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Antecedentes y objetivos: El desarrollo de lesión renal aguda (FRA) durante la hospitalización por SARS-Cov2 se ha asociado a elevada morbi-mortalidad. El Registro FRA-COVID 19 SEN ha recogido datos de pacientes con este perfil durante los meses de la pandemia. El objetivo de este trabajo fue caracterizar la población española ingresada por COVID-19 que desarrolló FRA, con/ sin necesidades de tratamiento renal sustitutivo (TRS), modalidades terapéuticas utilizadas y resultados en términos de mortalidad Material y método: Estudio retrospectivo de datos procedentes de 30 hospitales españoles, recopilados en el Registro Español FRA COVID-19 desde mayo de 2020 hasta noviembre 2021. Se registraron variables clínicas y demográficas, datos relacionados con la gravedad de la COVID-19, con el FRA y de supervivencia. Mediante estudio de regresión multivariante se analizan los factores relacionados con la necesidad de TRS y con mortalidad. Resultados: Se registraron datos de 730 pacientes. El 71,9% eran hombres, con edad media 70 años (60-78). El 70,1% eran hipertensos, 32,9% diabéticos, 33,3% con enfermedad cardiovascular y 23,9% presentaban algún grado de enfermedad renal crónica. Desarrollaron neumonía el 94,6%, con necesidad de soporte ventilatorio en el 54,2% e ingreso en UCI en un 44,1% de los casos.La mediana de tiempo desde el inicio de síntomas COVID hasta la aparición de FRA fue de 6 días (4-10). En cuanto a la gravedad: 37,1% KDIGO I, 18,3% KDIGO II, 44,6% KDIGO III;requirieron TRS 235 pacientes (33,9%). Las técnicas continuas (TCRR) fueron las más empleadas (155 pac), seguidas de hemodiálisis (HD) intermitente (89 pac), HD diaria (36 pacientes), hemodiafiltración (HDF) 17 pac., y HD expandida (HDexp) en 24 casos. El hábito tabáquico (OR 3,41), la necesidad de soporte ventilatorio (OR 20,2), la cifra de creatinina máxima (OR 2,41) y el tiempo transcurrido desde el inicio de los síntomas hasta la aparición del FRA (OR 1,13) se identificaron como predictores de la necesidad de TRS en nuestra cohorte, mientras la edad se comporta como protector (OR: 0,95). El grupo que no requirió TRS se caracterizó por presentar mayor edad, menor gravedad de FRA y un tiempo de aparición y recuperación de la lesión renal más corto (p<0,05).El 38,6% de la cohorte falleció durante la hospitalización. La gravedad del FRA y la necesidad de TRS fue más frecuente en el grupo de fallecidos. En el análisis multivariante, edad (OR 1.03), enfermedad renal crónica previa (OR 2,21), desarrollo de neumonía (OR 2,89), soporte ventilatorio (OR 3,34) y TRS (OR: 2,28) se comportaron como factores predictores mientras que el tratamiento crónico con ARAII se comportó como factor protector (OR 0,55). Conclusiones: Del análisis del Registro FRA-COVID 19 SEN se deduce que los pacientes que presentaron FRA durante la hospitalización por COVID-19 tenían una edad media elevada, mayores comorbilidades y presentaron un cuadro de infección grave. Definimos dos patrones clínicos diferentes: un FRA de aparición precoz, en pacientes más ancianos que se resuelve en pocos días sin necesidad de TRS;y otro patrón más grave, con mayor requerimiento de TRS, y aparición tardía en el curso de la enfermedad, que se relacionó con mayor gravedad de la misma. La gravedad de la infección, la edad y la presencia de ERC previa al ingreso fueron factores determinantes en la mortalidad de estos pacientes, identificando el tratamiento crónico con ARA II como un factor protector de mortalidad.
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Purpose: The increased COVID-19 severity observed in kidney transplant recipients (KTR) has been widely reported. In addition, several studies have shown a reduced humoral and cellular response after mRNA vaccination in this population compared to hemodialysis patients. However, there is currently no information on real-life clinical protection (deaths and hospitalizations), a gap that this study aims to fill. Method(s): Observational prospective study. A total population of 1336 KTR and hemodialysis patients from three dialysis units affiliated to Hospital Clinic of Barcelona, Spain, vaccinated with two doses of mRNA-1273 (Moderna) or BNT162b2 (Pfizer-BioNTech) SARS-CoV-2 mRNA vaccines. The outcomes measured were SARS-CoV-2 infection diagnosed by a positive RT-PCR fourteen days after the second vaccine dose, hospital admissions derived from infection, and a severe COVID-19 composite outcome, defined as either ICU admission, invasive and non-invasive mechanical ventilation, or death. Result(s): Six per cent (18/302) of patients on hemodialysis were infected, of whom four required hospital admission (1.3%), only one (0.3%) had severe COVID-19, and none of them died. In contrast, 4.3% (44/1034) of KTR were infected, and presented more hospital admissions (26 patients, 2.5%), severe COVID-19 (11 patients, 1.1%) or death (4 patients, 0.4%). There were no correlations on the multivariate analysis between measured outcomes and baseline characteristics nor immunosuppressive treatment. Conclusion(s): The study highlights the need for further booster doses in KTR. In contrast, the hemodialysis population appears to have an adequate clinical response to vaccination, at least up to four months from its administration.
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Purpose: Health systems need tools to deal with COVID-19, especially for high-risk population,such as transplant recipients. Predictive models are necessary to improve management of patients and optimize resources. Methods: A retrospective study of hospitalized transplant patients due to COVID-19 was evaluated(March 3-April 24,2020). Admission data were integrated to develop a prediction model to evaluate a composite-event defined as Intensive Care Unit admission or intensification treatment with antiinflamatory agents. Predictions were made using a Data Envelopment Analysis(DEA)-Artificial Neural Network(ANN) hybrid, whose accuracy relative to several alternative configurations has been validated through a battery of clustering techniques. Results: Of 1006 recipients with a planned or an unscheduled visit during the observation period, thirty-eight were admitted due to COVID-19. Twenty-five patients(63.2%) exhibited poor clinical course(mortality rate:13.2%), within a mean of 12 days of admission stay. Cough as a presenting symptom(P=0.000), pneumonia(P=0.011), and levels of LDH(P=0.031) were admission factors associated with poor outcomes. The prediction hybrid model working with a set of 17 input variables displays an accuracy of 96.3%, outperforming any competing model, such as logistic regression(65.5%) and Random forest(denoted by Bagged Trees,44.8%). Moreover, the prediction model allows us to categorize the evolution of patients through the values at hospital admission. Conclusions: The prediction model based in Data Envelopment Analysis-Artificial Neural Network hybrid forecasts the progression towards severe COVID-19 disease with an accuracy of 96.3%, and may help to guide COVID-19 management by identification of key predictors that permit a sustainable distribution of resources in a patient-centered model. Improving efficiency and patient parformance in the AAN with DEA, we can get high accurancy even with no-big cohorts. (Table Presented).
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SDRA secondary due to SARS-Cov 2 infection is one of the causes of tracheal intubation. In patients with acute respiratory failure. In ICU patientes with difficcult weaning of mechanical ventilation tracheostomy is one of the therapeutic approach. Percutaneous tracheostomy have so many advantages instead of surgical tracheostomy wiht less complications rate, cheaper and quicker. One of the complications of this technique is the risk of tracheal rupture. We present a case of tracheal rupture in a patient with severe SARS- Cov-2 pneumonia secondary a percutaneous tracheostomy solved with conservative management and a literatura review.
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Background: SARS-CoV-2 coronavirus pandemic has significant impact on the general population, and chronic hemodialysis patients presented a poor prognosis with a mortality rate around 25%. Data from severe acute kidney injury(AKI) and acute renal replacement therapy(RRT) is scarce. We present the preliminary results of AKI COVID-19 Registry of the Spanish Society of Nephrology. Methods: The online Registry began operating on May 21th. It collects epidemiological variables, contagion and diagnosis data, signs and symptoms, treatments and outcomes. Patients were diagnosed with SARS-Cov-2 infection based on PCR of the virus. Results: One week after the AKI COVID registry started, 54 patients with AKI with RRT and COVID-19 from 11 Hospitals. Age was 64+9years and 55% men. 65% hypertension, 31% diabetes mellitus, 14% cardiovascular disease, 26% chronic kidney disease, 6% neoplasm, 29% obesity, 8% chronic obstructive pulmonary disease, and6% smokers. Previous treatment: 10% immunosuppressive, 20% ACEi, 25% ARBs, 14% antiplatelets, and 10% anticoagulants. Clinical characteristics: 92% common respiratory symptoms, 96% pneumonia, 90% required intensive care unit(ICU) and 87% mechanic ventilation. 32% albuminuria, 18% hematuria, and 50% AKI with preserved urine output. Time from COVID-19 symptoms start to AKI 12.3+8days, time ICU 19.8+5days. APACHE at UCI admission 15+7. 81% lymphopenia. RRT was needed in 91% 13.4+12days: 55% received continuous RRT, and 72% anticoagulation. Kidney biopsy was not performed. Mortality 46.3% (60% males), and 4% remained under RRT. Time from AKI to renal function recovery 25+14 days. 65.2% death patients had hypertension. No differences were observed in comorbidities, chronic treatments, renal clinical characteristics, dialysis modality and mortality. Decreased lymphocyte count was associated with worse patient prognosis (dead 495±260 vs. survivors 789±460,p=0.023). Conclusions: The mortality in AKI with RRT and COVID-19 is alarming high. Severe AKI associated with COVID-19 disease is more frequent in males. Interestingly, half of the patients preserved urine output. Severe lymphopenia was associated with mortality. More data from the AKI COVID-19 registry will help us to enlighten the prognosis and risk factors associated to mortality.
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Background: Health systems need tools to deal with COVID-19, especially for highrisk population, such as transplant recipients. Predictive models are necessary to improve management of patients and optimize resources. Methods: A retrospective study of hospitalized transplant patients due to COVID-19 was evaluated(March 3-April 24,2020). Admission data were integrated to develop a prediction model to evaluate a composite-event defined as Intensive Care Unit admission or intensification treatment with antiinflamatory agents. Predictions were made using a Data Envelopment Analysis(DEA)-Artificial Neural Network(ANN) hybrid, whose accuracy relative to several alternative configurations has been validated through a battery of clustering techniques. Results: Of 1006 recipients with a planned or an unscheduled visit during the observation period, thirty-eight were admitted due to COVID-19. Twenty-five patients(63.2%) exhibited poor clinical course(mortality rate:13.2%), within a mean of 12 days of admission stay. Cough as a presenting symptom(P=0.000), pneumonia(P=0.011), and levels of LDH(P=0.031) were admission factors associated with poor outcomes. The prediction hybrid model working with a set of 17 input variables displays an accuracy of 96.3%, outperforming any competing model, such as logistic regression(65.5%) and Random forest(denoted by Bagged Trees, 44.8%). Moreover, the prediction model allows us to categorize the evolution of patients through the values at hospital admission. Conclusions: The prediction model based in Data Envelopment Analysis-Artificial Neural Network hybrid forecasts the progression towards severe COVID-19 disease with an accuracy of 96.3%, and may help to guide COVID-19 management by identification of key predictors that permit a sustainable distribution of resources in a patient-centered model.