Failure Monitoring Research Articles

Bioimpedance Spectroscopy-Based Edema Supervision Wearable System for Noninvasive Monitoring of Heart Failure

Heart failure (HF) is a complex syndrome in which the heart is unable to pump enough blood containing oxygen and nutrients to meet the body’s demands. HF is the leading cause of hospitalization for patients over 65 years of age. After a patient is diagnosed with HF, the mortality rate is 50% within the first 5 years. Presently, there is no unanimous diagnostic criteria for HF. Bioimpedance (BI) analysis has been proposed in recent years as a technique to detect one of the main symptoms: changes in body volume due to edema. This research presents a portable device (Volum), capable of performing real-time BI measurements in a low cost and non-invasive way. The goal is to improve patient monitoring at home to ensure rapid intervention in cases of worsening conditions, either with timely hospitalizations or adjustments to a patient’s usual treatment. Volum is a small, wearable, wireless, lightweight, low-power clinical pilot prototype, that takes measurements through four electrodes and sends the data via Bluetooth to an Android device.

Remote monitoring for heart failure by a ready-to-use solution in France: Comparison of digital versus illectronic patients

Remote monitoring for heart failure by a ready-to-use solution in France: Comparison of digital versus illectronic patients

Usefulness of telemedicine-based heart failure monitoring according to “eHealth literacy” domains: insights from the iCOR randomised controlled trial

Abstract Background/Introduction The potential positive effect of electronic health (eHealth)-based heart failure (HF) monitoring remains uncertain, largely due to the heterogeneity of the studies published to date. A subgroup of patients in which its key role is particularly uncertain is in the “low literacy” or “computer or digital illiterate” patients mainly due to telemedicine (TM)-based strategies have been often discouraged on the basis of a foreseeable limited benefit in them. Purpose The aim of this study was to determine the effectiveness of a TM-based managed care solution across literacy levels and information and communications technology (ICT) skills. Methods We performed a sub-analysis on the basis of two literacy domains (traditional and computer literacy) encompassed in the definition of “eHealth literacy” to the HF-patients included in the “insuficiència Cardíaca Optimitzaciό Remota” (iCOR) randomised study comparing TM vs. usual care (UC) in HF-patients. The primary study endpoint was the incidence of a non-fatal HF event after 6 months of inclusion. The event rates of primary (the occurrence of non-fatal HF event) and secondary (all-cause hospitalization, all cause death and the composite endpoint combining of allcause death or non-fatal HF events) study endpoints were calculated for each literacy domains and its combination. Cox proportional-hazards regression models were used to evaluate the effect of “eHealth literacy” dimensions, treatment group and the interaction term “eHealth literacy” domains by treatment group on study endpoints, and p-value was used to compare the different curves. Results 178 patients were analysed (81 TM vs 90 UC). As far as the “traditional literacy” analyses, 65% of patients had only elementary education and the 6% had no literacy qualifications, defining the low-educated subgroup as “lower literacy” (126 patients, 71%). All the other patients were classified in the “middle or higher literacy” (52 patients, 29%) subgroup. Moreover, concerning new technological management, 128 patients (72%) referred presenting moderate to high difficulties in its handling, constituting the “lower ICT skills” subgroup. The remaining 28% (50 patients) were allocated into the “middle or higher ICT skills” subgroup due to their minor handicap in their ICT use. The beneficial effect of TM compared to UC strategy was consistent across all literacy domains (pvalue for interaction 0.207 and 0.117 respectively). The risk of experiencing a primary event was significantly lower in patients that underwent allocation to the TM arm compared to UC in both clustered in the “lower literacy” (p-value=0.001) (Picture 1) and those allocated to the “lower ICT skills” (p-value=0.001) (Picture 2) subgroup. Conclusions Non-invasive eHealth-based HF monitoring tools are effective compared to UC in preventing HF events in the early post-discharge period, regardless of two “eHealth literacy” domains (“traditional and computer literacy”). Funding Acknowledgement Type of funding sources: None.

Use of a combined transient elastography and biochemical strategy to determine liver fibrosis in pediatric intestinal failure

Non-invasive monitoring of intestinal failure (IF) associated liver disease is an ongoing challenge in children with IF. Our objective was to develop a combined algorithm of clinical, transient elastography (TE) and biochemical parameters to identify liver fibrosis in this population. A retrospective cohort study of IF patients followed by our intestinal rehabilitation program between November 2015 to October 2019. Patients with a liver biopsy and TE were included. Demographic and liver function tests were collected. Fibrosis on liver biopsies was graded using the modified Scheuer score. Decision tree based algorithms classified low (F0-F1) versus high (F2-F4) fibrosis scores based on a combination of TE, biochemical and demographic parameters, using 6-fold classification error, sensitivity and specificity cross-validation (CV) scores. 42 patients (74% male, median age 7.6 (4.6; 42.7) months) were evaluated. Median length of PN therapy was 182 (121; 556) days. High fibrosis was present in 40.5% with a median TE of 12.1 (6.7; 12.9) kPa in high fibrosis children. An algorithm, based on cut-off values for TE of 11.3kPa and AST of 40 U/L, and grouping of the underlying etiology resulted in a correct classification of 88.1% of the pathology scores; with sensitivity 0.82 (95% CI 0.57; 0.96), specificity 0.92 (95% CI 0.74; 0.99), positive predictive value 0.88 (95% CI 0.64; 0.96) and negative predictive value 0.88 (95% CI 0.73; 0.96). The CV classification error was 28.6%, CV sensitivity 72.2% and CV specificity 75.5%. This algorithm shows promising results that could simplify non-invasive monitoring of liver fibrosis in children with IF. Validation in additional IF cohorts is needed.

Electrical Machine Breakdown Monitoring System Using IoT

Nowadays, electrical machines of different types and designs are used in hybrid production systems. Electrical machines are needed to control and implement various mechanisms to produce final products. However, sometimes, electrical machines stop working and cause downtime. This affects workers’ productivity during peak hours and reduces business profits. To overcome this problem, a suitable machine-monitoring system is required so that the problem can be addressed and rectified immediately. In this paper, an IoT prototype for an electrical machine failure monitoring system using Heltec LoRa ESP32 as the microcontroller to communicate with the Blynk IoT platform server was proposed and developed. The monitored parameter values are voltage, current and motion sensor readings. A ZMPT101b voltage sensor is used to measure the AC voltage, while an ACS712 current sensor is used to measure the AC current flowing through the machine. In addition, a tilt sensor is used to detect the vibrating motion of the motor. The measured values indicate machine health and are monitored using the developed Blynk IoT GUI platform, which is accessible from mobile phones and the web. A notification from Blynk application will be sent to users to alert them about a specific condition when a value measured and uploaded to the Blynk cloud exceeds the configured average. Three tests were performed using the serial monitor, the Blynk application and the multimeter, and the results showed that the lowest percentage of error between the voltage sensor readings extracted from the serial monitor and the multimeter was 1.19% and the lowest percentage of error between the current sensor readings extracted from the serial monitor and the multimeter was 17.63%. Overall, all parameter values were successfully displayed on the IoT platform. In future works, the accuracy of the current sensor can be improved by integrating a signal conditioning circuit to filter out noise.

Adsorption and gas sensing properties of Co modified InN to SF6-N2 mixture decomposition components

• It is the first time that Co-InN is used as the adsorbent to remove the decomposition components of SF 6 -N 2. • Co-InN shows good adsorption properties to NO 2 , SOF 4 , SOF 2 , SO 2 F 2 by chemisorption. • The theoretical calculation results are important to guide the development of resistive gas sensor based on the Co-InN. This paper presents a detection method of partial discharge decomposition components of SF 6 -N 2 mixture by Co-doped InN. The severity of partial discharge in gas insulated switchgear (GIS) can be judged by the gas components when SF 6 -N 2 decomposes. Exploring sensors with excellent gas-sensing detection capabilities can provide method to partial discharge decomposition component detection and fault diagnosis of GIS. Undoped InN is a wide bandgap semiconductor with good sensing properties. In this paper, the adsorption of SF 6 -N 2 decomposition components on Co-doped InN monolayer, as well as the adsorption mechanism and gas sensitivity characteristics were investigated by density functional theory (DFT), including adsorption energy, charge transfer, the density of state theory and molecular orbital theory. Our calculations found that Co-doped InN has the optimal structure at the T N site and has good adsorption performance for NO 2 , SOF 4 , SOF 2 , and SO 2 F 2 gas molecule. All adsorption reactions belong to chemisorption. These results could direct the development of gas sensor based on the Co-doped InN monolayer, and provides methods to online monitoring of GIS discharge failures.

Multi-Instrumental Approach to Slope Failure Monitoring in a Landslide Susceptible Newly Built-Up Area: Topo-Geodetic Survey, UAV 3D Modelling and Ground-Penetrating Radar

Slope failures and landslides cause economic damage and deaths worldwide. These losses can be minimized by integrating different methodologies, instruments, and data monitoring to predict future landslides. In the constantly growing metropolitan area of Cluj-Napoca, Romania, changes in land cover, land use, and build-up areas are an issue. The unprecedented urban sprawl pushed the city limits from the Somes River to hilly terrain prone to landslides and erosion. This study focuses on a landslide-prone area where a previous slope failure caused significant economic losses. It combines topo-geodetic measurements, UAV monitoring of surface displacement, GIS spatial analysis, ground-penetrating radar investigations, and geotechnical assessment. Two years of data show that the slope is undergoing surface erosion, with soil displacements of a few centimeters. Geodetic monitoring of the retaining wall’s control points indicates a small rotation. Coupled with georadar profile imaging showing changes in soil and rock layers with an uplift trend, it was deduced that the area suffers from a global instability. The findings provide valuable information about the dynamics of landslides and erosion for forecasting future movements and developing preventative strategies based on a new methodology that combines affordable and prevalent instrumentation and techniques.

Monitoring of Right Ventricular Failure With Daily Pressure Volume Loops Obtained via an Application and 3-Dimensional Echocardiography.

Monitoring of Right Ventricular Failure With Daily Pressure Volume Loops Obtained via an Application and 3-Dimensional Echocardiography.

Home care nursing practice for older persons with heart failure living at home.

To enhance knowledge of how older people with heart failure, living at home, manage their illness with the support of their family caregivers and home care nursing services. Heart failure monitoring and self-care have been important means of reducing the serious impact of heart failure. Drawing on theories of practice as enacted and conceptualising service users and their family caregivers as active, the idea of attunement was used to explore how home care nurses work in supporting them. Ethnographic case study. Data collection involved home visits and interviews (10 home care users, 10 caregivers, five home care team leaders). Data were field-notes and transcribed interviews. Themes were deductively developed from the findings, informed by the theoretical background, using content analysis. The COREQ checklist was used. Three themes were developed from the data: (1) Practices of attunement in relations, (2) Becoming among difficulties and (3) Off track-difficult to attune around self-care. The findings reflect the complexity of heart failure monitoring at home, showing how, in addition to self-care measures, users are supported by an array of informal and formal care. The caregiving situation is shaped by relations among participants and involves making oneself available to the situation. We suggest a stance of attunement for home care nurses, which demands tact and calls for interest, engagement and openness. Study findings caution against limiting heart failure monitoring to circumscribed tasks, instead calling for a holistic understanding of what may be helpful for users. Home care nurses need time to attune with users living with heart failure and their caregivers to prevent exacerbations and promote well-being. Although patients were not formally involved in study design ethnography favours their voice.

Abstract 15095: A Rare Cardiomyopathy Caught Offsides: Discovery of a New Phenotype

A 51-year-old male with Wolff-Parkinson-White (WPW) syndrome, atrial flutter, and third-degree heart block necessitating pacemaker placement presented with asymptomatic atrial flutter. The patient’s father and paternal aunt were diagnosed with WPW and died suddenly at approximate ages of 40 and 60 years, respectively. An echocardiogram showed RV dilation and moderate to severe RV systolic dysfunction with normal LV ejection fraction. Cardiac CT demonstrated thinning of the RV free wall and free wall outpouchings during systole (Figure), as well as moderate asymmetric LV hypertrophy, aneurysmal changes of the basal inferior wall, and dyskinesis of the LV apex. At this time, the differential for RV dysfunction included group 1 pulmonary hypertension, ischemic cardiomyopathy, sarcoidosis, and arrhythmogenic right ventricular cardiomyopathy (ARVC). Subsequent testing excluded all diagnoses other than ARVC. Additionally, 2010 Task Force Criteria for ARVC were not met. However, genetic testing revealed p.Arg302Gln mutation in the PRKAG2 gene. AMP-activated protein kinase (AMPK) is a highly conserved kinase encoded by the PRKAG2 gene that is responsible for controlling cellular energy homeostasis. This PRKAG2 mutation, within the spectrum of genetic mutations resulting in the rare autosomal dominant PRKAG2 syndrome, is characterized by glycogen storage cardiomyopathy and a familial form of WPW, supraventricular arrhythmias, and left ventricular hypertrophy. To the best of our knowledge, this is the first report of PRKAG2 syndrome with prominent abnormalities of the RV, and it expands the scope of PRKAG2 syndrome. Management included implantation of an AICD and ongoing careful monitoring for arrhythmia and heart failure. The patient’s siblings and children were referred for genetic counseling. This case highlights the importance of a broad differential diagnosis and exhaustive evaluation of patients presenting with right ventricular abnormalities.

3D Modelling of Rockfall Hazard at Gunung Lang, Ipoh

Densely populated hillside regions in humid tropical climatic zones are often prone to various types of mass movement. The development in Kinta Valley is no exception from the rapid development and has prompted the opening of new areas approaching slopes. This type of activity ultimately increased the level of rockfall vulnerability in this area. In this research, we conducted an assessment on the hazard level of Gunung Lang and created a 3D hazard model using the CONEFALL software. Aerial photograph, Landsat ETM+ image and DEM data of Gunung Lang were used in this study for lineament tracing and subsequently used to identify rockfall source and generated a 3D model of our study area using a high-resolution DEM. Based on our model, the maximum velocity of rockfall is approximately 36ms-1 and could travel up to 113 m from its source. Therefore, in order to minimize casualty and property damage, regular monitoring of slope failure are needed.

A nonlinear total variation based denoising method for electrostatic signal of low signal-to-noise ratio

Aero-engine electrostatic monitoring technology (EMT) is a novel and effective condition monitoring technology. With the help of EMT, effective monitoring of early failures can be achieved. Since the electrostatic monitoring of the running engine will be strongly interfered, the sampled electrostatic signal has various noise components and low signal-to-noise ratio (SNR). After analyzing the source of the noise components carried by the electrostatic signal, this paper proposes a method for electrostatic signal denoising in a strong interference environment, which is based on the nonlinear total variation theory. In the experiments, the simulated electrostatic measurement signal and the actual test-run electrostatic measurement signal were used as the analysis objects, and the denoising test was carried out by using the proposed method. Meanwhile, the denoising effect was compared and analyzed with other classical methods. The experimental results show that the proposed denoising method can effectively remove random noise, electromagnetic pulse and periodic noise in electrostatic signal, and is more applicable to the measured electrostatic signal with low SNR than the classical electrostatic signal denoising methods such as wavelet threshold denoising method and empirical mode decomposition method.

Usefulness of telemedicine-based heart failure monitoring according to “eHealth literacy” domains: insights from the iCOR randomised controlled trial

Abstract Background/Introduction The potential positive effect of electronic health (eHealth)-based heart failure (HF) monitoring remains uncertain, largely due to the heterogeneity of the studies published to date. A subgroup of patients in which its key role is particularly uncertain is in the “low literacy” or “computer or digital illiterate” patients mainly due to telemedicine (TM)-based strategies have been often discouraged on the basis of a foreseeable limited benefit in them. Purpose The aim of this study was to determine the effectiveness of a TM-based managed care solution across literacy levels and information and communications technology (ICT) skills. Methods We performed a sub-analysis on the basis of two literacy domains (traditional and computer literacy) encompassed in the definition of “eHealth literacy” to the HF-patients included in the “insuficiència Cardíaca Optimitzaciό Remota” (iCOR) randomised study comparing TM vs. usual care (UC) in HF-patients. The primary study endpoint was the incidence of a non-fatal HF event after 6 months of inclusion. The event rates of primary (the occurrence of non-fatal HF event) and secondary (all-cause hospitalization, all cause death and the composite endpoint combining of allcause death or non-fatal HF events) study endpoints were calculated for each literacy domains and its combination. Cox proportional-hazards regression models were used to evaluate the effect of “eHealth literacy” dimensions, treatment group and the interaction term “eHealth literacy” domains by treatment group on study endpoints, and p-value was used to compare the different curves. Results 178 patients were analysed (81 TM vs 90 UC). As far as the “traditional literacy” analyses, 65% of patients had only elementary education and the 6% had no literacy qualifications, defining the low-educated subgroup as “lower literacy” (126 patients, 71%). All the other patients were classified in the “middle or higher literacy” (52 patients, 29%) subgroup. Moreover, concerning new technological management, 128 patients (72%) referred presenting moderate to high difficulties in its handling, constituting the “lower ICT skills” subgroup. The remaining 28% (50 patients) were allocated into the “middle or higher ICT skills” subgroup due to their minor handicap in their ICT use. The beneficial effect of TM compared to UC strategy was consistent across all literacy domains (pvalue for interaction 0.207 and 0.117 respectively). The risk of experiencing a primary event was significantly lower in patients that underwent allocation to the TM arm compared to UC in both clustered in the “lower literacy” (p-value=0.001) (Picture 1) and those allocated to the “lower ICT skills” (p-value=0.001) (Picture 2) subgroup. Conclusions Non-invasive eHealth-based HF monitoring tools are effective compared to UC in preventing HF events in the early post-discharge period, regardless of two “eHealth literacy” domains (“traditional and computer literacy”). Funding Acknowledgement Type of funding sources: None.

An Energy-Efficient Bridge-to-Digital Converter for Implantable Pressure Monitoring Systems.

This paper presents an energy-efficient, duty-cycled, and spinning excitation bridge-to-digital converter (BDC) designed for implantable pressure sensing systems. The circuit provides the measure of the pulmonary artery pressure that is particularly relevant for the monitoring of heart failure and pulmonary hypertension patients. The BDC is made of a piezoresistive pressure sensor and a readout integrated circuit (IC) that comprises an instrumentation amplifier (IA) followed by an analog-to-digital converter (ADC). The proposed design spins both the bridge excitation and the ADC's sampling input voltages simultaneously and exploits duty cycling to reduce the static power consumption of the bridge sensor and IA while cancelling the IA's offset and 1/f noise at the same time. The readout IC has been designed and fabricated in a standard 180-nm CMOS process and achieves 8.4 effective number of bits (ENOB) at 1 kHz sampling rate while drawing 0.53 μA current from a 1.2 V supply. The BDC, built with the readout IC and a differential pressure sensor having 5 k Ω bridge resistances, achieves 0.44 mmHg resolution in a 270 mmHg pressure range at 1 ms conversion time. The current consumption of the bridge sensor by employing duty cycling is reduced by 99.8% thus becoming 0.39 μA from a 1.2 V supply. The total conversion energy of the pressure sensing system is 1.1 nJ, and achieves a figure-of-merit (FoM) of 3.3 pJ/conversion, which both represent the state of the art.

Study and Application of an Elevator Failure Monitoring System Based on the Internet of Things Technology

To prevent the occurrence of elevator safety accidents, in this study, an Internet of things-based elevator failure monitoring system is investigated. First, it introduces the Internet of things technology, preprocesses the relevant data, and extracts the features of the elevator operation data continuously collected by the elevator sensors. The objective of this paper is to study the application of IoT in elevators. Method. The Relief-F algorithm is used to evaluate the potential influencing factors. The results show that, as the batch size increases, the accuracy rate will gradually increase, but after more than 20, the accuracy rate will decrease. When the batch size is 20, the training result is the best. It can be seen that, as the time step increases, the accuracy of the prediction will be significantly improved. When the time step is 24, the prediction accuracy is the highest; after 24, the prediction rate will decline. In the diagram of the influence of learning rate on the model, the blue line indicates that the learning rate is 1.0, the red line is 0.1, and the black line is 0.01. With the increase of the iteration times, the effect is the best when the learning rate is 0.1. This system has high research value and broad application prospects and can make up for the current lack of monitoring in the elevator industry.

Online correction method of IGBT lifetime evaluation based on bonding wire failure monitoring

Insulated gate bipolar transistor (IGBT) modules are widely used as power switching devices. In actual working conditions, the failure of bonding wires is one of the most important failure modes. At present, the influence of bonding wire failure is not considered in the reliability evaluation of the device, resulting in a large error in the life prediction result. Therefore, this paper proposes a life evaluation method for IGBT modules that takes into account the failure of the bonding wire. The transient thermal impedance of 1200 V/50 A IGBT module under the failure conditions of different numbers of bonding wires is calculated by constructing a finite element simulation model of device electro-thermal coupling. Both simulation and experimental results show that with the increase of the number of bonding wire failures, the transient thermal impedance curve of the device rises and the steady-state thermal impedance increases. The Foster thermal network models of the devices are updated, and the number of power cycles of the device with bonding wire failure under DC and sinusoidal pulse width modulation (SPWM) conditions is calculated. When the number of bonding wire failures and fatigue accumulation have a linear relationship, the life will be reduced by 48.29% compared to the healthy state.

A Test Method for Finding Early Dynamic Fracture of Rock: Using DIC and YOLOv5.

Intelligent monitoring and early warning of rock mass failure is vital. To realize the early intelligent identification of dynamic fractures in the failure process of complex fractured rocks, 3D printing of the fracture network model was used to produce rock-like specimens containing 20 random joints. An algorithm for the early intelligent identification of dynamic fractures was proposed based on the YOLOv5 deep learning network model and DIC cloud. The results demonstrate an important relationship between the overall strength of the specimen with complex fractures and dynamic fracture propagation, and the overall specimen strength can be judged semi-quantitatively by counting dynamic fracture propagation. Before the initiation of each primary fracture, a strain concentration area appears, which indicates new fracture initiation. The dynamic evolution of primary fractures can be divided into four types: primary fractures, stress concentration areas, new fractures, and cross fractures. The cross fractures have the greatest impact on the overall strength of the specimen. The overall identification accuracy of the four types of fractures identified by the algorithm reached 88%, which shows that the method is fast, accurate, and effective for fracture identification and location, and classification of complex fractured rock masses.

Analysis of SLAM-Based Lidar Data Quality Metrics for Geotechnical Underground Monitoring

Adverse ground behavior events, such as convergence and ground falls, pose critical risks to underground mine safety and productivity. Today, monitoring of such failures is primarily conducted using legacy techniques with low spatial and temporal resolution while exposing workers to hazardous environments. This study assesses the potential of novel simultaneous localization and mapping (SLAM)-based light detection and ranging (Lidar) data quality for rapid, digital, and eventually autonomous mine-wide underground geotechnical monitoring. We derive a comprehensive suite of quality metrics based on tests in two underground mines for two state-of-the-art mobile laser scanning (MLS) systems. Our results provide evidence that SLAM-based MLS provides data of the quality required to detect geotechnically relevant changes while being significantly more efficient for large mine layouts when compared to traditional static systems. Additionally, we show that SLAM-specific processing can achieve an order of magnitude better relative accuracy relevant for change detection than quality metrics derived from traditionally deployed tests would suggest while reducing SLAM drift error by up to 90%. In collaboration with an operating block cave mine, we confirm these capabilities in field tests on a mine-wide scale and, for the first time, demonstrate methods of rockfall detection using MLS data. While more work is required to investigate optimal collection, processing, and utilization of MLS data, we demonstrate its potential to become an effective and widely applicable data source for rapid, accurate, and comprehensive geotechnical inspections.

Prevalence of Fungal Drug Resistance in COVID-19 Infection: a Global Meta-analysis.

PurposeSecondary bacterial or fungal infections are one of the most important medical complications among patients with Coronavirus Disease 2019 (COVID-19). The emergence of multidrug-resistant (MDR) candida can cause many problems such as treatment failure, adverse clinical outcomes, and even disease outbreaks. This systematic review and meta-analysis aims to investigate the prevalence and outcomes of fungal drug-resistant in COVID-19 patients.MethodsPubMed, Embase, Scopus, Cochrane Library, and Web of Science databases were searched for peer reviewed-articles published in English up to May 20, 2021. Heterogeneity across studies was evaluated using Cochrane’s Q test and the I2 index. The pooled point prevalence and their corresponding 95% confidence intervals (CIs) were considered to estimate the prevalence of fungal drug resistance infection in COVID-19 patients.ResultsEight eligible articles were included in our meta-analysis. The number of COVID-19 patients with fungal co-infection varied from 5 to 35 among selected studies. The overall pooled prevalence of fungal drug resistance among patients with co-infections of fungal and COVID-19 was 69% (95% CI: 37%, 94%) by using a random-effects model. In terms of specific species, the pooled meta-analysis for Candida Auris was estimated to be 100% (95%CI: 98%, 100%; I2 = 0%), for Multi-Candida 59% (95%CI: 38%, 79%; I2 = 12.5%), and for Aspergillus 15% (95%CI: 0%, 42%; I2 = 0%).ConclusionOur study shows the high prevalence of fungal drug resistance in COVID-19 patients and emphasizes the need to strengthen antimicrobial stewardship programs, close monitoring for treatment failure, and the emergence of resistance upon treatment.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12281-022-00439-9.

Accuracy of a New Pulse Oximetry in Detection of Arterial Oxygen Saturation and Heart Rate Measurements: The SOMBRERO Study.

Early diagnosis and continuous monitoring of respiratory failure (RF) in the course of the most prevalent chronic cardio-vascular (CVD) and respiratory diseases (CRD) are a clinical, unresolved problem because wearable, non-invasive, and user-friendly medical devices, which could grant reliable measures of the oxygen saturation (SpO2) and heart rate (HR) in real-life during daily activities are still lacking. In this study, we investigated the agreement between a new medical wrist-worn device (BrOxy M) and a reference, medical pulseoximeter (Nellcor PM 1000N). Twelve healthy volunteers (aged 20–51 years, 84% males, 33% with black skin, obtaining, during the controlled hypoxia test, the simultaneous registration of 219 data pairs, homogeneously deployed in the levels of Sat.O2 97%, 92%, 87%, 82% [ISO 80601-2-61:2017 standard (paragraph EE.3)]) were included. The paired T test 0 and the Bland-Altman plot were performed to assess bias and accuracy. SpO2 and HR readings by the two devices resulted significantly correlated (r = 0.91 and 0.96, p < 0.001, respectively). Analyses excluded the presence of proportional bias. For SpO2, the mean bias was −0.18% and the accuracy (ARMS) was 2.7%. For HR the mean bias was 0.25 bpm and the ARMS3.7 bpm. The sensitivity to detect SpO2 ≤ 94% was 94.4%. The agreement between BrOxy M and the reference pulse oximeter was “substantial” (for SpO2 cut-off 94% and 90%, k = 0.79 and k = 0.80, respectively). We conclude that BrOxy M demonstrated accuracy, reliability and consistency in measuring SpO2 and HR, being fully comparable with a reference medical pulseoxymeter, with no adverse effects. As a wearable device, Broxy M can measure continually SpO2 and HR in everyday life, helping in detecting and following up CVD and CRD subjects.