Electrocardiographic T wave alterations and prediction of hyperkalemia in patients with acute kidney injury.
Giuseppe RegolistiUmberto MaggiorePaolo GrecoCaterina MaccariElisabetta ParentiFrancesca Di MarioValentina PistolesiSanto MorabitoEnrico FiaccadoriPublished in: Internal and emergency medicine (2019)
Electrocardiographic (ECG) alterations are common in hyperkalemic patients. While the presence of peaked T waves is the most frequent ECG alteration, reported findings on ECG sensitivity in detecting hyperkalemia are conflicting. Moreover, no studies have been conducted specifically in patients with acute kidney injury (AKI). We used the best subset selection and cross-validation methods [via linear and logistic regression and leave-one-out cross-validation (LOOCV)] to assess the ability of T waves to predict serum potassium levels or hyperkalemia (defined as serum potassium ≥ 5.5 mEq/L). We included the following clinical variables as a candidate for the predictive models: peaked T waves, T wave maximum amplitude, T wave/R wave maximum amplitude ratio, age, and indicator variates for oliguria, use of ACE-inhibitors, sartans, mineralocorticoid receptor antagonists, and loop diuretics. Peaked T waves poorly predicted the serum potassium levels in both full and test sample (R2 = 0.03 and R2 = 0.01, respectively), and also poorly predicted hyperkalemia. The selection algorithm based on Bayesian information criterion identified T wave amplitude and use of loop diuretics as the best subset of variables predicting serum potassium. Nonetheless, the model accuracy was poor in both full and test sample [root mean square error (RMSE) = 0.96 mEq/L and adjR2 = 0.08 and RMSE = 0.97 mEq/L, adjR2 = 0.06, respectively]. T wave amplitude and the use of loop diuretics had also poor accuracy in predicting hyperkalemia in both full and test sample [area-under-curve (AUC) at receiver-operator curve (ROC) analysis 0.74 and AUC 0.72, respectively]. Our findings show that, in patients with AKI, electrocardiographic changes in T waves are poor predictors of serum potassium levels and of the presence of hyperkalemia.
Keyphrases
- acute kidney injury
- cardiac surgery
- heart rate
- resting state
- machine learning
- end stage renal disease
- transcription factor
- heart rate variability
- newly diagnosed
- chronic kidney disease
- left atrial
- heart failure
- blood pressure
- social media
- deep learning
- prognostic factors
- patient reported outcomes
- clinical evaluation