Portable single-lead electrocardiogram device is accurate for QTc evaluation in hospitalized patients

Screening for Left Ventricular Hypertrophy Using Artificial Intelligence Algorithms Based on 12 Leads of the Electrocardiogram-Applicable in Clinical Practice?-Critical Literature Review with Meta-Analysis

Background/Objectives: The increasing utilization of artificial intelligence (AI) in the medical field holds the potential to address the global shortage of doctors. However, various challenges, such as usability, privacy, inequality, and misdiagnosis, complicate its application. This literature review focuses on AI's role in cardiology, specifically its impact on the diagnostic accuracy of AI algorithms analyzing 12-lead electrocardiograms (ECGs) to detect left ventricular hypertrophy (LVH). Methods: Following PRISMA 2020 guidelines, we conducted a comprehensive search of PubMed, CENTRAL, Google Scholar, Web of Science, and Cochrane Library. Eligible studies included randomized controlled trials (RCTs), observational studies, and case-control studies across various settings. This review is registered in the PROSPERO database (registration number 531468). Results: Seven significant studies were selected and included in our review. Meta-analysis was performed using RevMan. Co-CNN (with incorporated demographic data and clinical variables) demonstrated the highest weighted average sensitivity at 0.84. 2D-CNN models (with demographic features) showed a balanced performance with good sensitivity (0.62) and high specificity (0.82); Co-CNN models excelled in sensitivity (0.84) but had lower specificity (0.71). Traditional ECG criteria (SLV and CV) maintained high specificities but low sensitivities. Scatter plots revealed trends between demographic factors and performance metrics. Conclusions: AI algorithms can rapidly analyze ECG data with high sensitivity. The diagnostic accuracy of AI models is variable but generally comparable to classical criteria. Clinical data and the training population of AI algorithms play a critical role in their efficacy. Future research should focus on collecting diverse ECG data across different populations to improve the generalizability of AI algorithms.

Clinical value of portable 12-lead electrocardiography devices in patients with heart disease: A validation study

OBJECTIVE

The present study was conducted to assess the accuracy and reliability of portable 12‑lead electrocardiography (ECG) devices in patients with heart disease.

MATERIALS AND METHODS

This single-center, prospective, blinded study enrolled 62 patients between September and October 2023 from the Heart Center of a Class III hospital. In sequential tests on each patient, heart rate (HR) and the PR, QT, QTc and QRS intervals of ECG recordings obtained with a portable 12‑lead device (Weheal, CN) were compared with those obtained via conventional 12‑lead ECG. ECG parameters were read in batches by 3 blinded electrophysiologists. Two-tailed paired t-tests were used to compare the continuous variables. Agreement was evaluated via Bland-Altman plots.

RESULTS

Sixty-two patients were included. HR and the QT, QTc and QRS intervals from the portable 12‑lead electrocardiogram recordings were essentially the same as those obtained via conventional ECG. Bland-Altman analysis revealed no significant differences in these values, indicating suitable agreement between the 2 measurements. The PR interval was 176.89 ± 29.53 ms in the portable group and 161.56 ± 17.78 ms in the standard group, which was statistically (p < 0.001) but not clinically significant.

CONCLUSIONS

ECG recordings obtained with a portable 12‑lead device (Weheal, CN) allow for accurate HR, PR, QT, QTc and QRS assessments. Considering its simplicity, this approach has advantages over conventional ECG and can provide an alternative for evaluating patients outside the hospital. How to improve patients' acceptance of portable ECG machines still needs further research.

Use of portable single-lead electrocardiogram device as an alternative for QTc monitoring in critically ill patients

PURPOSE

Acquired QT prolongation is frequent and leads to a higher mortality rate in critically ill patients. KardiaMobile 1L® (KM1L) is a portable, user-friendly single lead, mobile alternative to conventional 12-lead electrocardiogram (12-L ECG) that could be more readily available, potentially facilitating more frequent QTc assessments in intensive care units (ICU); however, there is currently no evidence to validate this potential use.

METHODS

We conducted a prospective diagnostic test study comparing QT interval measurement using KM1L with conventional 12-L ECG ordered for any reason in patients admitted to an ICU. We compared the mean difference using a paired t-test, agreement using Bland-Altman analysis, and Lin's concordance coefficient, numerical precision (proportion of QT measurements with <10 ms difference between KM1L and conventional 12-L ECG), and clinical precision (concordance for adequate discrimination of prolonged QTc).

RESULTS

We included 114 patients (61.4% men, 60% cardiovascular etiology of hospitalization) with 131 12-L ECG traces. We found no statistical difference between corrected QT measurements (427 ms vs. 428 ms, p = .308). Lin's concordance coefficient was 0.848 (95% CI 0.801-0.894, p = .001). Clinical precision was excellent in males and substantial in females (Kappa 0.837 and 0.781, respectively). Numerical precision was lower in patients with vasoactive drugs (-13.99 ms), QT-prolonging drugs (13.84 ms), antiarrhythmic drugs (-12.87 ms), and a heart rate (HR) difference of ≥5 beats per minute (bpm) between devices (-11.26 ms).

CONCLUSION

Our study validates the clinical viability of KM1L, a single-lead mobile ECG device, for identifying prolonged QT intervals in ICU patients. Caution is warranted in patients with certain medical conditions that may affect numerical precision.

Development of Prediction Model for Intensive Care Unit Admission Based on Heart Rate Variability: A Case-Control Matched Analysis

This study aimed to develop a predictive model for intensive care unit (ICU) admission by using heart rate variability (HRV) data. This retrospective case-control study used two datasets (emergency department [ED] patients admitted to the ICU, and patients in the operating room without ICU admission) from a single academic tertiary hospital. HRV metrics were measured every 5 min using R-peak-to-R-peak (R-R) intervals. We developed a generalized linear mixed model to predict ICU admission and assessed the area under the receiver operating characteristic curve (AUC). Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated from the coefficients. We analyzed 610 (ICU: 122; non-ICU: 488) patients, and the factors influencing the odds of ICU admission included a history of diabetes mellitus (OR [95% CI]: 3.33 [1.71-6.48]); a higher heart rate (OR [95% CI]: 3.40 [2.97-3.90] per 10-unit increase); a higher root mean square of successive R-R interval differences (RMSSD; OR [95% CI]: 1.36 [1.22-1.51] per 10-unit increase); and a lower standard deviation of R-R intervals (SDRR; OR [95% CI], 0.68 [0.60-0.78] per 10-unit increase). The final model achieved an AUC of 0.947 (95% CI: 0.906-0.987). The developed model effectively predicted ICU admission among a mixed population from the ED and operating room.

Serial electrocardiogram recordings revealed a high prevalence of QT interval prolongation in patients with tuberculosis receiving fluoroquinolones

BACKGROUND

Fluoroquinolones, crucial components of treatment regimens for drug-resistant tuberculosis (TB), are associated with QT interval prolongation and risks of fatal cardiac arrhythmias. However, few studies have explored dynamic changes in the QT interval in patients receiving QT-prolonging agents.

METHODS

This prospective cohort study recruited hospitalized patients with TB who received fluoroquinolones. The study investigated the variability of the QT interval by using serial electrocardiograms (ECGs) recorded four times daily. This study analyzed the accuracy of intermittent and single-lead ECG monitoring in detecting QT interval prolongation.

RESULTS

This study included 32 patients. The mean age was 68.6 ± 13.2 years. The results revealed mild-to-moderate and severe QT interval prolongation in 13 (41%) and 5 (16%) patients, respectively. The incremental yields in sensitivity of one to four daily ECG recordings were 61.0%, 26.1%, 5.6%, and 7.3% in detecting mild-to-moderate QT interval prolongation, and 66.7%, 20.0%, 6.7%, and 6.7% in detecting severe QT interval prolongation. The sensitivity levels of lead II and V5 ECGs in detecting mild-to-moderate and severe QT interval prolongation exceeded 80%, and their specificity levels exceeded 95%.

CONCLUSION

This study revealed a high prevalence of QT interval prolongation in older patients with TB who receive fluoroquinolones, particularly those with multiple cardiovascular risk factors. Sparsely intermittent ECG monitoring, the prevailing strategy in active drug safety monitoring programs, is inadequate owing to multifactorial and circadian QT interval variability. Additional studies performing serial ECG monitoring are warranted to enhance the understanding of dynamic QT interval changes in patients receiving QT-prolonging anti-TB agents.

Validation of a multiple‑lead smartphone-based electrocardiograph with automated lead placement for layman use in patients with hypertrophic cardiomyopathy

BACKGROUND

A smartphone 12-Lead ECG that enables layman ECG screening is still lacking. We aimed to validate D-Heart ECG device, a smartphone 8/12 Lead electrocardiograph with an image processing algorithm to guide secure electrode placement by non-professional users.

METHODS

One-hundred-fourty-five patients with HCM were enrolled. Two uncovered chest images were acquired using the smartphone camera. An image with virtual electrodes placement by imaging processing algorithm software was compared to the 'gold standard' electrode placement by a doctor. D-Heart 8 and 12-Lead ECG were obtained, immediately followed by 12‑lead ECGs and were assessed by 2 independent observers. Burden of ECG abnormalities was defined by a score based on the sum of 9 criteria, identifying four classes of increasing severity.

RESULTS

A total of 87(60%) patients presented a normal/mildly abnormal ECG, whereas 58(40%) had moderate or severe ECG alteration. Eight(6%) patients had ≥1 misplaced electrode. D-Heart 8-Lead and 12‑lead ECGs concordance according to Cohen's weighted kappa test was 0,948 (p < 0,001, agreement of 97.93%). Concordance was high for the Romhilt-Estes score (k_w = 0,912; p < 0.01). Concordance between D-Heart 12-Lead ECG and standard 12-Lead ECG was perfect (k_w = 1). PR and QRS intervals measurements comparison with Bland-Altman method showed good accuracy (95% limit of agreement ±18 ms for PR and ± 9 ms for QRS).

CONCLUSIONS

D-Heart 8/12-Lead ECGs proved accurate, allowing an assessment of ECG abnormalities comparable to the standard 12‑lead ECG in patients with HCM. The image processing algorithm provided accurate electrode placement, standardizing exam quality, potentially opening perspectives for layman ECG screening campaigns.