ABSTRACT
BACKGROUND: Previous works seem to agree in the higher mortality of cancer patients with COVID-19. Identifying potential prognostic factors upon admission could help identify patients with a poor prognosis. METHODS: We aimed to explore the characteristics and evolution of COVID-19 cancer patients admitted to hospital in a multicenter international registry (HOPE COVID-19).Our primary objective is to define those characteristics that allow us to identify cancer patients with a worse prognosis (mortality within 30 days after the diagnosis of COVID-19). RESULTS: 5838 patients have been collected in this registry, of whom 770 had cancer among their antecedents. In hospital mortality reached 258 patients (33.51%). The median was 75 years (65-82). Regarding the distribution by sex, 34.55% of the patients (266/770) were women.The distribution by type of cancer: genitourinary 238/745 (31.95%), digestive 124/745 (16.54%), hematologic 95/745 (12.75%).In multivariate regression analysis, factors that are independently associated with mortality at admission are: renal impairment (OR 3.45, CI 97.5% 1.85-6.58), heart disease (2.32, 1.47-3.66), liver disease (4.69, 1.94-11.62), partial dependence (2.41, 1.34-4.33), total dependence (7.21, 2.60-21.82), fatigue (1.84, 1.16-2.93), arthromialgias (0.45, 0.26-0.78), SatO2 < 92% (4.58, 2.97-7.17), elevated LDH (2.61, 1.51-4.69) and abnormal decreased Blood Pressure (3.57, 1.81-7.15). Analitical parameters are also significant altered. CONCLUSION: In patients with cancer from the HOPE registry, 30-day mortality from any cause is high and is associated with easily identifiable clinical factors upon arrival at the hospital. Identifying these patients can help initiate more intensive treatments from the start and evaluate the prognosis of these patients.
ANTECEDENTES: Trabajos previos parecen coincidir en la mayor mortalidad de los pacientes con cáncer y COVID-19. La identificación de posibles factores pronósticos en el momento del ingreso podría ayudar a identificar a los pacientes con mal pronóstico. MÉTODOS: Nos propusimos explorar las características y la evolución de los pacientes con cáncer y COVID-19 ingresados en un registro internacional multicéntrico (HOPE COVID-19).Nuestro objetivo principal es definir aquellas características que nos permitan identificar a los pacientes con cáncer de peor pronóstico (mortalidad en los 30 días siguientes al diagnóstico de COVID-19). RESULTADOS: En este registro se ha recogido a 5.838 pacientes, de los cuales 770 tenían cáncer entre sus antecedentes. La mortalidad hospitalaria alcanzó a 258 pacientes (33,51%). La mediana fue de 75 años (65-82). En cuanto a la distribución por sexo, el 34,55% de los pacientes eran mujeres (266/770).La distribución por tipo de cáncer: genitourinario 238/745 (31,95%), digestivo 124/745 (16,54%) y hematológico 95/745 (12,75%).En el análisis de regresión multivariante, los factores que se asocian de forma independiente con la mortalidad al ingreso son: insuficiencia renal (OR 3,45; IC 97,5%: 1,85-6,58), cardiopatía (2,32; 1,47-3,66), hepatopatía (4,69; 1,94-11,62), dependencia parcial (2,41; 1,34-4,33), dependencia total (7,21; 2,60-21,82), fatiga (1,84, 1;16-2,93), artromialgias (0,45; 0,26-0,78), SatO2 < 92% (4,58; 2,97-7,17), LDH elevada (2,61; 1,51-4,69) y disminución anormal de la presión arterial (3,57; 1,81-7,15). Los parámetros analíticos también están significativamente alterados. CONCLUSIÓN: En los pacientes con cáncer del registro HOPE, la mortalidad a los 30 días por cualquier causa es elevada y se asocia a factores clínicos fácilmente identificables a su llegada al hospital. La identificación de estos pacientes puede ayudar a iniciar tratamientos más intensivos desde el principio y evaluar el pronóstico de estos pacientes.
ABSTRACT
BACKGROUND: Previous works seem to agree in the higher mortality of cancer patients with COVID-19. Identifying potential prognostic factors upon admission could help identify patients with a poor prognosis. METHODS: We aimed to explore the characteristics and evolution of COVID-19 cancer patients admitted to hospital in a multicenter international registry (HOPE COVID-19). Our primary objective is to define those characteristics that allow us to identify cancer patients with a worse prognosis (mortality within 30 days after the diagnosis of COVID-19). RESULTS: 5838 patients have been collected in this registry, of whom 770 had cancer among their antecedents. In hospital mortality reached 258 patients (33.51%). The median was 75 years (65-82). Regarding the distribution by sex, 34.55% of the patients (266/770) were women. The distribution by type of cancer: genitourinary 238/745 (31.95%), digestive 124/745 (16.54%), hematologic 95/745 (12.75%). In multivariate regression analysis, factors that are independently associated with mortality at admission are: renal impairment (OR 3.45, CI 97.5% 1.85-6.58), heart disease (2.32, 1.47-3.66), liver disease (4.69, 1.94-11.62), partial dependence (2.41, 1.34-4.33), total dependence (7.21, 2.60-21.82), fatigue (1.84, 1.16-2.93), arthromialgias (0.45, 0.26-0.78), SatO2<92% (4.58, 2.97-7.17), elevated LDH (2.61, 1.51-4.69) and abnormal decreased Blood Pressure (3.57, 1.81-7.15). Analitical parameters are also significant altered. CONCLUSION: In patients with cancer from the HOPE registry, 30-day mortality from any cause is high and is associated with easily identifiable clinical factors upon arrival at the hospital. Identifying these patients can help initiate more intensive treatments from the start and evaluate the prognosis of these patients.
Subject(s)
COVID-19 , Neoplasms , Humans , Neoplasms/diagnosis , Neoplasms/therapy , Prognosis , Registries , SARS-CoV-2ABSTRACT
The objective of this study was to estimate the components of variance and genetic parameters of test-day milk yield in first lactation Girolando cows, using a random regression model. A total of 126,892 test-day milk yield (TDMY) records of 15,351 first-parity Holstein, Gyr, and Girolando breed cows were used, obtained from the Associação Brasileira dos Criadores de Girolando. To estimate the components of (co) variance, the additive genetic functions and permanent environmental covariance were estimated by random regression in three functions: Wilmink, Legendre Polynomials (third order) and Linear spline Polynomials (three knots). The Legendre polynomial function showed better fit quality. The genetic and permanent environment variances for TDMY ranged from 2.67 to 5.14 and from 9.31 to 12.04, respectively. Heritability estimates gradually increased from the beginning (0.13) to mid-lactation (0.19). The genetic correlations between the days of the control ranged from 0.37 to 1.00. The correlations of permanent environment followed the same trend as genetic correlations. The use of Legendre polynomials via random regression model can be considered as a good tool for estimating genetic parameters for test-day milk yield records.(AU)
O objetivo deste estudo foi estimar os componentes de variância e os parâmetros genéticos da produção de leite no dia do teste (TDMY) em vacas Girolando de primeira lactação, usando modelo de regressão aleatória. Foram utilizados 126.892 registros de produção de leite no dia controle de 15.351 vacas primíparas das raças Holandesa, Gir e Girolando, obtidas na Associação Brasileira dos Criadores de Girolando. Para estimar os componentes de (co) variância, as funções genéticas aditivas e de covariância ambiental permanente foram estimadas por regressão aleatória em três funções: Wilmink, polinômios de Legendre (terceira ordem) e polinômios splines lineares (três nós). A função polinomial de Legendre apresentou melhor qualidade de ajuste. As variâncias genéticas e de ambiente permanente para produção de leite no dia do controle variaram de 2,67 a 5,14 e de 9,31 a 12,04, respectivamente. As estimativas de herdabilidade aumentaram gradativamente do início (0,13) para o meio da lactação (0,19). As correlações genéticas entre os dias do controle variaram de 0,37 a 1,00. As correlações de ambiente permanente seguiram a mesma tendência das correlações genéticas. A utilização dos polinômios de Legendre via modelos de regressão aleatória pode ser considerada como uma boa ferramenta para estimação de parâmetros genéticos da produção de leite no dia do teste.(AU)
