Abstract Introduction Ideal timing of Renal Replacement Therapy (RRT) discontinuation in Acute Kidney Injury (AKI) is still unknown. We aimed to study the role of creatinine-related variables in predicting RRT successful discontinuation and to propose a clinical predictive score. Methods In this single-centre retrospective study, we evaluated all AKI patients in whom RRT was interrupted for at least 48 hours. Patients who were still RRT-independent 7 days after initial RRT cessation were included in the “Success” group and opposed to the “Failure” group. We evaluated baseline characteristics and variables collected at the time of RRT interruption, as well as the Kinetic estimated Glomerular Filtration Rate (KeGFR), the simple variation in serum Creatinine (ΔsCr), and the incremental creatinine ratio on the first three days after RRT interruption. Multivariable analysis was performed to evaluate prediction of success. Internal validation using a simple binomial generalized regression model with Lasso estimation and 5-fold cross validation method was performed. Results We included 124 patients, 49 in the “Failure” group and 75 in the “Success” group. All creatinine-related variables predicted success in simple and multiple logistic regression models. The best model generated a clinical score based on the odds ratio obtained for each variable and included urine output, non-renal SOFA score, fluid balance, serum urea, serum potassium, blood pH, and the variation in sCr values after RRT discontinuation. The score presented an area under the ROC of 0.86 (95% CI 0.76‒1.00). Conclusion Creatinine variation between the first 2 consecutive days after RRT discontinuation might predict success in RRT discontinuation. The developed clinical score based on these variables might be a useful clinical decision tool to guide hemodialysis catheter safe removal.

O objetivo deste trabalho foi modelar o fator k e as funções DRIS para a diagnose foliar de mangueiras cultivadas. Dez pomares comerciais, no estágio de produção, localizados no vale do Rio São Francisco, foram monitorados, mensalmente, durante dois anos, por meio da coleta de amostras foliares para determinação dos teores de N, P, K, Ca, Mg, B, Fe, Mn, Zn e Cu. Os dados foram testados quanto à normalidade e as relações entre as concentrações dos nutrientes foram usadas para calcular as normas DRIS, obtendo-se média, variância e limites máximo e mínimo de cada relação dentro da população amostrada. Os nutrientes foram classificados como macronutrientes de resposta freqüente (MAF) (N, P e K), macronutrientes de resposta rara (MAR) (Ca e Mg); micronutrientes de resposta freqüente (MIF) (B, Fe, Mn e Zn) e, micronutrientes de resposta rara (MIR) (Cu). Funções DRIS foram desenvolvidas para cada classe de nutrientes. O modelo desenvolvido expressa o balanço nutricional das plantas cultivadas ajustado a cada nutriente e reflete o comportamento biológico das plantas como resultado da variação da disponibilidade dos nutrientes.

The objective of this work was to model DRIS functions and k factor on foliar diagnosis of mango crops. Ten commercial orchards, at yield stage, in the São Francisco River valley were monitored, monthly, during a two-year period, by means of collecting leaf samples to determine N, P, K, Ca, Mg, B, Fe, Mn, Zn and Cu concentrations. Data were tested for normality and bivariate relationships between nutrient concentrations were used to calculate DRIS norms. Mean, variance as well as minimum and maximum values were calculated for each relationship within the population. Nutrients were classified as follows: highly responsive macronutrients (HRMa) (N, P and K); rarely responsive macronutrients (RRMa) (Ca and Mg); highly responsive micronutrients (HRMi) (B, Fe, Mn and Zn), and rarely responsive micronutrients (RRMi) (Cu). DRIS functions were developed for each nutrient class. Results showed that the developed model expresses the nutritional balance adjusted for each nutrient, and reflects the expected biological behavior of plants as a result of variation in the availability of nutrients.