Development of prediction model for identifying heart failure patients with high risk of developing hyponatremia

Abstract

Listen

Translate article

Background: Despite its significant contribution to morbidity and mortality, studies reported that hyponatremia is still inadequately recognised and treated. Objective: To obtain a prediction model for predicting the risk of hyponatremia in patients hospitalized from heart failure. Methods: Patients included in this research were patients hospitalized from heart failure at Fatmawati Hospital in Jakarta, Indonesia during the 2011 – 2014 period. Logistic regression analysis was performed for the derivation of prediction model by including variables obtained during admission as the predictors. Brier-score and Nagelkerke R2 (NR2) were measured to assess overall predictive ability and area under the curve (AUC) of the Receiver Operating Characteristics (ROC) and calibration plot along with Hosmer-Lemeshow test were measured to assess discrimination and calibration ability, respectively. Internal validation was performed using a bootstrapping approach. Results: Out of 464 patients included in the research 102 (22%) were hyponatremic during hospitalization. Accordingly, 306 non-hyponatremic patients were selected as controls matched by age and gender. Variables significantly associated with hyponatremia were serum sodium level, fatigue, ascites, positive inotropes, heparin and antibiotics. Prediction model containing those six variables exhibits good predictive ability both overall (brier-score=0.107, NR2=0.531) and specifically of discrimination (AUC of ROC curve=0.90) and calibration ability (p-value of HL test=0.899). Optimism observed from internal validation did not reduce its predictive performance. Conclusion: Risk prediction for predicting the risk of hyponatremia in patients hospitalized from heart failure can be derived by including predictors taken from information obtained during admission.