Abstract
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.
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References
Faubel S, Shah PB (2016) Immediate consequences of acute kidney injury: the impact of traditional and nontraditional complications on mortality in acute kidney injury. Adv Chron Kidney Dis 23:179–185
Bagshaw SM, Wald R (2017) Strategies for the optimal timing to start renal replacement therapy in critically ill patients with acute kidney injury. Kidney Int 91:1022–1032
Liborio AB, Leite TT, Neves FM, Teles F, Bezerra CT (2015) AKI complications in critically ill patients: association with mortality rates and RRT. Clin J Am Soc Nephrol 10:21–28
Freeman K, Feldman JA, Mitchell P et al (2008) Effects of presentation and electrocardiogram on time to treatment of hyperkalemia. Acad Emerg Med 15:239–249
Long B, Warix JR, Koyfman A (2018) Controversies in management of hyperkalemia. J Emerg Med 55:192–205
Mattu A, Brady WJ, Robinson DA (2000) Electrocardiographic manifestations of hyperkalemia. Am J Emerg Med 18:721–729
Aslam S, Friedman E, Ifudu O (2002) Electrocardiography is unreliable in detecting potentially lethal hyperkalemia in hemodialysis patients. Nephrol Dial Transpl 17:1639–1642
Moulik PK, Nethaji C, Khaleeli AA (2002) Misleading electrocardiographic results in patient with hyperkalemia and diabetic ketoacidosis. BMJ 325:1346–1347
Wang K (2004) “Pseudoinfarction” pattern due to hyperkalemia. New Engl J Med 351:593
Fiaccadori E, Maggiore U, Clima B, Melfa L, Rotelli C, Borghetti A (2011) Incidence, risk factors and prognosis of gastrointestinal hemorrhage complcating acute renal failure. Kidney Int 59:1510–1519
Fiaccadori E, Maggiore U, Lombardi M, Leonardi S, Rotelli C, Borghetti A (2000) Predicting patient outcome from acute renal failure comparing three general severity illness scoring systems. Kidney Int 58:283–292
Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D (1999) A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Ann Intern Med 130:461–470
Rafique Z, Aceves J, Espina I, Peacock F, Sheikh-Hamad D, Kuo D (2019) Can physicians detect hyperkalemia based on the electrocardiogram? Am J Emerg Med. 10:11. doi: 10.1016/j.ajem.2019.04.036
Montague BT, Ouellette JR, Buller GK (2008) Retrospective review of ECG changes in hyperkalemia. Clin J Am Soc Nephrol 3:324–330
James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning. Springer, New York, pp 178–183
Dreyfuss D, Jondeau G, Couturier R et al (1989) Tall T waves during metabolic acidosis without hyperkalemia: a prospective study. Crit Care Med 17:404–408
Martinez-Vea A, Bardaji A, Garcia C, Oliver JA (1999) Severe hyperkalemia with minimal electrocardiographic manifestations. J Electrocardiol 32:45–49
Yu AS (1996) Atypical electrocardiographic changes in severe hyperkalemia. Am J Cardiol 77:906–908
Green D, Green HD, New DI, Kalra PA (2013) The clinical significance of hyperkalemia-associated repolarization abnormalities in end-stage renal disease. Nephrol Dial Transpl 28:99–105
Wrenn KD, Slovis CM, Slovis BS (1991) The ability of physicians to predict hyperkalemia from the ECG. Ann Emerg Med 20:1229–1232
Dillon JJ, DeSimone CV, Sapir Y et al (2015) Noninvasive potassium determination using a mathematically processed ECG: proof of concept for a novel “blood-less blood test”. J Electrocardiol 48:12–18
Somers MP, Brady WJ, Perron AD, Mattu A (2002) The prominant T wave: electrocardiographic differential diagnosis. Am J Emerg Med 20:243–251
Littmann L, Gibbs MA (2018) Electrocardiograhic manifestations of severe hyperkalemia. J Electrocardiol 51:814–817
Dittrich KL, Walls RM (1986) Hyperkalemia: ECG manifestations and clinical considerations. J Emerg Med 4:449–455
Varga C, Kalma Z, Szakall A et al (2019) ECG alterations suggestive of hyperkalemia in normokalemic versus hyperkalemic patients. BMC Emerg Med 19:33
Einhorn LM, Zhan M, Hsu VD et al (2019) The frequency of hyperkalemia and its significance in chronic kidney disease. Arch Intern Med 169:1156–1162
Bleyer AJ, Hartman J, Brannon PC, Reeves-Daniel A, Satko SG, Russell G (2006) Characteristics of sudden death in hemodialysis patients. Kidney Int 69:2268–2273
Corsi C, Cortesi M, Callisesi G et al (2017) Noninvasive quantification of blood potassium concentration from ECG in hemodialysis patients. Sci Rep 7:42492
Galloway CD, Valys AV, Shreibati JB et al (2019) Development and validation of a deep-learning model to screen for hyperkalemia from the electrocardiogram. JAMA Cardiol 4:428–436
El-Sherif N, Turitto G (2011) Electrolyte disorders and arrhythmogenesis. Cardiol J 18:233–245
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The study was approved by the local ethical board Comitato Etico dell’Area Vasta Emilia Nord).
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Regolisti, G., Maggiore, U., Greco, P. et al. Electrocardiographic T wave alterations and prediction of hyperkalemia in patients with acute kidney injury. Intern Emerg Med 15, 463–472 (2020). https://doi.org/10.1007/s11739-019-02217-x
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DOI: https://doi.org/10.1007/s11739-019-02217-x