Prediction of hyperkalemia in ESRD patients by identification of multiple leads and multiple features on ECG

References

• Hunter RW, Bailey MA. Hyperkalemia: pathophysiology, risk factors and consequences. Nephrol Dial Transplant. 2019;34(Suppl 3):1–10.
   PubMedGoogle Scholar
• Kovesdy CP. Updates in hyperkalemia: outcomes and therapeutic strategies. Rev Endocr Metab Disord. 2017;18(1):41–47.
   PubMed Web of Science ®Google Scholar
• Montford JR, Linas S. How dangerous is hyperkalemia? J Am Soc Nephrol. 2017;28(11):3155–3165.
   PubMed Web of Science ®Google Scholar
• Nakhoul GN, Huang H, Arrigain S, et al. Serum potassium, end-stage renal disease and mortality in chronic kidney disease. Am J Nephrol. 2015;41(6):456–463.
   PubMed Web of Science ®Google Scholar
• Luo J, Brunelli SM, Jensen DE, et al. Association between serum potassium and outcomes in patients with reduced kidney function. Clin J Am Soc Nephrol. 2016;11(1):90–100.
   PubMed Web of Science ®Google Scholar
• Kovesdy CP, Regidor DL, Mehrotra R, et al. Serum and dialysate potassium concentrations and survival in hemodialysis patients. Clin J Am Soc Nephrol. 2007;2(5):999–1007.
   PubMed Web of Science ®Google Scholar
• Kashihara N, Kohsaka S, Kanda E, et al. Hyperkalemia in real-world patients under continuous medical care in Japan. Kidney Int Rep. 2019;4(9):1248–1260.
   PubMed Web of Science ®Google Scholar
• Grodzinsky A, Goyal A, Gosch K, et al. Prevalence and prognosis of hyperkalemia in patients with acute myocardial infarction. Am J Med. 2016;129(8):858–865.
   PubMed Web of Science ®Google Scholar
• Thomsen RW, Nicolaisen SK, Hasvold P, et al. Elevated potassium levels in patients with chronic kidney disease: occurrence, risk factors and clinical outcomes – a Danish population-based cohort study. Nephrol Dial Transplant. 2018;33(9):1610–1620.
   PubMed Web of Science ®Google Scholar
• Nilsson E, Gasparini A, Ärnlöv J, et al. Incidence and determinants of hyperkalemia and hypokalemia in a large healthcare system. Int J Cardiol. 2017;245:277–284.
   PubMed Web of Science ®Google Scholar
• Diercks DB, Shumaik GM, Harrigan RA, et al. Electrocardiographic manifestations: electrolyte abnormalities. J Emerg Med. 2004;27(2):153–160.
   PubMed Web of Science ®Google Scholar
• Handelman GS, Kok HK, Chandra RV, et al. eDoctor: machine learning and the future of medicine. J Intern Med. 2018;284(6):603–619.
   PubMed Web of Science ®Google Scholar
• Stoltzfus JC. Logistic regression: a brief primer. Acad Emerg Med. 2011;18(10):1099–1104.
   PubMed Web of Science ®Google Scholar
• Pochet NLMM, Suykens JAK. Support vector machines versus logistic regression: improving prospective performance in clinical decision-making. Ultrasound Obstet Gynecol. 2006;27(6):607–608.
   PubMed Web of Science ®Google Scholar
• Freund Y, Schapire R, Sciences S. A decision-theoretic generalization of on-line learning and an application to boosting. 1997.
   Google Scholar
• Chen T, Guestrin CJA. XGBoost: a scalable tree boosting system. 2016.
   Google Scholar
• LeCun Y, Boser B, Denker JS, et al. Backpropagation applied to handwritten zip code recognition. Neural Comput. 1989;1(4):541–551.
   Web of Science ®Google Scholar
• Brinker TJ, Hekler A, Utikal JS, et al. Skin cancer classification using convolutional neural networks: systematic review. J Med Internet Res. 2018;20(10):e11936.
   PubMed Web of Science ®Google Scholar
• Mohan BP, Khan SR, Kassab LL, et al. High pooled performance of convolutional neural networks in computer-aided diagnosis of GI ulcers and/or hemorrhage on wireless capsule endoscopy images: a systematic review and meta-analysis. Gastrointestinal Endoscopy. 2021;93(2):356–364.e4.
   PubMed Web of Science ®Google Scholar
• Anwar SM, Majid M, Qayyum A, et al. Medical image analysis using convolutional neural networks: a review. J Med Syst. 2018;42(11):226.
   PubMed Web of Science ®Google Scholar
• Kim H, Jeon J, Han YJ, et al. Convolutional neural network classifies pathological voice change in laryngeal cancer with high accuracy. JCM. 2020;9(11):3415.
   Google Scholar
• Bonmati E, Hu Y, Grimwood A, et al. Voice-assisted image labeling for endoscopic ultrasound classification using neural networks. IEEE Trans Med Imaging. 2022;41(6):1311–1319.
   PubMed Web of Science ®Google Scholar
• Clase CM, Carrero J-J, Ellison DH, et al. Potassium homeostasis and management of dyskalemia in kidney diseases: conclusions from a kidney disease: improving global outcomes (KDIGO) controversies conference. Kidney Int. 2020;97(1):42–61.
   PubMed Web of Science ®Google Scholar
• Mattu A, Brady WJ, Robinson DA. Electrocardiographic manifestations of hyperkalemia. Am J Emerg Med. 2000;18(6):721–729.
   PubMed Web of Science ®Google Scholar
• Littmann L, Gibbs MA. Electrocardiographic manifestations of severe hyperkalemia. J Electrocardiol. 2018;51(5):814–817.
   PubMed Web of Science ®Google Scholar
• Dittrich KL, Walls RM. Hyperkalemia: ECG manifestations and clinical considerations. J Emerg Med. 1986;4(6):449–455.
   PubMedGoogle Scholar
• Martinez-Vea A, Bardají A, Garcia C, et al. Severe hyperkalemia with minimal electrocardiographic manifestations: a report of seven cases. J Electrocardiol. 1999;32(1):45–49.
   PubMed Web of Science ®Google Scholar
• Weiss JN, Qu Z, Shivkumar K. Electrophysiology of hypokalemia and hyperkalemia. Circ: Arrhythmia and Electrophysiology. 2017;10(3):e004667.
   Web of Science ®Google Scholar
• Palmer BF, Clegg DJ. Physiology and pathophysiology of potassium homeostasis. Adv Physiol Educ. 2016;40(4):480–490.
   PubMed Web of Science ®Google Scholar
• Sims DB, Sperling LS. Images in cardiovascular medicine. ST-segment elevation resulting from hyperkalemia. Circulation. 2005;111(19):e295–e296.
   PubMed Web of Science ®Google Scholar
• Varga C, Kálmán Z, Szakáll A, et al. ECG alterations suggestive of hyperkalemia in normokalemic versus hyperkalemic patients. BMC Emerg Med. 2019;19(1):33.
   PubMedGoogle Scholar
• Sadiq I, Perez-Alday EA, Shah AJ, et al. Breathing rate and heart rate as confounding factors in measuring T wave alternans and morphological variability in ECG. Physiol Meas. 2021;42(1):015002.
   PubMed Web of Science ®Google Scholar
• Sohar E, Shoenfeld Y, Shapiro Y, et al. Effects of exposure to Finnish sauna. Israel Journal of Medical Sciences. 1976;12(11):1275–1282.
   PubMedGoogle Scholar
• Bayir H, et al. Effect of perioperative inadvertent hypothermia on the ECG parameters in patients undergoing transurethral resection. Eur Rev Med Pharmacol Sci. 2016;20(8):1445–1449.
   PubMed Web of Science ®Google Scholar
• Kania M, Rix H, Fereniec M, et al. The effect of precordial lead displacement on ECG morphology. Med Biol Eng Comput. 2014;52(2):109–119.
   PubMed Web of Science ®Google Scholar
• Rafique Z, Aceves J, Espina I, et al. Can physicians detect hyperkalemia based on the electrocardiogram? Am J Emerg Med. 2020;38(1):105–108.
   PubMed Web of Science ®Google Scholar
• Montague BT, Ouellette JR, Buller GK. Retrospective review of the frequency of ECG changes in hyperkalemia. Clin J Am Soc Nephrol. 2008;3(2):324–330.
   PubMed Web of Science ®Google Scholar
• Green D, Green HD, New DI, et al. The clinical significance of hyperkalaemia-associated repolarization abnormalities in end-stage renal disease. Nephrology, Dialysis, Transplantation. 2013;28(1):99–105.
   PubMed Web of Science ®Google Scholar
• Mei Z, Chen J, Chen P, et al. A nomogram to predict hyperkalemia in patients with hemodialysis: a retrospective cohort study. BMC Nephrol. 2022;23(1):351.
   PubMed Web of Science ®Google Scholar
• Velagapudi V, O’Horo JC, Vellanki A, et al. Computer-assisted image processing 12 lead ECG model to diagnose hyperkalemia. J Electrocardiol. 2017;50(1):131–138.
   PubMed Web of Science ®Google Scholar
• Galloway CD, Valys AV, Shreibati JB, et al. Development and validation of a Deep-Learning model to screen for hyperkalemia from the electrocardiogram. JAMA Cardiol. 2019;4(5):428–436.
   PubMed Web of Science ®Google Scholar
• Sun Y, et al. Classification of imbalanced data: a review. Int J Pattern Recognit Artif Intell. 2009;23(4):687–719.
   Web of Science ®Google Scholar
• Zhang Z, Ho KM, Hong Y. Machine learning for the prediction of volume responsiveness in patients with oliguric acute kidney injury in critical care. Crit Care. 2019;23(1):112.
   PubMedGoogle Scholar
• Saraiva MM, Pouca MV, Ribeiro T, et al. Artificial intelligence and anorectal manometry: automatic detection and differentiation of anorectal motility patterns – a proof of concept study. Clin Transl Gastroenterol. 2022. Publish Ahead of Print.
   Google Scholar
• Zhang Z, Liu J, Xi J, et al. Derivation and validation of an ensemble model for the prediction of agitation in mechanically ventilated patients maintained under light sedation. Crit Care Med. 2021;49(3):e279–e290.
   PubMed Web of Science ®Google Scholar