Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a disease with no validated specific and sensitive biomarker, and no standard approved treatment. In this observational study with no intervention, participants used a Fitbit activity tracker. The aims were to explore natural symptom variation, feasibility of continuous activity monitoring, and to compare activity data with patient reported outcome measures (PROMs).In this pilot study, 27 patients with mild to severe ME/CFS, of mean age 42.3 years, used the Fitbit Charge 3 continuously for six months. Patients wore a SenseWear activity bracelet for 7 days at baseline, at 3 and 6 months. At baseline and follow-up they completed the Short Form 36 Health Survey (SF-36) and the DePaul Symptom Questionnaire–Short Form (DSQ-SF).The mean number of steps per day decreased with increasing ME/CFS severity; mild 5566, moderate 4991 and severe 1998. The day-by-day variation was mean 47% (range 25%–79%). Mean steps per day increased from the first to the second three-month period, 4341 vs 4781 steps, p = 0.022. The maximum differences in outcome measures between 4-week periods (highest vs lowest), were more evident in a group of eight patients with milder disease (baseline SF-36 PF > 50 or DSQ-SF < 55) as compared to 19 patients with higher symptom burden (SF-36 PF < 50 and DSQ-SF > 55), for SF-36 PF raw scores: 16.9 vs 3.4 points, and for steps per day: 958 versus 479 steps. The correlations between steps per day and self-reported SF-36 Physical function, SF-36 Social function, and DSQ-SF were significant. Fitbit recorded significantly higher number of steps than SenseWear. Resting heart rates were stable during six months.Continuous activity registration with Fitbit Charge 3 trackers is feasible and useful in studies with ME/CFS patients to monitor steps and resting heart rate, in addition to self-reported outcome measures.NCT04195815.Clinicaltrials.gov:  Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a disease of unknown etiology with high symptom burden, no validated specific and sensitive biomarker, and no standard approved effective treatment. Defined by the Canadian consensus criteria, it affects 0.1–0.8% of the population . ME/CFS Our working hypothesis is that ME/CFS in a subgroup could be a variant of an autoimmune disease. We have previously conducted clinical trials in patients with ME/CFS using the immunomodulatory drugs cyclophosphamide and rituximab –8. In thIn the randomized and placebo-controlled trial assessing rituximab induction and maintenance infusions versus placebo in ME/CFS patients, the primary and secondary outcome measures were negative with no significant differences between the rituximab and placebo groups . There wIn addition, comparison of data from different clinical trials are hampered by the use of different outcome measures and, importantly, inclusion of patient populations with different levels of symptom severity. The establishment of new, improved and broadly accepted outcome measures would aid such comparisons, which we believe are important when assessing trial outcomes. Until now, consensus on outcome measures is lacking, but there are some reports evaluating different PROMs in ME/CFS –15. SeveAiming to improve the outcome measures for future clinical trials on ME/CFS, this study evaluates the use of data obtainable from activity trackers, which are growing in popularity. Several studies for evaluation of accuracy have been performed . Both ouIn this prospective observational study with no intervention, 27 participants used Fitbit activity trackers continuously throughout the six-month study period. We explored the feasibility of continuous activity monitoring in a clinical trial and whether the armbands could be used to assess levels of physical activity in ME/CFS patients. Further, we compared the continuous activity monitoring with PROMs for health-related quality of life and for ME/CFS symptoms, and asked the participants to assess which data best reflected their own perception of activity level and symptom severity. Data from the Fitbit monitor were compared with the SenseWear activity armbands, which have been validated and used in our previous clinical trials.The study  was designed as a prospective observational study with continuous monitoring of physical activity using Fitbit Charge 3 activity trackers for 6 months, including assessment of feasibility, comparison with PROMs for quality of life (SF-36 ver.1.2) and for ME/CFS symptoms (DSQ-SF), and comparison with the validated activity bracelet Sensewear.Recruitment was performed by advertising via the local ME association’s Facebook page, and the research group’s e-mail newsletter. Of 122 candidates who applied for participation, 14 were excluded due to long travel distance to the study center, and 16 because they had participated in earlier studies. From the remaining 92 candidates, 30 were randomly selected and informed about the study by phone. Three patients decided not to participate, and the remaining 27 proceeded to clinical assessment and inclusion. Due to the Covid-19 pandemic, the inclusion process was closed after the first 27 included patients.Inclusion criteria were: a diagnosis of ME/CFS according to the Canadian consensus criteria ; age 18 Recruitment and follow-up lasted from March 2020 until November 2020. All 27 patients were included and monitored at the Department of Oncology and Medical Physics, Haukeland University Hospital (HUH).The patients used Fitbit Charge 3 trackers on their non-dominant wrist  for continuous monitoring of physical activity, day and night. They were instructed to only remove the trackers for recharging, roughly for one hour per week.https://dev.fitbit.com/build/reference/web-api/developer-guide/application-design/). For each participant we registered an Oauth 2.0 application with type set as “personal”. The scopes were set to heartrate+profile+sleep+settings+activity+weight, with “time” set to 365 days.A Data Protection Impact Assessment was performed prior to study start. In an effort to protect the participants’ privacy, we used pseudonymisation toward third parties. Each participant Fitbit account was set up using a study-specific e-mail address, initials instead of name and a fictitious date of birth. Fitbit’s terms of use comply with the General Data Protection Regulation (GDPR) directive. Fitbit activity data from each participant were downloaded at the study center weekly, using the Fitbit web API . Sensewear armbands have been validated for use in clinical studies , 24.During recent years, several clinical studies for validation of older generations of Fitbit trackers, in different diseases, have also been performed. The overall impression is that from the different measures available, number of steps had the best accuracy , 20, 21.At baseline, the patients recorded self-reported symptom score for Fatigue, PEM, and need for rest using a scale of 1 to 10 (higher number denotes more severe symptoms), and Function level  according to a table with examples in which 100% was completely healthy .At baseline and every four weeks, they completed Norwegian-language versions of the SF-36 questionnaire for health-related quality of life  , and theDuring follow-up, patients completed the Composite Autonomic Symptom Score-31 (COMPASS-31) questionnaire, used to assess symptoms related to dysautonomia.One week after completing the study, the participants were asked to answer an evaluation of the study and the activity armband. We used an online survey from enalyzer.com. The answers were anonymous.For Fitbit data, steps per 24 hours and resting heart rate, with continuous data for 168 days (24 weeks), with no replacement for missing data, were used in the analyses. Means and standard deviations (SD) were calculated per 4-week period, and also dichotomized into days 1–84 vs days 85–168, to assess changes over time. Groups were compared by t-test , or by Mann-Whitney test, as appropriate.Repeated measures of variables, by ME/CFS severity and by categories of baseline SF-36 PF, were assessed by General Linear Model (GLM) for repeated measures.Correlations (Spearman’s rho) were performed between different variables describing aspects of ME/CFS such as Function (%), SF-36 domains, Compass sum score and domains, DSQ-SF total score, mean steps and resting HR.All tests were two-sided with a significance level of p < 0.05. Missing data were replaced only for one patient at the 24-weeks recording for SF-36 and DFQ-SF (i.e. 2 recordings out of 378) using the last value carried forward (LVCF) method. All analyses were performed using IBM SPSS Statistics ver.26 , and Graphpad Prism ver.9 .The study was approved by the Regional Committees for Medical and Health Research Ethics in Norway (No. 28780). Concerns about patients’ privacy are discussed under “Activity armbands” in Methods. A Data Protection Impact Assessment was performed in consultation with the IT Security Manager and Data Protection Officer for the Bergen Hospital Trust and user representatives. The user representatives have also assessed and contributed to the study protocol and all written information given to the participants. All participants gave written consent.Flow diagram of participants through enrollment, inclusion and follow-up and analysis are shown in Except for one participant who started a low carbohydrate diet aimed at the ME/CFS symptoms, the participants did not undergo any intervention for their ME/CFS disease during the course of the study.A dataset with clinical data, PROMS, steps and resting heartrate for all patients during follow up  and TRENThere were little missing data from the Fitbit recordings of activity level. For the 24-week study period, the mean number of days with valid recording of steps per 24 hours was 166 out of 168 days. There were recordings of steps per 24 hours for all 168 days in 20 patients, while 2 had recordings from 167 days, and 5 patients had valid recordings spanning from 152 to 166 days.For resting heart rate, the mean number of days with valid recordings was 162. Sexteen patients had recordings from at least 166 days, and 11 patients had valid recordings spanning from 132 to 165 days. For steps per 24 hours and resting heart rate, the observed data were used as input for statistical analyses, with no replacement for missing values .At baseline there were no missing data for SF-36 and DSQ-SF, and only missing data from one patient for SenseWear, Function level and COMPASS-31. During follow-up one patient failed to report Function level, and at 24 weeks (end of study) there were missing data for SenseWear and Function level for two patients, the observed data were used with no replacement for missing values.For SF-36 and DSQ-SF one patient had missing data at the 24-week recording . These two data for SF-36 and DSQ-SF were replaced by the LVCF method.Mean steps per 24 hours for all 27 patients during 168 days’ follow-up were 4560. A representative profile for steps per 24 hours and resting HR throughout the study period is shown in For day-by day variation of steps per 24 hours, the mean coefficient of variation  was 47%, however with a broad range 25%–79% among patients, and with no significant difference between clinical severities, or between categories of SF-36 PF.Mean steps per 24 hours for all 27 patients through days 1–84 compared to days 85–168, assessed by paired sample t-test, was significantly higher in the last period . The absolute changes in mean steps per 24 hours (with minimum and maximum) from the first to the second half of the study were mean 723 steps (-960 to 4031) in patients with mild disease, mean 350 steps (-415 to 1236) in patients with moderate severity, and mean 68 (-263 to 291) in patients with severe ME/CFS. Although the increase in mean steps per day through follow-up was largest among patients with mild severity, or with higher baseline SF-36 PF, the changes were not significantly different by ME/CFS severity, , or by cMean resting heart rates were stable during follow-up in the three severity groups, and also by categories of baseline SF-36 PF. Correlation between steps per 24 hours and resting heart rate (recordings days 1–168) was not significant (p = 0.58). Note the considerable individual variation between minimum and maximum mean resting heartrate .A dataset with mean steps and resting heartrate per day for each patient is found in Recordings of steps per 24 hours using the Fitbit Charge 3 tracker (on the non-dominant wrist) were compared with recordings from the SenseWear activity device (on the non-dominant upper arm) for seven days at baseline, at 3 months and at 6 months. Values from the two activity trackers correlated significantly at all three timepoints, but Fitbit recorded significantly higher number of steps . A BlandA dataset with the comparison between Sensewear and Fitbit is found in The raw scores  for the six SF-36 domains , Physical function (SF-36 PF), Bodily pain (SF-36 BP), General health (SF-36 GH), Social function (SF-36 SF) and Vitality (SF-36 VF), recorded at four-week intervals during follow-up, are shown in Mean steps per 24 hours correlated significantly with baseline SF-36 PF (p = 0.01), SF-36 SF (p = 0.03), and baseline DSQ-SF score (p = 0.007). DSQ-SF score correlated significantly with SF-36 PF, SF-36 BP, Function level and Compass sum score. All baseline correlations are shown in Baseline DSQ-SF scores for ME/CFS symptoms are reported in In an explorative attempt to define groups of patients with the largest variations in outcome measures during six months’ follow-up, we combined baseline SF-36 PF and DSQ-SF scores. One group of 8 patients with milder ME/CFS symptoms had either SF-36 PF > 50 or DSQ-SF < 55, while the remaining group of 19 patients with more pronounced ME/CFS symptoms had both SF-36 PF < 50 and DSQ-SF > 55. In this observational study with no intervention, the maximum differences between each 4-week period were more evident in the group of patients with milder ME/CFS, for mean number of steps per 24 hours . There were significant negative correlations between “Orthostatic intolerance” and the patient’s age, the SF-36 domain “General health” and Function level .One week after completing the study the participants were asked to answer an evaluation of the study and the activity armband. We used an online survey from enalyzer.com. The answers were anonymous and 22 of the 27 participants answered .Several patients reported that their Fitbit devices recorded steps when they were not walking, but engaged in other activites which involved arm movement or vibration, such as knitting, cooking and driving slowly in a car or electric wheelchair on bumpy roads.The present study shows that continuous activity registration with Fitbit Charge 3 trackers is feasible in studies with ME/CFS patients. The mean number of steps per day decreased with increasing severity. Mean steps per day increased in the second as compared to the first half of the study. The correlations between steps per day and self-reported SF-36 Physical function, Social function, and DSQ-SF were significant. The study had a low number of participants, too few to draw firm conclusions, but using the combination of SF-36 PF and DSQ-SF we identified a group of eight patients with milder disease that showed considerable variation in outcome measures during follow-up compared to the remaining participants.In attempts to evaluate data from our previous clinical intervention trials of treatment involving the anti-CD20 B-cell depleting antibody rituximab –7, 30 orWe wanted to evaluate and optimize the possible outcome measures for use in clinical trials by exploring the feasibility of continuous activity monitoring using the Fitbit Charge 3 armband to assess levels of physical activity in ME/CFS patients. Objective measures are important, but in view of the complexity of symptoms involved in this disease, it is necessary to combine the activity measures with self-reported questionnaires. An important question that should be further investigated in larger trials is which technologies and parameters are the most useful to reflect the ME/CFS symptoms and variations over time. Uniformly accepted outcome measures are lacking and criteria for improvement and recovery have been inconsistently defined across studies, making it difficult to assess treatment outcomes and compare different interventional studies.In recent years, it has been quite common to combine patient-reported questionnaires with activity recordings such as number of steps per day as outcome measures, both in ME/CFS , 17–19 aWhen chosing a device for this project, our priorities were simplicity of use, performance on the basic functionalities (steps and heart rate), and privacy. The Fitbit privacy terms and conditions were more specific on their compliance with the General Data Protection Regulation (GDPR) directive than several comparable trackers in the same price range. Fitbit Charge 3 has been validated and showed acceptable accuracy during rest and treadmill activities, but performed poorly during sprint running and cycling. However, data in the range of activities typical for ME/CFS patients were acceptable .The present study shows that it is feasible to use activity trackers for continuous registration of steps and resting heart rate in a study with ME/CFS patients. Our clinical impression from previous trials, and pilot experiences with ME/CFS patients, was that resting heartrate decreased when patients reported clinical improvement. We did not see the same tendency in this study with no intervention.Continous Fitbit data for mean steps per 24 hours and for resting heart rate seemed useful, and may be used as outcome measures. However, due to the complexity of symptoms in individual patients, it is still important to also use the patient-reported outcome measures. Patients with ME/CFS cannot be evaluated based exclusively on measures of physical activity.Generally, both PROMs and number of steps per 24 hours showed slight improvements during six months follow-up. By comparing mean steps per 24 hours in the first 12 weeks versus the last 12 weeks, we found a significant increase in the second part of the study. 23 of 27 patients were included between December and March, which means the first three-month period was winther to spring and the second three-month period was spring to summer. Some of the clinical improvement seen, could be explained by the fact that ME/CFS patients living in the Northern hemisphere often have less severe symptoms when the weather is warm.The increase in steps and improvements in patient-reported measures were more evident among patients with mild disease. In patients with moderate (mainly housebound) or severe (partly bedridden) ME/CFS, there was little variation in symptom scores or number of steps over time. However, this observation may not be valid for ME/CFS patients in general, due to the low number of patients in the present study. The cause of the increased activity during the study period is not certain. In a recent metaanalysis, feedback through activity monitors was found to increase the daily steps by 1235 in mixed groups of adults . Such anWhen including patients in clinical studies, self-reported medical history and clinical assessments are used for severity grading . A recenA systematic review of 56 randomized controlled trials (RCT) for chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) showed a total of 31 primary measurement tools used to assess the main outcome. The Checklist Individual Strength (CIS) was the most frequently used (35.7%), and others included the SF-36 (32.1%) [We have used SF-36 in our studies, combined with other self-reported questionnaires. The general experience from our previous studies is that there are overall acceptable agreements between the clinical severity categories, questionnaires such as SF-36 PF and DSQ-SF and steps per day.When validating and comparing different trials, the characteristics of the included study population are important, and may influence the outcome of the trial. Although the SF-36 PF has major limitations for interpretation and is an imperfect measure, this item is often presented in clinical ME/CFS studies, both as a baseline characteristic and a possible measure of clinical outcome, as shown in As previously noted, in this study we made explorative efforts to identify groups of patients with larger symptom variations that would have had a significant influence on outcome measures in a clinical study. Dividing the patients into clinical severity groups, we observed that the mild group tended to increase more in SF-36 PF and steps per 24 hours during six months’ follow-up as compared to patients in the moderate and severe groups. When we combined SF-36 PF > 50 or DSQ-SF score < 55, we identified a group with the largest changes in SF-36 PF between four-week intervals during follow-up, i.e. 16.9 points among these 8 patients, compared to an increase of 3.4 points in the remaining 19 patients. Although we cannot generalize from this small observational study with no intervention, our data underline the difficulties in distinguishing fluctuations in the natural course of the disease from the true effect of an intervention. If similar natural variations in patient-reported and physical activity measures occurred during a clinical trial, they could be wrongfully interpreted as response to an intervention, and could affect conclusions on response and effect sizes. Natural variation of symptoms over time, and associations with baseline disease severity, are therefore important to have in mind when planning clinical trials, and should also be included in the interpretation and discussion of clinical trial results.In order to reduce the impact of natural symptom variation in future studies, one option could be to include a run-in period before start of intervention, to identify individual variation of symptoms e.g. over a time period of three to six months, and make it easier to interpret any changes occurring after active intervention.The feedback from the patients assessing the use of Fitbit trackers was generally positive. Most found participation in the study useful, and they considered the Fitbit app and activity armband easy to use. Most patients reported that the Fitbit measures gave an accurate reflection of their activity level. Half of the patients reported that using the activity armband influenced their activity level. From the consultations with patiens in previous studies we have learned that some patients use activity trackers to monitor and pace their physical activity, partly as a tool to prevent post-exertional malaise (PEM) and “crashes”. For some patients with ME/CFS, it is possible that wearing a tracker will decrease daily steps, at least in some periods, and give the opposite effect than the previously mentioned metaanalysis that showed increase in daily steps by wearing activity monitors .The most important limitation of the study is the low number of participants in an observational study, too few to draw firm conclusions. The study population included more patients with mild disease than we usually include in our studies. There were little missing data both at baseline and follow-up. As discussed, the patients pointed at possible sources of error regarding Fitbit step registration, indicating that step count accuracy must be expected to vary between individuals as well as between devices. Nevertheless, the trackers can be a useful tool to monitor day to day changes for one individual, when the same technology is used over time.In this study we have observed the course of 27 ME/CFS patients during 6 months’ follow-up without any intervention. It is feasible to use activity trackers for the continuous registration of steps and resting heart rate in a study with ME/CFS patients. According to feedback from patients, the Fitbit trackers were easy to use, and gave a fair reflection of their physical activity levels. The correlations between steps per day and SF-36 PF, SF-36 SF, and DSQ-SF scores were significant. SF-36 PF has been reported in many studies of ME/CFS, with large differences in baseline values reflecting inclusion of patient populations which may not be easily comparable. After exploring different combinations of PROMs, activity measures and clinical assessment, we found that the combination of lower SF36-PF and higher DSQ-SF defined patients with more stable symptoms during follow-up in this study with no intervention. The knowledge from this study could be useful for the design of study protocols and assessments of outcome measures in future interventional studies. We propose including a run-in period with activity tracking and PROMs pre-intervention to evaluate normal fluctuations of the disease in individual patients. Due to the complexity of symptoms, it is necessary to combine the activity measures with patient-reported outcome measures to assess different aspects of disease.S1 File(PDF)Click here for additional data file.S2 File(R)Click here for additional data file.S3 File(XLSX)Click here for additional data file.S4 File(PDF)Click here for additional data file.S5 File(XLSX)Click here for additional data file.S6 File(XLSX)Click here for additional data file.


To assess cost-effectiveness of direct oral anticoagulants (DOACs) compared with vitamin K antagonists (VKAs) for stroke prevention in atrial fibrillation (AF) by pooling incremental net benefits (INBs).Systematic review and meta-analysis.We searched PubMed, Scopus and Centre for Evaluation of Value and Risks in Health Registry from inception to December 2019.Patients with AF.2 statistic. Risk of bias was assessed using the economic evaluations bias (ECOBIAS) checklist.The INB was defined as a difference of incremental effectiveness multiplied by willing to pay threshold minus the incremental cost; a positive INB indicated favour treatment. These INBs were pooled  with a random-effects model if heterogeneity existed, otherwise a fixed effects model was applied. Heterogeneity was assessed using Q test and I2=59.9%), $6353.24 , $7664.58  and $8573.07  for dabigatran, apixaban, rivaroxaban and edoxaban relative to VKA, respectively but only dabigatran was significantly cost-effective from societal perspective (SP) with an INB of $11 746.96 . The pooled INBs of all comparisons for upper-middle income countries (UMICs) were not significantly cost-effective. The ECOBIAS checklist indicated that risk of bias was mostly low for most items with the exception of five items which should be less influenced on pooling INBs.A total of 100 eligible economic evaluation studies (224 comparisons) were included. For high-income countries (HICs) from a third-party payer (TPP) perspective, the pooled INBs for DOAC versus VKA pairs were significantly cost-effective with INBs (95% CI) of $6632 ($2961.67 to $10 303.72; IOur meta-analysis provides comprehensive economic evidence that allows policy makers to generalise cost-effectiveness data to their local context. All DOACs may be cost-effective compared with VKA in HICs with TPP perspective. The pooling results produced moderate to high heterogeneity particularly in UMICs. Further studies are required to inform UMICs with SP.CRD 42019146610. A large number of economic evaluation studies on direct acting oral anticoagulants (DOACs) and vitamin K antagonists (VKAs) were conducted in various healthcare settings to guide health policy makers in relation to reimbursement of DOACs.The previous systematic reviews that compared DOACs with VKAs for stroke prevention in atrial fibrillation did not provide an overall quantitative synthesis.This is the first quantitative meta-analysis of 100 economic evaluations (that included 144 comparisons) of all four DOACs with VKAs applying pooled incremental net benefit.Our findings indicated that DOACs might be significantly more cost-effective than VKAs in high-income countries using a third-party payer perspective while no DOACs were more cost-effective in upper-middle income countries (UMICs), regardless of any perspective was used.We found that country socioeconomic status and the methodological approach used potentially influenced the cost-effectiveness of DOACs compared with VKAs.While clinical efficacy and safety of DOACs over VKAs are established, these agents, at their current pricing, are cost-effective only in high -income countries but not in UMICs due partly to the lower socioeconomic status and the small number of studies available.Policy makers and pharmaceutical companies should together consider potential pathways to increase access to these useful agents by considering the impact of socioeconomic status on the cost-effectiveness for UMICs and potentially low-income and middle-income countries.Atrial fibrillation (AF), the most common cardiac arrhythmia,Oral anticoagulants such as vitamin K antagonists  and direct oral anticoagulants (DOACs) are the cornerstone of stroke prevention in AF.et alMultiple cost-effectiveness studies have compared DOACs with VKAs in various healthcare settings to inform health policy including five systematic reviews (SRs) of economic evaluations.This SR-MA was reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis 2020 statement and the review protocol was registered at PROSPERO.We performed a comprehensive search in PubMed, Scopus and Centre for Evaluation of Value and Risks in Health (CEVR) databases from inception to 7 December 2019, see 10.1136/bmjebm-2020-111634.supp1Supplementary dataTwo investigators (RN and BSB) independently extracted data. Disagreement was resolved in consultation with senior authors (SY and AT). Extracted data included study characteristics, study population, interventions, economic data  and findings. In addition, data for pooling were also extracted including mean cost, incremental cost, clinical effectiveness, incremental effectiveness and ICERs together with SE, or 95% CI. Incremental costs and effectiveness were also extracted from the cost-effective plane using Web-Plot-Digitizer software V.4.2.We assessed risk of bias for included studies using the economic evaluations bias (ECOBIAS) checklist.et al2 statistic and considered present if I2 ≥25% or if the p value was <0.1. The INBs were pooled across studies, stratified by country income (classified by the World Bank),The primary outcome of interest was INB. Economic data were harmonised by converting all currency data using purchasing power parity for the year 2019.Meta-regression, sensitivity or subgroup analyses were undertaken to explore sources of heterogeneity such as discount rate, WTP threshold, data source and funding source. Publication bias was assessed using Egger’s test and funnel plots where number of studies/comparisons was 10 or more. Where a funnel plot was asymmetrical, a contour-enhanced funnel plot was constructed to assess if the asymmetry was due to missing studies or heterogeneity. All analyses were performed using STATA V.16. A two-sided p<0.05 was considered statistically significant except for heterogeneity tests, in which case p<0.10 was used.Of the 1585 studies identified, 100 met the inclusion criteria. List of 14 excluded studies along with reasons are provided in Characteristics are summarised in Clinical and utility parameters were mostly taken from published literature. Country-specific and GDP-based thresholds were used for WTP in 73Across all 22 items from the ECOBIAS checklist, 17 items where more than 70% of studies were graded as low risk of bias, see 2=59.9%) and $11 746.96 from an SP . The corresponding pooled INBs in UMICs (nine studies with 13 comparisons) were $49 000.59 from a TPP  and −$14 709.67 from an SP . Dabigatran was cost-effective compared with VKAs in HICs, but not in UMICs  and $1516.13 from an SP . The corresponding pooled INBs in UMICs (eight studies with 11 comparisons) were −$2440.41 from a TPP , and −$17 300.33 from an SP . Apixaban was cost-effective compared with VKAs in HICs with a TPP but not with an SP (see Based on 31 studies (33 comparisons) in HICs, the pooled INBs were $6353.24 from a TPP (95% CI) $4076.03 to $8630.45; In SP see . Accordin SP see . There w2=0%) and $10 345.74 from an SP . The corresponding pooled INBs in UMICs (seven studies with 10 comparisons) were −$27 567.34 from a TPP , and −$14 350.24 from an SP . Rivaroxaban was cost-effective compared with VKAs in HICs with lifetime-horizon from TPP, but not from SP, see Based on 26 studies with 28 comparisons in HICs, the pooled INBs were $7664.58 from a TPP . The pooled INBs in UMICs (three studies with five comparisons) were −$11 062.53 from a TPP  and −$15 547.36 from an SP . Edoxaban was cost-effective compared with VKAs from TPP only in HICs, but not cost-effective in UMICs in both TPP and SP, see Based on 13 studies with 15 comparisons in HICs, the pooled INBs (95% CI) were $8573.07 from a TPP  from a TPP were significantly more cost-effective in HICs compared with VKAs. However, outcomes varied if the evaluation was conducted from an SP; with only dabigatran remaining cost-effective compared with VKAs. Conversely, all DOACs were not cost-effective compared with VKAs in UMICs with SP.To our knowledge, this is the first SR-MA of cost-effectiveness that includes all four commonly used DOACs providing quantitative economic evidence. Given the variable reporting of economic outcomes, the use of INBs provides direct interpretation and supporting evidence for policy decision making. To minimise the heterogeneity across economic studies, we initially pooled INBs from similar studies based on strata including country incomes, economic model, perspectives and time-horizon. Heterogeneity was therefore reduced in studies from HICs but remained moderate to high in UMICs. This may be due to variation in the characteristics and assumptions that underlie the key model features, different reporting mechanisms, and measures of dispersion for point estimates within individual studies. As such, different approaches, data simulations and variance values were considered from similar studies in our analyses.Our study found that country socioeconomic status and methodological approach used potentially influenced the cost-effectiveness of DOACs versus VKAs. DOACs were cost-effective in HICs when the evaluation was conducted using Markov models and lifetime-horizon from TPP-perspective but only dabigatran was cost-effective when using SP. This paradox could be explained by the much smaller number of previous studies analysed from SP in HICs. Moreover, many of them originated from the USA where the WTP thresholds were higher than those from other HICs. Hence, even though DOACs were cost-effective in comparison to VKAs in some individual studies, once their INBs were pooled, the effect was lost.It is noteworthy that subgroup analyses highlighted that dabigatran was significantly cost-effective compared with VKAs from TPP when WTP thresholds were less than $50 000. Therefore, policy makers in HICs should consider these conditions in their decision making especially when the SP is preferred or the WTP threshold is less than $50 000 per QALY.Our findings confirm the individual economic evaluations in UMICs that all DOACs were less cost-effective than VKAs particularly with SP and low WTP thresholds. However, apixaban might be more cost-effective than VKAs when considered according to WTP threshold. In general, DOACs would not be the optimum choice compared with VKAs in UMICs. Many of the economic evaluations of DOACs versus VKAs for stroke prevention in patients with AF are represented by diverse methods.Our study provides comprehensive economic evidence for policy makers to assess cost-effectiveness data in their local context, considering perspectives, time horizons, discounting, sources of data and WTP thresholds. Our study had several limitations. Pooling INBs produced moderate to high heterogeneity particularly in UMICs. A meta-regression could be performed in a few pooling because of small number of studies particularly in UMIC, only a few factors could identify leading to subgroup analysis. Although we considered data from variable scenarios, we were still left with some estimated INBs that had no variances, and we had to ‘borrow’ the variances from similar studies. Although we limited the extent of heterogeneity by using several simulation methods, this was not possible for studies from UMICs. This highlights a need for uniformity of data reporting in economic analyses, particularly measures of dispersion, to enable SR-MA of economic evaluations. Our findings for rivaroxaban and edoxaban may be limited given the small number of evaluations published. Furthermore, the analyses from UMICs may also be affected by the quality of VKA monitoring; there is evidence that time in therapeutic range is lower in developing countries


Knowledge of the window of SARS-CoV-2 infectiousness is crucial in developing policies to curb transmission. Mathematical modelling based on scarce empirical evidence and key assumptions has driven isolation and testing policy, but real-world data are needed. We aimed to characterise infectiousness across the full course of infection in a real-world community setting.The Assessment of Transmission and Contagiousness of COVID-19 in Contacts (ATACCC) study was a UK prospective, longitudinal, community cohort of contacts of newly diagnosed, PCR-confirmed SARS-CoV-2 index cases. Household and non-household exposed contacts aged 5 years or older were eligible for recruitment if they could provide informed consent and agree to self-swabbing of the upper respiratory tract. The primary objective was to define the window of SARS-CoV-2 infectiousness and its temporal correlation with symptom onset. We quantified viral RNA load by RT-PCR and infectious viral shedding by enumerating cultivable virus daily across the course of infection. Participants completed a daily diary to track the emergence of symptoms. Outcomes were assessed with empirical data and a phenomenological Bayesian hierarchical model.Between Sept 13, 2020, and March 31, 2021, we enrolled 393 contacts from 327 households ; and between May 24, 2021, and Oct 28, 2021, we enrolled 345 contacts from 215 households (the delta variant wave). 173 of these 738 contacts were PCR positive for more than one timepoint, 57 of which were at the start of infection and comprised the final study population. The onset and end of infectious viral shedding were captured in 42 cases and the median duration of infectiousness was 5 (IQR 3–7) days. Although 24 (63%) of 38 cases had PCR-detectable virus before symptom onset, only seven (20%) of 35 shed infectious virus presymptomatically. Symptom onset was a median of 3 days before both peak viral RNA and peak infectious viral load . Notably, 22 (65%) of 34 cases and eight (24%) of 34 cases continued to shed infectious virus 5 days and 7 days post-symptom onset, respectively . Correlation of lateral flow device (LFD) results with infectious viral shedding was poor during the viral growth phase (sensitivity 67% [95% CI 59–75]), but high during the decline phase (92% [86–96]). Infectious virus kinetic modelling suggested that the initial rate of viral replication determines the course of infection and infectiousness.Less than a quarter of COVID-19 cases shed infectious virus before symptom onset; under a crude 5-day self-isolation period from symptom onset, two-thirds of cases released into the community would still be infectious, but with reduced infectious viral shedding. Our findings support a role for LFDs to safely accelerate deisolation but not for early diagnosis, unless used daily. These high-resolution, community-based data provide evidence to inform infection control guidance.National Institute for Health and Care Research. Widespread community transmission of SARS-CoV-2 continues to occur, even in populations with high levels of immunity. Reducing transmission remains central to the public health response; however, as virus circulation becomes endemic, there is a need for a pragmatic approach, ideally limiting self-isolation to the duration of infectiousness. Delineation of the window of infectiousness and how the degree of infectiousness changes with time since infection, symptom onset, and diagnostic test results is therefore fundamental to developing a better understanding of transmission and more effective, evidence-based infection control policies.Mathematical modelling based on scarce empirical evidence and key assumptions has substantially driven isolation and testing policy internationally.5Evidence before this studyA search of PubMed from database inception to March 10, 2022, for studies with the keywords “individuals” or “persons”, “viral dynamics”, and “SARS-CoV-2” in the title or abstract, without language or other restrictions, identified 19 results (with no duplicates). All 19 results were evaluated, with ten deemed to be relevant on the basis that they were longitudinal human studies of COVID-19. Of these, six studies analysed the viral RNA trajectories in participants who were PCR positive from study onset , thereby missing the crucial growth phase of viral shedding. Three studies reported viral RNA trajectories in a cohort of professional US sports players, including cases who became PCR positive during the study period, but without measuring cultivable infectious virus. Only one cohort study, involving prospective surveillance of college-enrolled students, did daily RT-PCR in addition to measuring infectious virus by in vitro culture.A second search, from inception to March 10, 2022, using title or abstract terms “viral kinetics”, and “SARS-CoV-2”, without language or other restrictions, returned 46 results (with one duplicate result), 17 of which were deemed to be relevant. Of these, 16 focused on viral RNA trajectories in prevalent cases. One study captured the onset of viral shedding with serial saliva and nasal sampling in a small cohort of seven cases, but did not attempt to measure culturable infectious virus.A third search, from inception to March 10, 2022, using title or abstract terms “antigen” or “lateral flow”, “SARS-CoV-2”, and “infectiousness”, without language or other restrictions, returned 25 results (with no duplicates). 11 were deemed to be relevant on the basis that they attempted to link lateral flow device (LFD) results with individual infectiousness. Of these, eight made assumptions of infectiousness based on viral copy number quantified by RT-PCR. The other three studies measured LFD sensitivity relative to in vitro cell culture  and found higher sensitivity than when compared with RT-PCR, but crucially only evaluated LFDs cross-sectionally rather than across the course of infection.Finally, we reviewed the human challenge study in which SARS-CoV-2-naive healthy volunteers were inoculated with a standardised dose of a specific SARS-CoV-2 strain directly into the nares. This study included both daily RT-PCR and viral culture of serial samples and found that infectious virus could be recovered from participants during a median period of 6·5 days. However, it is unknown whether these observations are generalisable to naturally acquired infection in the real-world setting of community contacts of COVID-19 cases, where most transmission occurs globally with broader demographics.Added value of this studyTo our knowledge, this is the first study to serially quantify both viral RNA and infectious, culturable virus from the start of naturally acquired SARS-CoV-2 infection. We did RT-PCR, in vitro cell culture-based quantitative plaque assays to enumerate infectious virus, and lateral flow antigen tests from the upper respiratory tract of 57 recently exposed cases in a real-world community setting, capturing the early viral growth phase during which most transmission occurs. This enabled us to define the window of infectiousness (median duration of infectiousness 5 [IQR 3–7] days) and its temporal relationship to symptom onset, which revealed that two-thirds of cases are still infectious 5 days post-symptom onset and one-third at 7 days. LFDs have poor sensitivity for detecting culturable virus during the growth phase of infection (67% [95% CI 59–75]), but high sensitivity during the decline phase (92% [86–96]), supporting a role for LFDs in de-isolation but not for early diagnosis, unless used daily.Implications of all the available evidenceOur results uniquely define the window and kinetics of SARS-CoV-2 infectiousness in naturally acquired infection. Our findings moreover suggest that the recent observations in the controlled experimental human challenge model are largely generalisable to community COVID-19 cases. However, there was wider inter-individual variability in the duration and amount of infectious viral shedding in our larger, real-world cohort. This probably reflects the greater demographic heterogeneity of the community cases as well as the variability in the infecting dose, and route, for transmission events in the community compared with the highly controlled experimental inoculation of pre-selected healthy volunteers in the challenge model. By delineating the period of infectiousness in mild COVID-19, and its correlation with symptom onset and commonly used diagnostic tests, our findings enable calibration of isolation guidance to the infectious window.Identifying such cases from the earliest timepoints after exposure and densely sampling them thereafter is essential to delineate the trajectory of infectious viral shedding. However, this is operationally challenging in naturally exposed people. The growth phase and peak of viral replication occur very early post-exposure, meaning studies of symptomatic cases cannot capture this crucial window where a large proportion of transmission occurs.8We therefore recruited community contacts recently exposed to PCR-confirmed SARS-CoV-2 cases in the UK; contacts self-performed daily upper respiratory tract (URT) swabs through the course of infection. Here, we analyse the contacts in whom we succeeded in capturing the crucial growth phase and peak of viral replication, with the aim of characterising the window of SARS-CoV-2 infectiousness and its temporal correlation with symptom onset. Our dataset provides a valuable opportunity to test the public health implications of shortening self-isolation periods to inform national and organisational case management policies, and how these might be facilitated by point-of-care testing with LFDs.The Assessment of Transmission and Contagiousness of COVID-19 in Contacts (ATACCC) study was a UK prospective, longitudinal, community cohort study of community contacts of newly diagnosed, PCR-confirmed SARS-CoV-2 index cases, as previously described and detailed .9 ATACCCPCR-positive contacts are hereafter referred to as cases. Household and non-household exposed contacts aged 5 years or older were eligible for recruitment if they could provide informed consent and agree to self-swabbing of the URT. Unvaccinated cases were defined as those who had not received any COVID-19 vaccination before index case symptom onset, our proxy for exposure. Fully vaccinated cases were defined as those who had received their second COVID-19 vaccination 14 days or more before index case symptom onset; none had received booster vaccinations. Cases who received only one dose of vaccine before index symptom onset were excluded.The study was approved by the Health Research Authority (Research Ethics Committee reference 20/NW/0231) and samples were obtained with written informed consent. All data were housed within a secure research environment and accessed only by approved researchers .All participants had mild-to-moderate ambulatory illness, which did not require hospitalisation. Participants completed a daily symptom diary. We defined symptomatic cases as those experiencing any one of the three canonical COVID-19 symptoms , or at least two of the following symptoms: muscle aches, headache, appetite loss, or sore throat, as per the criteria defined and validated by Houston and colleaguesAll contacts underwent daily, longitudinal URT sampling for up to 20 days. For each contact, viral RNA load was quantified daily by RT-PCR and the ORF1ab cycle threshold (Ct) values were converted to viral genome copies, as previously described.All laboratory assays were done by a scientist (JZ) masked to the variant and vaccination status of the contact from which the samples were derived and their timepoint of infection . Plaque Our study provided a unique opportunity to triangulate the kinetics of SARS-CoV-2 RNA viral loads and infectious viral loads with viral antigen-detecting LFDs. We used the Innova LFD  to evaluate the performance of antigen-detecting LFDs. LFDs were done on PCR-positive samples, as well as 1 day before and 1 day after the positive PCR result. Independent validation experiments were carried out at the UKHSA site in Colindale  to identify the amount of thawed VTM that would be most likely to give a result consistent with a concentration of virus in fresh samples,PCR-positive samples were submitted for viral whole-genome sequencing to assign lineages, as described in the The primary objective was to define the window of SARS-CoV-2 infectiousness from the onset of infection and its temporal correlation with symptom onset. The secondary outcome was to longitudinally correlate LFD positivity with infectious viral shedding. We compared the infectious virus kinetics  between vaccinated and unvaccinated cases as a separate exploratory analysis.t tests.To estimate values and uncertainties of the kinetic parameters, and account for test accuracy and sensitivity, individual fits of RNA viral load and infectious viral load trajectories were implemented using Bayesian hierarchical modelling, as previously describedThe funder of the study had no role in the study design, data collection, data analysis, data interpretation, the writing of the report and the decision to submit.ATACCC1 enrolled 393 contacts from 327 households from Sept 13, 2020, to March 31, 2021, during the SARS-CoV-2 pre-alpha and alpha variant waves; and ATACCC2 enrolled 345 contacts from 215 households from May 24, 2021, to Oct 28, 2021, during the delta variant wave. 738 contacts provided at least one URT swab, of whom 173 (23%) were PCR positive for more than one timepoint, referred to here as cases. 40 (23%) of 173 cases tested PCR negative at enrolment and subsequently became PCR positive, denoted as incident cases. A further 17 cases were PCR positive at enrolment, but met all the following criteria for being captured within the viral growth phase, and are denoted as early prevalent cases: first, low viral load on the day of enrolment ; second, an observable increase in viral load after enrolment; and third, PCR positive for three or more timepoints. Thus, in total, 57 cases comprised the final study population .Figure 12), with few reporting comorbidities or pregnancy  of 57 cases were fully vaccinated and were infected with the delta variant, confirmed by whole-genome sequencing. Of the 32 unvaccinated cases, 13 (41%) were infected with pre-alpha, 12 (38%) with alpha, and seven (22%) with delta SARS-CoV-2 variants, as confirmed by whole-genome sequencing. The final study population was mostly White (51 [89%] of 57), middle-aged (median 41 [IQR 29–49] years), of a healthy BMI (median 25·2 [IQR 21·2–28·8] kg/m53 (93%) of 57 cases shed viral RNA for over 7 days . 51 93% and foun10 8·4 [IQR 7·9–8·8] RNA copies per mL) and peak infectious viral load (log10 4·5 PFU per mL [3·6–5·1]) attained were fairly homogeneous across cases  of 57 cases recorded symptom information and, of these, three 6%; , plot 23Fits of RNA viral load and infectious viral load trajectories, implemented using Bayesian hierarchical modelling , are sumWithin our overall cohort (n=57), RNA viral load growth rate positively correlated with the peak RNA viral load  and negatively with the RNA viral load decline rates  of 51 cases shedding infectious virus; both cases had sporadic, low-level infectious viral shedding , with loLFD sensitivity was high for infectious virus during the viral decline phase .Our study provided the opportunity to rigorously test whether LFDs align more with infectious viral shedding than PCR positivity. LFD sensitivity was poor during the viral growth phase, especially at lower PFU concentrations, which is crucial for early diagnosis. During the decline phase, LFD sensitivity against viral culture was much higher than during the growth phase regardless of PFU concentration, with a median 2-day lag in LFD positivity after cessation of infectious viral shedding. Prolonged LFD positivity in the absence of evidence for active viral replication probably reflects persisting viral antigens from remnants of infected cells as seen with RT-PCR with lingering viral RNA fragments.Our findings provide a rationale for using LFDs to safely accelerate deisolation, as embodied in certain policiesOur small cohort size rendered statistical comparison between subgroups underpowered and this was not the primary objective of our study. Notwithstanding, since approximately half of our cases were vaccinated before exposure, we took the opportunity to compare them with unvaccinated cases in an exploratory analysis. The unvaccinated cases were infected with pre-alpha, alpha, and delta variants, whereas all vaccinated cases were delta variant-infected, which could confound comparison if viral load kinetics differ between different strains.Our study has several limitations. Very young and older age groups, with their attendant risk factors for severe illness and hospitalisation, were under-represented in our cohort. However, our cohort does reflect the population and setting responsible for most SARS-CoV-2 transmission globally. URT samples were self-performed by study participants, which can result in variable sensitivity and specificity.The timeframe of our study precluded analysis of the omicron variant, which became prevalent at the end of 2021. Although some recent studies suggest that omicron infections have lower RNA viral load compared with delta infections,Given that no reference standard for infectiousness exists, our data should be interpreted by policy makers as defining the window of potential infectiousness, because the presence of infectious virus in the URT does not inevitably lead to secondary transmission, which depends on several other host, behavioural, and environmental factors, including the propensity for viral aerosolisation.In conclusion, we defined the infectious window and dynamics of SARS-CoV-2 infectiousness and its inter-individual variability. Preliminary evidence from our study has already informed policyThis online publication has been corrected. The corrected version first appeared at thelancet.com/respiratory on September 5, 2022GitHub repository.An anonymised, de-identified version of the dataset can be made available upon request to the corresponding author to allow all results to be reproduced. Modelling code will also be made publicly available on the eLife journal. All other authors declare no competing interests.NF reports grants from the UK Medical Research Council, UK National Institute of Health and Care Research (NIHR), UK Research and Innovation, Community Jameel, Janssen Pharmaceuticals, Bill & Melinda Gates Foundation, and Gavi, the Vaccine Alliance; consulting fees from the World Bank; payment or honoraria from the Wellcome Trust; travel expenses from WHO; advisory board participation for Takeda; and is a senior editor of the


These variables had a 27% explanatory power for nursing intention . Conclusions: To foster undergraduate nursing students’ nursing intention toward patients with emerging infectious diseases (EIDs), a program focused on cultivating a positive attitude toward EID patient care should be developed and implemented. The curriculum should further include education on EID patient care.Purpose: This study examines final-year undergraduate nursing students to determine the educational needs for Coronavirus disease 2019 (COVID-19), knowledge of COVID-19, attitude toward COVID-19 patient care, and nursing intention toward COVID-19 patients. Methods: A structured questionnaire was used to collect data from 21 April to 6 May 2022. The participants included 144 final-year (4th year) undergraduate nursing students in Gangwon-do, Daegu-si, and Chungcheong-do. The SPSS/WIN 21.0 program was used to analyze the data; Pearson’s correlation coefficients and multiple regression were further performed. Results: The attitude toward COVID-19 patient care (β = 0.38,  Coronavirus disease-19 (COVID-19) is an acute respiratory syndrome, first reported on 8 December 2019, in Wuhan, China; it was declared a global pandemic by the World Health Organization (WHO) on 11 March 2020 [While infection control in hospitals has been reinforced and the problems of overcrowding of emergency rooms and environment of healthcare utilization have been resolved since the outbreak of the Middle East Respiratory Syndrome (MERS) , the perThe nursing departments in South Korean universities accredit 20,000 nurses annually . HoweverThe theory of planned behavior (TPB) states that a behavior is induced by the intention to adopt the behavior . Hence, Many studies have identified knowledge as a factor influencing intention ,20. StudAccordingly, this study determines the educational needs for COVID-19, knowledge of COVID-19, attitude toward COVID-19 patient care, and the nursing intention toward COVID-19 patients among final-year undergraduate nursing students before joining the field of COVID-19 patient care. It also aims to provide the basic data for developing educational programs on the care of patients with infectious diseases for the pre-registration nurses.(1)To determine the educational needs, knowledge, attitude, and nursing intention regarding COVID-19 patient care among the participants.(2)To determine the variation in nursing intention toward COVID-19 patients according to the general characteristics of the participants.(3)To determine the correlations between knowledge, attitude, and nursing intention regarding COVID-19 patient care among the participants.(4)To identify the factors influencing nursing intention toward COVID-19 patients among the participants.This study aims to investigate the educational needs, knowledge, attitude, and nursing intention regarding COVID-19 patient care among undergraduate nursing students to provide basic data for devising practical educational strategies for these students to acquire the response capacity against the COVID-19 pandemic. The specific goals of the study are:This was a descriptive survey aimed at determining the educational needs and nursing intentions of final-year (4th year) undergraduate nursing students regarding COVID-19 patient care and identifying the respective influencing factors.The data were collected from 4th year undergraduate nursing students at universities in Gangwon-do, Daegu-si, and Chungcheong-do in South Korea. The sample size was estimated using the G-power 3.1 program based on the following conditions: a moderate effect size of 0.15, a testing power of 0.90, and a significance level of 0.05, with eight predicted independent variables for the regression analysis. The estimated sample size was 136; considering a dropout rate of approximately 10%, 150 individuals were recruited. Among these, 6 were excluded owing to providing incomplete responses, leaving a total of 144 participants.The instrument used for evaluating educational needs in this study was developed by the authors based on a study on undergraduate nursing students’ demands for education on COVID-19 infection control  and the This study modified and employed the Instrument for Predictive Nursing Interaction for EID Patient Care, a tool developed by Yoo, Gwon, Jang, & Yoon  and subsThe instrument for evaluating knowledge was based on the tool developed by Yun , which wTo assess nurses’ attitudes toward COVID-19 patient care, this study used two items from the Instrument for Predictive Nursing Interaction for EID Patient Care, a tool developed by Yoo, Gwon, Jang, & Yoon  and subsData collection was approved by the Institutional Review Board (IRB) of the institution to which the author belongs (IRB No 22-01-0304). The data were collected through a web-based questionnaire from 21 April to 6 May 2022.For the survey, the head of the department at one university in each of the three regions, that is, Gangwon-do, Daegu-si, and Chungcheong-do, was provided an explanation of the study’s purpose and methodology via a phone call. After obtaining their consent for cooperation, the participant recruitment advertisements with a document explaining the study were distributed to the students by the fourth year student representative of the department. The online survey was conducted with students who voluntarily agreed to participate. The participants read the advertisement and clicked on the link provided to read the explanation and the guidelines on study participation. Those who agreed to participate were further directed to the web-based questionnaire. The participants were informed that the data collected would be used solely for research purposes and that they could withdraw from the study at any time. After providing consent, the participants proceeded with the questionnaire.The survey was conducted online. To prevent duplication, only one response was allowed from each IP address. To prevent multiple responses, only a single response could be selected for each question. The participants’ mobile numbers were collected to send a gift of appreciation to them at the end of the study and were double-checked to avoid duplication.The data were anonymized by assigning a unique ID code to each participant, and their details were instantly deleted as the data were stored. The data were coded for storage and were to be deleted completely three years after the completion of the study. The mobile numbers were deleted instantly after the gift of appreciation was sent.(1)The participants’ educational needs, knowledge, attitudes, and nursing intention regarding COVID-19 patient care were analyzed and evaluated using frequency, percentage, mean, and standard deviation.(2)t-test and ANOVA, with the Scheffé test as the post-hoc test.The variations in nursing intention toward COVID-19 patients according to the participants’ general characteristics were analyzed using the (3)The correlations between knowledge, attitudes, and nursing intention regarding COVID-19 were analyzed using Pearson’s correlation analysis.(4)The factors influencing the participants’ nursing intention toward COVID-19 patients were analyzed using multiple regression analysis.The SPSS 21.0 was used to analyze the collected data.p = 0.031), the experience of screening tests owing to suspicious symptoms , the educational experience for the theoretical basis of wearing protective gear , and the intention to study COVID-19 patient care  displayed significantly high levels of nursing intention toward COVID-19 patients according to the general characteristics (The cohabitants’ (family or friend) COVID-19 infection histories  of attitudes toward COVID-19 patient care was 2.00 ± 0.91, in the range between −3 and 3. The total score of educational needs for COVID-19 patient care was 2.71 ± 0.31; for each subcategory, the score was the highest at 2.87 ± 0.36 for COVID-19 patient treatment, followed by antiviral administration of COVID-19 patients and setting-up of ventilators .The score for the participants’ knowledge of COVID-19 patient care was 9.07 out of 11, with a mean rate of 80.4% for correct answers. The question with the highest correct response rate (100%) was “The symptoms of COVID-19 include fever, sore throat, labored breathing, and loss of taste or smell”, followed by “The treatment for COVID-19 should be administered at a health center with a negative pressure ward or quarantine facility” (92.4%). The question with the lowest correct response rate (65.3%) was “In case of faulty protective gear amid treatment, the gear should not be taken off or substituted until completion”, followed by “For COVID-19 patient care, the precautions of standard treatment, saliva droplets, and physical contact should be complied with, while the precaution of air is applicable during aerosol procedures” (69.4%) .p < 0.001). Knowledge of COVID-19 was not significantly correlated with nursing intention   .To determine the factors influencing nursing intention toward COVID-19 patients among undergraduate nursing students in their final year, a multiple regression analysis was performed on the following independent variables that significantly varied in the nursing intention toward COVID-19 patients: the experience of a screening test, a family member formerly infected with COVID-19, the intention to study COVID-19 patient care, and attitudes toward COVID-19 patient care. p = 0.001), a family member formerly infected with COVID-19 , and the intention to study COVID-19 patient care . These factors had an explanatory power of 27% for nursing intention toward COVID-19 patients, with statistical significance .The results of the regression analysis showed that the factors influencing nursing intention toward COVID-19 patients included attitudes toward COVID-19 patient care (β = 0.38, The results of this study showed that, regarding nursing students’ educational needs for COVID-19, the score on the negative pressure ward structure and principles was the lowest, contrary to our expectations. This is presumably attributable to the fact that the participants might not have thought that this was directly required in nursing owing to their lack of experience in such wards during field practice. The educational needs score on COVID-19 pathophysiology was also low, which is presumably related to the high score on the knowledge of the characteristics of the COVID-19 pathogen. Most undergraduate nursing students expected that they could learn EID-related knowledge using public media . During The scores on educational needs for COVID-19 patient treatment and direct nursing skills were high. In a previous study on nurses without experience in respiratory EID patient care, the educational needs for nursing skills and treatment were high, supporting the current study . This suThe score for nursing intention toward COVID-19 patients was 1.50 in this study. According to previous studies, the scores for nursing intention toward patients with infectious diseases were 1.21 out of 3 among undergraduate nursing students and 0.31 out of 3 among nurses ,17; furtThis study identified attitudes toward COVID-19 patient care as a factor related to nursing intention for COVID-19 patients, to support the TPB, which states that intention affects attitude . The repThe score for nursing intention toward COVID-19 patients was high among the participants with a cohabitant formerly infected with COVID-19. A family member or friend as a cohabitant is someone with whom higher levels of intimacy are formed compared with other members of society. Moreover, the national policy required those with COVID-19 infection to be quarantined. During the pandemic, many media outlets introduced nurses as medical personnel at the forefront of COVID-19 and highlighted their efforts, improving nurses’ professional identity , and nurParticipants with an educational need for COVID-19 patient care showed high nursing intention toward COVID-19 patients. This suggests that the purpose of acquiring the knowledge necessary for COVID-19 patient care was rooted in the anticipation to participate in COVID-19 patient care. As a case of COVID-19 infection was first reported in South Korea in January 2020 and the WHO declared a pandemic on 12 March of the same year . Hence, This study was aimed at determining the educational needs of COVID-19 patient care among undergraduate nursing students, and identifying the factors related to nursing intention to further provide basic data to foster new nurses with the capacity for EID patient care in this era of EID outbreaks. The results of this study show that the educational needs for the areas related to direct nursing performance were high. Furthermore, nursing intention toward COVID-19 patients increased as the nurses’ attitudes became more positive and when they had a cohabitant formerly infected with COVID-19; the willingness to study COVID-19 patient care also positively influenced nursing intention toward COVID-19 patients. Therefore, to help undergraduate nursing students increase nursing intention toward EID patients, a program focused on cultivating positive attitudes toward EID patient care should be developed and implemented; the curriculum should include education on EID patient care. This study is meaningful in that it analyzed nursing education needs and nursing intention among nursing students who entered the university in 2019 and received nursing education in the COVID-19 era in 2020 to the endemic after several peaks. The results are expected to be used as fundamental data for education related to new infectious diseases for nursing students in the future.This study has some limitations. First, the results cannot be generalized as the study targeted nursing students in specific regions. Second, in this study, the researchers modified and used existing infectious disease-related tools to assess knowledge and attitudes regarding COVID-19. In particular, the tool used to assess knowledge showed low internal consistency as the characteristics of COVID-19 were revealed, and response guidelines were continuously updated. New measurement tools are required to compensate for these weaknesses and accurately measure nursing intention toward patients with EIDs and related factors. Third, it is also possible that there are unidentified variables associated with nursing intention. In future, therefore, efforts should be undertaken to reproduce these results for generalization and to identify the factors influencing nursing intention among nursing students.


Deep learning methods have been developed to classify sleep states of mouse electroencephalogram (EEG) and electromyogram (EMG) recordings with accuracy reported as high as 97%. However, when applied to independent datasets, with a variety of experimental and recording conditions, sleep state classification accuracy often drops due to distributional shift. Mixture z-scoring, a pre-processing standardization of EEG/EMG signals, has been suggested to account for these variations. This study sought to validate mixture z-scoring in combination with a deep learning method on an independent dataset. The open-source software Accusleep, which implements mixture z-scoring in combination with deep learning via a convolutional neural network, was used to classify sleep states in 12, three-hour EEG/EMG recordings from mice sleeping in a head-fixed position. Mixture z-scoring with deep learning classified sleep states on two independent recordings with 85-92% accuracy and a Cohen’s κ of 0.66-0.71. These results validate mixture z-scoring in combination with deep learning to classify sleep states with the potential for widespread use. Classification of rodent sleep recordings requires expert human researchers to manually score the EEG/EMG into various sleep states, at the cost of high labor and time bottlenecks. To overcome these limitations, machine learning methods were developed to automatically classify sleep states with accuracies ranging from 70% to 95% . Recently, deep learning methods to classify sleep states have reported accuracies up to 97% . While these deep learning methods have proven to be highly accurate when trained and validated on their own datasets, generalization to independent datasets has been difficult . Generalizability across datasets is challenging because independent datasets are often collected under experimental  or recording  conditions different from the original training dataset. Thus, a pre-processing method that standardizes EEG/EMG recordings is needed prior to classifying sleep states by use of deep learning methods.One way to standardize EEG/EMG recordings is through mixture z-scoring . To prevent systematic bias and preserve the distributional shifts that occur across datasets, mixture z-scoring calibrates each mouse’s recording using a small amount of labeled data. This standardization combined with a deep learning-based classification model has been successfully implemented and tested on a single dataset with an accuracy of 97% . Unfortunately, this method has not been validated on an independent dataset. The purpose of this study is to validate mixture z-scoring combined with deep learning of an EEG/EMG dataset collected under specific experimental (head-fixation) and recording  conditions. We find that despite experimental and recording conditions unique to this independent dataset, mixture z-scoring followed by deep learning classifies sleep state with a high level of accuracy. Therefore, this method holds promise for widespread use.RESULTSTo validate mixture z-scoring combined with deep learning-based sleep state classification on an independent dataset, data from 11 mice undergoing sleep recordings and optical imaging in the head fixed position were trained and validated using the open-source software Accusleep. When applied to test data, mixture z-scoring combined with deep learning yielded an overall accuracy of 86% and a Cohen’s κ of 0.71 indicating substantial agreement  (Landis and Koch 1977). For the individual sleep states the precision  was 0.91 (0.88) for wakefulness, 0.76 (0.80) for NREM, and 0.81 (0.91) for REM .To further illustrate the ability of mixture z-scoring combined with a deep learning-based model to classify sleep states, two unseen 3-hour recordings were classified. As depicted by the hypnogram , there was a substantial agreement in the temporal pattern (sleep cycles) of transitions between wakefulness, NREM, and REM between the human raters and the deep learning method with an accuracy of 92% and a Cohen’s κ of 0.71. For individual sleep states the precision  was 0.94 (0.96) for wakefulness, 0.76 (0.67) for NREM, and 0.95 (0.87) for REM. For the other individual recording, the deep learning method yielded 85% accuracy and a Cohen’s κ of 0.66. Precision  was 0.94 (0.86) for wakefulness, 0.65 (0.86) for NREM, and 0.96 (0.70) for REM. Together, these results show that sleep states classified by mixture z-scoring combined with deep learning-based model can achieve a high degree of accuracy (85-92%).DISCUSSIONThe purpose of this study was to assess the generalizability of mixture z-scoring combined with a deep learning model to classify mouse sleep states. When applied to an independent EEG/EMG sleep dataset, this approach successfully classified sleep states with high accuracy.Accurate sleep state classification by deep learning often relies on large sample sizes and high-quality recordings. Previous attempts of applying machine learning algorithms to small-scale datasets (N=14 for a training set) yielded an accuracy of 91-96%, which is lower than the 94-97% accuracy achieved when applied to large-scale datasets  . In this study, mixture z-scoring combined with a deep learning-based model applied to a small-scale (N=11) dataset achieved an accuracy of 85-92%, but was not as high as the 97% previously reported when applied to a different dataset . One potential reason for the decreased accuracy may be due to the decreased size of the dataset compared to the original dataset . Another potential reason could be the lateral placement of the EEG electrodes used in this study. The EEG electrodes were placed laterally, compared to the typical somatosensory or motor cortex locations, to avoid obstruction of the field of view of the plexiglass window used for simultaneous optical fluorescence imaging. As a result, the electrodes were located adjacently to muscles attached to the skull and could have led to poorer signal quality, changes in the EEG spectral profile, and subsequent poorer performance of the model. Another cause of the disrupted signal could be due to the head fixed position of the mice during recordings . This unnatural sleeping position (mice prefer to sleep in a curled-up position) may have prevented a clear differentiation of the relatively decreased muscle tone of NREM and REM sleep in the EMG, potentially leading to decreased performance of the model. A fourth possible source of signal quality disruption is the inclusion of artifacts into the training of the sleep classification model. Often, artifacts will be removed prior to sleep-state classification model training. In this study, artifacts were not excluded when training the model in order to replicate real-world settings where artifacts are typically part of the recordings, which may have decreased the accuracy. To address potential causes for decreased accuracy, a future study using EEG electrodes placed in typical skull locations in a higher number of freely moving mice with all artifacts excluded is needed.Manual scoring of EEG/EMG sleep recordings is laborious, requires highly-trained experts and is prone to inter-individual variability. Expert human scorers of EEG/EMG often only agree upwards of 92%, and the level of disagreement increases during sleep-wakefulness transitions . In our study, the largest source of disagreement between human scored EEG/EMG and the mixture z-scoring and deep learning model was also at transitions between wakefulness and NREM which is known to often contain a mixture of sleep states and historically has the lowest inter-rater reliability among human scorers (Merica and Fortune 2004). To avoid sleep-wake transition misclassifications, other studies have assigned sleep states based on a consensus of multiple experts independently scoring the same recording . Our study, however, used EEG/EMG recordings scored by a single sleep scoring expert (E.L.), increasing the possibility for human error with the subsequent decreased performance of the model. Future studies assessing the generalizability and accuracy of sleep state classification models may be improved by using datasets scored by a consensus of sleep-scoring experts.Mixture z-scoring is designed to allow for generalizability across datasets by calibrating the deep learning model using representative samples of each state in the recording of each subject. However, the selection of representative samples could have had a significant influence on performance by biasing the deep learning classifier to look for similar samples within the recording. For example, NREM sleep is defined by the presence of slow waves and spindles whereas REM sleep can be phasic or tonic in humans and intermediate sleep with high amplitude theta activity can precede the onset of REM in rodents . In this study, 100 representative seconds per state  were used for calibration. If some of these NREM/REM characteristics were over or underrepresented in the calibration, it may have biased classification toward or away from certain EEG/EMG characteristics. Alternatives in the future would be to increase the amount of representative scoring samples or to examine the effect of systematically varying which representative samples are used for mixture z-scoring.In summary, applying mixture z-scoring combined with deep learning-based classification model using an independent dataset with unique recording and experimental conditions was able to classify sleep states with an accuracy of 85% to 92%. While this accuracy is not as high as previously reported methods, the accuracy of the model may have been limited by, the size of the dataset, changes in signal quality, human error, or the choice of samples used for mixture z-scoring calibration, which should be accounted for in future studies. Despite its limitations, this study will be useful to sleep researchers planning to implement machine learning models on their own experimental datasets using automated methods that can achieve ~90% accuracy. Future studies replicating these findings across multiple independent datasets would further strengthen the claim that mixture z-scoring followed by deep learning is a valid methodology and would encourage widespread use.This study was approved by the Washington University School of Medicine Institutional Animal Care and Use Committee and performed in accordance with National Institutes of Health Guide for the Care and Use of Laboratory Animals. A total of 12 transgenic mice (12-16 weeks of age) expressing GCaMP6f in excitatory neurons (driven by a Thy1 promotor) acquired from Jackson Laboratories (JAX Strain: C57BL/6J-Tg(Thy1-GCaMP6f)GP5.5Dkim; stock: 024276) were used. Mice were housed in 12:12 hour light/dark cycles with lights on at 6:00 AM and given ad libitum access to food and water.Experimental DesignEEG/EMG data of head-fixed mice was collected during wakefulness, NREM (Non-rapid Eye Movement) sleep, and REM (Rapid Eye Movement) Sleep reported in a previously published dataset . Prior to data collection, mice were acclimated to head fixation while secured in a black felt hammock for one to three sessions ranging from 30 to 180 minutes until the EEG/EMG signals showed the presence of sleep. Once sleep was established in the head-fixed position, the mouse then underwent a three-hour undisturbed recording session. All recordings occurred between 9:00 AM and 1:00 PM during the mice’s normal sleeping hours to maximize the chance of recording sleep. After 12 three-hour recordings were collected, human experts scored recordings in 10-second epochs as wakefulness, NREM, or REM as previously described .Surgical TechniquesAs part of the previously published dataset, mice had been fitted with EEG/EMG electrodes and plexiglass windows to allow simultaneous EEG/EMG and optical fluorescence imaging . Following scalp retraction, EEG pins  were placed at the surface  of the brain overlying the somatosensory cortex  and fixed with Fusio dental cement. An EEG pin placed on the surface of the cerebellum served as a bipolar reference. To record muscle activity, two 23-gauge stainless steel needles were inserted bilaterally into the neck muscles. A plexiglass head cap was fixed with a translucent adhesive cement  to allow for chronic, repeated imaging . Following surgery, mice were allowed to recover for 7 days in their home cages.Deep learning and mixture z-scoringEleven, 3-hour EEG/EMG recordings were manually scored in 10-second epochs for a total of 11,880 epochs. Then the pooled 11,880 epochs were randomly split into a training (80%), validation (10%), and test (10%) set. The EEG/EMG spectrogram of the pooled data of the training set was used to train the convolutional neural network (CNN) deep learning model described in Barger et al. From the training set, 100 seconds of expert-scored wakefulness, NREM, and REM from each record underwent mixture z-scoring to create calibration files specific to each subject. Then the remaining test set from each record was classified by the CNN. To further evaluate the performance of deep learning sleep state classification on a recording that was previously unseen by the model, two complete 3-hour recordings from two independent mice were withheld from the initial training. These recordings had mixture z-scoring applied to 100 seconds of each sleep state and were then classified with the CNN.Deep learning sleep classification and mixture z-scoring was implemented using the open-source software program AccuSleep (custom MATLAB package). https://github.com/zekebarger/AccuSleepStatistical comparisonsMetrics including precision=TP/(TP+FP), recall=TP/(TP+FN), and accuracy=(TP+TN)/(TP+TN+FP+FN), where TP, TN, FP, FN are the numbers representing true positive, true negative, false positive, and false negative, respectively, were used to evaluate the model performance. The Cohen’s kappa statistic, κ, was computed to assess the inter-rater reliability between manual EEG/EMG-based scoring and mixture-z scoring with deep learning-based classification results. The kappa statistic is thought to be a more robust measure than percent agreement, and a kappa magnitude between 0.61 and 0.80 indicates a substantial agreement between the two raters .


Objectives: To report the safety and effectiveness of office-based carpal tunnel release (CTR) using ultrasound (US) guidance in a general community population.Methods: This was a prospective single-center study that evaluated patients treated with CTR using US guidance between March 2019 and August 2020 for whom one-year data were available. Procedures were performed in an office-based procedure room using local anesthesia. Main outcomes of this study included complications, Boston Carpal Tunnel Questionnaire scores  and Quick Disabilities of the Shoulder and Hand (QDASH) scores.Results: Among 88 patients (123 hands) aged 30 to 89 years with available one-year data, 29 patients had simultaneous bilateral procedures. No intraoperative complications were reported. Statistically significant and clinically important improvements in BCTQ-SSS, BCTQ-FSS and QDASH scores occurred within two weeks and persisted through one year of follow-up (p < 0.001). Outcomes were similar for simultaneous bilateral releases. During follow-up, one patient developed complex regional pain syndrome that was successfully treated and subsequently had CTR using US guidance on the contralateral hand. No other complications were observed during one-year follow-up.Conclusion: Office-based CTR using US guidance is safe, effective and provides sustained clinical improvements at one-year follow-up in a community population. Simultaneous bilateral procedures were well-tolerated and resulted in similar clinical improvements. Carpal tunnel syndrome (CTS) is a peripheral nerve entrapment syndrome that affects 3% to 6% of adults in the United States resulting in annual healthcare costs of over 2 billion dollars -5. CarpaUltrasound (US) is an excellent soft tissue imaging modality that provides the opportunity to perform CTR through small incisions (< 5 mm) while dynamically visualizing the at-risk structures in the carpal tunnel region. Although multiple publications have documented the safety and effectiveness of CTR using US guidance -23, no pStudy designThis was a prospective single-center study that evaluated patients treated with office-based CTR using US guidance for whom one-year follow-up data were available. Study procedures and methodology followed the principles set forth in the Helsinki Declaration. This study was reviewed and approved by the Institutional Review Board affiliated with the authors’ practice (Mayo Clinic IRB#18-010140). Written informed consent was obtained from all patients.Study participantsThe authors' practice in a general rural community practice setting serving approximately 50,000 people. All patients meeting the following criteria were offered the option of receiving office-based CTR using US guidance as part of a shared decision-making process: (1) clinical diagnosis of CTS; (2) a minimum of six months nonsurgical management; and (3) confirmatory electrodiagnostic testing consistent with median neuropathy at the wrist. All eligible patients were subsequently evaluated with US to detect anatomic variations that would preclude the procedure. No patients were excluded based on US findings.Procedural descriptionPrior to the study, the first author had 10 years of experience in diagnostic and interventional ultrasound and the second author had one year of experience in interventional ultrasound. Each author completed a formal, cadaver-based training program for CTR-US prior to starting the procedure. All procedures were performed in an office-based procedure room using sterile technique (including sterile US probe cover and sterile gel), local anesthesia without a tourniquet, a high frequency linear array transducer (4-18 MHz or 3-14 MHz) and one of two cart-based US machines .The patient was placed supine with the arm abducted and the hand resting comfortably on the arm board or other supportive stand with the wrist slightly extended. The carpal tunnel was scanned to identify relevant anatomic landmarks including the median nerve, thenar motor branch, digital nerves, ulnar artery and superficial arch, transverse carpal ligament (TCL), transverse safe zone , and any anatomic variations that would affect the procedure. Local anesthesia was obtained using direct US guidance and standard 22- or 25-gauge stainless steel needles to deliver 5-15 ml of 1% lidocaine without epinephrine subcutaneously at the incision site and into the carpal tunnel. Local anesthetic was also used to hydrodissect the synovial tissue, median nerve, and flexor tendons away from the TCL and further delineate the boundaries of the ligament . US guidance was used to position the device within the TSZ at the distal TCL Figure .Following confirmation of accurate device positioning relative to the distal TCL and surrounding neurovascular structures, the device handle was depressed to expand the balloons and create space within the TSZ. Following reconfirmation of safe and accurate device placement, the blade was deployed, and the TCL transected distal to proximal using continuous US guidance Figures -4. The blade was then recessed, the balloons deflated, and the device removed under US visualization. The elevator was then re-inserted, and the TCL probed under direct US visualization to ensure complete transection. If incomplete transection was suspected, the device was re-inserted, and the procedure was repeated accordingly. A post-procedure scan was then completed to assess the integrity of the regional neurovascular structures. Following device removal and probing, excessive fluid was allowed to drain from the wound, and pressure was applied. The wound was covered with three to four sterile adhesive strips, a gauze pad, and a sterile transparent film dressing. A post-procedure exam was performed to assess for neurologic or tendon injuries.OutcomesMain outcomes of this study were complications, symptom severity and functional status scores of the Boston Carpal Tunnel Questionnaire  -26 and tStatistical analysisAt each postoperative time-point, BCTQ-SSS, BCTQ-FSS, and QDASH scores for the patient cohort were summarized with mean and standard error (SE). Statistical significance for pre- versus postoperative differences was analyzed using a nonparametric Wilcoxon test. The minimal clinically important differences (MCIDs) were defined as 1.14 points on the BCTQ-SSS, 0.74 points on the BCTQ-FSS, and 15 points on the QDASH ,28. Diff2.Between March 2019 and August 2020, the authors treated 200 hands in 140 patients, for which one-year follow-up was available for 123 hands  in 88 patients . Follow-up was available for over 90% of the 123 hands at both two weeks and one month, and for 85% of hands at three months. Confirmatory electrodiagnostic testing was available for all 123 hands, of which 81 (66%) had moderately severe or severe disease. The most common comorbidities were hypertension (43%), obesity (38%), and diabetes mellitus (24%) (Table Twenty-nine patients (57 hands) were treated as simultaneous bilateral procedures (one patient did not provide the 12-month symptom scores on one hand), 18 patients (25 hands) were treated with staged bilateral procedures, and 41 were treated with a unilateral procedure. All office-based procedures were successfully completed with local anesthesia. No supplementary intraoperative analgesics or anxiolytics were required. The TCL was transected using one to two blade passes in 96% of cases, with 4% requiring three blade passes. All incisions were < 5 mm in length. No intraoperative complications occurred.BCTQ-SSS, BCTQ-FSS, and QDASH scores significantly improved by two weeks, with mean reductions of 1.19, 0.64, and 14.06, respectively  Table -6. Mean Two (2.3%) patients required a supplementary three-day supply of tramadol to control postoperative pain. One patient developed symptoms consistent with complex regional pain syndrome including unilateral arm pain, paresthesias, and trace edema two weeks postoperative. Follow-up examination showed no erythema or signs of infection. Follow-up ultrasound examination showed complete transection of the TCL, no evidence of nerve injury, and no signs of seroma or abscess formation. The patient was treated with a short course of oral prednisone and vitamin C, with symptom resolution within two weeks. The same patient subsequently had the contralateral hand released using the same office-based procedure without complication. No neurovascular injuries, tendon injuries, infections or significant post-surgical wound complications occurred in any hand. There were no recurrences or reoperations.The primary finding of this study was that office-based CTR using US guidance is well-tolerated, safe, and results in statistically significant and clinically important CTS symptom improvement in a community population followed for one year postprocedure. All 123 hands were successfully treated in the office using only local anesthesia without the need for supplementary anxiolysis or analgesia. There were no intraoperative complications, and no conversions were required due to inadequate visualization. Furthermore, during the one-year follow-up period, there were no recurrences or reoperations, and only one significant complication occurred - a patient with complex regional pain syndrome unaccompanied by neurovascular injury that was promptly recognized and successfully treated.Although traditional CTR using larger incisions has an established record of safety and effectiveness, over time there has been a trend to use smaller incisions to minimize surgical morbidity and accelerate recovery ,6-10. InTo date, seven studies have documented the long-term (> one-year) safety and effectiveness of CTR using US guidance, including one prospective, randomized study demonstrating significantly faster recovery compared to mini-open CTR (mOCTR) ,15,19-22As the number of publications pertaining to CTR using US guidance continues to grow, it becomes increasingly important to address the generalizability of results with respect to typical patients encountered in clinical practice. All patients treated in the current investigation were seen within the context of a rural community practice, and the cohort with a mean age of 66 years, chronic symptoms (> six months), and a female predominance (57%) was typical of patients treated with CTR [Many patients with CTS present with bilateral disease and some are candidates for bilateral CTRs -31. In tStudy strengths include a prospective design, long-term follow-up on a large patient cohort consisting of >100 hands, excellent generalizability of results, inclusion of a large number of hands with severe disease, statistical comparison of patient outcomes with unilateral, simultaneous bilateral, and staged bilateral procedures, and use of validated patient-reported outcomes. However, several study limitations also warrant discussion. First, there was no comparison group. However, the primary purpose of this investigation was to specifically document the safety and effectiveness of office-based CTR using US guidance in our community practice. Of note, the current results are similar to those reported by Leiby et al. for 76 hands in 47 patients treated in the ambulatory surgery center or hospital operating room setting with the same US-guided CTR technique . Second,Office-based CTR using US guidance is safe, effective and results in significant and sustained clinical improvements at one-year follow-up within the context of a community practice. Clinically important reductions in pain and improvements in function were reported early in follow-up and maintained throughout the course of the study. When clinically indicated, simultaneous bilateral procedures were well-tolerated and resulted in similar clinical improvements as unilateral or staged bilateral procedures. Overall, office-based CTR using US guidance is an attractive minimally invasive treatment option for patients with severe or refractory CTS.