COVID-19 pandemic and the opportunity to accelerate remote monitoring of patients

Júnior, Antonio Paulo Nassar

doi:10.36416/1806-3756/e20210238

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…Thirteen patients (8%) were asymptomatic, while the rest had at least one symptom for a median of 11 (7)(8)(9)(10)(11)(12)(13)(14)(15)(16) days. Patients experienced a median of three (2-4) symptoms during the 14-day monitoring period.…”

Section: Symptomatologymentioning

confidence: 99%

“…The mass-produced wearables and portable sensors and the telemedicine tools for diagnostics, monitoring, and management could enhance control over the COVID-19 pandemic. The potential benefits of employing these telemedicine methods during a pandemic have been previously discussed and reviewed [5][6][7][8]. The healthcare delivery models underwent significant changes, leading to a rapid implementation of telemedicine solutions providing healthcare services to COVID-19 patients [9,10].…”

Section: Introductionmentioning

confidence: 99%

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Telehealth-Based Information Retrieval and Extraction for Analysis of Clinical Characteristics and Symptom Patterns in Mild COVID-19 Patients

Jahaj,

Gallos,

Tziomaka

et al. 2024

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Clinical characteristics of COVID-19 patients have been mostly described in hospitalised patients, yet most are managed in an outpatient setting. The COVID-19 pandemic transformed healthcare delivery models and accelerated the implementation and adoption of telemedicine solutions. We employed a modular remote monitoring system with multi-modal data collection, aggregation, and analytics features to monitor mild COVID-19 patients and report their characteristics and symptoms. At enrolment, the patients were equipped with wearables, which were associated with their accounts, provided the respective in-system consents, and, in parallel, reported the demographics and patient characteristics. The patients monitored their vitals and symptoms daily during a 14-day monitoring period. Vital signs were entered either manually or automatically through wearables. We enrolled 162 patients from February to May 2022. The median age was 51 (42–60) years; 44% were male, 22% had at least one comorbidity, and 73.5% were fully vaccinated. The vitals of the patients were within normal range throughout the monitoring period. Thirteen patients were asymptomatic, while the rest had at least one symptom for a median of 11 (7–16) days. Fatigue was the most common symptom, followed by fever and cough. Loss of taste and smell was the longest-lasting symptom. Age positively correlated with the duration of fatigue, anorexia, and low-grade fever. Comorbidities, the number of administered doses, the days since the last dose, and the days since the positive test did not seem to affect the number of sick days or symptomatology. The i-COVID platform allowed us to provide remote monitoring and reporting of COVID-19 outpatients. We were able to report their clinical characteristics while simultaneously helping reduce the spread of the virus through hospitals by minimising hospital visits. The monitoring platform also offered advanced knowledge extraction and analytic capabilities to detect health condition deterioration and automatically trigger personalised support workflows.

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Section: Symptomatologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Telehealth-Based Information Retrieval and Extraction for Analysis of Clinical Characteristics and Symptom Patterns in Mild COVID-19 Patients

Jahaj,

Gallos,

Tziomaka

et al. 2024

Information

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“…In low- and middle-income countries, where over 75% of deaths are related to cardiovascular disease and where there is limited access to ECG equipment and cardiologists, many patients with heart disease need regular ECG checks and reviews, both to check for disease progression and for surveillance of drug therapies [ 6 ]. This demand can place an insurmountable burden on the available pool of specialists, including in areas with a high concentration of populations susceptible to heart disease (eg, South Asian people), particularly in the COVID-19 pandemic era [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Comparison Between QT and Corrected QT Interval Assessment by an Apple Watch With the AccurBeat Platform and by a 12‑Lead Electrocardiogram With Manual Annotation: Prospective Observational Study

Chokshi¹,

Tologonova²,

Calixte³

et al. 2022

JMIR Form Res

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Background Abnormal prolongation or shortening of the QT interval is associated with increased risk for ventricular arrhythmias and sudden cardiac death. For continuous monitoring, widespread use, and prevention of cardiac events, advanced wearable technologies are emerging as promising surrogates for conventional 12‑lead electrocardiogram (ECG) QT interval assessment. Previous studies have shown a good agreement between QT and corrected QT (QTc) intervals measured on a smartwatch ECG and a 12-lead ECG, but the clinical accuracy of computerized algorithms for QT and QTc interval measurement from smartwatch ECGs is unclear. Objective The prospective observational study compared the smartwatch-recorded QT and QTc assessed using AccurKardia’s AccurBeat platform with the conventional 12‑lead ECG annotated manually by a cardiologist. Methods ECGs were collected from healthy participants (without any known cardiovascular disease) aged >22 years. Two consecutive 30-second ECG readings followed by (within 15 minutes) a 10-second standard 12-lead ECG were recorded for each participant. Characteristics of the participants were compared by sex using a 2-sample t test and Wilcoxon rank sum test. Statistical comparisons of heart rate (HR), QT interval, and QTc interval between the platform and the 12-lead ECG, ECG lead I, and ECG lead II were done using the Wilcoxon sign rank test. Linear regression was used to predict QTc and QT intervals from the ECG based on the platform’s QTc/QT intervals with adjustment for age, sex, and difference in HR measurement. The Bland-Altman method was used to check agreement between various QT and QTc interval measurements. Results A total of 50 participants (32 female, mean age 46 years, SD 1 year) were included in the study. The result of the regression model using the platform measurements to predict the 12-lead ECG measurements indicated that, in univariate analysis, QT/QTc intervals from the platform significantly predicted QT/QTc intervals from the 12-lead ECG, ECG lead I, and ECG lead II, and this remained significant after adjustment for sex, age, and change in HR. The Bland-Altman plot results found that 96% of the average QTc interval measurements between the platform and QTc intervals from the 12-lead ECG were within the 95% confidence limit of the average difference between the two measurements, with a mean difference of –10.5 (95% limits of agreement –71.43, 50.43). A total of 94% of the average QT interval measurements between the platform and the 12-lead ECG were within the 95% CI of the average difference between the two measurements, with a mean difference of –6.3 (95% limits of agreement –54.54, 41.94). Conclusions QT and QTc intervals obtained by a smartwatch coupled with the platform’s assessment were comparable to those from a 12-lead ECG. Accordingly, with further refinements, remote monitoring using this technology holds promise for the identification of QT interval prolongation.

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Response to “The COVID-19 pandemic and the opportunity to accelerate remote monitoring of patients”

et al. 2021

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COVID-19 pandemic and the opportunity to accelerate remote monitoring of patients

Cited by 3 publications

References 3 publications

Telehealth-Based Information Retrieval and Extraction for Analysis of Clinical Characteristics and Symptom Patterns in Mild COVID-19 Patients

Telehealth-Based Information Retrieval and Extraction for Analysis of Clinical Characteristics and Symptom Patterns in Mild COVID-19 Patients

Comparison Between QT and Corrected QT Interval Assessment by an Apple Watch With the AccurBeat Platform and by a 12‑Lead Electrocardiogram With Manual Annotation: Prospective Observational Study

Response to “The COVID-19 pandemic and the opportunity to accelerate remote monitoring of patients”

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