ABSTRACT
BACKGROUND: Wearing face shields and masks, which used to have very limited public use before the COVID-19 outbreak, has been highly recommended by organizations, such as CDC and WHO, during this pandemic period. AIMS: The aim of this prospective study is to scrutinize the dynamic changes in vital parameters, change in end tidal CO2 (PETCO2) levels, the relationship of these changes with taking a break, and the subjective complaints caused by respiratory protection, while healthcare providers are performing their duties with the N95 mask. METHODS: The prospective cohort included 54 healthcare workers (doctors, nurses, paramedics) who worked in the respiratory unit of the emergency department (ED) and performed their duties by wearing valved N95 masks and face shields. The vital parameters and PETCO2 levels were measured at 0-4th-5th and 9th hours of the work-shift. RESULTS: Only the decrease in diastolic BP between 0 and 9 h was statistically significant (p = 0.038). Besides, mean arterial pressure (MAP) values indicated a significant decrease between 0-9 h and 5-9 h (p = 0.024 and p = 0.049, respectively). In terms of the vital parameters of the subjects working with and without breaks, only PETCO2 levels of those working uninterruptedly increased significantly at the 4th hour in comparison to the beginning-of-shift baseline levels (p = 0.003). CONCLUSION: Although the decrease in systolic blood pressure (SBP) and MAP values is assumed to be caused by increased fatigue due to workload and work pace as well as increase in muscle activity, the increase in PETCO2 levels in the ED healthcare staff working with no breaks between 0 and 4 h should be noted in terms of PPE-induced hypoventilation.
ABSTRACT
Healthcare sectors are majorly moving towards Remote Health Monitoring Systems (RHMS) after the COVID-19 pandemic outbreak across the world. RHMS involves monitoring the patient's vital parameters remotely and providing advice and consultation online. Alerts are generated whenever a particular health parameter exceeds the threshold and sent to the medical officers for further actions. However, it is observed that these thresholds are applicable only when a patient is at rest and can change drastically during patient's physical activity such as walking, climbing the staircase, during exercise etc., which can mislead in understanding the patient's health condition. Hence there is a requirement to correlate these parameter values with the current activity the patient is in and to generate activity-based dynamic thresholds. In this paper, a method to correlate the sensor values with physical activities is proposed. The activity-based RHMS (aRHMS) uses the motion sensors available in the patient's smartphone to predict the activity and will automatically adjust the threshold values in co-relation with the activities and provides alarms/alerts accordingly. © 2022 IEEE.
ABSTRACT
Telemedicine platforms have been largely used to manage multiple problems during the Covid-19 pandemic. In fact, they have given the possibility of remotely monitoring infected and high-risk patients, reducing hospitalisations. Telemonitoring systems with Global Navigation Satellite System technology allow to geo-localise all patients' measurements and enable the tracking of positions. These data can be used for contact tracing or to support doctors in epidemiological analysis. This paper presents the integration of satellite technologies in an existing telemedicine system (E@syCare), during the current outbreak. In particular, the platform has been enhanced with GPS, to geo-tag all vital parameters collected by the tablet gateway and the smartwatch. Geographical data are processed, after a request through the improved web-based medical interface based on some filters (e.g., vital parameters and their thresholds, considered period of time, and maximum cluster radius), with two sequential clustering algorithms. Agglomerative Clustering is used to find the optimal number of clusters given a maximum radius, and K-Means to effectively generate the predefined number of clusters. Resulting clusters are shown on an interactive epidemiological map in the webbased medical interface. This additional feature gives the possibility to healthcare authorities to correlate the spread of a disease or a virus with specific geographical areas or environmental conditions, to monitor fitness/movement habits of patients (also when the pandemic is over), and to track contact among patients. ©2021 IEEE.
ABSTRACT
OBJECTIVE: This study aimed to improve the accuracy of CT for detection of COVID-19-associated pneumonia and to identify patient subgroups who might benefit most from CT imaging. METHODS: A total of 269 patients who underwent CT for suspected COVID-19 were included in this retrospective analysis. COVID-19 was confirmed by reverse-transcription-polymerase-chain-reaction. Basic demographics (age and sex) and initial vital parameters (O2-saturation, respiratory rate, and body temperature) were recorded. Generalized mixed models were used to calculate the accuracy of vital parameters for detection of COVID-19 and to evaluate the diagnostic accuracy of CT. A clinical score based on vital parameters, age, and sex was established to estimate the pretest probability of COVID-19 and used to define low, intermediate, and high risk groups. A p-value of <0.05 was considered statistically significant. RESULTS: The sole use of vital parameters for the prediction of COVID-19 was inferior to CT. After correction for confounders, such as age and sex, CT showed a sensitivity of 0.86, specificity of 0.78, and positive predictive value of 0.36. In the subgroup analysis based on pretest probability, positive predictive value and sensitivity increased to 0.53 and 0.89 in the high-risk group, while specificity was reduced to 0.68. In the low-risk group, sensitivity and positive predictive value decreased to 0.76 and 0.33 with a specificity of 0.83. The negative predictive value remained high (0.94 and 0.97) in both groups. CONCLUSIONS: The accuracy of CT for the detection of COVID-19 might be increased by selecting patients with a high-pretest probability of COVID-19.