ABSTRACT
Background: In recent years, a number of predictive models have appeared to predict the risk of medium-term mortality in hemodialysis patients, but only one, limited to patients aged over 70 years, has undergone sufficiently powerful external validation. Recently, using a national learning database and an innovative approach based on Bayesian networks and 14 carefully selected predictors, we have developed a clinical prediction tool to predict all-cause mortality at 2 years in all incident hemodialysis patients. In order to generalize the results of this tool and propose its use in routine clinical practice, we carried out an external validation using an independent external validation database. Methods: A regional, multicenter, observational, retrospective cohort study was conducted to externally validate the tool for predicting 2-year all-cause mortality in incident and prevalent hemodialysis patients. This study recruited a total of 142 incident and 697 prevalent adult hemodialysis patients followed up in one of the eight Association pour l'Utilisation du Rein Artificiel dans la région Lyonnaise (AURAL) Alsace dialysis centers. Results: In incident patients, the 2-year all-cause mortality prediction tool had an area under the receiver curve (AUC-ROC) of 0.73, an accuracy of 65%, a sensitivity of 71% and a specificity of 63%. In prevalent patients, the performance for the external validation were similar in terms of AUC-ROC, accuracy and specificity, but was lower in term of sensitivity. Conclusion: The tool for predicting all-cause mortality at 2 years, developed using a Bayesian network and 14 routinely available explanatory variables, obtained satisfactory external validation in incident patients, but sensitivity was insufficient in prevalent patients.
ABSTRACT
BACKGROUND: International recommendations promote a strict potassium diet in order to avoid hyperkalemia in chronic kidney disease (CKD) patients. However, the efficiency of such a dietary recommendation has never been demonstrated. The objectives of this study were to define the relationship between kalemia, dietary potassium intake estimated by kaliuresis and renal function, and to define the factors associated with kalemia in patients using artificial intelligence. METHODS: To this extent, data from patients followed in a nephrology unit, included in the UniverSel study and whose kalemia (measured on the day of urine collection; n = 367) were analyzed. RESULTS: The patients included had a wide range of estimated glomerular filtration rate (eGFR), but few had stage 5 CKD. Kalemia was negatively and linearly correlated to eGFR (P < .001) but was not correlated to kaliuresis (P = .55). Kaliuresis was not correlated to eGFR (P = .08). Factors associated with kalemia were analyzed using a Bayesian network. The five variables most associated with kalemia were, in descending order, eGFR, original nephropathy, age, diabetes and plasma bicarbonate level. CONCLUSION: The results of this study do not support a strict dietary potassium control to regulate kalemia in stage 1-4 CKD patients.
