ABSTRACT
OBJECTIVE: To assess the impact of different phase-out measures approved by several European governments. DESIGN: This is a longitudinal observational study. SETTINGS: European countries, from 20 February 2020 to 11 May 2020. PARTICIPANTS: All European countries that implemented at least one phase-out measure dictated by governments, during the follow-up period. MAIN OUTCOME: New COVID-19 cases, analysed as daily rate by countries. METHODS: We compared the observed versus the predicted rates of new confirmed cases, hospital admission, intensive care unit (ICU) admission and deaths by regions in Spain, to assess the accuracy of the proposed generalised estimating equations and hurdle models. Based on these models, we defined and calculated two indices to quantify the impact of the phase-out measures approved in several European countries. RESULTS: After 2-month follow-up, we confirmed the good performance of these models for the prediction of the incidence of new confirmed cases, hospital admission, ICU admission and death in a 7-day window. We found that certain phase-out measures implemented in Italy, Spain and Denmark showed moderate impact in daily new confirmed cases. Due to these different phase-out measures, in Italy, the estimated increment of new confirmed cases per 100 000 inhabitants was 4.61, 95% CI (4.42 to 4.80), in Spain 2.58, 95% CI (2.54 to 2.62) and in Denmark 2.55, 95% CI (2.40 to 2.69). Other significant measures applied in other countries had no impact. CONCLUSION: The two indices proposed can be used to quantify the impact of the phase-out measures and to help other countries to make the best decision. Monitoring these phase-out measures over time can minimise the negative effects on citizens.
Subject(s)
COVID-19 , Humans , COVID-19/epidemiology , Pandemics , Spain/epidemiology , Longitudinal Studies , HospitalizationABSTRACT
ANTECEDENTES Y OBJETIVO: El diagnóstico de insuficiencia renal aguda (IRA) todavía se basa en la creatinina sérica y la diuresis. Sin embargo, el incremento de la creatinina a menudo se retrasa 48 h o más con respecto al momento de la lesión. El objetivo de este estudio es determinar la utilidad de las pruebas analíticas de función renal habituales en el postoperatorio, para predecir la IRA con uno o 2días de antelación, en una cohorte de pacientes intervenidos mediante cirugía cardíaca. PACIENTES Y MÉTODOS: A partir de una base de datos prospectiva, se seleccionó una muestra de pacientes operados de cirugía cardíaca mayor, entre enero de 2002 y diciembre de 2013. La definición de IRA se basó en el criterio de la creatinina sérica utilizado por la Acute Kidney Injury Network. La cohorte de 3.962 casos se dividió en 2grupos de tamaño similar, uno exploratorio y otro de validación. El grupo exploratorio se utilizó para demostrar los objetivos principales y el de validación para confirmar los resultados. La capacidad de predicción de la IRA, de varios parámetros de función renal medidos en la analítica postoperatoria habitual, se evaluó utilizando curvas ROC tiempo-dependientes. Como variable principal se consideró el tiempo transcurrido desde la medida del marcador hasta el diagnóstico de la IRA. RESULTADOS: Se observaron 610 (30,8%) y 623 (31,4%) episodios de IRA en los grupos exploratorio y de validación, respectivamente. La tasa de filtrado glomerular estimada por la ecuación MDRD-4 demostró la mejor capacidad predictiva de IRA, con valores del área bajo la curva ROC entre 0,700 y 0,946. Se calcularon distintos puntos de corte para dicho parámetro, en función de la gravedad de la IRA y del tiempo transcurrido entre la cirugía y su medición. Los resultados obtenidos se confirmaron en el grupo de validación. CONCLUSIÓN: La tasa de filtrado glomerular postoperatoria, estimada por la ecuación MDRD-4, mostró una alta capacidad de predicción de IRA con uno o 2 días de antelación, en pacientes operados de cirugía cardíaca
BACKGROUND: and objective Acute kidney injury (AKI) diagnosis is still based on serum creatinine and diuresis. However, increases in creatinine are typically delayed 48h or longer after injury. Our aim was to determine the utility of routine postoperative renal function blood tests, to predict AKI one or 2days in advance in a cohort of cardiac surgery patients. PATIENTS AND METHODS: Using a prospective database, we selected a sample of patients who had undergone major cardiac surgery between January 2002 and December 2013. The ability of the parameters to predict AKI was based on Acute Kidney Injury Network serum creatinine criteria. A cohort of 3,962 cases was divided into 2groups of similar size, one being exploratory and the other a validation sample. The exploratory group was used to show primary objectives and the validation group to confirm results. The ability to predict AKI of several kidney function parameters measured in routine postoperative blood tests, was measured with time-dependent ROC curves. The primary endpoint was time from measurement to AKI diagnosis. RESULTS: AKI developed in 610 (30.8%) and 623 (31.4%) patients in the exploratory and validation samples, respectively. Estimated glomerular filtration rate using the MDRD-4 equation showed the best AKI prediction capacity, with values for the AUC ROC curves between 0.700 and 0.946. We obtained different cut-off values for estimated glomerular filtration rate depending on the degree of AKI severity and on the time elapsed between surgery and parameter measurement. Results were confirmed in the validation sample. CONCLUSIONS: Postoperative estimated glomerular filtration rate using the MDRD-4 equation showed good ability to predict AKI following cardiac surgery one or 2 days in advance
Subject(s)
Humans , Male , Female , Middle Aged , Aged , Aged, 80 and over , Glomerular Filtration Rate , Renal Insufficiency/diagnosis , Biomarkers , Severity of Illness Index , Retrospective Studies , Cohort Studies , Acute Disease , Thoracic Surgery , ROC CurveABSTRACT
BACKGROUND: and objective Acute kidney injury (AKI) diagnosis is still based on serum creatinine and diuresis. However, increases in creatinine are typically delayed 48h or longer after injury. Our aim was to determine the utility of routine postoperative renal function blood tests, to predict AKI one or 2days in advance in a cohort of cardiac surgery patients. PATIENTS AND METHODS: Using a prospective database, we selected a sample of patients who had undergone major cardiac surgery between January 2002 and December 2013. The ability of the parameters to predict AKI was based on Acute Kidney Injury Network serum creatinine criteria. A cohort of 3,962 cases was divided into 2groups of similar size, one being exploratory and the other a validation sample. The exploratory group was used to show primary objectives and the validation group to confirm results. The ability to predict AKI of several kidney function parameters measured in routine postoperative blood tests, was measured with time-dependent ROC curves. The primary endpoint was time from measurement to AKI diagnosis. RESULTS: AKI developed in 610 (30.8%) and 623 (31.4%) patients in the exploratory and validation samples, respectively. Estimated glomerular filtration rate using the MDRD-4 equation showed the best AKI prediction capacity, with values for the AUC ROC curves between 0.700 and 0.946. We obtained different cut-off values for estimated glomerular filtration rate depending on the degree of AKI severity and on the time elapsed between surgery and parameter measurement. Results were confirmed in the validation sample. CONCLUSIONS: Postoperative estimated glomerular filtration rate using the MDRD-4 equation showed good ability to predict AKI following cardiac surgery one or 2days in advance.
Subject(s)
Acute Kidney Injury/diagnosis , Cardiac Surgical Procedures , Glomerular Filtration Rate , Postoperative Complications/diagnosis , Acute Kidney Injury/blood , Acute Kidney Injury/urine , Aged , Biomarkers/blood , Biomarkers/urine , Female , Humans , Male , Postoperative Complications/blood , Postoperative Complications/urine , Predictive Value of Tests , Retrospective Studies , Time FactorsABSTRACT
The generalized estimating equations (GEE) derived by Liang and Zeger to analyze longitudinal data have been used in a wide range of medical and biological applications. To make regression a useful and meaningful statistical tool, emphasis should be placed not only on inference or fitting, but also on diagnosing potential data problems. Most of the usual diagnostics for linear regression models have been generalized for GEE. However, global influence measures based on the volume of confidence ellipsoids are not available for GEE analysis. This article presents an extension of these measures that is valid for correlated-measures regression analysis using GEEs. The proposed measures are illustrated by an analysis of epileptic seizure count data arising from a study of prograbide as an adjuvant therapy for partial seizures and some simulated data sets.
