The Long-COVID autonomic syndrome in hospitalized patients: A one-year prospective cohort study.

Long-COVID syndrome is characterized by fatigue, orthostatic intolerance, tachycardia, pain, memory difficulties, and brain fog, which may be associated with autonomic nervous system abnormalities. We aimed to evaluate the short and long-term course of COVID-19 autonomic symptoms and quality of life (QoL) after SARS-CoV-2 infection through a one-year follow-up combined with validated questionnaires. Additionally, we aimed to identify patients with worsening autonomic symptoms at 6 and 12 months by dividing the patient cohort into two sub-groups: the Post-COVID healed Control sub-group (total score<16.4) and the Long-COVID autonomic syndrome sub-group (total score>16.4). This prospective cohort studied 112 SARS-CoV-2 positive patients discharged from Humanitas Research Hospital between January and March 2021. Autonomic symptoms and QoL were assessed using the composite autonomic symptom scale 31 (COMPASS-31) and Short Form Health Survey (SF-36) questionnaires at various time points: before SARS-CoV-2 infection (PRE), at hospital discharge (T0), and at 1 (T1), 3 (T3), 6 (T6), and 12 (T12) months of follow-up. COMPASS-31 total score, Orthostatic Intolerance and Gastrointestinal function indices, QoL, physical functioning, pain, and fatigue scores worsened at T0 compared to PRE but progressively improved at T1 and T3, reflecting the acute phase of COVID-19. Unexpectedly, these indices worsened at T6 and T12 compared to T3. Subgroup analysis revealed that 47% of patients experienced worsening autonomic symptoms at T6 and T12, indicating Long-COVID autonomic syndrome. Early rehabilitative and pharmacological therapy is recommended for patients at the T1 and T3 stages after SARS-CoV-2 infection to minimize the risk of developing long-term autonomic syndrome.

Ventricular tachycardia and in-hospital mortality in the intensive care unit.

Background: Continuous electrocardiographic (ECG) monitoring is used to identify ventricular tachycardia (VT), but false alarms occur frequently. Objective: The purpose of this study was to assess the rate of 30-day in-hospital mortality associated with VT alerts generated from bedside ECG monitors to those from a new algorithm among intensive care unit (ICU) patients. Methods: We conducted a retrospective cohort study in consecutive adult ICU patients at an urban academic medical center and compared current bedside monitor VT alerts, VT alerts from a new-unannotated algorithm, and true-annotated VT. We used survival analysis to explore the association between VT alerts and mortality. Results: We included 5679 ICU admissions (mean age 58 ± 17 years; 48% women), 503 (8.9%) experienced 30-day in-hospital mortality. A total of 30.1% had at least 1 current bedside monitor VT alert, 14.3% had a new-unannotated algorithm VT alert, and 11.6% had true-annotated VT. Bedside monitor VT alert was not associated with increased rate of 30-day mortality (adjusted hazard ratio [aHR] 1.06; 95% confidence interval [CI] 0.88-1.27), but there was an association for VT alerts from our new-unannotated algorithm (aHR 1.38; 95% CI 1.12-1.69) and true-annotated VT(aHR 1.39; 95% CI 1.12-1.73). Conclusion: Unannotated and annotated-true VT were associated with increased rate of 30-day in-hospital mortality, whereas current bedside monitor VT was not. Our new algorithm may accurately identify high-risk VT; however, prospective validation is needed.

Agreement of Computerized QT and QTc Interval Measurements Between Both Bedside and Expert Nurses Using Electronic Calipers.

BACKGROUND: In hospitalized patients, QT/QTc (heart rate corrected) prolongation on the electrocardiogram (ECG) increases the risk of torsade de pointes. Manual measurements are time-consuming and often inaccurate. Some bedside monitors automatically and continuously measure QT/QTc; however, the agreement between computerized versus nurse-measured values has not been evaluated. OBJECTIVE: The aim of this study was to examine the agreement between computerized QT/QTc and bedside and expert nurses who used electronic calipers. METHODS: This was a prospective observational study in 3 intensive care units. Up to 2 QT/QTc measurements (milliseconds) per patient were collected. Bland-Altman test was used to analyze measurement agreement. RESULTS: A total of 54 QT/QTc measurements from 34 patients admitted to the ICU were included. The mean difference (bias) for QT comparisons was as follows: computerized versus expert nurses, -11.04 ± 4.45 milliseconds (95% confidence interval [CI], -2.3 to -19.8; P = .016), and computerized versus bedside nurses, -13.72 ± 6.70 (95% CI, -0.70 to -26.8; P = .044). The mean bias for QTc comparisons was as follows: computerized versus expert nurses, -12.46 ± 5.80 (95% CI, -1.1 to -23.8; P = .035), and computerized versus bedside nurses, -18.49 ± 7.90 (95% CI, -3.0 to -33.9; P = .022). CONCLUSION: Computerized QT/QTc measurements calculated by bedside monitor software and measurements performed by nurses were in close agreement; statistically significant differences were found, but differences were less than 20 milliseconds (on-half of a small box), indicating no clinical significance. Computerized measurements may be a suitable alternative to nurse-measured QT/QTc. This could reduce inaccuracies and nurse burden while increasing adherence to practice recommendations. Further research comparing computerized QT/QTc from bedside monitoring to standard 12-lead electrocardiogram in a larger sample, including non-ICU patients, is needed.

A Systematic Survey of Data Augmentation of ECG Signals for AI Applications.

AI techniques have recently been put under the spotlight for analyzing electrocardiograms (ECGs). However, the performance of AI-based models relies on the accumulation of large-scale labeled datasets, which is challenging. To increase the performance of AI-based models, data augmentation (DA) strategies have been developed recently. The study presented a comprehensive systematic literature review of DA for ECG signals. We conducted a systematic search and categorized the selected documents by AI application, number of leads involved, DA method, classifier, performance improvements after DA, and datasets employed. With such information, this study provided a better understanding of the potential of ECG augmentation in enhancing the performance of AI-based ECG applications. This study adhered to the rigorous PRISMA guidelines for systematic reviews. To ensure comprehensive coverage, publications between 2013 and 2023 were searched across multiple databases, including IEEE Explore, PubMed, and Web of Science. The records were meticulously reviewed to determine their relevance to the study's objective, and those that met the inclusion criteria were selected for further analysis. Consequently, 119 papers were deemed relevant for further review. Overall, this study shed light on the potential of DA to advance the field of ECG diagnosis and monitoring.

Evaluation of premature ventricular complexes during in-hospital ECG monitoring as a predictor of ventricular tachycardia in an intensive care unit cohort.

In-hospital electrocardiographic (ECG) monitors are typically configured to alarm for premature ventricular complexes (PVCs) due to the potential association of PVCs with ventricular tachycardia (VT). However, no contemporary hospital-based studies have examined the association of PVCs with VT. Hence, the benefit of PVC monitoring in hospitalized patients is largely unknown. This secondary analysis used a large PVC alarm data set to determine whether PVCs identified during continuous ECG monitoring were associated with VT, in-hospital cardiac arrest (IHCA), and/or death in a cohort of adult intensive care unit patients. Six PVC types were examined (i.e., isolated, bigeminy, trigeminy, couplets, R-on-T, and run PVCs) and were compared between patients with and without VT, IHCA, and/or death. Of 445 patients, 48 (10.8%) had VT; 11 (2.5%) had IHCA; and 49 (11%) died. Isolated and run PVC counts were higher in the VT group (p = 0.03 both), but group differences were not seen for the other four PVC types. The regression models showed no significant associations between any of the six PVC types and VT or death, although confidence intervals were wide. Due to the small number of cases, we were unable to test for associations between PVCs and IHCA. Our findings suggest that we should question the clinical relevance of activating PVC alarms as a forewarning of VT, and more work should be done with larger sample sizes. A more precise characterization of clinically relevant PVCs that might be associated with VT is warranted.

Ischemic Pre-Conditioning is Present in Patients with Non-ST Elevation Myocardial Infarction and ECG Derived Moderate Obstructive Sleep Apnea.

Background: Obstructive Sleep Apnea (OSA) is associated with an increased risk of cardiovascular events, including Acute Coronary Syndrome (ACS). There is conflicting evidence that suggests OSA has a cardioprotective effect (i.e., lower troponin), via ischemic pre-conditioning, in patients with ACS. Purpose: This study had two aims: (1) compare peak troponin between non-ST elevation (NSTE) ACS patients with and without moderate OSA identified using a Holter derived respiratory disturbance index (HDRDI); and (2) determine the frequency of transient myocardial ischemia (TMI) between NSTE-ACS patients with and without moderate HDRDI. Method: This was a secondary analysis. OSA events were identified from 12-lead ECG Holter recordings using QRSs, R-R intervals, and the myogram. Moderate OSA was defined as an HDRDI ≥15 events per/hour. TMI was defined as ≥1 millimeter of ST-segment ↑ or ↓, in ≥ 1 ECG lead, ≥ 1 minute. Results: In 110 NSTE-ACS patients, 39% (n=43) had moderate HDRDI. Peak troponin was higher in patients with moderate HDRDI (6.8 ng/ml yes vs. 10.2 ng/ml no; p=0.037). There was a trend for fewer TMI events, but there were no differences (16% yes vs. 30% no; p=0.081). Conclusions: NSTE-ACS patients with moderate HDRDI have less cardiac injury than those without moderate HDRDI measured using a novel ECG derived method. These findings corroborate prior studies suggesting a possible cardioprotective effect of OSA in ACS patients via ischemic pre-condition. There was a trend for fewer TMI events in moderate HDRDI patients, but there was no statistical difference. Future research should explore the underlying physiologic mechanisms of this finding.

An annotated ventricular tachycardia (VT) alarm database: Toward a uniform standard for optimizing automated VT identification in hospitalized patients.

BACKGROUND: False ventricular tachycardia (VT) alarms are common during in-hospital electrocardiographic (ECG) monitoring. Prior research shows that the majority of false VT can be attributed to algorithm deficiencies. PURPOSE: The purpose of this study was: (1) to describe the creation of a VT database annotated by ECG experts and (2) to determine true vs. false VT using a new VT algorithm created by our group. METHODS: The VT algorithm was processed in 5320 consecutive ICU patients with 572,574 h of ECG and physiologic monitoring. A search algorithm identified potential VT, defined as: heart rate >100 beats/min, QRSs > 120 ms, and change in QRS morphology in >6 consecutive beats compared to the preceding native rhythm. Seven ECG channels, SpO2 , and arterial blood pressure waveforms were processed and loaded into a web-based annotation software program. Five PhD-prepared nurse scientists performed the annotations. RESULTS: Of the 5320 ICU patients, 858 (16.13%) had 22,325 VTs. After three levels of iterative annotations, a total of 11,970 (53.62%) were adjudicated as true, 6485 (29.05%) as false, and 3870 (17.33%) were unresolved. The unresolved VTs were concentrated in 17 patients (1.98%). Of the 3870 unresolved VTs, 85.7% (n = 3281) were confounded by ventricular paced rhythm, 10.8% (n = 414) by underlying BBB, and 3.5% (n = 133) had a combination of both. CONCLUSIONS: The database described here represents the single largest human-annotated database to date. The database includes consecutive ICU patients, with true, false, and challenging VTs (unresolved) and could serve as a gold standard database to develop and test new VT algorithms.

Premature ventricular complexes during continuous electrocardiographic monitoring in the intensive care unit: Occurrence rates and associated patient characteristics.

AIMS AND OBJECTIVES: This study examined the occurrence rate of specific types of premature ventricular complex (PVC) alarms and whether patient demographic and/or clinical characteristics were associated with PVC occurrences. BACKGROUND: Because PVCs can signal myocardial irritability, in-hospital electrocardiographic (ECG) monitors are typically configured to alert nurses when they occur. However, PVC alarms are common and can contribute to alarm fatigue. A better understanding of occurrences of PVCs could help guide alarm management strategies. DESIGN: A secondary quantitative analysis from an alarm study. METHODS: The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist was followed. Seven PVC alarm types (vendor-specific) were described, and included isolated, couplet, bigeminy, trigeminy, run PVC (i.e. VT >2), R-on-T and PVCs/min. Negative binomial and hurdle regression analyses were computed to examine the association of patient demographic and clinical characteristics with each PVC type. RESULTS: A total of 797,072 PVC alarms (45,271 monitoring hours) occurred in 446 patients, including six who had disproportionately high PVC alarm counts (40% of the total alarms). Isolated PVCs were the most frequent type (81.13%) while R-on-T were the least common (0.29%). Significant predictors associated with higher alarms rates: older age (isolated PVCs, bigeminy and couplets); male sex and presence of PVCs on the 12-lead ECG (isolated PVCs). Hyperkalaemia at ICU admission was associated with a lower R-on-T type PVCs. CONCLUSIONS: Only a few distinct demographic and clinical characteristics were associated with the occurrence rate of PVC alarms. Further research is warranted to examine whether PVCs were associated with adverse outcomes, which could guide alarm management strategies to reduce unnecessary PVC alarms. RELEVANCE TO CLINICAL PRACTICE: Targeted alarm strategies, such as turning off certain PVC-type alarms and evaluating alarm trends in the first 24 h of admission in select patients, might add to the current practice of alarm management.

Incorporation of Serial 12-Lead Electrocardiogram With Machine Learning to Augment the Out-of-Hospital Diagnosis of Non-ST Elevation Acute Coronary Syndrome.

STUDY OBJECTIVE: Ischemic electrocardiogram (ECG) changes are subtle and transient in patients with suspected non-ST-segment elevation (NSTE)-acute coronary syndrome. However, the out-of-hospital ECG is not routinely used during subsequent evaluation at the emergency department. Therefore, we sought to compare the diagnostic performance of out-of-hospital and ED ECG and evaluate the incremental gain of artificial intelligence-augmented ECG analysis. METHODS: This prospective observational cohort study recruited patients with out-of-hospital chest pain. We retrieved out-of-hospital-ECG obtained by paramedics in the field and the first ED ECG obtained by nurses during inhospital evaluation. Two independent and blinded reviewers interpreted ECG dyads in mixed order per practice recommendations. Using 179 morphological ECG features, we trained, cross-validated, and tested a random forest classifier to augment non ST-elevation acute coronary syndrome (NSTE-ACS) diagnosis. RESULTS: Our sample included 2,122 patients (age 59 [16]; 53% women; 44% Black, 13.5% confirmed acute coronary syndrome). The rate of diagnostic ST elevation and ST depression were 5.9% and 16.2% on out-of-hospital-ECG and 6.1% and 12.4% on ED ECG, with ∼40% of changes seen on out-of-hospital-ECG persisting and ∼60% resolving. Using expert interpretation of out-of-hospital-ECG alone gave poor baseline performance with area under the receiver operating characteristic (AUC), sensitivity, and negative predictive values of 0.69, 0.50, and 0.92. Using expert interpretation of serial ECG changes enhanced this performance (AUC 0.80, sensitivity 0.61, and specificity 0.93). Interestingly, augmenting the out-of-hospital-ECG alone with artificial intelligence algorithms boosted its performance (AUC 0.83, sensitivity 0.75, and specificity 0.95), yielding a net reclassification improvement of 29.5% against expert ECG interpretation. CONCLUSION: In this study, 60% of diagnostic ST changes resolved prior to hospital arrival, making the ED ECG suboptimal for the inhospital evaluation of NSTE-ACS. Using serial ECG changes or incorporating artificial intelligence-augmented analyses would allow correctly reclassifying one in 4 patients with suspected NSTE-ACS.

Thoracic Impedance Pneumography-Derived Respiratory Alarms and Associated Patient Characteristics.

BACKGROUND: Respiratory rate (RR) alarms alert clinicians to a change in a patient's condition. However, RR alarms are common occurrences. To date, no study has examined RR alarm types and associated patient characteristics, which could guide alarm management strategies. OBJECTIVES: To characterize RR alarms by type, frequency, duration, and associated patient demographic and clinical characteristics. METHODS: A secondary data analysis of alarms generated with impedance pneumography in 461 adult patients admitted to either a cardiac, a medical/surgical, or a neurological intensive care unit (ICU). The RR alarms included high parameter limit (≥30 breaths/min), low parameter limit (≤5 breaths/min), and apnea (no breathing ≥20 s). The ICU type; total time monitored; and alarm type, frequency, and duration were evaluated. RESULTS: Of 159 771 RR alarms, parameter limit alarms (n = 140 975; 88.2%) were more frequent than apnea alarms (n = 18 796; 11.8%). High parameter limit alarms were most frequent (n = 131 827; 82.5%). After ICU monitoring time was controlled for, multivariate analysis showed that alarm rates were higher in patients in the cardiac and neurological ICUs (P = .001), patients undergoing mechanical ventilation (P = .005), and patients without a ventricular assist device or pacemaker (P = .02). Male sex was associated with low parameter limit (P = .01) and apnea (P = .005) alarms. CONCLUSION: High parameter limit RR alarms were most frequent. Factors associated with RR alarms included monitoring time, ICU type, male sex, and mechanical ventilation. Although these factors are not modifiable, these data could be used to guide management strategies.

Agreement between respiratory rate measurement using a combined electrocardiographic derived method versus impedance from pneumography.

BACKGROUND: Impedance pneumography (IP) is the current device-driven method used to measure respiratory rate (RR) in hospitalized patients. However, RR alarms are common and contribute to alarm fatigue. While RR derived from electrocardiographic (ECG) waveforms hold promise, they have not been compared to the IP method. PURPOSE: Study examined the agreement between the IP and combined-ECG derived (EDR) for normal RR (≥12 or ≤20 breaths/minute [bpm]); low RR (≤5 bpm); and high RR (≥30 bpm). METHODOLOGY: One-hundred intensive care unit patients were included by RR group: (1) normal RR (n = 50; 25 low RR and 25 high RR); (2) low RR (n = 50); and (3) high RR (n = 50). Bland-Altman analysis was used to evaluate agreement. RESULTS: For normal RR, a significant bias difference of -1.00 + 2.11 (95% CI -1.60 to -0.40) and 95% limit of agreement (LOA) of -5.13 to 3.13 was found. For low RR, a significant bias difference of -16.54 + 6.02 (95% CI: -18.25 to -14.83) and a 95% LOA of -28.33 to - 4.75 was found. For high RR, a significant bias difference of 17.94 + 12.01 (95% CI: 14.53 to 21.35) and 95% LOA of -5.60 to 41.48 was found. CONCLUSION: Combined-EDR method had good agreement with the IP method for normal RR. However, for the low RR, combined-EDR was consistently higher than the IP method and almost always lower for the high RR, which could reduce the number of RR alarms. However, replication in a larger sample including confirmation with visual assessment is warranted.

Effects of a cool classroom microclimate on cardiac autonomic control and cognitive performances in undergraduate students.

An inverted U-shape relationship between cognitive performance and indoor temperature with best performance peaking at 21.6 °C was previously described. Little is known on classroom temperature reduction effects on cognitive performances and cardiac autonomic profile, during the cold season. Fifteen students underwent electrocardiogram recording during a lecture in two days in December when classroom temperatures were set as neutral (NEUTRAL, 20-22 °C) and cool (COOL, 16-18 °C). Cognitive performance (memory, verbal ability, reasoning, overall cognitive C-score) was assessed by Cambridge Brain Science cognitive evaluation tool. Cardiac autonomic control was evaluated via the analysis of spontaneous fluctuations of heart period, as the temporal distance between two successive R-wave peaks (RR). Spectral analysis provided the power in the high frequency (HF, 0.15-0.40 Hz) and low frequency (LF, 0.04-0.15 Hz) bands of RR variability. Sympatho-vagal interaction was assessed by LF to HF ratio (LF/HF). Symbolic analysis provided the fraction of RR patterns composed by three heart periods with no variation (0 V%) and two variations (2 V%), taken as markers of cardiac sympathetic and vagal modulations, respectively. The students' thermal comfort was assessed during NEUTRAL and COOL trials. Classroom temperatures were 21.5 ± 0.8 °C and 18.4 ± 0.4 °C during NEUTRAL and COOL. Memory, verbal ability, C-Score were greater during COOL (13.01 ± 3.43, 12.32 ± 2.58, 14.29 ± 2.90) compared to NEUTRAL (9.98 ± 2.26, p = 0.002; 8.57 ± 1.07, p = 0.001 and 10.35 ± 3.20, p = 0.001). LF/HF (2.4 ± 1.7) and 0 V% (23.2 ± 11.1%) were lower during COOL compared to NEUTRAL (3.7 ± 2.8, p = 0.042; 28.1 ± 12.2.1%, p = 0.031). During COOL, 2 V% was greater (30.5 ± 10.9%) compared to NEUTRAL (26.2 ± 11.3, p = 0.047). The students' thermal comfort was slightly reduced during COOL compared to NEUTRAL trial. During cold season, a better cognitive performance was obtained in a cooler indoor setting enabling therefore energy saving too.

Deep learning analysis of electrocardiogram for risk prediction of drug-induced arrhythmias and diagnosis of long QT syndrome.

AIMS: Congenital long-QT syndromes (cLQTS) or drug-induced long-QT syndromes (diLQTS) can cause torsade de pointes (TdP), a life-threatening ventricular arrhythmia. The current strategy for the identification of drugs at the high risk of TdP relies on measuring the QT interval corrected for heart rate (QTc) on the electrocardiogram (ECG). However, QTc has a low positive predictive value. METHODS AND RESULTS: We used convolutional neural network (CNN) models to quantify ECG alterations induced by sotalol, an IKr blocker associated with TdP, aiming to provide new tools (CNN models) to enhance the prediction of drug-induced TdP (diTdP) and diagnosis of cLQTS. Tested CNN models used single or multiple 10-s recordings/patient using 8 leads or single leads in various cohorts: 1029 healthy subjects before and after sotalol intake (n = 14 135 ECGs); 487 cLQTS patients (n = 1083 ECGs: 560 type 1, 456 type 2, 67 type 3); and 48 patients with diTdP (n = 1105 ECGs, with 147 obtained within 48 h of a diTdP episode). CNN models outperformed models using QTc to identify exposure to sotalol [area under the receiver operating characteristic curve (ROC-AUC) = 0.98 vs. 0.72, P ≤ 0.001]. CNN models had higher ROC-AUC using multiple vs. single 10-s ECG (P ≤ 0.001). Performances were comparable for 8-lead vs. single-lead models. CNN models predicting sotalol exposure also accurately detected the presence and type of cLQTS vs. healthy controls, particularly for cLQT2 (AUC-ROC = 0.9) and were greatest shortly after a diTdP event and declining over time (P ≤ 0.001), after controlling for QTc and intake of culprit drugs. ECG segment analysis identified the J-Tpeak interval as the best discriminator of sotalol intake. CONCLUSION: CNN models applied to ECGs outperform QTc measurements to identify exposure to drugs altering the QT interval, congenital LQTS, and are greatest shortly after a diTdP episode.

Computerized automated algorithm-based analyses of digitized paper ECGs in Brugada syndrome.

BACKGROUND: Brugada syndrome is a rare inherited arrhythmic syndrome with a coved type 1 ST-segment elevation on ECG and an increased risk of sudden death. Many studies have evaluated risk stratification performance based on ECG-derived parameters. However, since historical Brugada patient cohorts included mostly paper ECGs, most studies have been based on manual ECG parameter measurements. We hypothesized that it would be possible to run automated algorithm-based analysis of paper ECGs. We aimed: 1) to validate the digitization process for paper ECGs in Brugada patients; and 2) to quantify the acute class I antiarrhythmic drug effect on relevant ECG parameters in Brugada syndrome. METHODS: A total of 176 patients (30% female, 43 ± 13 years old) with induced type 1 Brugada syndrome ECG were included in the study. All of the patients had paper ECGs before and during class I antiarrhythmic drug challenge. Twenty patients also had a digital ECG, in whom printouts were used to validate the digitization process. Paper ECGs were scanned and then digitized using ECGScan software, version 3.4.0 (AMPS, LLC, New York, NY, USA) to obtain FDA HL7 XML format ECGs. Measurements were automatically performed using the Bravo (AMPS, LLC, New York, NY, USA) and Glasgow algorithms. RESULTS: ECG parameters obtained from digital and digitized ECGs were closely correlated (r = 0.96 ± 0.07, R2 = 0.93 ± 0.12). Class I antiarrhythmic drugs significantly increased the global QRS duration (from 113 ± 20 to 138 ± 23, p < 0.0001). On lead V2, class I antiarrhythmic drugs increased ST-segment elevation (from 110 ± 84 to 338 ± 227 µV, p < 0.0001), decreased the ST slope (from 14.9 ± 23.3 to -27.4 ± 28.5, p < 0.0001) and increased the TpTe interval (from 88 ± 18 to 104 ± 33, p < 0.0001). CONCLUSIONS: Automated algorithm-based measurements of depolarization and repolarization parameters from digitized paper ECGs are reliable and could quantify the acute effects of class 1 antiarrhythmic drug challenge in Brugada patients. Our results support using computerized automated algorithm-based analyses from digitized paper ECGs to establish risk stratification decision trees in Brugada syndrome.

Association of thyroid-stimulating hormone with corrected QT interval variation: A prospective cohort study among patients with type 2 diabetes.

BACKGROUND: Patients with type 2 diabetes mellitus (T2DM) have a prolonged QT interval and are at high risk of sudden cardiac death. A prolonged QT interval, indicative of impaired ventricular repolarization, is a risk factor for lethal ventricular arrhythmias, such as torsades-de-pointes (TdP). AIMS: To identify key clinical and biochemical covariates associated with Fridericia's corrected QT interval (QTcF) among euthyroid patients with T2DM, and to describe the temporal relationship between these factors and QTcF. METHODS: We performed prospective, clinical, biochemical and electrocardiographic measurements among patients with T2DM enrolled in the DIACART study at Pitié-Salpêtrière Hospital, at T1 (baseline) and T2 (follow-up), with a median interval of 2.55 years. RESULTS: Mean age (63.9±8.5 years), sex (22.35% women), drugs with known risk of TdP according to the CredibleMeds website (Cred-drugsTdP) and serum thyroid-stimulating hormone (TSH) concentrations correlated with QTcF in univariate analysis at both T1 and T2. In multivariable analysis, all these covariates except age were significantly associated with QTcF at both T1 (women: standardized ß=0.24±0.07, P=0.001; Cred-drugsTdP: ß=0.19±0.07, P=0.007; TSH concentration: ß=0.18±0.07, P=0.01) and T2 (women: ß=0.25±0.08, P=0.002; Cred-drugsTdP: ß=0.25±0.08, P=0.001; TSH concentration: ß=0.19±0.08, P=0.01). Furthermore, variation in QTcF over the years was associated with variation in TSH concentration (r=0.24, P=0.007) and changes in use of Cred-drugsTdP (r=0.2, P=0.02). CONCLUSIONS: Serum TSH concentration and its variation were associated with QTcF and its variation, even after correcting for the main determinants of QTcF. Interventional optimization of TSH concentration in T2DM warrants further investigation to establish its impact on the risk of TdP and sudden cardiac death.

ECG alarms during left ventricular assist device (LVAD) therapy in the ICU.

BACKGROUND: In hospitalized patients with left ventricular assist device (LVAD), electrical interference and low amplitude QRS complexes are common, which could impact the accuracy of electrocardiographic (ECG) arrhythmia detection and create technical alarms. This could contribute to provider alarm fatigue and threaten patient safety. OBJECTIVES: We examined three LVAD patients in the cardiac intensive care unit (ICU) to determine: 1) the frequency and accuracy of audible arrhythmia alarms; 2) occurrence rates of technical alarms; and 3) alarm burden (# alarms/hour of monitoring) METHODS: Secondary analysis. RESULTS: During 593 h, there were 549 audible arrhythmia alarms and 98% were false. There were 25,232 technical alarms and 93% were for artifact, which was configured as an inaudible text alert. CONCLUSION: False-arrhythmia and technical alarms are frequent in LVAD patients. Future studies are needed to identify both clinical and algorithm-based strategies to improve arrhythmia detection and reduce technical alarms in LVAD patients.

Validation of an algorithm for continuous monitoring of atrial fibrillation using a consumer smartwatch.

BACKGROUND: Consumer devices with broad reach may be useful in screening for atrial fibrillation (AF) in appropriate populations. However, currently no consumer devices are capable of continuous monitoring for AF. OBJECTIVE: The purpose of this study was to estimate the sensitivity and specificity of a smartwatch algorithm for continuous detection of AF from sinus rhythm in a free-living setting. METHODS: We studied a commercially available smartwatch with photoplethysmography (W-PPG) and electrocardiogram (W-ECG) capabilities. We validated a novel W-PPG algorithm combined with a W-ECG algorithm in a free-living setting, and compared the results to those of a 28-day continuous ECG patch (P-ECG). RESULTS: A total of 204 participants completed the free-living study, recording 81,944 hours with both P-ECG and smartwatch measurements. We found sensitivity of 87.8% (95% confidence interval [CI] 83.6%-91.0%) and specificity of 97.4% (95% CI 97.1%-97.7%) for the W-PPG algorithm (every 5-minute classification); sensitivity of 98.9% (95% CI 98.1%-99.4%) and specificity of 99.3% (95% CI 99.1%-99.5%) for the W-ECG algorithm; and sensitivity of 96.9% (95% CI 93.7%-98.5%) and specificity of 99.3% (95% CI 98.4%-99.7%) for W-PPG triggered W-ECG with a single W-ECG required for confirmation of AF. We found a very strong correlation of W-PPG in quantifying AF burden compared to P-ECG (r = 0.98). CONCLUSION: Our findings demonstrate that a novel algorithm using a commercially available smartwatch can continuously detect AF with excellent performance and that confirmation with W-ECG further enhances specificity. In addition, our W-PPG algorithm can estimate AF burden. Further research is needed to determine whether this algorithm is useful in screening for AF in select at-risk patients.

Sex influences on ventricular repolarization duration in normal subjects and in type 1, 2 and 3 long QT syndrome patients: Different effect in acquired and congenital type 2 LQTS.

AIM: To evaluate the interaction between sex and rate corrected QT interval (QTc) duration in normal subjects after drug-induced QT prolongation and in LQTS patients. METHODS: Semi-automated measurements were performed on 875 digital ECGs (200 normal subjects off drugs (100 females), 200 normal subjects on Moxifloxacin (100 females), 259 LQT1 patients (161 females), 183 LQT2 patients (100 females) and 33 LQT3 patients (15 females)). A sex specific coefficient was calculated in each group and was used to calculate group specific corrected QT intervals (QTci). RESULTS: The mean sex difference (female minus male) in QTci interval duration was 17 ms 95%CI(12.7; 21.3) in normal subjects, 19 ms (14.5; 23.5) on Moxifloxacin, and 13 ms (4.8; 21.2) in LQT1 patients. The mean difference was 2 ms (-7.9; 11.9) in LQT2 and - 5 ms (-32.2; 22.2) in LQT3 patients (p = 0.0067 for the group and sex interaction). In the subgroup of patients above 15 years and without beta blocker treatment, the sex effect (female minus male) on QTci interval duration was 17 ms (4.1; 29.9) in LQT1 patients. QTc duration was not different between sex in LQT2 and in LQT3 patients (mean difference - 3 ms (-21.6; 15.6) and 12 ms (-28.4; 52.4), respectively) (p = 0.0191 for group and sex interaction). CONCLUSIONS: The interaction between sex and QTc interval is preserved in type 1 LQTS and drug-induced QTc prolongation but blurred in type 2 LQTS. Further experimental studies are warranted to better understand the interaction of sexual hormones with malfunctioning KCNH2 encoded repolarizing potassium channel.

Actionable Ventricular Tachycardia During In-Hospital ECG Monitoring and Its Impact on Alarm Fatigue.

BACKGROUND: Ventricular tachycardia (V-tach) is the most common lethal arrhythmia, yet 90% of alarms are false and contribute to alarm fatigue. We hypothesize that some true V-tach also causes alarm fatigue because current criteria are too sensitive (i.e., ≥6 beats ≥100 beats/min [bpm]). PURPOSE: This study was designed to determine (1) the proportion of clinically actionable true V-tach events; (2) whether true actionable versus nonactionable V-tach differs in terms of heart rate and/or duration (seconds); and (3) if actionable V-tach is associated with adverse outcomes. METHODS: This was a secondary analysis in 460 intensive care unit (ICU) patients. Electronic health records were examined to determine if a V-tach event was actionable or nonactionable. Actionable V-tach was defined if a clinical action(s) was taken within 15 minutes of its occurrence (i.e., new and/or change of medication, defibrillation, and/or laboratory test). Maximal heart rate and duration for each V-tach event were measured from bedside monitor electrocardiography. Adverse patient outcomes included a code blue event and/or death. RESULTS: In 460 ICU patients, 50 (11%) had 151 true V-tach events (range 1-20). Of the 50 patients, 40 (80%) had only nonactionable V-tach (97 events); 3 (6%) had both actionable and nonactionable V-tach (32 events); and 7 patients (14%) had only actionable V-tach (23 events). There were differences in duration comparing actionable versus nonactionable V-tach (mean 56.19 ± 116.87 seconds vs. 4.28 ± 4.09 seconds; P = 0.001) and maximal heart rate (188.81 ± 116.83 bpm vs. 150.79 ± 28.26 bpm; P = 0.001). Of the 50 patients, 3 (6%) had a code blue, 2 died, and all were in the actionable V-tach group. CONCLUSIONS: In our sample, <1% experienced a code blue following true V-tach. Heart rate and duration for actionable V-tach were much faster and longer than that for nonactionable V-tach. Current default settings typically used for electrocardiographic monitoring (i.e., ≥6 beats ≥100 bpm) appear to be too conservative and can lead to crisis/red level nuisance alarms that contribute to alarm fatigue. A prospective study designed to test whether adjusting default settings to these higher levels is safe for patients is needed.

Correction of QRS voltage for body mass index does not improve the prediction of fatal and nonfatal cardiovascular events. The Moli-sani study.

BACKGROUND AND AIMS: The diagnosis of LVH by ECG may particularly difficult in obese individuals. The aim of this study was to prospectively investigate whether the correction for body mass index (BMI) might improve the prognostic significance for cerebro and cardiovascular events of two electrocardiographic (ECG) criteria for left ventricular hypertrophy (LVH) in a large cohort of Italian adults. METHODS AND RESULTS: In 18,330 adults (54 ± 11 years, 55% women) from the Moli-sani cohort, obesity was defined using the ATPIII criteria. The Sokolow-Lyon (SL) and Cornell Voltage (CV) criteria were used for ECG-LVH. In overweight and obese subjects, as compared with normal weight, the prevalence of ECG-LVH by the SL index was lower. During follow-up (median 4.3 yrs), 503 cerebro and cardiovascular events occurred. One standard deviation (1-SD) increment in uncorrected and in BMI-corrected SL index and CV was associated with an increased risk of events (HR 1.12, 95% CI 1.02-1.22 and HR 1.16, 95% CI 1.06-1.26 and HR 1.12, 95% CI 1.03-1.23 and HR 1.17, 95% CI 1.07-1.27, respectively for SL and CV). In obese subjects, 1-SD increment in uncorrected CV and in BMI-corrected CV was not associated to a significant risk of events (HR 1.05, 95% CI 0.910-1.22 and HR 1.08, 95% CI 0.95-1.23 respectively). Uncorrected SL index showed a significant association with events, which was marginally stronger with BMI-corrected SL voltage (HR 1.18, 95% CI 1.02-1.37 and HR 1.17, 95% CI 1.04-1.33 respectively, Akaike information criterion change from 3220 to 3218). CONCLUSIONS: BMI correction of ECG LVH voltage criteria does not significantly improve the prediction of cerebro and cardiovascular events in obese patients in a large cohort at low cardiovascular risk.