Refractory hyperkalaemic cardiac arrest – What to do first: Treat the reversible cause or initiate E-CPR?

Refractory ventricular fibrillation secondary to hyperkalemia resuscitated with extracorporeal membrane oxygenation: A case report

The routine use of extracorporeal cardiopulmonary resuscitation (ECPR) is not recommended for patients with cardiac arrest. However, ECPR is considered for selected patients with cardiac arrest of reversible cause. Extracorporeal membrane oxygenation (ECMO) provides temporary cardiopulmonary support and adequate perfusion to the end organs, thereby shortening ischemic organ time and minimizing complications. One indication for ECPR therapy is prolonged ventricular fibrillation despite optimal conventional CPR. Here, we report a successful recovery case from ECPR, in which the patient suffered from refractory ventricular fibrillation and was predisposed to severe hyperkalemia. Ventricular fibrillation failed to respond despite prolonged conventional CPR and defibrillation management for 32 min. After successfully initiating ECPR 54 min after cardiac arrest, spontaneous circulation returned sooner. He demonstrated clear consciousness after treatment and was discharged without any neurological disability on day 11.

High-Dose Insulin for Hyperkalemic Cardiac Arrest

ABSTRACT

Hyperkalemic cardiac arrest diagnosis can be elusive and management difficult as the cardiac rhythm restoration is often not achieved until the potassium level decreases to a relatively normal level for the patient who suffers the arrest. Current treatment modalities can take hours to achieve this goal. We describe two patients who survived a witnessed hyperkalemic cardiac arrest after being managed with conventional advanced cardiac life support and unconventionally high doses of intravenous insulin.

[Cardiac arrest under special circumstances]

These guidelines of the European Resuscitation Council (ERC) Cardiac Arrest under Special Circumstances are based on the 2020 International Consensus on Cardiopulmonary Resuscitation Science with Treatment Recommendations. This section provides guidelines on the modifications required for basic and advanced life support for the prevention and treatment of cardiac arrest under special circumstances; in particular, specific causes (hypoxia, trauma, anaphylaxis, sepsis, hypo-/hyperkalaemia and other electrolyte disorders, hypothermia, avalanche, hyperthermia and malignant hyperthermia, pulmonary embolism, coronary thrombosis, cardiac tamponade, tension pneumothorax, toxic agents), specific settings (operating room, cardiac surgery, cardiac catheterization laboratory, dialysis unit, dental clinics, transportation [in-flight, cruise ships], sport, drowning, mass casualty incidents), and specific patient groups (asthma and chronic obstructive pulmonary disease, neurological disease, morbid obesity, pregnancy).

European Resuscitation Council Guidelines 2021: Cardiac arrest in special circumstances

These European Resuscitation Council (ERC) Cardiac Arrest in Special Circumstances guidelines are based on the 2020 International Consensus on Cardiopulmonary Resuscitation Science with Treatment Recommendations. This section provides guidelines on the modifications required to basic and advanced life support for the prevention and treatment of cardiac arrest in special circumstances; specifically special causes (hypoxia, trauma, anaphylaxis, sepsis, hypo/hyperkalaemia and other electrolyte disorders, hypothermia, avalanche, hyperthermia and malignant hyperthermia, pulmonary embolism, coronary thrombosis, cardiac tamponade, tension pneumothorax, toxic agents), special settings (operating room, cardiac surgery, catheter laboratory, dialysis unit, dental clinics, transportation (in-flight, cruise ships), sport, drowning, mass casualty incidents), and special patient groups (asthma and COPD, neurological disease, obesity, pregnancy).

Artificial intelligence for detecting electrolyte imbalance using electrocardiography

Introduction

The detection and monitoring of electrolyte imbalance is essential for appropriate management of many metabolic diseases; however, there is no tool that detects such imbalances reliably and noninvasively. In this study, we developed a deep learning model (DLM) using electrocardiography (ECG) for detecting electrolyte imbalance and validated its performance in a multicenter study.

Methods and Results

This retrospective cohort study included two hospitals: 92,140 patients who underwent a laboratory electrolyte examination and an ECG within 30 min were included in this study. A DLM was developed using 83,449 ECGs of 48,356 patients; the internal validation included 12,091 ECGs of 12,091 patients. We conducted an external validation with 31,693 ECGs of 31,693 patients from another hospital, and the result was electrolyte imbalance detection. During internal, the area under the receiving operating characteristic curve (AUC) of a DLM using a 12‐lead ECG for detecting hyperkalemia, hypokalemia, hypernatremia, hyponatremia, hypercalcemia, and hypocalcemia were 0.945, 0.866, 0.944, 0.885, 0.905, and 0.901, respectively. The values during external validation of the AUC of hyperkalemia, hypokalemia, hypernatremia, hyponatremia, hypercalcemia, and hypocalcemia were 0.873, 0.857, 0.839, 0.856, 0.831, and 0.813 respectively. The DLM helped to visualize the important ECG region for detecting each electrolyte imbalance, and it showed how the P wave, QRS complex, or T wave differs in importance in detecting each electrolyte imbalance.

Conclusion

The proposed DLM demonstrated high performance in detecting electrolyte imbalance. These results suggest that a DLM can be used for detecting and monitoring electrolyte imbalance using ECG on a daily basis.