = 4.2 × 10-5 respectively). Conclusions: Meteorological and geographical factors could influence the evolution of the pandemic in Spain. Knowledge regarding the seasonality of the virus would help to predict new waves of COVID-19 infections.
Introducción: Varios estudios han analizado la influencia de factores meteorológicos y geográficos en la incidencia de COVID-19. La estacionalidad podría tener importancia en la transmisión de SARS-CoV-2. Nuestro estudio evalúa el patrón geográfico de la COVID-19 en España y su relación con las distintas variables meteorológicas. Métodos: Estudio ecológico a escala provincial que analiza la influencia de factores meteorológicos y geográficos en la incidencia acumulada de COVID-19 en las 52 provincias españolas (24 costeras y 28 del interior) durante las tres primeras olas. La incidencia acumulada se calculó con los datos del Instituto Nacional Estadística (INE) y la Red Nacional de Vigilancia Epidemiológica (RENAVE), las variables meteorológicas se obtuvieron de la Agencia estatal de meteorología (AEMET). Resultados: La incidencia acumulada total, en los tres periodos, fue menor en las provincias costeras que en las del interior (566 ± 181 vs. 782 ± 154; P = 2,5 × 10−5). La incidencia acumulada correlacionó negativamente con la temperatura media (r = −0,49; P = 2,2 × 10−4) y las precipitaciones (r = −0,33; P = ,01), y positivamente con la altitud (r = 0,56; P = 1,4 × 10−5). Las provincias españolas con una temperatura media <10 °C tuvieron casi el doble de incidencia acumulada que las provincias con temperaturas >16 °C. La temperatura media y las precipitaciones fueron las variables asociadas con la incidencia acumulada provincial de COVID-19, con independencia de otros factores (Coeficiente Beta de −0,62; P = 3,7 × 10−7 y −0,47; P = 4,2 × 10−5 respectivamente). Conclusiones: Los factores meteorológicos y geográficos podrían influir en la evolución de la pandemia en España. El reconocimiento de la estacionalidad del COVID-19 ayudaría a predecir nuevas olas.
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INTRODUCTION: Several studies have analyzed the influence of meteorological and geographical factors on the incidence of COVID-19. Seasonality could be important in the transmission of SARS-CoV-2. This study aims to evaluate the geographical pattern of COVID-19 in Spain and its relationship with different meteorological variables. METHODS: A provincial ecological study analyzing the influence of meteorological and geographical factors on the cumulative incidence of COVID-19 in the 52 (24 coastal and 28 inland) Spanish provinces during the first three waves was carried out. The cumulative incidence was calculated with data from the National Statistical Institute (INE) and the National Epidemiological Surveillance Network (RENAVE), while the meteorological variables were obtained from the Spanish Meteorological Agency (AEMET). RESULTS: The total cumulative incidence, in all three waves, was lower in the coastal provinces than in the inland ones (566±181 vs. 782±154; p=2.5×10-5). The cumulative incidence correlated negatively with mean air temperature (r=-0.49; p=2.2×10-4) and rainfall (r=-0.33; p=0.01), and positively with altitude (r=0.56; p=1. 4×10-5). The Spanish provinces with an average temperature <10°C had almost twice the cumulative incidence than the provinces with temperatures >16°C. The mean air temperature and rainfall were associated with the cumulative incidence of COVID-19, regardless of other factors (Beta Coefficient of -0.62; p=3.7×10-7 and -0.47; p=4.2×10-5 respectively). CONCLUSIONS: Meteorological and geographical factors could influence the evolution of the pandemic in Spain. Knowledge regarding the seasonality of the virus would help to predict new waves of COVID-19 infections.
Subject(s)
COVID-19 , Weather , Altitude , COVID-19/epidemiology , Humans , Incidence , Meteorological Concepts , SARS-CoV-2 , Spain/epidemiology , TemperatureABSTRACT
Introducción: Varios estudios han analizado la influencia de factores meteorológicos y geográficos en la incidencia de COVID-19. La estacionalidad podría tener importancia en la transmisión de SARS-CoV-2. Nuestro estudio evalúa el patrón geográfico de la COVID-19 en España y su relación con las distintas variables meteorológicas. Métodos: Estudio ecológico a escala provincial que analiza la influencia de factores meteorológicos y geográficos en la incidencia acumulada de COVID-19 en las 52 provincias españolas (24 costeras y 28 del interior) durante las tres primeras olas. La incidencia acumulada se calculó con los datos del Instituto Nacional Estadística (INE) y la Red Nacional de Vigilancia Epidemiológica (RENAVE), las variables meteorológicas se obtuvieron de la Agencia estatal de meteorología (AEMET). Resultados: La incidencia acumulada total, en los tres periodos, fue menor en las provincias costeras que en las del interior (566±181 vs. 782±154;p=2,5x10 -5 ). La incidencia acumulada correlacionó negativamente con la temperatura media (r=-0,49;p=2,2x10 -4 ) y las precipitaciones (r=-0,33;p=0,01), y positivamente con la altitud (r=0,56;p=1,4x10 -5 ). Las provincias españolas con una temperatura media < 10ºC tuvieron casi el doble de incidencia acumulada que las provincias con temperaturas >16ºC. La temperatura media y las precipitaciones fueron las variables asociadas con la incidencia acumulada provincial de COVID-19, con independencia de otros factores (Coeficiente Beta de -0,62;p=3,7x10 -7 y -0,47;p=4,2x10 -5 respectivamente). Conclusiones: Los factores meteorológicos y geográficos podrían influir en la evolución de la pandemia en España. El reconocimiento de la estacionalidad del COVID-19 ayudaría a predecir nuevas olas.
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INTRODUCTION: Social vulnerability is a known determinant of health in respiratory diseases. Our aim was to identify whether there are socio-demographic factors among COVID-19 patients hospitalized in Spain and their potential impact on health outcomes during the hospitalization. METHODS: A multicentric retrospective case series study based on administrative databases that included all COVID-19 cases admitted in 19 Spanish hospitals from 1 March to 15 April 2020. Socio-demographic data were collected. Outcomes were critical care admission and in-hospital mortality. RESULTS: We included 10,110 COVID-19 patients admitted to 18 Spanish hospitals (median age 68 (IQR 54-80) years old; 44.5% female; 14.8% were not born in Spain). Among these, 779 (7.7%) cases were admitted to critical care units and 1678 (16.6%) patients died during the hospitalization. Age, male gender, being immigrant, and low hospital saturation were independently associated with being admitted to an intensive care unit. Age, male gender, being immigrant, percentile of average per capita income, and hospital experience were independently associated with in-hospital mortality. CONCLUSIONS: Social determinants such as residence in low-income areas and being born in Latin American countries were associated with increased odds of being admitted to an intensive care unit and of in-hospital mortality. There was considerable variation in outcomes between different Spanish centers.
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COVID-19 , Aged , Aged, 80 and over , Female , Hospital Mortality , Hospitalization , Humans , Intensive Care Units , Male , Middle Aged , Prognosis , Retrospective Studies , Risk Factors , SARS-CoV-2 , Social VulnerabilitySubject(s)
COVID-19/epidemiology , COVID-19/prevention & control , Health Policy , Pandemics , Physical Distancing , Europe/epidemiology , Humans , Time FactorsABSTRACT
OBJECTIVES: The primary objective was to describe the clinical characteristics and 30-day mortality rates in emergency department patients with coronavirus disease 2019 (COVID-19) in different diagnostic groupings. MATERIAL AND METHODS: Secondary analysis of the COVID-19 registry compiled by the emergency department of Hospital Clínico San Carlos in Madrid, Spain. We selected suspected COVID-19 cases treated in the emergency department between February 28 and March 31, 2020. The cases were grouped as follows: 1) suspected, no polymerase chain reaction (PCR) test (S/no-PCR); 2) suspected, negative PCR (S/PCR-); 3) suspected, positive PCR (S/PCR+); 4) highly suspected, no PCR, or negative PCR (HS/no or PCR-); and 5) highly suspected, positive PCR (HS/PCR+). We collected clinical, radiologic, and microbiologic data related to the emergency visit. The main outcome was 30-day all-cause mortality. Secondary outcomes were hospitalization and clinical severity of the episode. RESULTS: A total of 1993 cases (90.9%) were included as follows: S/no-PCR, 17.2%; S/PCR-, 11.4%; S/PCR+, 22.1%; HS/no PCR or PCR-, 11.7%; and HS/PCR+, 37.6%. Short-term outcomes differed significantly in the different groups according to demographic characteristics; comorbidity and clinical, radiographic, analytical, and therapeutic variables. Thirty-day mortality was 11.5% (56.5% in hospitalized cases and 19.6% in cases classified as severe). The 2 HS categories and the S/PCR+ category had a greater adjusted risk for 30-day mortality and for having a clinically severe episode during hospitalization in comparison with S/PCR- cases. Only the 2 HS categories showed greater risk for hospitalization than the S/PCR- cases. CONCLUSION: COVID-19 diagnostic groups differ according to clinical and laboratory characteristics, and the differences are associated with the 30-day prognosis.
OBJETIVO: El objetivo principal fue describir el perfil clínico y la mortalidad a los 30 días de diferentes categorías diagnósticas en los casos de COVID-19 atendidos en un servicio de urgencias (SU). METODO: Análisis secundario del registro COVID-19_URG-HCSC. Se seleccionaron los casos sospechosos de COVID-19 atendidos en un SU de Madrid desde el 28 de febrero hasta el 31 de marzo de 2020. La muestra se dividió: 1) sospecha con PCR no realizada (S/PCR NR); 2) sospecha con PCR negativa (S/PCR); 3) sospecha con PCR positiva (S/ PCR+); 4) alta sospecha con PCR negativa o no realizada (AS/PCR o NR); y 5) alta sospecha con PCR positiva (AS/ PCR+). Se recogieron variables clínicas, radiológicas y microbiológicas del episodio de urgencias. La variable de resultado principal fue la mortalidad por cualquier causa a los 30 días. Las variables secundarias fueron el ingreso y la gravedad del episodio. RESULTADOS: Se incluyeron 1.993 pacientes; 17,2% S/PCR NR, 11,4% S/PCR, 22,1% S/PCR+, 11,7% AS/PCR o NR y 37,6% AS/PCR+. Se hallaron diferencias estadísticamente significativas respecto a las variables demográficas, comorbilidad, clínicas, radiográficas, analíticas y terapéuticas y de resultados a corto plazo en función las categorías diagnósticas. La mortalidad global a los 30 días fue de un 11,5%, 56,5% casos fueron hospitalizados y 19,6% casos sufrieron un episodio grave. Las categorías de AS y de S/PCR+ tuvieron un incremento del riesgo ajustado de mortalidad a los 30 días y de sufrir un episodio grave durante el ingreso hospitalario respecto a S/PCR. En relación al ingreso, solo las categorías de AS tuvieron un incremento del riesgo ajustado de hospitalización respecto a la categoría de S/PCR. CONCLUSIONES: Existen diferentes categorías diagnósticas de la enfermedad COVID-19 en función del perfil clínico y microbiológico que tienen correlato con el pronóstico a 30 días.
Subject(s)
Betacoronavirus , Coronavirus Infections/diagnosis , Coronavirus Infections/mortality , Pneumonia, Viral/diagnosis , Pneumonia, Viral/mortality , Adult , COVID-19 , Cause of Death , Comorbidity , Confidence Intervals , Coronavirus Infections/complications , Coronavirus Infections/therapy , Diagnosis-Related Groups , Emergency Service, Hospital/statistics & numerical data , Female , Humans , Male , Middle Aged , Pandemics , Pneumonia, Viral/complications , Pneumonia, Viral/therapy , Polymerase Chain Reaction/statistics & numerical data , Registries/statistics & numerical data , SARS-CoV-2 , Spain/epidemiology , Symptom Assessment , Time Factors , Treatment OutcomeABSTRACT
OBJETIVO: EL objetivo principal fue describir el perfil clínico y la mortalidad a los 30 días de diferentes categorías diagnósticas en los casos de COVID-19 atendidos en un servicio de urgencias (SU). MÉTODO: Análisis secundario del registro COVID-19_URG-HCSC. Se seleccionaron los casos sospechosos de COVID-19 atendidos en un SU de Madrid desde el 28 de febrero hasta el 31 de marzo de 2020. La muestra se dividió: 1) sospecha con PCR no realizada (S/PCR NR);2) sospecha con PCR negativa (S/PCR-);3) sospecha con PCR positiva (S/PCR+);4) alta sospecha con PCR negativa o no realizada (AS/PCR- o NR);y 5) alta sospecha con PCR positiva (AS/PCR+). Se recogieron variables clínicas, radiológicas y microbiológicas del episodio de urgencias. La variable de resultado principal fue la mortalidad por cualquier causa a los 30 días. Las variables secundarias fueron el ingreso y la gravedad del episodio. RESULTADOS: Se incluyeron 1.993 pacientes;17,2% S/PCR NR, 11,4% S/PCR-, 22,1% S/PCR+, 11,7% AS/PCR- o NR y 37,6% AS/PCR+. Se hallaron diferencias estadísticamente significativas respecto a las variables demográficas, comorbilidad, clínicas, radiográficas, analíticas y terapéuticas y de resultados a corto plazo en función las categorías diagnósticas. La mortalidad global a los 30 días fue de un 11,5%, 56,5% casos fueron hospitalizados y 19,6% casos sufrieron un episodio grave. Las categorías de AS y de S/PCR+ tuvieron un incremento del riesgo ajustado de mortalidad a los 30 días y de sufrir un episodio grave durante el ingreso hospitalario respecto a S/PCR-. En relación al ingreso, solo las categorías de AS tuvieron un incremento del riesgo ajustado de hospitalización respecto a la categoría de S/PCR-. CONCLUSIONES: Existen diferentes categorías diagnósticas de la enfermedad COVID-19 en función del perfil clínico y microbiológico que tienen correlato con el pronóstico a 30 días OBJECTIVE: The primary objective was to describe the clinical characteristics and 30-day mortality rates in emergency department patients with coronavirus disease 2019 (COVID-19) in different diagnostic groupings. METHODS: Secondary analysis of the COVID-19 registry compiled by the emergency department of Hospital Clínico San Carlos in Madrid, Spain. We selected suspected COVID-19 cases treated in the emergency department between February 28 and March 31, 2020. The cases were grouped as follows: 1) suspected, no polymerase chain reaction (PCR) test (S/no-PCR);2) suspected, negative PCR (S/PCR-);3) suspected, positive PCR (S/PCR+);4) highly suspected, no PCR, or negative PCR (HS/no or PCR-);and 5) highly suspected, positive PCR (HS/PCR+). We collected clinical, radiologic, and microbiologic data related to the emergency visit. The main outcome was 30-day all-cause mortality. Secondary outcomes were hospitalization and clinical severity of the episode. RESULTS: A total of 1993 cases (90.9%) were included as follows: S/no-PCR, 17.2%;S/PCR-, 11.4%;S/PCR+, 22.1%;HS/no PCR or PCR-, 11.7%;and HS/PCR+, 37.6%. Short-term outcomes differed significantly in the different groups according to demographic characteristics;comorbidity and clinical, radiographic, analytical, and therapeutic variables. Thirty-day mortality was 11.5% (56.5% in hospitalized cases and 19.6% in cases classified as severe). The 2 HS categories and the S/PCR+ category had a greater adjusted risk for 30-day mortality and for having a clinically severe episode during hospitalization in comparison with S/PCR- cases. Only the 2 HS categories showed greater risk for hospitalization than the S/PCR- cases
ABSTRACT
PURPOSE: To determine the differences by age-dependent categories in the clinical profile, presentation, management, and short-term outcomes of patients with laboratory-confirmed COVID-19 admitted to a Spanish Emergency Department (ED). METHODS: Secondary analysis of COVID-19_URG-HCSC registry. We included all consecutive patients with laboratory-confirmed COVID-19 admitted to the ED of the University Hospital Clinico San Carlos (Madrid, Spain). The population was divided into six age groups. Demographic, baseline and acute clinical data, and in-hospital and 30-day outcomes were collected. RESULTS: 1379 confirmed COVID-19 cases (mean age 62 (SD 18) years old; 53.5% male) were included (18.1% < 45 years; 17.8% 45-54 years; 17.9% 55-64 years; 17.2% 65-74 years; 17.0% 75-84 years; and 11.9% ≥ 85 years). A statistically significant association was found between demographic, comorbidity, clinical, radiographic, analytical, and therapeutic variables and short-term results according to age-dependent categories. There were less COVID-specific symptoms and more atypical symptoms among older people. Age was a prognostic factor for hospital admission (aOR = 1.04; 95% CI 1.02-1.05) and in-hospital (aOR = 1.08; 95% CI 1.05-1.10) and 30-day mortality (aOR = 1.07; 95% CI 1.04-1.09), and was associated with not being admitted to intensive care (aOR = 0.95; 95% CI 0.93-0.98). CONCLUSIONS: Older age is associated with less COVID-specific symptoms and more atypical symptoms, and poor short-term outcomes. Age has independent prognostic value and may help in shared decision-making in patients with confirmed COVID-19 infection.