Quantitative Evaluation of the Relationship between T-Wave-Based Features and Serum Potassium Level in Real-World Clinical Practice

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.

Differences in ventricular wall composition may explain inter-patient variability in the ECG response to variations in serum potassium and calcium

Objective: Chronic kidney disease patients have a decreased ability to maintain normal electrolyte concentrations in their blood, which increases the risk for ventricular arrhythmias and sudden cardiac death. Non-invasive monitoring of serum potassium and calcium concentration, [K+] and [Ca2+], can help to prevent arrhythmias in these patients. Electrocardiogram (ECG) markers that significantly correlate with [K+] and [Ca2+] have been proposed, but these relations are highly variable between patients. We hypothesized that inter-individual differences in cell type distribution across the ventricular wall can help to explain this variability. Methods: A population of human heart-torso models were built with different proportions of endocardial, midmyocardial and epicardial cells. Propagation of ventricular electrical activity was described by a reaction-diffusion model, with modified Ten Tusscher-Panfilov dynamics. [K+] and [Ca2+] were varied individually and in combination. Twelve-lead ECGs were simulated and the width, amplitude and morphological variability of T waves and QRS complexes were quantified. Results were compared to measurements from 29 end-stage renal disease (ESRD) patients undergoing hemodialysis (HD). Results: Both simulations and patients data showed that most of the analyzed T wave and QRS complex markers correlated strongly with [K+] (absolute median Pearson correlation coefficients, r, ranging from 0.68 to 0.98) and [Ca2+] (ranging from 0.70 to 0.98). The same sign and similar magnitude of median r was observed in the simulations and the patients. Different cell type distributions in the ventricular wall led to variability in ECG markers that was accentuated at high [K+] and low [Ca2+], in agreement with the larger variability between patients measured at the onset of HD. The simulated ECG variability explained part of the measured inter-patient variability. Conclusion: Changes in ECG markers were similarly related to [K+] and [Ca2+] variations in our models and in the ESRD patients. The high inter-patient ECG variability may be explained by variations in cell type distribution across the ventricular wall, with high sensitivity to variations in the proportion of epicardial cells. Significance: Differences in ventricular wall composition help to explain inter-patient variability in ECG response to [K+] and [Ca2+]. This finding can be used to improve serum electrolyte monitoring in ESRD patients.

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.

Monitoring of Serum Potassium and Calcium Levels in End-Stage Renal Disease Patients by ECG Depolarization Morphology Analysis

Objective: Non-invasive estimation of serum potassium, [K+], and calcium, [Ca2+], can help to prevent life-threatening ventricular arrhythmias in patients with advanced renal disease, but current methods for estimation of electrolyte levels have limitations. We aimed to develop new markers based on the morphology of the QRS complex of the electrocardiogram (ECG). Methods: ECG recordings from 29 patients undergoing hemodialysis (HD) were processed. Mean warped QRS complexes were computed in two-minute windows at the start of an HD session, at the end of each HD hour and 48 h after it. We quantified QRS width, amplitude and the proposed QRS morphology-based markers that were computed by warping techniques. Reference [K+] and [Ca2+] were determined from blood samples acquired at the time points where the markers were estimated. Linear regression models were used to estimate electrolyte levels from the QRS markers individually and in combination with T wave morphology markers. Leave-one-out cross-validation was used to assess the performance of the estimators. Results: All markers, except for QRS width, strongly correlated with [K+] (median Pearson correlation coefficients, r, ranging from 0.81 to 0.87) and with [Ca2+] (r ranging from 0.61 to 0.76). QRS morphology markers showed very low sensitivity to heart rate (HR). Actual and estimated serum electrolyte levels differed, on average, by less than 0.035 mM (relative error of 0.018) for [K+] and 0.010 mM (relative error of 0.004) for [Ca2+] when patient-specific multivariable estimators combining QRS and T wave markers were used. Conclusion: QRS morphological markers allow non-invasive estimation of [K+] and [Ca2+] with low sensitivity to HR. The estimation performance is improved when multivariable models, including T wave markers, are considered. Significance: Markers based on the QRS complex of the ECG could contribute to non-invasive monitoring of serum electrolyte levels and arrhythmia risk prediction in patients with renal disease.

Effects of lower limb segmental muscle vibration on primary motor cortex short-latency intracortical inhibition and spinal excitability in healthy humans

We examined the effects of lower limb segmental muscle vibration (SMV) on intracortical and spinal excitability in 13 healthy participants (mean age: 34.9 ± 7.8 years, 12 males, 1 female). SMV at 30 Hz was applied to the hamstrings, gastrocnemius, and soleus muscles for 5 min. Paired-pulse transcranial magnetic stimulation protocols were used to investigate motor-evoked potential (MEP)  amplitude, short-interval intracortical inhibition (SICI) and short-interval intracortical facilitation (SICF) from the abductor hallucis muscle (AbdH). These assessments were compared to the results of a control experiment (i.e., non-vibration) in the same participants. F-waves were evaluated from the AbdH on the right (vibration side) and left (non-vibration side) sides, and we calculated the ratio of the F-wave amplitude to the M-response amplitude (F/M ratio). These assessments were obtained before, immediately after, and 10, 20, and 30 min after SMV. For SICI, there was no change immediately after SMV, but there was a decrease over time (before vs. 30 min after, p = 0.021; immediately after vs. 30 min after, p = 0.015). There were no changes in test MEP amplitude, SICF, or the F/M ratio. SMV causes a gradual decrease in SICI over time perhaps owing to long-term potentiation. The present results may have implications for the treatment of spasticity.

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.

Discovering hidden information in biosignals from patients using artificial intelligence

Biosignals such as electrocardiogram or photoplethysmogram are widely used for determining and monitoring the medical condition of patients. It was recently discovered that more information could be gathered from biosignals by applying artificial intelligence (AI). At present, one of the most impactful advancements in AI is deep learning. Deep learning-based models can extract important features from raw data without feature engineering by humans, provided the amount of data is sufficient. This AI-enabled feature presents opportunities to obtain latent information that may be used as a digital biomarker for detecting or predicting a clinical outcome or event without further invasive evaluation. However, the black box model of deep learning is difficult to understand for clinicians familiar with a conventional method of analysis of biosignals. A basic knowledge of AI and machine learning is required for the clinicians to properly interpret the extracted information and to adopt it in clinical practice. This review covers the basics of AI and machine learning, and the feasibility of their application to real-life situations by clinicians in the near future.

Can physicians detect hyperkalemia based on the electrocardiogram?

OBJECTIVE

Although there is no consensus on how to use an electrocardiogram (ECG) in patients with hyperkalemia, physicians often obtain it in the acute setting when diagnosing and treating hyperkalemia. The objective of this study is to evaluate if physicians are able to detect hyperkalemia based on the ECG.

METHODS

The study was conducted at a large county hospital with a population of end stage renal disease (ESRD) patients who received hemodialysis (HD) solely on an emergent basis. Five hundred twenty eight ECGs from ESRD patients were evaluated. The prevalence of hyperkalemia was approximately 60% in this cohort, with at least half of them in the severe hyperkalemia range (K ≥ 6.5 mEq/L).

RESULTS

The mean sensitivity and specificity of the emergency physicians detecting hyperkalemia were 0.19 (± 0.16) and 0.97(± 0.04) respectively. The mean positive predictive value of evaluators for detecting hyperkalemia was 0.92 (±0.13) and the mean negative predictive value was 0.46 (± 0.05). In severe hyperkalemia (K ≥ 6.5 mEq/L), the mean sensitivity improved to 0.29 (± 0.20), while specificity decreased to 0.95 (±0.07).

CONCLUSION

An ECG is not a sensitive method of detecting hyperkalemia and should not be relied upon to rule it out. However, the ECG has a high specificity for detecting hyperkalemia and could be used as a rule in test.