Diagnostic Performance, Triage Safety, and Usability of a Clinical Decision Support System Within a University Hospital Emergency Department: Algorithm Performance and Usability Study.

Background: Computerized clinical decision support systems (CDSSs) are increasingly adopted in health care to optimize resources and streamline patient flow. However, they often lack scientific validation against standard medical care. Objective: The purpose of this study was to assess the performance, safety, and usability of a CDSS in a university hospital emergency department setting in Kuopio, Finland. Methods: Patients entering the emergency department were asked to voluntarily participate in this study. Patients aged 17 years or younger, patients with cognitive impairments, and patients who entered the unit in an ambulance or with the need for immediate care were excluded. Patients completed the CDSS web-based form and usability questionnaire when waiting for the triage nurse's evaluation. The CDSS data were anonymized and did not affect the patients' usual evaluation or treatment. Retrospectively, 2 medical doctors evaluated the urgency of each patient's condition by using the triage nurse's information, and urgent and nonurgent groups were created. The International Statistical Classification of Diseases, Tenth Revision diagnoses were collected from the electronic health records. Usability was assessed by using a positive version of the System Usability Scale questionnaire. Results: In total, our analyses included 248 patients. Regarding urgency, the mean sensitivities were 85% and 19%, respectively, for urgent and nonurgent cases when assessing the performance of CDSS evaluations in comparison to that of physicians. The mean sensitivities were 85% and 35%, respectively, when comparing the evaluations between the two physicians. Our CDSS did not miss any cases that were evaluated to be emergencies by physicians; thus, all emergency cases evaluated by physicians were evaluated as either urgent cases or emergency cases by the CDSS. In differential diagnosis, the CDSS had an exact match accuracy of 45.5% (97/213). The usability was good, with a mean System Usability Scale score of 78.2 (SD 16.8). Conclusions: In a university hospital emergency department setting with a large real-world population, our CDSS was found to be equally as sensitive in urgent patient cases as physicians and was found to have an acceptable differential diagnosis accuracy, with good usability. These results suggest that this CDSS can be safely assessed further in a real-world setting. A CDSS could accelerate triage by providing patient-provided data in advance of patients' initial consultations and categorize patient cases as urgent and nonurgent cases upon patients' arrival to the emergency department.

Novel Technologies in the Detection of Atrial Fibrillation: Review of Literature and Comparison of Different Novel Technologies for Screening of Atrial Fibrillation.

Atrial fibrillation (AF) is globally the most common arrhythmia associated with significant morbidity and mortality. It impairs the quality of the patient's life, imposing a remarkable burden on public health, and the healthcare budget. The detection of AF is important in the decision to initiate anticoagulation therapy to prevent thromboembolic events. Nonetheless, AF detection is still a major clinical challenge as AF is often paroxysmal and asymptomatic. AF screening recommendations include opportunistic or systematic screening in patients ≥65 years of age or in those individuals with other characteristics pointing to an increased risk of stroke. The popularities of well-being and taking personal responsibility for one's own health are reflected in the continuous development and growth of mobile health technologies. These novel mobile health technologies could provide a cost-effective solution for AF screening and an additional opportunity to detect AF, particularly its paroxysmal and asymptomatic forms.

Continuous mHealth Patch Monitoring for the Algorithm-Based Detection of Atrial Fibrillation: Feasibility and Diagnostic Accuracy Study.

BACKGROUND: The detection of atrial fibrillation (AF) is a major clinical challenge as AF is often paroxysmal and asymptomatic. Novel mobile health (mHealth) technologies could provide a cost-effective and reliable solution for AF screening. However, many of these techniques have not been clinically validated. OBJECTIVE: The purpose of this study is to evaluate the feasibility and reliability of artificial intelligence (AI) arrhythmia analysis for AF detection with an mHealth patch device designed for personal well-being. METHODS: Patients (N=178) with an AF (n=79, 44%) or sinus rhythm (n=99, 56%) were recruited from the emergency care department. A single-lead, 24-hour, electrocardiogram-based heart rate variability (HRV) measurement was recorded with the mHealth patch device and analyzed with a novel AI arrhythmia analysis software. Simultaneously registered 3-lead electrocardiograms (Holter) served as the gold standard for the final rhythm diagnostics. RESULTS: Of the HRV data produced by the single-lead mHealth patch, 81.5% (3099/3802 hours) were interpretable, and the subject-based median for interpretable HRV data was 99% (25th percentile=77% and 75th percentile=100%). The AI arrhythmia detection algorithm detected AF correctly in all patients in the AF group and suggested the presence of AF in 5 patients in the control group, resulting in a subject-based AF detection accuracy of 97.2%, a sensitivity of 100%, and a specificity of 94.9%. The time-based AF detection accuracy, sensitivity, and specificity of the AI arrhythmia detection algorithm were 98.7%, 99.6%, and 98.0%, respectively. CONCLUSIONS: The 24-hour HRV monitoring by the mHealth patch device enabled accurate automatic AF detection. Thus, the wearable mHealth patch device with AI arrhythmia analysis is a novel method for AF screening. TRIAL REGISTRATION: ClinicalTrials.gov NCT03507335; https://clinicaltrials.gov/ct2/show/NCT03507335.

Automatic Mobile Health Arrhythmia Monitoring for the Detection of Atrial Fibrillation: Prospective Feasibility, Accuracy, and User Experience Study.

BACKGROUND: Atrial fibrillation (AF) is the most common tachyarrhythmia and associated with a risk of stroke. The detection and diagnosis of AF represent a major clinical challenge due to AF's asymptomatic and intermittent nature. Novel consumer-grade mobile health (mHealth) products with automatic arrhythmia detection could be an option for long-term electrocardiogram (ECG)-based rhythm monitoring and AF detection. OBJECTIVE: We evaluated the feasibility and accuracy of a wearable automated mHealth arrhythmia monitoring system, including a consumer-grade, single-lead heart rate belt ECG device (heart belt), a mobile phone application, and a cloud service with an artificial intelligence (AI) arrhythmia detection algorithm for AF detection. The specific aim of this proof-of-concept study was to test the feasibility of the entire sequence of operations from ECG recording to AI arrhythmia analysis and ultimately to final AF detection. METHODS: Patients (n=159) with an AF (n=73) or sinus rhythm (n=86) were recruited from the emergency department. A single-lead heart belt ECG was recorded for 24 hours. Simultaneously registered 3-lead ECGs (Holter) served as the gold standard for the final rhythm diagnostics and as a reference device in a user experience survey with patients over 65 years of age (high-risk group). RESULTS: The heart belt provided a high-quality ECG recording for visual interpretation resulting in 100% accuracy, sensitivity, and specificity of AF detection. The accuracy of AF detection with the automatic AI arrhythmia detection from the heart belt ECG recording was also high (97.5%), and the sensitivity and specificity were 100% and 95.4%, respectively. The correlation between the automatic estimated AF burden and the true AF burden from Holter recording was >0.99 with a mean burden error of 0.05 (SD 0.26) hours. The heart belt demonstrated good user experience and did not significantly interfere with the patient's daily activities. The patients preferred the heart belt over Holter ECG for rhythm monitoring (85/110, 77% heart belt vs 77/109, 71% Holter, P=.049). CONCLUSIONS: A consumer-grade, single-lead ECG heart belt provided good-quality ECG for rhythm diagnosis. The mHealth arrhythmia monitoring system, consisting of heart-belt ECG, a mobile phone application, and an automated AF detection achieved AF detection with high accuracy, sensitivity, and specificity. In addition, the mHealth arrhythmia monitoring system showed good user experience. TRIAL REGISTRATION: ClinicalTrials.gov NCT03507335; https://clinicaltrials.gov/ct2/show/NCT03507335.

Continuous 24-h Photoplethysmogram Monitoring Enables Detection of Atrial Fibrillation.

Aim: Atrial fibrillation (AF) detection is challenging because it is often asymptomatic and paroxysmal. We evaluated continuous photoplethysmogram (PPG) for signal quality and detection of AF. Methods: PPGs were recorded using a wrist-band device in 173 patients (76 AF, 97 sinus rhythm, SR) for 24 h. Simultaneously recorded 3-lead ambulatory ECG served as control. The recordings were split into 10-, 20-, 30-, and 60-min time-frames. The sensitivity, specificity, and F1-score of AF detection were evaluated for each time-frame. AF alarms were generated to simulate continuous AF monitoring. Sensitivities, specificities, and positive predictive values (PPVs) of the alarms were evaluated. User experiences of PPG and ECG recordings were assessed. The study was registered in the Clinical Trials database (NCT03507335). Results: The quality of PPG signal was better during night-time than in daytime (67.3 ± 22.4% vs. 30.5 ± 19.4%, p < 0.001). The 30-min time-frame yielded the highest F1-score (0.9536), identifying AF correctly in 72/76 AF patients (sensitivity 94.7%), only 3/97 SR patients receiving a false AF diagnosis (specificity 96.9%). The sensitivity and PPV of the simulated AF alarms were 78.2 and 97.2% at night, and 49.3 and 97.0% during the daytime. 82% of patients were willing to use the device at home. Conclusion: PPG wrist-band provided reliable AF identification both during daytime and night-time. The PPG data's quality was better at night. The positive user experience suggests that wearable PPG devices could be feasible for continuous rhythm monitoring.

Wrist Band Photoplethysmography Autocorrelation Analysis Enables Detection of Atrial Fibrillation Without Pulse Detection.

Atrial fibrillation is often asymptomatic and intermittent making its detection challenging. A photoplethysmography (PPG) provides a promising option for atrial fibrillation detection. However, the shapes of pulse waves vary in atrial fibrillation decreasing pulse and atrial fibrillation detection accuracy. This study evaluated ten robust photoplethysmography features for detection of atrial fibrillation. The study was a national multi-center clinical study in Finland and the data were combined from two broader research projects (NCT03721601, URL: https://clinicaltrials.gov/ct2/show/NCT03721601 and NCT03753139, URL: https://clinicaltrials.gov/ct2/show/NCT03753139). A photoplethysmography signal was recorded with a wrist band. Five pulse interval variability, four amplitude features and a novel autocorrelation-based morphology feature were calculated and evaluated independently as predictors of atrial fibrillation. A multivariate predictor model including only the most significant features was established. The models were 10-fold cross-validated. 359 patients were included in the study (atrial fibrillation n = 169, sinus rhythm n = 190). The autocorrelation univariate predictor model detected atrial fibrillation with the highest area under receiver operating characteristic curve (AUC) value of 0.982 (sensitivity 95.1%, specificity 93.7%). Autocorrelation was also the most significant individual feature (p < 0.00001) in the multivariate predictor model, detecting atrial fibrillation with AUC of 0.993 (sensitivity 96.4%, specificity 96.3%). Our results demonstrated that the autocorrelation independently detects atrial fibrillation reliably without the need of pulse detection. Combining pulse wave morphology-based features such as autocorrelation with information from pulse-interval variability it is possible to detect atrial fibrillation with high accuracy with a commercial wrist band. Photoplethysmography wrist bands accompanied with atrial fibrillation detection algorithms utilizing autocorrelation could provide a computationally very effective and reliable wearable monitoring method in screening of atrial fibrillation.

Effectiveness of the Chest Strap Electrocardiogram to Detect Atrial Fibrillation.

Atrial fibrillation (AF) is a significant cause of cardioembolic strokes. AF is often symptomless and intermittent, making its detection challenging. The aim of this study was to assess the possibility to use a chest strap (Suunto Movesense) to detect AF both by cardiologists and automated algorithms. A single channel electrocardiogram (ECG) from a chest strap of 220 patients (107 AF and 111 sinus rhythm SR with 2 inconclusive rhythms) were analyzed by 2 cardiologists (Doc1 and Doc2) and 2 different algorithms (COSEn and AFEvidence). A 3-lead Holter served as the gold standard ECG for rhythm analysis. Both cardiologists evaluated the quality of the chest strap ECG to be superior to the quality of the Holter ECG; p <0.05/p <0.001 (Doc1/Doc 2). Accurate automated algorithm-based AF detection was achieved with sensitivity of 95.3%/96.3% and specificity of 95.5/98.2% with 2 AF detection algorithms from chest strap and 93.5%/97.2% and 98.2%/95.5% from Holter, respectively. P waves were detectable in 93.7% (Doc1) and 94.6% (Doc2) of the cases from the chest strap ECG with sinus rhythm and 98.2% (Doc1) and 95.5% (Doc2) from the Holter (p = n.s). In conclusion, the ECGs from both methods enabled AF detection by a cardiologist and by automated algorithms. Both methods studied enabled P-wave detection in sinus rhythm.

Epinephrine induces tissue perfusion deficit in porcine endotoxin shock: evaluation by regional CO(2) content gradients and lactate-to-pyruvate ratios.

Epinephrine is widely used as a vasoconstrictor or inotrope in shock, although it may typically induce or augment lactic acidosis. Ongoing debate addresses the question of whether hyperlactatemia per se is a sign of tissue perfusion deficit or aerobic glycolysis. We wanted to test the hypothesis that epinephrine has selective detrimental effects on visceral perfusion and metabolism. We performed rigorous regional venous blood gas analyses as well as intraperitoneal microdialysis. We used a mathematical model to calculate regional arteriovenous CO(2) content gradients and estimated the magnitude of the Haldane effect in a porcine model of prolonged hypotensive shock induced by endotoxin infusion (mean arterial blood pressure < 60 mmHg). Subsequently, vasopressors (epinephrine or norepinephrine) were administered and adjusted to maintain systemic mean arterial pressure > 70 mmHg for 4 h. Epinephrine caused systemic hyperlactatemia and acidosis. Importantly, both systemic and regional venous lactate-to-pyruvate ratios increased. Epinephrine was associated with decreasing portal blood flow despite apparently maintained total splanchnic blood flow. Epinephrine increased gastric venous-to-arterial Pco(2) gradients and CO(2) content gradients with decreasing magnitude of the Haldane effect, and the regional gastric respiratory quotient remained higher after epinephrine as opposed to norepinephrine infusion. In addition, epinephrine induced intraperitoneal lactate and glycerol release. We did not observe these adverse hemodynamic or metabolic changes related to norepinephrine with the same arterial pressure goal. We conclude that high CO(2) content gradients with decreasing magnitude of the Haldane effect pinpoint the most pronounced perfusion deficiency to the gastric wall when epinephrine, as opposed to norepinephrine, is used in experimental endotoxin shock.

The effects of vasopressin on systemic and splanchnic hemodynamics and metabolism in endotoxin shock.

UNLABELLED: We compared the effects of vasopressin and norepinephrine on systemic and splanchnic circulation and metabolism in endotoxin shock in pigs. Twenty-one pigs were randomized to endotoxin shock (Escherichia coli endotoxin infusion) (n = 6), endotoxin and vasopressin (VASO; n = 6), endotoxin and norepinephrine (NE; n = 6), and controls (n = 3). Endotoxin infusion was increased to induce hypotension, after which vasopressin or norepinephrine was started to keep systemic mean arterial blood pressure >70 mm Hg. Regional blood flows and arterial and regional lactate concentrations were measured. Tonometers with microdialysis capillaries were inserted into the stomach, jejunum, and colon. Systemic mean arterial blood pressure >70 mm Hg was achieved in the VASO and NE groups. Vasopressin decreased cardiac output, superior mesenteric artery, and portal vein blood flow, whereas hepatic arterial blood flow increased. Arterial lactate concentration increased from 2.0 mM (1.6-2.1 mM) to 4.7 mM (4.7-4.9 mM) (P = 0.007). Systemic and mesenteric oxygen delivery and consumption decreased and oxygen extraction increased in the VASO group. Vasopressin increased mucosal-arterial PCO(2) gradients in all three locations, whereas luminal lactate release occurred only in the jejunum. Animals in the NE group remained stable. Vasopressin reversed hypotension but decreased systemic and gut blood flow. This was associated with hyperlactatemia, signs of visceral dysoxia, and jejunal luminal lactate release. IMPLICATIONS: Although vasopressin induces vasoconstriction in visceral region, its effects on splanchnic circulation and metabolism during septic-endotoxin shock are still poorly characterized. We evaluated the metabolic and hemodynamic effects of vasopressin and norepinephrine within the splanchnic area in porcine endotoxin shock.