Electrocardiographic T wave alterations and prediction of hyperkalemia in patients with acute kidney injury

Monitoring serum potassium concentration in patients with severe hyperkalemia: the role of bloodless artificial intelligence-enabled electrocardiography

Background

Severe hyperkalemia is a life-threatening emergency requiring prompt management and close surveillance. Although artificial intelligence-enabled electrocardiography (AI-ECG) has been developed to rapidly detect hyperkalemia, its application to monitor potassium (K+) levels remains unassessed. This study aimed to evaluate the effectiveness of AI-ECG for monitoring K+ levels in patients with severe hyperkalemia.

Methods

This retrospective study was performed at an emergency department of a single medical center over 2.5 years. Patients with severe hyperkalemia defined as Lab-K+ ≥6.5 mmol/l with matched ECG-K+ ≥5.5 mmol/l were included. ECG-K+ was quantified by ECG12Net analysis of the AI-ECG system. The following paired ECG-K+ and Lab-K+ were measured at least twice, almost simultaneously, during and after K+-lowering therapy in 1 day. Clinical characteristics, pertinent intervention, and laboratory data were analyzed.

Results

Seventy-six patients fulfilling the inclusion criteria exhibited initial Lab-K+ 7.4 ± 0.7 and ECG-K+ 6.8 ± 0.5 mmol/l. Most of them had chronic kidney disease (CKD) or were on chronic hemodialysis (HD). The followed Lab-K+ and ECG-K+ measured with a mean time difference of 11.4 ± 5.6 minutes significantly declined in parallel both in patients treated medically (n = 39) and with HD (n = 37). However, there was greater decrement in Lab-K⁺ (mean 7.3 to 4.1) than ECG-K⁺ (mean 6.6 to 5.0) shortly after HD. Three patients with persistent ECG-K+ hyperkalemia despite normalized Lab-K+ exhibited concomitant acute cardiovascular comorbidities.

Conclusions

AI-ECG for K+ prediction may help monitor K+ level for severe hyperkalemia and reveal more severe cardiac disorders in the patients with persistent AI-ECG hyperkalemia.

Association between admission baseline blood potassium levels and all-cause mortality in patients with acute kidney injury combined with sepsis: A retrospective cohort study

INTRODUCTION

Imbalances in blood potassium (K) homeostasis is a significant contributor to the emergence of severe complications, especially among critically ill patients. Hypokalemia and hyperkalemia are both associated with an increased risk of adverse events. However, it is not known about the impact of blood K levels on risk of intensive care units (ICU) mortality for Acute kidney injury (AKI) combined with sepsis patients. This study aimed to explore the relationship between admission blood K levels and ICU 30-day mortality in patients with AKI combined with sepsis.

METHODS

We selected patients diagnosed with AKI and sepsis on their first ICU admission from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. The first blood K levels within 24 hours of admission were categorized into three groups according to tertiles (T1 < 3.9 mmol/L, 3.9 ≤ T2 < 4.5 mmol/L, and T3 ≥ 4.5 mmol/L), with T2 serving as the reference. We examined the association between blood K levels and ICU 30-day mortality using accelerated failure time (AFT) models and survival analysis.

RESULTS

A total of 8,242 ICU patients with AKI combined with sepsis were included. In multivariate AFT models, each 1 mmol/L increase in blood K levels was associated with a 13% increase in the risk of ICU 30-day mortality (p < 0.001, 95% confidence interval (CI): 1.06-1.20). Extended multivariable AFT models showed that, compared to the middle category, patients with high blood K levels (≥ 4.5 mmol/L) were associated with all-cause mortality (p = 0.002, adjusted hazard ratio (HR) = 1.22, 95% CI: 1.08-1.38), whereas those with low blood K levels (< 3.9 mmol/L) showed no significant difference (p = 0.385, adjusted HR = 1.06, 95% CI: 0.93-1.21). Kaplan-Meier curves indicated that patients with high blood K levels had higher mortality, and those with middle blood potassium levels (3.9 ≤ K < 4.5 mmol/L) had the lowest mortality.

CONCLUSION

The admission baseline blood K levels were significantly associated with ICU 30-day mortality in intensive care patients suffering from AKI in conjunction with sepsis. Therefore, immediate and careful correction of blood potassium imbalances may prove to be a crucial approach in improving outcomes for these patients.

Rapid and non-invasive diagnosis of hyperkalemia in patients with systolic myocardial failure using a model based on machine learning algorithms

Background

Hyperkalemia is a potentially life-threatening electrolyte disturbance that if not diagnosed on time may lead to devastating conditions and sudden cardiac death. Blood sampling for potassium level checks is time-consuming and can delay the treatment of severe hyperkalemia on time. So, we propose a non-invasive method for correct and rapid hyperkalemia detection.

Methods

The cardiac signal of patients referred to the Pediatrics Emergency room of Shahid Rejaee Hospital was measured by a 12-lead Philips electrocardiogram (ECG) device. Immediately, the blood samples of the patients were sent to the laboratory for potassium serum level determination. We defined 16 features for each cardiac signal at lead 2 and extracted them automatically using the algorithm developed. With the help of the principal component analysis (PCA) algorithm, the dimension reduction operation was performed. The algorithms of decision tree (DT), random forest (RF), logistic regression, and support vector machine (SVM) were used to classify serum potassium levels. Finally, we used the receiver operation characteristic (ROC) curve to display the results.

Results

In the period of 5 months, 126 patients with a serum level above 4.5 (hyperkalemia) and 152 patients with a serum potassium level below 4.5 (normal potassium) were included in the study. Classification with the help of a RF algorithm has the best result. Accuracy, Precision, Recall, F1, and area under the curve (AUC) of this algorithm are 0.71, 0.87, 0.53, 0.66, and 0.69, respectively.

Conclusions

A lead2-based RF classification model may help clinicians to rapidly detect severe dyskalemias as a non-invasive method and prevent life-threatening cardiac conditions due to hyperkalemia.

Electrocardiographic Abnormalities in Patients with Hyperkalemia: A Retrospective Study in an Emergency Department in Colombia

Introduction

Hyperkalemia is a prevalent electrolyte disorder related to elevated serum potassium levels, resulting in diverse abnormal electrocardiographic findings and associated clinical signs and symptoms, often necessitating specific treatment. However, in some patients, these abnormal findings may not be present on the electrocardiogram even in elevated serum potassium levels. This study aims to identify electrocardiographic abnormalities related to the severity of hyperkalemia and the clinical outcomes in an emergency department in southwestern Colombia.

Methodology

This is a retrospective cross-sectional descriptive study. We described the electrocardiographic findings, clinical characteristics, treatment, and outcomes related to the degrees of hyperkalemia. The potential association between the severity of hyperkalemia and electrocardiographic findings was evaluated.

Results

A total of 494 patients were included. The median of the potassium level was 6.6 mEq/L. Abnormal electrocardiographic findings were reported in 61.5% of the cases. Mild and severe hyperkalemia groups reported abnormalities in 59.9% and 61.2%, respectively. The most common electrocardiography abnormalities were the peaked T wave 36.2%, followed by wide QRS 83 (16.8%). Only 1.4% of patients had adverse outcomes. The abnormal findings were registered in 61.5%. Mortality was 11.9%. The peaked T wave was the most common finding across different levels of hyperkalemia severity.

Conclusion

High serum potassium levels are related with abnormal ECG. However, patients with different degrees of hyperkalemia could not describe abnormal ECG findings. In a high proportion of patients with renal chronic disease and hyperkalemia, the abnormalities in the ECG could be minimal or absent.

Machine learning models for early prediction of potassium lowering effectiveness and adverse events in patients with hyperkalemia

The aim of this study was to develop a model for early prediction of adverse events and treatment effectiveness in patients with hyperkalemia. We collected clinical data from patients with hyperkalemia in the First Hospital of Zhejiang University School of Medicine between 2015 and 2021. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were used to analyze the predictors on the full dataset. We randomly divided the data into a training group and a validation group, and used LASSO to filter variables in the training set. Six machine learning methods were used to develop the models. The best model was selected based on the area under the curve (AUC). Shapley additive exPlanations (SHAP) values were used to explain the best model. A total of 1074 patients with hyperkalemia were finally enrolled. Diastolic blood pressure (DBP), breathing, oxygen saturation (SPO2), Glasgow coma score (GCS), liver disease, oliguria, blood sodium, international standardized ratio (ISR), and initial blood potassium were the predictors of the occurrence of adverse events; peripheral edema, estimated glomerular filtration rate (eGFR), blood sodium, actual base residual, and initial blood potassium were the predictors of therapeutic effect. Extreme gradient boosting (XGBoost) model achieved the best performance (adverse events: AUC = 0.87; therapeutic effect: AUC = 0.75). A model based on clinical characteristics was developed and validated with good performance.

Comparison of quantifiable electrocardiographic changes associated with severe hyperkalemia

BACKGROUND

Hyperkalemia (HK) is a life-threatening condition that is frequently evaluated by electrocardiogram (ECG). ECG changes in severe HK (≥ 6.3 mEq/L) are not well-characterized. This study sought to compare and correlate ECG metrics in severe HK to baseline normokalemic ECGs and serum potassium.

METHODS

A retrospective analysis of 340 severe HK encounters with corresponding normokalemic ECGs was performed.

RESULTS

Various ECG metrics were analyzed. P wave amplitude in lead II, QRS duration, T wave slope, ratio of T wave amplitude: duration, and ratios of T wave: QRS amplitudes were significantly different between normokalemic and HK ECGs. P wave amplitude attenuation in lead II correlated better with serum potassium than in V1. T wave metrics that incorporated both T wave and QRS amplitudes correlated better than metrics utilizing T wave metrics alone.

CONCLUSION

Multiple statistically significant and quantifiable differences among ECG metrics were observed between normokalemic and HK ECGs and correlated with increasing degrees of serum potassium and along the continuum of serum potassium. When incorporated into a logistic regression model, the ability to distinguish HK versus normokalemia on ECG improved significantly. These findings could be integrated into an ECG acquisition system that can more accurately identify severe HK.

Recent Progresses in Non-Dialysis Chronic Kidney Disease Patients with Hyperkalemia: Outcomes and Therapeutic Strategies

Chronic kidney disease (CKD) affects about 10% of the world's population. Hyperkalemia is a life-threatening complication in patients with CKD, as it is associated with adverse cardiovascular and kidney outcomes. There are still many challenges and questions to address to improve the currently available therapeutic strategies to treat hyperkalemia, such as how to approach the emergency management of hyperkalemia. In recent years, in addition to novel oral potassium binders, great progress has been made in the application of novel kidney protective strategies, such as mineralocorticoid receptor antagonists and sodium-glucose cotransporter 2 inhibitors (SGLT2i) in hyperkalemia therapy. This review will discuss the recent advances from clinical trials in the effective management of hyperkalemia in non-dialysis CKD patients, enhancing the knowledge of physicians and internists concerning these newer agents and providing a helpful reference for clinical practice.

Effect of calcium in patients with pulseless electrical activity and electrocardiographic characteristics potentially associated with hyperkalemia and ischemia-sub-study of the Calcium for Out-of-hospital Cardiac Arrest (COCA) trial

OBJECTIVE

The Calcium for Out-of-hospital Cardiac Arrest (COCA) trial was recently conducted and published. This pre-planned sub-study evaluated the effect of calcium in patients with pulseless electrical activity (PEA) including subgroup analyses based on electrocardiographic characteristics potentially associated with hyperkalemia and ischemia.

METHODS

Patients aged ≥ 18 years were included if they had a non-traumatic out-of-hospital cardiac arrest and received adrenaline. The trial drug consisted of calcium chloride (5 mmol) or saline placebo given after the first, and again after the second, dose of adrenaline for a maximum of two doses. This sub-study analyzed patients with PEA as their last known rhythm prior to receiving the trial drug. Outcomes were return of spontaneous circulation and survival at 30 days.

RESULTS

104 patients were analyzed. In the calcium group, 9 patients (20 %) achieved return of spontaneous circulation vs 23 patients (39 %) in the placebo group (risk ratio 0.51; 95 %CI 0.26, 1.00). Subgroup analyses based on electrocardiographic characteristics potentially associated with hyperkalemia and ischemia showed similar results. At 30 days, 1 patient (2.2 %) was alive in the calcium group while 8 patients (13.6 %) were alive in the placebo group (risk ratio 0.16; 95 %CI 0.02, 1.26).

CONCLUSION

In adults with out-of-hospital cardiac arrest presenting with PEA, effect estimates suggested harm of calcium administration as compared to placebo but with wide confidence intervals. Results were consistent for patients with electrocardiographic characteristics potentially associated with hyperkalemia and ischemia. The results do not support calcium administration based strictly on electrocardiographic findings seen during out-of-hospital cardiac arrest.

Prophylactic administration of rosmarinic acid ameliorates depression-associated cardiac abnormalities in Wistar rats: Evidence of serotonergic, oxidative, and inflammatory pathways

Psychiatric disorders and associated cardiac comorbidities have increased the risk of mortality worldwide. Researchers reported that depression increases the possibility of future cardiac abnormalities by approximately 30%. Therefore, there is an unmet need to develop therapeutic interventions to treat depression and associated cardiac abnormalities. The present study was conducted to evaluate the prophylactic effect of rosmarinic acid (RA) against chronic unpredictable stress (CUS)-induced depression associated cardiac abnormalities in Wistar rats. The CUS paradigm, which comprised several stressors, was employed for 40 days to induce depressive-like behavior and associated cardiac abnormalities in rats. Along with CUS, RA at a dose of 25 and 50 mg/kg was administered orally to two groups of animals for 40 days. Behavioral tests (forced swim test and sucrose consumption test) and molecular biomarkers (corticosterone and serotonin) were performed. Electrocardiography was performed before CUS (Day 0), Day 20, and Day 40 to study electrocardiogram parameters. Furthermore, changes in body weight, organ weight, tissue lipid peroxidation, glutathione, catalase, cTn-I, MMP-2, and proinflammatory cytokines (TNF-α and IL-6) were estimated. Our results showed that RA treatment caused a reduction in immobility period, adrenal hyperplasia, corticosterone level, tissue lipid peroxidation, cTn-I, MMP-2, proinflammatory cytokines, and QRS complex duration, while an increase in sucrose consumption, brain serotonin level, T-wave width, glutathione, and catalase activity as compared with the CUS-control group. The results of our study proved that RA administration ameliorates CUS-induced depression-associated cardiac abnormalities in rats via serotonergic, oxidative, and inflammatory pathways.

Initial and terminal T wave angle as hyperkalemia indicator in chronic kidney disease

BACKGROUND

Hyperkalemia is one prevalent complication in chronic kidney disease and is considered fatal since it potentially causes malignant arrhythmias and mortality. It is associated with electrocardiography (ECG) changes, such as peaked T wave in all ECG leads. However, the universal definition of the peaked T wave is still unclear, with low sensitivity and specificity.

AIM

: This study aims to determine the predictive value of initial and terminal T wave angle in detecting hyperkalemia among CKD patients.

METHODS

: A cross-sectional study was conducted at Dr. Soetomo General Hospital, including all adult hospitalized CKD patients. A caliper was used to measure T wave morphology. The initial deflection angle (Tia) and terminal deflection angle (Tta) were calculated from an arctan of T peak amplitude and the respective initial or terminal length. The receiver operating characteristics (ROC) curve was analyzed to determine the area under the curve (AUC) and optimal cut-off.

RESULTS

: A total of 220 CKD patients were enrolled in this study, with 98 patients with hyperkalemia (potassium >5.0). The majority of the patients were male, with a mean age of 51.12±12.58 years. Ti-Tp duration, Tp-Tt duration, Tia, Tta, and Tp amplitude were significantly higher in the hyperkalemia group (all p<0.05). A Spearman correlation analysis demonstrated a significant positive correlation of Tia (r=0.346 and p<0.001) and Tta (r=0.445 and p<0.001) with potassium levels in the participants. The optimal cut-off angle for Tta was 66.20^o (sensitivity=67.3% and specificity=73.8%) and Tia was 61.07^o (sensitivity=66.3% and specificity=69.7%).

CONCLUSION

: The terminal T wave angle outperformed the initial angle in predicting hyperkalemia in CKD patients.

The conundrum of the complex relationship between acute kidney injury and cardiac arrhythmias

Acute kidney injury (AKI) is defined by a rapid increase in serum creatinine levels, reduced urine output, or both. Death may occur in 16%-49% of patients admitted to an intensive care unit with severe AKI. Complex arrhythmias are a potentially serious complication in AKI patients with pre-existing or AKI-induced heart damage and myocardial dysfunction, fluid overload, and especially electrolyte and acid-base disorders representing the pathogenetic mechanisms of arrhythmogenesis. Cardiac arrhythmias, in turn, increase the risk of poor renal outcomes, including AKI. Arrhythmic risk in AKI patients receiving kidney replacement treatment may be reduced by modifying dialysis/replacement fluid composition. The most common arrhythmia observed in AKI patients is atrial fibrillation. Severe hyperkalemia, sometimes combined with hypocalcemia, causes severe bradyarrhythmias in this clinical setting. Although the likelihood of life-threatening ventricular arrhythmias is reportedly low, the combination of cardiac ischemia and specific electrolyte or acid-base abnormalities may increase this risk, particularly in AKI patients who require kidney replacement treatment. The purpose of this review is to summarize the available epidemiological, pathophysiological, and prognostic evidence aiming to clarify the complex relationships between AKI and cardiac arrhythmias.

Electrocardiographic changes in the acute hyperkalaemia produced by intragastric KCl load in rats

NEW FINDINGS

What is the central question of this study? This study presents a new model for studying the rapid onset of severe, acute hyperkalaemia in rats with intact kidney function by administering an intragastric KCl load. What is the main finding and its importance? This new model of intragastric KCl load produces a reliable and reproducible model for studying the rapid onset of severe, acute hyperkalaemia in rats with intact kidney function. We report unprecedented rapid changes (30 min) in ECG, blood pressure and various arterial blood analyses with this new model, providing a solid foundation for future experiments in this field.

ABSTRACT

A variety of animal models have been proposed to study hyperkalaemia, but most of them have meaningful limitations when the goal is to study the effect of potassium overload on healthy kidneys. In this study, we aimed to introduce a new approach for induction of hyperkalaemia in a reliable and reproducible animal model. We used intragastric administration of potassium chloride [KCl 2.3 M, 10 ml/(kg body weight)] to male Holtzman rats (300-350 g) to induce hyperkalaemia. The results showed that this potassium load can temporarily overwhelm the renal and extrarenal handling of this ion, causing an acute and severe hyperkalaemia that can be useful to study the effect of potassium imbalance in a variety of scenarios. Severe hyperkalaemia (>8 meqiv/l) and very profound ECG alterations, characterized by lengthening waves and intervals, were seen as early as 30 min after intragastric administration of KCl in rats. In addition, a transient increase in arterial blood pressure and time-dependent bradycardia were also seen after the KCl administration. No metabolic acidosis was present in the animals, and the potassium ion did not increase proportionally to chloride ion in the blood, leading to an increased anion gap. In conclusion, the results suggest that intragastric KCl loading is a reliable model to promote rapid and severe hyperkalaemia that can be used for further research on this topic.

Monitoring blood potassium concentration in hemodialysis patients by quantifying T-wave morphology dynamics

We investigated the ability of time-warping-based ECG-derived markers of T-wave morphology changes in time ([Formula: see text]) and amplitude ([Formula: see text]), as well as their non-linear components ([Formula: see text] and [Formula: see text]), and the heart rate corrected counterpart ([Formula: see text]), to monitor potassium concentration ([Formula: see text]) changes ([Formula: see text]) in end-stage renal disease (ESRD) patients undergoing hemodialysis (HD). We compared the performance of the proposed time-warping markers, together with other previously proposed [Formula: see text] markers, such as T-wave width ([Formula: see text]) and T-wave slope-to-amplitude ratio ([Formula: see text]), when computed from standard ECG leads as well as from principal component analysis (PCA)-based leads. 48-hour ECG recordings and a set of hourly-collected blood samples from 29 ESRD-HD patients were acquired. Values of [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] were calculated by comparing the morphology of the mean warped T-waves (MWTWs) derived at each hour along the HD with that from a reference MWTW, measured at the end of the HD. From the same MWTWs [Formula: see text] and [Formula: see text] were also extracted. Similarly, [Formula: see text] was calculated as the difference between the [Formula: see text] values at each hour and the [Formula: see text] reference level at the end of the HD session. We found that [Formula: see text] and [Formula: see text] showed higher correlation coefficients with [Formula: see text] than [Formula: see text]-Spearman's ([Formula: see text]) and Pearson's (r)-and [Formula: see text]-Spearman's ([Formula: see text])-in both SL and PCA approaches being the intra-patient median [Formula: see text] and [Formula: see text] in SL and [Formula: see text] and [Formula: see text] in PCA respectively. Our findings would point at [Formula: see text] and [Formula: see text] as the most suitable surrogate of [Formula: see text], suggesting that they could be potentially useful for non-invasive monitoring of ESRD-HD patients in hospital, as well as in ambulatory settings. Therefore, the tracking of T-wave morphology variations by means of time-warping analysis could improve continuous and remote [Formula: see text] monitoring of ESRD-HD patients and flagging risk of [Formula: see text]-related cardiovascular events.

Quantification and classification of potassium and calcium disorders with the electrocardiogram: What do clinical studies, modeling, and reconstruction tell us?

Diseases caused by alterations of ionic concentrations are frequently observed challenges and play an important role in clinical practice. The clinically established method for the diagnosis of electrolyte concentration imbalance is blood tests. A rapid and non-invasive point-of-care method is yet needed. The electrocardiogram (ECG) could meet this need and becomes an established diagnostic tool allowing home monitoring of the electrolyte concentration also by wearable devices. In this review, we present the current state of potassium and calcium concentration monitoring using the ECG and summarize results from previous work. Selected clinical studies are presented, supporting or questioning the use of the ECG for the monitoring of electrolyte concentration imbalances. Differences in the findings from automatic monitoring studies are discussed, and current studies utilizing machine learning are presented demonstrating the potential of the deep learning approach. Furthermore, we demonstrate the potential of computational modeling approaches to gain insight into the mechanisms of relevant clinical findings and as a tool to obtain synthetic data for methodical improvements in monitoring approaches.