Errors of Classification With Potassium Blood Testing: The Variability and Repeatability of Critical Clinical Tests

Validation of Noninvasive Detection of Hyperkalemia by Artificial Intelligence-Enhanced Electrocardiography in High Acuity Settings

Background

Artificial intelligence (AI) electrocardiogram (ECG) analysis can enable detection of hyperkalemia. In this validation, we assessed the algorithm's performance in two high acuity settings.

Methods

An emergency department (ED) cohort (February to August 2021) and a mixed intensive care unit (ICU) cohort (August 2017 to February 2018) were identified and analyzed separately. For each group, pairs of laboratory-collected potassium and 12 lead ECGs obtained within 4 hours of each other were identified. The previously developed AI ECG algorithm was subsequently applied to leads 1 and 2 of the 12 lead ECGs to screen for hyperkalemia (potassium >6.0 mEq/L).

Results

The ED cohort (N=40,128) had a mean age of 60 years, 48% were male, and 1% (N=351) had hyperkalemia. The area under the curve (AUC) of the AI-enhanced ECG (AI-ECG) to detect hyperkalemia was 0.88, with sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and positive likelihood ratio (LR+) of 80%, 80%, 3%, 99.8%, and 4.0, respectively, in the ED cohort. Low-eGFR (<30 ml/min) subanalysis yielded AUC, sensitivity, specificity, PPV, NPV, and LR+ of 0.83, 86%, 60%, 15%, 98%, and 2.2, respectively, in the ED cohort. The ICU cohort (N=2636) had a mean age of 65 years, 60% were male, and 3% (N=87) had hyperkalemia. The AUC for the AI-ECG was 0.88 and yielded sensitivity, specificity, PPV, NPV, and LR+ of 82%, 82%, 14%, 99%, and 4.6, respectively in the ICU cohort. Low-eGFR subanalysis yielded AUC, sensitivity, specificity, PPV, NPV, and LR+ of 0.85, 88%, 67%, 29%, 97%, and 2.7, respectively in the ICU cohort.

Conclusions

The AI-ECG algorithm demonstrated a high NPV, suggesting that it is useful for ruling out hyperkalemia, but a low PPV, suggesting that it is insufficient for treating hyperkalemia.

Development and validation of a deep learning model to screen hypokalemia from electrocardiogram in emergency patients

Background:

A deep learning model (DLM) that enables non-invasive hypokalemia screening from an electrocardiogram (ECG) may improve the detection of this life-threatening condition. This study aimed to develop and evaluate the performance of a DLM for the detection of hypokalemia from the ECGs of emergency patients.

Methods:

We used a total of 9908 ECG data from emergency patients who were admitted at the Second Affiliated Hospital of Nanchang University, Jiangxi, China, from September 2017 to October 2020. The DLM was trained using 12 ECG leads (lead I, II, III, aVR, aVL, aVF, and V₁–₆) to detect patients with serum potassium concentrations <3.5 mmol/L and was validated using retrospective data from the Jiangling branch of the Second Affiliated Hospital of Nanchang University. The blood draw was completed within 10 min before and after the ECG examination, and there was no new or ongoing infusion during this period.

Results:

We used 6904 ECGs and 1726 ECGs as development and internal validation data sets, respectively. In addition, 1278 ECGs from the Jiangling branch of the Second Affiliated Hospital of Nanchang University were used as external validation data sets. Using 12 ECG leads (leads I, II, III, aVR, aVL, aVF, and V₁–₆), the area under the receiver operating characteristic curve (AUC) of the DLM was 0.80 (95% confidence interval [CI]: 0.77–0.82) for the internal validation data set. Using an optimal operating point yielded a sensitivity of 71.4% and a specificity of 77.1%. Using the same 12 ECG leads, the external validation data set resulted in an AUC for the DLM of 0.77 (95% CI: 0.75–0.79). Using an optimal operating point yielded a sensitivity of 70.0% and a specificity of 69.1%.

Conclusions:

In this study, using 12 ECG leads, a DLM detected hypokalemia in emergency patients with an AUC of 0.77 to 0.80. Artificial intelligence could be used to analyze an ECG to quickly screen for hypokalemia.

Hyperkalemia excursions are associated with an increased risk of mortality and hospitalizations in hemodialysis patients

BACKGROUND

Hyperkalemia is common among hemodialysis (HD) patients and has been associated with adverse clinical outcomes. Previous studies considered a single serum potassium (K) measurement or time-averaged values, but serum K excursions out of the target range may be more reflective of true hyperkalemia events. We assessed whether hyperkalemia excursions lead to an elevated risk of adverse clinical outcomes.

METHODS

Using data from 21 countries in Phases 4-6 (2009-18) of the Dialysis Outcomes and Practice Patterns Study (DOPPS), we investigated the associations between peak serum K level, measured monthly predialysis, over a 4-month period ('peak K') and clinical outcomes over the subsequent 4 months using Cox regression, adjusted for potential confounders.

RESULTS

The analysis included 62 070 patients contributing a median of 3 (interquartile range 2-6) 4-month periods. The prevalence of hyperkalemia based on peak K was 58% for >5.0, 30% for >5.5 and 12% for >6.0 mEq/L. The all-cause mortality hazard ratio for peak K (reference ≤5.0 mEq/L) was 1.15 [95% confidence interval (CI) 1.09, 1.21] for 5.1-5.5 mEq/L, 1.19 (1.12, 1.26) for 5.6-6.0 mEq/L and 1.33 (1.23, 1.43) for >6.0 mEq/L. Results were qualitatively consistent when analyzing hospitalizations and a cardiovascular composite outcome.

CONCLUSIONS

Among HD patients, we identified a lower K threshold (peak K 5.1-5.5 mEq/L) than previously reported for increased risk of hospitalization and mortality, with the implication that a greater proportion (>50%) of the HD population may be at risk. A reassessment of hyperkalemia severity ranges is needed, as well as an exploration of new strategies for effective management of chronic hyperkalemia.

[Symptom control in heart failure patients - how to handle GFR decrease and hyperkalaemia]

Bei Patienten mit Herzinsuffizienz und reduzierter Ejektionsfraktion wird durch eine optimierte medikamentöse Therapie sowohl die Symptomkontrolle verbessert als auch die Mortalität gesenkt. Eckpfeiler der Herzinsuffizienztherapie sind dabei Medikamente mit Einfluss auf das Renin-Angiotensin-Aldosteron-System, sogenannte RAAS-Inhibitoren. Dieser Artikel stellt einen kardiologisch-nephrologischen Konsens zur praxisorientierten Hilfestellung bei abnehmender glomerulärer Filtrationsrate oder Anstieg des Serum-Kaliumspiegels vor. Dies sind die 2 häufigsten Gründe für eine Dosisreduktion oder das Absetzen von prognoseverbessernden Medikamenten bei Herzinsuffizienzpatienten.

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.

Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram

Importance

For patients with chronic kidney disease (CKD), hyperkalemia is common, associated with fatal arrhythmias, and often asymptomatic, while guideline-directed monitoring of serum potassium is underused. A deep-learning model that enables noninvasive hyperkalemia screening from the electrocardiogram (ECG) may improve detection of this life-threatening condition.

Objective

To evaluate the performance of a deep-learning model in detection of hyperkalemia from the ECG in patients with CKD.

Design, Setting, and Participants

A deep convolutional neural network (DNN) was trained using 1 576 581 ECGs from 449 380 patients seen at Mayo Clinic, Rochester, Minnesota, from 1994 to 2017. The DNN was trained using 2 (leads I and II) or 4 (leads I, II, V3, and V5) ECG leads to detect serum potassium levels of 5.5 mEq/L or less (to convert to millimoles per liter, multiply by 1) and was validated using retrospective data from the Mayo Clinic in Minnesota, Florida, and Arizona. The validation included 61 965 patients with stage 3 or greater CKD. Each patient had a serum potassium count drawn within 4 hours after their ECG was recorded. Data were analyzed between April 12, 2018, and June 25, 2018.

Exposures

Use of a deep-learning model.

Main Outcomes and Measures

Area under the receiver operating characteristic curve (AUC) and sensitivity and specificity, with serum potassium level as the reference standard. The model was evaluated at 2 operating points, 1 for equal specificity and sensitivity and another for high (90%) sensitivity.

Results

Of the total 1 638 546 ECGs, 908 000 (55%) were from men. The prevalence of hyperkalemia in the 3 validation data sets ranged from 2.6% (n = 1282 of 50 099; Minnesota) to 4.8% (n = 287 of 6011; Florida). Using ECG leads I and II, the AUC of the deep-learning model was 0.883 (95% CI, 0.873-0.893) for Minnesota, 0.860 (95% CI, 0.837-0.883) for Florida, and 0.853 (95% CI, 0.830-0.877) for Arizona. Using a 90% sensitivity operating point, the sensitivity was 90.2% (95% CI, 88.4%-91.7%) and specificity was 63.2% (95% CI, 62.7%-63.6%) for Minnesota; the sensitivity was 91.3% (95% CI, 87.4%-94.3%) and specificity was 54.7% (95% CI, 53.4%-56.0%) for Florida; and the sensitivity was 88.9% (95% CI, 84.5%-92.4%) and specificity was 55.0% (95% CI, 53.7%-56.3%) for Arizona.

Conclusions and Relevance

In this study, using only 2 ECG leads, a deep-learning model detected hyperkalemia in patients with renal disease with an AUC of 0.853 to 0.883. The application of artificial intelligence to the ECG may enable screening for hyperkalemia. Prospective studies are warranted.