Children with prolonged hospital admissions for CHD often develop delirium. Antipsychotic medications (APMs) have been used to treat delirium but are known to prolong the QTc duration. There is concern for prolongation of the QTc interval in cardiac patients who may be more vulnerable to electrocardiogram (ECG) changes and may have postoperative QTc prolongation already. The goal of this study was to determine the effect of APM on QTc duration in postoperative paediatric cardiac patients and determine the effect of quetiapine and risperidone in treating delirium and QTc prolongation.

Retrospective study, July 1, 2017–May 31, 2022.

Tertiary children’s hospital.

Included were patients admitted to the paediatric cardiac ICU at Children’s Healthcare of Atlanta.

None.

ECGs, delirium scores, and drug information were collected. Delirium was defined as Cornell Assessment of Pediatric Delirium (CAPD) score >9. Mixed effect models were performed to evaluate the effect of surgery on QTc change and the effect of antipsychotics on QTc and CAPD changes. There were 139 children, 55% male and 67% surgical admissions. Median age was 5.9 months. Mean QTc increased after cardiac surgery by 18 ms (p = 0.014, 95% CI 3.65–32.4). There was no significant change in QTc after antipsychotic administration (p = 0.064). The mean CAPD score decreased (12.5–7.2; p < 0.001). Quetiapine had the most improvement in delirium, and risperidone had the least improvement (77.8%, n = 14; 37.8%, n = 34, respectively; p = 0.002).

The QTc interval did not have a statistically significant change after the administration of antipsychotics, while there was improvement in the CAPD score. APMs may be administered safely without significant prolongation of the QTc and are an effective treatment for delirium.

Early detection of ST-segment elevation myocardial infarction (STEMI) on the prehospital electrocardiogram (ECG) improves patient outcomes. Current software algorithms optimize sensitivity but have a high false-positive rate. The authors propose an algorithm to improve the specificity of STEMI diagnosis in the prehospital setting.

A dataset of prehospital ECGs with verified outcomes was used to validate an algorithm to identify true and false-positive software interpretations of STEMI. Four criteria implicated in prior research to differentiate STEMI true positives were applied: heart rate <130, QRS <100, verification of ST-segment elevation, and absence of artifact. The test characteristics were calculated and regression analysis was used to examine the association between the number of criteria included and test characteristics.

There were 44,611 cases available. Of these, 1,193 were identified as STEMI by the software interpretation. Applying all four criteria had the highest positive likelihood ratio of 353 (95% CI, 201-595) and specificity of 99.96% (95% CI, 99.93-99.98), but the lowest sensitivity (14%; 95% CI, 11-17) and worst negative likelihood ratio (0.86; 95% CI, 0.84-0.89). There was a strong correlation between increased positive likelihood ratio (r2 = 0.90) and specificity (r2 = 0.85) with increasing number of criteria.

Prehospital ECGs with a high probability of true STEMI can be accurately identified using these four criteria: heart rate <130, QRS <100, verification of ST-segment elevation, and absence of artifact. Applying these criteria to prehospital ECGs with software interpretations of STEMI could decrease false-positive field activations, while also reducing the need to rely on transmission for physician over-read. This can have significant clinical and quality implications for Emergency Medical Services (EMS) systems.

Telemedicine gained an increasing use throughout the last years. Lifestyle tools like the Apple watch seem to have an increasing spread even in remote areas and underdeveloped regions. The increasing availability of these tools offers the chance to use the health care functions of these devices to improve provision of professional medical care. First data on the use of the Apple Watch as a remote monitoring device in children have been reported, showing good acceptability and usability of the Apple Watch for symptom monitoring in children. This study aimed to evaluate the accuracy of the Apple Watch iECG in comparison to a standard 12-lead ECG in pre-term babies.

In this prospective, single-arm study, consecutive preterm neonates hospitalised in Leipzig University Hospital neonatal ICU were eligible. A 12-lead ECG and an iECG using Apple Watch 4 were performed. iECG and 12-lead ECG measurements were performed by a paediatric cardiologist. Cardiac rhythm was classified and amplitudes and timing intervals were analysed for comparability.

Fifty preterm neonates, gestational week (23–36 weeks), and body weight (0.65–3.09 kg) were enrolled. Overall good quality and excellent correlation of the Apple Watch generated iECG in comparison to the standard 12-lead ECG could be demonstrated (p < 0.001). When interpreted by a paediatric cardiologist, a correct rhythm classification could be done in 100% of cases.

The Apple Watch iECG seems to be a valuable tool to record an ECG comparable to lead I of the standard 12-lead ECG even in pre-term neonates. With a widespread availability and excellent connectivity, the Apple Watch iECG function may provide practitioners with a tool to send an iECG for interpretation to a paediatric cardiac specialist.

Studies on fear conditioning have made important contributions to the understanding of affective learning mechanisms as well as its applications (e.g., anxiety disorders, post-traumatic stress disorder). However, central mechanisms of sleep related consolidation of fear memory in humans have been almost neglected by previous studies.

In the current study we aimed to test effects of sleep and a period wakefulness on fear conditioned responses.

In our experiment in a group 18 healthy volunteers event-related brain potentials (ERP), heart rate variability (HRV) and behavioral responses were recorded during a fear conditioning procedure presented twice, before daytime sleep (2h) or control intervention (a period of wakefulness) and after. The conditioning procedure involved pairing of a neutral tone (CS+) with a highly unpleasant sound (UCS+).

Differential conditioning manifested itself in the contingent negative variance (CNV)-like slow ERP component. Both period of sleep and wakefulness resulted in an increased amplitude of the CNV to CS+. But we did not find an interaction effect of Time (Pre-Post) by Intervention (Sleep-Wake), suggesting that sleep did not affect the conditioned response differently as compared to a period of wakefulness. An apparent increase in HRV after a period of wakefulness did not affect fear conditioned responses (CNV and valence ratings).

To summarize, the data indicate that fear memories are consolidated with the course of time with no beneficial effect of sleep; relearning of fear causes stronger differential responses as measured by slow wave amplitude but not behavior; increase of HRV does not affect fear learning.

It currently remains unclear in how far supratherapeutic lithium serum concentrations can affect the cardiac conduction system. Prolonged QT interval, arrhythmias and cardiac death have all been anecdotally reported, but the systematic studies are few.

To examine ECG changes occurring with supratherapeutic lithium concentrations that have given rise to lithium toxicity.

We examined all episodes of lithium intoxication defined as serum lithium level (≥ 1.5 mmol/L). We analyzed ECG before, during and after intoxication and recorded ECG changes. These, we then assessed according to type of intoxications, clinical and other pharmacological characteristics. The study is based on 20-year data (1997-2020) from the retrospective cohort study (LiSIE) in the Swedish region of Norrbotten.

Of 1101 patients treated with lithium, 77 patients had experienced lithium intoxications. 12 patients had more than one episode of intoxication, yielding 91 episodes. 39 had ECG available both as reference and during lithium intoxication. We found no statistically significant prolongation of the QTc interval during lithium intoxication, compared to respective reference ECG (p = 0.364). Heart rate during lithium intoxication was significantly lower, mean 73 beats/min (SD 16,8, range 43 - 112), compared to the reference ECG, mean 79 beats/min (SD 15,3, range 48-112; p = 0.006). No patient died. All findings were independent of whether an intoxication was acute or chronic.

In our study, heart rate was significantly lower during episodes of intoxication. However, this decrease was of no clinical relevance in most cases. Lithium intoxication was not associated with prolonged QT time.

M. Ott: scientific advisory board member of Astra Zeneca, Sweden. U. Werneke: received funding for educational activities on behalf of Norrbotten Region; Astra Zeneca, Eli Lilly, Janssen, Novartis, Otsuka/Lundbeck, Servier, Shire, Sunovion. Others: None

Point-of-care ultrasound (POCUS) is used increasingly during resuscitation. The aim of this study was to assess whether combining POCUS and electrocardiogram (ECG) rhythm findings better predicts outcomes during cardiopulmonary resuscitation in the emergency department (ED).

We completed a health records review on ED cardiac arrest patients who underwent POCUS. Primary outcome measurements included return of spontaneous circulation (ROSC), survival to hospital admission, and survival to hospital discharge.

POCUS was performed on 180 patients; 45 patients (25.0%; 19.2%–31.8%) demonstrated cardiac activity on initial ECG, and 21 (11.7%; 7.7%–17.2%) had cardiac activity on initial POCUS; 47 patients (26.1%; 20.2%–33.0%) achieved ROSC, 18 (10.0%; 6.3%–15.3%) survived to admission, and 3 (1.7%; 0.3%–5.0%) survived to hospital discharge. As a predictor of failure to achieve ROSC, ECG had a sensitivity of 82.7% (95% CI 75.2%–88.7%) and a specificity of 46.8% (32.1%–61.9%). Overall, POCUS had a higher sensitivity of 96.2% (91.4%–98.8%) but a similar specificity of 34.0% (20.9%–49.3%). In patients with ECG-asystole, POCUS had a sensitivity of 98.18% (93.59%–99.78%) and a specificity of 16.00% (4.54%–36.08%). In patients with pulseless electrical activity, POCUS had a sensitivity of 86.96% (66.41%–97.22%) and a specificity of 54.55% (32.21%–75.61%). Similar patterns were seen for survival to admission and discharge. Only 0.8% (0.0–4.7%) of patients with ECG-asystole and standstill on POCUS survived to hospital discharge.

The absence of cardiac activity on POCUS, or on both ECG and POCUS together, better predicts negative outcomes in cardiac arrest than ECG alone. No test reliably predicted survival.

ST-segment elevation myocardial infarction (STEMI) is a time-sensitive entity that has been shown to benefit from prehospital diagnosis by electrocardiogram (ECG). Current computer algorithms with binary decision making are not accurate enough to be relied on for cardiac catheterization lab (CCL) activation.

An algorithmic approach is proposed to stratify binary STEMI computerized ECG interpretations into low, intermediate, and high STEMI probability tiers.

Based on previous literature, a four-criteria algorithm was developed to rule out/in common causes of prehospital STEMI false-positive computer interpretations: heart rate, QRS width, ST elevation criteria, and artifact. Prehospital STEMI cases were prospectively collected at a single academic center in Salt Lake City, Utah (USA) from May 2012 through October 2013. The prehospital ECGs were applied to the algorithm and compared against activation of the CCL by an emergency department (ED) physician as the outcome of interest. In addition to calculating test characteristics, linear regression was used to look for an association between number of criteria used and accuracy, and logistic regression was used to test if any single criterion performed better than another.

There were 63 ECGs available for review, 39 high probability and 24 intermediate probability. The high probability STEMI tier had excellent test characteristics for ruling in STEMI when all four criteria were used, specificity 1.00 (95% CI, 0.59-1.00), positive predictive value 1.00 (0.91-1.00). Linear regression showed a strong correlation demonstrating that false-positives increased as fewer criteria were used (adjusted r-square 0.51; P <.01). Logistic regression showed no significant predictive value for any one criterion over another (P = .80). Limiting physician overread to the intermediate tier only would reduce the number of ECGs requiring physician overread by a factor of 0.62 (95% CI, 0.48-0.75; P <.01).

Prehospital STEMI ECGs can be accurately stratified to high, intermediate, and low probabilities for STEMI using the four criteria. While additional study is required, using this tiered algorithmic approach in prehospital ECGs could lead to changes in CCL activation and decreased requirements for physician overread. This may have significant clinical and quality implications.