Diagnostic Accuracy of Single-Lead Electrocardiograms Using the Kardia Mobile App and the Apple Watch 4: Validation Study.

BACKGROUND: To date, the 12-lead electrocardiogram (ECG) is the gold standard for cardiological diagnosis in clinical settings. With the advancements in technology, a growing number of smartphone apps and gadgets for recording, visualizing, and evaluating physical performance as well as health data is available. Although this new smart technology is innovative and time- and cost-efficient, less is known about its diagnostic accuracy and reliability. OBJECTIVE: This study aimed to examine the agreement between the mobile single-lead ECG measurements of the Kardia Mobile App and the Apple Watch 4 compared to the 12-lead gold standard ECG in healthy adults under laboratory conditions. Furthermore, it assessed whether the measurement error of the devices increases with an increasing heart rate. METHODS: This study was designed as a prospective quasi-experimental 1-sample measurement, in which no randomization of the sampling was carried out. In total, ECGs at rest from 81 participants (average age 24.89, SD 8.58 years; n=58, 72% male) were recorded and statistically analyzed. Bland-Altman plots were created to graphically illustrate measurement differences. To analyze the agreement between the single-lead ECGs and the 12-lead ECG, Pearson correlation coefficient (r) and Lin concordance correlation coefficient (CCCLin) were calculated. RESULTS: The results showed a higher agreement for the Apple Watch (mean deviation QT: 6.85%; QT interval corrected for heart rate using Fridericia formula [QTcF]: 7.43%) than Kardia Mobile (mean deviation QT: 9.53%; QTcF: 9.78%) even if both tend to underestimate QT and QTcF intervals. For Kardia Mobile, the QT and QTcF intervals correlated significantly with the gold standard (rQT=0.857 and rQTcF=0.727; P<.001). CCCLin corresponded to an almost complete heuristic agreement for the QT interval (0.835), whereas the QTcF interval was in the range of strong agreement (0.682). Further, for the Apple Watch, Pearson correlations were highly significant and in the range of a large effect (rQT=0.793 and rQTcF=0.649; P<.001). CCCLin corresponded to a strong heuristic agreement for both the QT (0.779) and QTcF (0.615) intervals. A small negative correlation between the measurement error and increasing heart rate could be found of each the devices and the reference. CONCLUSIONS: Smart technology seems to be a promising and reliable approach for nonclinical health monitoring. Further research is needed to broaden the evidence regarding its validity and usability in different target groups.

Design and evaluation of an interactive training system for scrub nurses.

OBJECTIVE: The current trend toward increasingly integrated technological support systems and the rise of streamlined processes in the OR have led to a growing demand for personnel with higher levels of training. Although simulation systems are widely used and accepted in surgical training, they are practically non-existent for perioperative nursing, especially scrub nursing. This paper describes and evaluates an interactive OR environment simulation to help train scrub nurses. METHODS: A system comprising multiple computers and monitors, including an interactive table and a touchscreen combined with a client-server software solution, was designed to simulate a scrub nurse's workplace. The resulting demonstrator was evaluated under laboratory conditions with a multicenter interview study involving three participating ear, nose, and throat (ENT) departments in Germany and Switzerland. RESULTS: The participant group of 15 scrub nurses had an average of 12.8 years hands-on experience in the OR. A series of 22 questions was used to evaluate various aspects of the demonstrator system and its suitability for training novices. DISCUSSION: The system received very positive feedback. The participants stated that familiarization with instrument names and learning the instrument table setup were the two most important technical topics for beginners. They found the system useful for acquiring these skills as well as certain non-technical aspects. CONCLUSIONS: Interactive training through simulation is a new approach for preparing novice scrub nurses for the challenges at the instrument table in the OR. It can also improve the lifelong training of perioperative personnel. The proposed system is currently unique in its kind. It can be used to train both technical and non-technical skills and, therefore, contributes to patient safety. Moreover, it is not dependent on a specific type of surgical intervention or medical discipline.

Web-tool to Support Medical Experts in Probabilistic Modelling Using Large Bayesian Networks With an Example of Hinosinusitis.

For many complex diseases, finding the best patient-specific treatment decision is difficult for physicians due to limited mental capacity. Clinical decision support systems based on Bayesian networks (BN) can provide a probabilistic graphical model integrating all necessary aspects relevant for decision making. Such models are often manually created by clinical experts. The modeling process consists of graphical modeling conducted by collecting of information entities, and probabilistic modeling achieved through defining the relations of information entities to their direct causes. Such expert-based probabilistic modelling with BNs is very time intensive and requires knowledge about the underlying modeling method. We introduce in this paper an intuitive web-based system for helping medical experts generate decision models based on BNs. Using the tool, no special knowledge about the underlying model or BN is necessary. We tested the tool with an example of modeling treatment decisions of Rhinosinusitis and studied its usability.