Repeatedly performing a submaximal motor task for a prolonged period of time leads to muscle fatigue comprising a central and peripheral component, which demands a gradually increasing effort. However, the brain contribution to the enhancement of effort to cope with progressing fatigue lacks a complete understanding. The intermittent motor tasks (IMTs) closely resemble many activities of daily living (ADL), thus remaining physiologically relevant to study fatigue. The scope of this study is therefore to investigate the EEG-based brain activation patterns in healthy subjects performing IMT until self-perceived exhaustion. Fourteen participants  repeated elbow flexion contractions at 40% maximum voluntary contraction by following visual cues displayed on an oscilloscope screen until subjective exhaustion. Each contraction lasted ≈5 s with a 2-s rest between trials. The force, EEG, and surface EMG (from elbow joint muscles) data were simultaneously collected. After preprocessing, we selected a subset of trials at the beginning, middle, and end of the study session representing brain activities germane to mild, moderate, and severe fatigue conditions, respectively, to compare and contrast the changes in the EEG time-frequency (TF) characteristics across the conditions. The outcome of channel- and source-level TF analyses reveals that the theta, alpha, and beta power spectral densities vary in proportion to fatigue levels in cortical motor areas. We observed a statistically significant change in the band-specific spectral power in relation to the graded fatigue from both the steady- and post-contraction EEG data. The findings would enhance our understanding on the etiology and physiology of voluntary motor-action-related fatigue and provide pointers to counteract the perception of muscle weakness and lack of motor endurance associated with ADL. The study outcome would help rationalize why certain patients experience exacerbated fatigue while carrying out mundane tasks, evaluate how clinical conditions such as neurological disorders and cancer treatment alter neural mechanisms underlying fatigue in future studies, and develop therapeutic strategies for restoring the patients' ability to participate in ADL by mitigating the central and muscle fatigue. Fatigue interferes with task performance in multiple ways—slowing down, improper execution, or failure to accomplish. Furthermore, unpleasant sensations such as pain, discomfort, and increased effort can accompany the muscle fatigue. In healthy population, the consequences due to fatigue may be reflected as reduced physical performance, difficulty to carry out strenuous or prolonged activity, and demand for extra effort to perform everyday tasks, which will subside after a period of rest or decreased activity. Nevertheless, in individuals suffering from muscular, neurological, cardiovascular, and respiratory diseases and those who are aging or frail, the effects of fatigue are amplified and posing restrictions on daily life  firing in the neuromuscular pathway: the cessation or slowing down of MU firing marks fatigue, whereas the increased recruitment of MUs serves as the compensatory mechanism. The central fatigue changes the MU firing by altering the intrinsic properties of motoneurons, feedback from the sensory input, and descending drive, which in turn modifies the strength and timing of muscle contractions. Besides, a sensation of discomfort and fatigue is facilitated by the firing of group III/IV muscle afferents. Furthermore, the modulation of brain neurotransmitters impacts the task endurance performance  studies were dedicated to observe the changes in the cortical activation due to single-limb contractions until self-perceived exhaustion. The sustained maximal  studies reported the effect of fatigue on the spectral power of signals recorded from the sensorimotor cortex. The post-movement beta rebound (PMBR) was reported to increase because of submaximal contractions until fatigue  in brain regions that include primary motor cortex (M1), SMA, and premotor cortex, e.g., studies related to upper limb  force of isometric elbow flexion was recorded. Next, they performed the intermittent motor task (IMT) comprising repetitive elbow flexion at 40% MVC until they were subjectively exhausted.The subjects were seated with their forearm in a neutral position making an elbow joint angle of ≈100°. They were instructed to exert the maximal strength while performing two elbow flexion contractions using their dominant arm. The MVC force displayed on an oscilloscope screen was recorded with a data acquisition system  in a computer. Simultaneously, the MVC force was also measured by a force transducer . Out of the two MVC measurements, the highest value was selected for the subsequent analysis. If the two measurements differed by more than 5%, the MVC task had to be repeated to ascertain that the maximum effort was exerted during the contraction.The IMT was intended to contract the elbow flexor muscles until the subjects perceived severe fatigue. Each submaximal intermittent contraction set at 40% MVC was maintained for 5 s. The effort level to perform submaximal contractions was chosen to be 40% MVC to ensure that severe fatigue would be induced within 30 min leading to task failure , and Triceps Brachii (elbow extensor). A fourth channel was used for recording the elbow flexion force signal sensed by the force transducer. After soaking the electrode net in the electrolyte—one liter of distilled water added with 1.5 teaspoons of potassium chloride—it was mounted on the subject's head. The connection quality of the electrodes was ensured by bringing their impedance values below 10 k℧. The signal from each channel was amplified by a factor of 75, 000, band-pass filtered (0.01 Hz to 100 Hz), and digitized at a sampling rate of 250 Hz using NeuroScan system  , in which the electrodes were arranged in a hat-like net and interconnected using nylon strings. Out of 128 channels, three were dedicated for EMG measurements from the muscles, namely C) Yang, .For more specific details on the experimental protocol, interested readers may refer to Yang ; Cai et The 124-channel EEG data were preprocessed as outlined in Filtering and Noise Removal. The EEG signals were first band-pass filtered using a Hamming windowed sinc finite impulse response filter with the lower and higher cut-off frequencies of 1 and 100 Hz, respectively. Next, they were filtered using a notch filter to remove the 60 Hz power-line noise and its higher harmonics. The noisy bursts in the filtered EEG data were corrected with Artifact Subspace Reconstruction (ASR) approach, which is an EEGLAB plug-in  was set as 20 as recommended in Chang et al.  were removed.ICA Decomposition and Dipole Estimation. The multichannel EEG data free of noisy epochs and stim pulses was decomposed by an Independent Component Analysis (ICA) algorithm, namely Extended Infomax  scalp map and the projection of the equivalent dipole is quantified using the residual scalp map variance (RV); for instance, the best-fitting model has the smallest RV. The ICs were classified with an artificial neural network framework into two categories: (i) “non-brain-related ICs” representing electrical artifacts stemming from muscle, eye, heart, line noise, and channel noise and (ii) “brain ICs” accounting predominantly for activities originating within the brain. The classifier was implemented with an EEGLAB plug-in  Source-Level ERSP Analysis. First, the ICs were estimated by decomposing the scalp EEG data with the Extended Infomax ICA algorithm  located within motor-related cortical regions. In both cases, we performed the ERSP analysis with the EEG data corresponding to the steady elbow flexion and post-contraction  a et al. , where VWe computed the ERSP based on the formal definition provided in Fuentemilla et al. , DelormeStatistical Framework. For notational convenience in the sequel, the subject (or component) index j and the fatigue condition are omitted in Equations (1)–(4). The mean event-related spectrum  being the spectral estimate for trial k at frequency f and time instant t. In the present setting, M denotes the total number of trials for a given subject (channel-level) or component in an IC cluster (source-level) corresponding to any one of the three fatigue conditions. The term k-th trial of subject (or component) j. We apply the gain model, which is the default choice in EEGLAB software that divides the contraction-induced spectral activity by the pre-contraction spectral activity. For a given frequency band, the absolute ERSP is computed by dividing the ERS power in Equation (1) computed for the steady contraction or post-contraction at each time-frequency point by the average spectral power in the pre-contraction baseline period at the same frequency.with As pointed out in Grandchamp and Delorme , the logwherer is the cardinality of the set R of steady- or post-contraction time points. In Equation (4), BAVG(f) denotes the pre-contraction spectral power as a function of f averaged over a total of q time points belonging to the set Q in the baseline period across p trials. We derived a common baseline for every subject (or component), i.e., the power spectra of the pre-contraction data for the j-th subject (or component) were averaged across the trials, time points, and fatigue conditions. The rationale for using a common baseline across fatigue conditions is to counteract the effect due to fatigue-induced changes in the baseline to the best possible extent. Consequently, p in Equation (4) represents the sum of selected number of trials for a subject (or component) under MinFatg, ModFatg, and SevFatg. We vertically concatenated the row vectors j = 1, 2, …, N to construct a matrix of size N × f as given by⊘ denotes Hadamard division, and To draw statistical inferences, we conducted the surrogate permutation test with the following: (i) log-transformed ERSP matrices Permutation Test and FDR Correction. We studied the log-transformed matrices p < 0.05 and 2000 permutations. The test results validated the differences among the three fatigue conditions in terms of ERSP values at each time-frequency point. We applied the FDR correction to control the type I errors in null hypothesis testing when conducting multiple comparisons with a p-value threshold of 0.05. The reason for employing FDR is that it is less conservative compared to family-wise error rate (FWER) controlling procedures and have greater power  with Bonferroni correction  channel-level ERSP for steady contraction; (ii) source-level ERSP for steady contraction; (iii) channel-level ERSP for post-contraction; and (iv) source-level ERSP for post-contraction.k-means algorithm based on the location and orientation of equivalent dipoles as described in Section 3. Third, the scalp maps of cluster centroids and the clusters of equivalent dipoles were visually inspected to identify the plausible neural sources that were located in close proximity to the cortical regions of interest responsible for the upper extremity motor task. The dipole clusters of interest (blue dots) and the respective cluster centroids (red dots) were shown for the steady-contraction EEG in the first row and for the post-contraction EEG in the second row of Out of 124 EEG channels, we selected six channels, namely FC3, C1, C3, C5, CP1, and CP3, corresponding to the scalp electrodes mounted over the contralateral cortical regions associated with the IMT (elbow flexion) for the channel-level analysis. Note that all the 14 healthy participants were right-handed. To perform the source-level analysis, first, the ICs were estimated from the multichannel EEG signals by the ICA algorithm and fitted with the equivalent dipoles via DIPFIT toolbox. Second, the ICs across the subjects were clustered with N subjects (components), denoted as p < 0.05. Interestingly, in all instances from the channel- and source-level analysis for the steady or post-contraction, we observed statistically significant differences across MinFatg, ModFatg, and SevFatg at a large collection of time-frequency points within the theta-, alpha-, and beta-band frequency range.For a given fatigue condition, we computed the log-transformed ERSP measure in Equation (2) from an EEG channel of interest for all the 14 subjects in the channel-level analysis. The EEG channel was replaced by an IC cluster of interest, while performing the source-level analysis with the ERSPs computed for the components of the cluster. We generated separate sets of 2-D patterns obtained by averaging j-th subject (component) to help investigate how the PSD modulates across the fatigue conditions in the steady- or post-contraction interval as the frequency varies within the range 3–90 Hz. Simply put, p < 0.05) in the theta, alpha, and beta band per the permutation test with FDR correction for multiple comparisons. Also noteworthy is that in IC Cluster 7, even though the mean PSD pertaining to ModFatg and SevFatg seems to overlap at around 25 Hz, the permutation test with FDR correction could detect the difference at those frequencies. In like manner, the channel- and source-level 1-D ERSPs for post-contraction are exemplified in The PSD values of time-frequency points in N for all the subjects (components). The statistical inferences help verify whether the averaged ERSP measure maintains a monotonic relationship with fatigue and differs between the three fatigue conditions with a statistical significance. To this end, we applied a one-way repeated measures ANOVA to p < 0.05), then we conducted the post-hoc analysis with Bonferroni correction to determine the significance of differences between pairs of group means. For a given frequency band, the pairwise differences of time- and frequency-averaged PSD between the three fatigue groups were visually inspected with the box plots. For the steady contraction, the box plots are provided for two EEG channels—CP1 (row 1) and CP3 (row 2)—and IC Cluster 7 (row 3) in We averaged N subjects (components). In AVG is derived using Equation (4) in our analysis in order to counteract the changes induced by fatigue in the baseline. To summarize, The subject-wise baseline PSD averaged over the trials in a given fatigue condition A critical aspect of this analysis stems from the fact that human EEG signals are inherently non-stationary due to noise and voluntary neural dynamics . Of note, the ERSP difference between ModFatg and SevFatg is seldom marginally significant (p < 0.1) and yet the ERSP-fatigue proportional relationship is well preserved. We conjecture that it could be because some subjects would not have encountered extreme fatigue and the EEG suffers from a large inter-subject variability. Based on this premise, we conclude that during the steady as well as post-contraction, as fatigue progresses due to a prolonged IMT involving upper extremities, the PSD estimates from EEG channels and IC clusters associated with sensory and motor cortex have the tendency to increase in proportion to the fatigue level.The p-values from the post-hoc analysis . Each feature vector comprises the ERSP from individual trials computed for six EEG channels  and two frequency bands  of interest. The accuracy (Ac) of binary classification (MinFatg and SevFatg) from a 10-fold cross validation is 78.22% for the steady-contraction and 71.72% for the post-contraction EEG data. When all the three classes were taken into account, as anticipated, Ac deteriorated to 55.78% and 55.23% for the data acquired during the steady- and post-contraction interval, respectively. The confusion matrices for the binary and three-class classification are presented in Cortical Regions/Channels of Interest. For the ERSP analysis, we have selected the EEG signals recorded from the electrodes, namely FC3, C1, C2, C3, CP1, and CP3, that overlay the following brain regions: (i) motor cortex comprising M1 (Brodmann area 4), premotor cortex, and SMA (Brodmann area 6); (ii) primary somatosensory cortex ; and (iii) superior parietal lobule (Brodmann area 5) , and left transverse temporal gyrus. These brain regions were found to have an association with the upper-extremity motor task performance and/or fatigue by the previous studies. The dorsal anterior cingulate cortex is responsible for the mediation of task-related motor control and has connections to premotor and sensorimotor areas ], which reflects the excitability of neurons due to activation in a specific cortical region  within the contralateral sensorimotor cortex concluded the following: the physical fatigue gives rise to (i) an enhanced PMBR and (ii) cancellation of attenuation in MRBD related to task habituation  and central contralateral (C3) electrode sites , beta 1 (C3 and Fz), and beta 2  bands . Therefore, we refrain from comparing the fatigue-related PSD modulation across different groups based on the age criterion, e.g., two groups consisting of young (< 65 years) and old (≥ 65 years) participants. Nevertheless, we repeated the analyses by excluding the EEG signals from older (≥ 65 years) subjects to verify whether the results could have been impacted by the data from the two outliers—subjects of age 70 and 72 years—in the former analyses. Interestingly, the outcome of both the channel- and source-level-analysis with the data from the remaining 12 subjects is in total agreement with what we have already inferred from the original dataset. Recall that the dataset consists of EEG from six male and eight female volunteers. To make sure that the conclusions arrived at are gender-independent, we carried out the channel-level analysis with the male and female participants separately and examined the outcome. Notwithstanding the gender difference, the spectral power varies relative to the fatigue level for the data from the channels of interest, provided theta-, alpha-, and beta-band frequencies are considered for statistical inferences  The theta, alpha, and beta PSDs scale with fatigue in cortical motor areas. These signal changes most likely are associated with progressively increasing the effort to continue the motor task to compensate for progressive fatigue. The fMRI , Cleveland Clinic (RPC6700), Department of Defense (DAMD17-01-1-0665), New Jersey Commission on Brain Injury Research (CBIR19PIL014), and National Institute on Disability, Independent Living, and Rehabilitation Research (ARHF17000003).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The pandemic of SARS-CoV-2, which causes COVID-19, has caused disruptions in ongoing clinical trials and is expected to accelerate interest in conducting research studies remotely.A quasi-experimental, mixed methods approach was used to examine the rates of visit completion as well as the opinions and experiences of participants enrolled in an ongoing clinical trial of smoking cessation who were required to change from in-person clinic visits to remote visits using video or telephone conferencing due to the COVID-19 pandemic.For quantitative comparisons, we used a quasi-experimental design, comparing a cohort of participants followed during the pandemic  to a comparable cohort of participants followed over a similar time period in the calendar years 2018 and 2019  to examine the rates of completion of scheduled visits and biospecimen collection. For the qualitative component, interviews were conducted with participants who experienced the transition from in-person to remote visits.Participants in the COVID-19 cohort completed an average of 83.6% of remote clinic visits (95% CI 73.1%-91.2%), which was not significantly different than the in-person completion rate of 89.8% in the pre–COVID-19 cohort. Participants in the COVID-19 cohort returned an average of 93.2% (95% CI 83.5%-98.1%) of saliva specimens for remote clinic visits completed, which was not significantly different than the in-person saliva specimen completion rate of 100% in the pre–COVID-19 cohort. Two broad themes emerged from the qualitative data: (1) the benefits of remote visits and (2) the challenges of remote counseling compared to in-person counseling. Despite limited experience with telehealth prior to this transition, most participants expressed a willingness to engage in remote visits in the future.Even in the context of a rapid transition from in-person to remote visits necessitated by the COVID-19 pandemic, rates of visit completion and return of biospecimens remained high. Participants were generally accepting of the transition. Further research is needed to identify the optimal mix of in-person and remote visits beyond the pandemic context and to better understand how these changes may impact study outcomes.ClinicalTrials.gov NCT03262662; https://clinicaltrials.gov/ct2/show/study/NCT03262662 Recent reviews have examined the growing interest in conducting remote trials for smoking cessation by incorporating existing applications for cell phones and other technology -3. HowevThe SARS-CoV-2 outbreak, which led to the COVID-19 pandemic, has caused disruptions in ongoing clinical trials and is expected to accelerate interest in conducting research studies remotely. Many trials were suspended for varying periods of time, while others were forced to transition from in-person to remote visits. In this context, the COVID-19 pandemic experience provided a unique opportunity to examine quantitative and qualitative data, albeit nonexperimentally, regarding the impact on relevant intermediate study outcomes such as completion of visits and return of biomarker specimens, as well as to consider participant perspectives on the transition and their preferences and recommendations for in-person versus remote visits.This paper uses a quasi-experimental, mixed methods approach to examine the opinions and experiences of participants enrolled in an ongoing clinical trial of smoking cessation who were required to change from in-person clinic visits to remote visits using video or telephone conferencing due to the COVID-19 pandemic. The quantitative component allows for direct  comparisons among a group of participants on objective measures of effect, which is complemented by qualitative data regarding the experiences and opinions of research participants. For quantitative comparisons, we used a quasi-experimental design, comparing a cohort of participants followed during the pandemic to a comparable cohort of participants followed over a similar time period in the calendar years 2018 and 2019 to examine rates of completion of scheduled clinic visits and biospecimen collection. We tested whether rates of visit completion and saliva collection were lower for remote visits than for in-person visits. For the qualitative approach, interviews were conducted with a subset of participants who experienced the transition from in-person to remote visits.Our ongoing randomized clinical trial examines the impact of an alternative varenicline dosing strategy to enhance rates of cessation among treatment-seeking adult cigarette smokers . FollowiIn March 2020, accruals to this trial were on target when the COVID-19 pandemic forced us to rapidly implement changes to our procedures. One of the primary changes involved a transition from in-person counseling to remote counseling via Zoom  or telephone calls (when Zoom was not feasible for the participant) for the 23 participants who had already begun the treatment phase but had not yet completed all clinic visits .A mixed methods approach, based on a concurrent embedded framework, was used to explore the impact of transitioning to remote clinic visits in a smoking cessation trial during the COVID-19 pandemic. A quasi-experimental design provided quantitative data to compare clinic visit completion rates among the COVID-19 cohort to a comparable cohort of participants with clinic visits that were scheduled during a similar time period in the calendar years 2018 and 2019 . Qualitative methods  were useA mixed methods design with a convergent approach was used to inform a more comprehensive understanding of the impact of transitioning from in-person to remote clinic visits by integrating both qualitative and quantitative data instead of only relying on one of these data sources. The quantitative data test the major outcomes of the quasi-experimental condition, while the qualitative data provide important insights about the process from people’s experiences ,13.The parent research project  continues to enroll eligible treatment-seeking cigarette smokers between the ages of 18 and 70 years in a randomized clinical trial evaluating extended pre-quit varenicline pharmacotherapy for smoking cessation. Following intake and randomization, each clinical trial participant is scheduled to complete 6 in-person clinic visits  as well as 2 follow-up appointments to assess long term abstinence. At each in-person clinic appointment, participants complete blood pressure and exhaled carbon monoxide (CO) assessments, fill out self-administered questionnaires, provide a saliva sample, receive brief behavioral counseling, and receive a supply of study medication.For the current investigation, we focused on a COVID-19 cohort, defined as participants (n=23) who had completed at least 1 in-person clinic visit in a 2-week transition window immediately before the PAUSE executive order was issued in New York State and had at least 1 of 6 clinic visits remaining (range: 1-5 remaining visits). In other words, these participants completed clinic visit #1, 2, 3, 4, or 5 during the 2-week transition period. The PAUSE executive order, issued in mid-March 2020, suspended 100% of nonessential business activities, including clinical research.During the transition window clinic visit, participants were provided with their remaining supply of study medication and remote visit packets containing instructions, paper copies of self-administered questionnaires, behavioral counseling handouts, and at-home saliva sample collection kits, along with postage-paid envelopes for returning the surveys and specimens. Procedures for either telephone or Zoom meetings were also briefly reviewed.For the remaining clinic visits, the questionnaires could be completed by following an email link to the surveys programmed in Research Electronic Data Capture (REDCap) or by filling out paper copies of the measures included in the remote visit packets. Participants received audio (telephone) or audiovisual (Zoom) instructions to guide saliva sample collection. To return the remote clinic visit packet to the study investigators, participants sealed their completed questionnaires and saliva specimen in a large padded envelope with pre-paid postage that could be picked up by their mail carrier.For comparative purposes, we retrospectively reviewed records to identify a comparator cohort of study participants who had completed an in-person clinic visit during the same 2-week time frame (March 18 to April 2) in the calendar year 2018 or 2019 and had 1 or more (range 1-5) remaining clinic visits scheduled . Participants followed during 2018 and 2019 did not significantly differ with regard to demographic or tobacco use characteristics and were combined into a single pre–COVID-19 cohort (n=51).We examined participant age, sex, race/ethnicity, household income, highest level of education completed, Fagerstrom Test of Cigarette Dependence (FTCD) cigarettes per day at time of enrollment, and baseline exhaled carbon monoxide level across the COVID-19 and pre–COVID-19 cohorts. The FTCD is a 6-item self-reported scale used to assess the severity of dependence on cigarettes; the level of dependence is inversely correlated with the ability to successfully quit ,15.We focused on the percentage of clinic visits completed and the rates of biospecimen  collection after the last in-person visit for the COVID-19 cohort and after the comparable in-person visit for the pre–COVID-19 cohort.Discussion section [This study relied upon a concurrent design in which quantitative and qualitative data collection and analyses were performed in parallel to yield complementary impressions of the impact of transitioning from in-person interviews and a desire to not burden participants to the extent that it would negatively affect the completion of long-term follow-up visits for participants still participating in the trial ,16. Const tests, and chi-square tests were used to compare means and categorical distributions by cohort; P values <.05 were considered significant. Analyses were conducted using SPSS, version 26 (IBM Corporation).Descriptive statistics, Persons in the COVID-19 cohort were contacted by telephone and invited to participate in a telephone-based structured interview to explore their experiences with transitioning from in-person to remote visits. Interviews were completed shortly after the final scheduled clinic visit (visit #6). Participants were informed verbally of the study aims and provided verbal informed consent; no additional compensation was provided. Interviews were completed by 2 trained research assistants using a structured interview guide. Items in the structured interview addressed how in-person counseling visits compared with remote visits, whether most remote visits were telephone calls or video sessions, desirable and undesirable features of remote visits, challenges encountered during remote visits, experiences with remote survey completion and saliva sample collection, privacy issues regarding in-person and remote visits, prior experience with telemedicine, preferences for remote versus in-person visits, and how changing from in-person to remote visits might have influenced the participant’s ability to stop smoking. The interviews lasted an average of 12:48 minutes (range 7:01-26:03), excluding informed consent. Interviews were completed with 15/21 persons (71%); 2 persons did not complete any remote visits and were not contacted, 4 persons declined to participate, and several telephone calls yielded no response for 2 participants. Initial interview audio transcripts were generated by the Zoom software. Individual transcripts were carefully reviewed by a coauthor (NJS), who made minor corrections when necessary to address errors or to improve clarity. This research was approved by the Institutional Review Board (IRB) at the University at Buffalo.A qualitative descriptive analysis was conducted using a conventional content analysis , and ATL21 of 23 participants in the COVID-19 cohort (91%) completed at least 1 remote clinic visit, while 48 of 51 participants (94%) in the pre–COVID-19 cohort completed at least 1 subsequent in-person visit; withdrawals and loss to follow-up were counted as incomplete visits. As presented in When given the option of recommending either in-person or remote clinic visits for a future cessation trial, 11/15 interviewed participants (73%) expressed a preference for a hybrid approach, with 7 participants recommending that future clinic visits be scheduled as a mix of in-person and remote visits and 4 respondents indicating that participants should be permitted to select their own schedule of clinic visits; 3 participants expressed a preference for all in-person visits, and 1 indicated no preference. Also, 13/15 participants (87%) reported that the change from in-person to remote visits had no impact on their ability to stop smoking; 1 participant reported a positive impact, and 1 reported a “potentially negative” impact from this change.Two broad themes emerged from the qualitative data: (1) the benefits of remote visits and (2) the challenges of remote counseling compared to in-person counseling. As presented in Among the 15 participants interviewed, 8 (53%) reported that the majority of their remote counseling visits were video calls (audio and video communication); a strong preference was expressed for video calls , as video sessions were viewed as more personal and interactive . Few participants  reported any previous experience with video or audio calls from their health care providers . Following the experience of these remote visits, 13/15 participants (87%) indicated a willingness to engage in telehealth visits in the future.The quasi-experimental component identified no differences between subcohorts in the proportion of participants lost to follow-up or withdrawal from the study. Participants in the COVID-19 cohort completed an average of 83.6% (95% CI 73.1%-91.2%) of scheduled remote clinic visits and returned an average of 93.2% (95% CI 83.5%-98.1%) of saliva specimens, which was comparable to the in-person clinic visit completion rate of 89.8% (95% CI 84.0%-94.1%) and the in-person saliva specimen completion rate of 100% (95% CI 97.4.0%-100%) in the pre-COVID-19 cohort. The qualitative findings suggested two broad themes: (1) the benefits of remote visits, such as convenience, comfort and safety, and (2) the challenges of remote visits, such as less personal interaction with the study staff, struggles with technology, less accountability to complete assignments, and decreased confidence in their ability to quit smoking. There was a strong preference expressed for video calls over telephone calls, as video sessions were viewed as more personal and interactive. Finally, while few participants reported any previous experience with video or audio calls with their health care providers, nearly all participants indicated a willingness to engage in telehealth visits in the future.The ongoing COVID-19 pandemic has heightened interest in remote, decentralized clinical trials. Dahne and colleagues [In March 2020, the US Food and Drug Administration offered recommendations for ongoing and planned clinical trials [The findings for visit completion were generally encouraging; we observed robust and sustained rates of completion for scheduled clinic visits after the migration to a remote platform of telephone and/or Zoom calls (83.6%) as a result of the COVID-19 pandemic. Importantly, the completion rate was not significantly lower than that of a historical comparison group, namely the pre–COVID-19 cohort comprising participants from the calendar years 2018 and 2019 who only experienced in-person visits (89.8%). Although the comparison is quasi-experimental, the two cohorts were comparable on all baseline participant and smoking characteristics, ruling out a variety of potential confounds. However, there is one critical confound between groups: participants in the COVID-19 cohort were also faced with the unique circumstances of the pandemic.It is important to note that we were not powered to detect small group differences in visit completion rates. Conversely, the transition from in-person to remote visits was made quite rapidly and may represent a “worst case scenario.” We would anticipate that remote visit completion rates would be even better if participants were preinformed of the remote visits and as a result of further refinement and standardization of our procedures. Regarding remote visits more generally, it is important to note that none of the current participants anticipated the possibility of remote visits upfront. Visit completion rates may be even higher in trials that are specifically designed to be remote .The second goal of this research was to understand the opinions and experiences of clinical trial participants who were required to change from in-person to remote visits after completing at least one in-person clinic visit. Interviews with 15 such participants suggested a mix of benefits and challenges for remote visits. Benefits included not having to travel to on-site appointments, avoiding potential exposure to COVID-19, and the general convenience of staying at home. Challenges of remote visits involved the less personal nature of interactions with project staff, technology challenges, and less accountability. Taken together, these data provide suggestions for more satisfying participant experiences in future intervention trials. For example, planning sufficient time and pre-educational sessions to deliver technology support may be necessary for successful remote visits via telephone or video. In addition, sending reminder calls or SMS text messages in advance of scheduled video or teleconferences seems to enhance participants’ accountability. Several participants in our study also commented on less privacy and more distractions at home, while others felt that privacy was greater at home. Few participants  who were required to transition to remote visits reported familiarity with virtual visits or telehealth at the time of migration to a remote platform. However, following their experience in this study, a large percentage 13/15, 87%) reported a willingness to participate with telehealth visits either via telephone or video in the future. After the experience of both in-person and remote visits, most participants recommended a mix of visit types or choosing from both options going forward. Studies comparing use of video counseling to telephone counseling have focused on specific groups of smokers  -8, limit7% reportParticipants in our clinical trial were already familiar with electronic data capture (REDCap) for completing many study measures; therefore, the transition to remote visits may have had less of an impact on that component. We did lose the ability to assess exhaled breath CO; however, we successfully retained our primary bioverification measure , as 93% of all remote visits were accompanied by the participant sending back their saliva sample.R2=0.047). These authors concluded that in-person visits compensated for weaknesses of the web visits, which was also noted in qualitative research among smokers in this trial [Adherence, measured as time spent in treatment, for face-to-face versus a mix of face-to-face and web visits was studied among 292 persons enrolled in a 10-session smoking cessation trial conducted in the Netherlands . Adherenis trial . Our stuLimitations of this study include the modest size of the cohort that transitioned to remote clinic visits, limited power to examine differences in rates of completion by individual visits or among specific subgroups of participants, and the inability to assess nonrespondent bias. Also, the duration of the interviews with participants who completed remote clinical visits was generally shorter than what is typical for qualitative studies; however, this was reflective of the focused nature of the interview guide and a desire to not burden respondents to an extent that would negatively affect the completion of long-term follow-up visits for participants still in the trial. These modest limitations are offset by the unique strengths of our mixed methods approach, which supported an examination of opinions and experiences of participants enrolled in an ongoing clinical trial of smoking cessation who were required to change from in-person clinic visits to remote visits and the quasi-experimental design comparing rates of visit completion to a comparable group of participants followed over a similar time period in calendar years 2018 and 2019.The experience of the COVID-19 pandemic has forced many investigators to more rapidly use and incorporate mobile health and technology applications into their ongoing clinical trials. This study suggests that participants in our smoking cessation trial were successfully transitioned from in-person to remote clinic visits based on high rates of visit and biospecimen completion and general satisfaction with the experience.These unique analyses provide valuable information on the experiences and perspectives of participants enrolled in a clinical trial of smoking cessation who were transitioned from in-person to remote clinic visits due to the COVID-19 pandemic. This synthesis of qualitative and quantitative data support a successful transition process from in-person to remote visits for this smoking cessation clinical trial, as Dahne and colleagues [In summary, our findings indicate strong acceptability among participants in this clinical trial for completing clinic visits remotely after starting initially with in-person visits and suggest the potential importance of considering individual preferences with regard to potentially transitioning to remote visits. These study findings are consistent with promising results of studies in which technology was used to support remote delivery of smoking cessation treatment ,6-8,23.


During the COVID-19 outbreak, healthcare providers might have avoided droplet/aerosol-generating procedures, such as non-invasive ventilation (NIV) and high-flow nasal cannula (HFNC) due to the concern of themselves being infected. We hypothesized that this change of practice could have also occurred to other non-COVID-infected patients in the Emergency Department (ED).A retrospective analytic study was conducted in the ED of Siriraj Hospital, Bangkok, Thailand, including adult patients presenting with signs and symptoms of respiratory distress between 1 March and 30 April 2020 (the COVID period). A comparison group using the same inclusion criteria was retrieved from 1 March to 30 April 2019 (the pre-COVID period). The primary outcome was rate of NIV and HFNC use. The secondary outcomes were rate of intubation, failure of NIV and HFNC, complications, and mortality.A total of 360 and 333 patients were included during the pre-COVID and COVID periods, respectively. After adjusting for baseline differences, patients in the COVID period were less likely to receive either NIV or HFNC than the pre-COVID period (adjusted OR 0.52 [95%CI 0.29–0.92]). Overall, intubation rate was similar between the two study periods. However, patients in respiratory distress with pulmonary edema had a relatively higher intubation rate in the COVID period. There were higher failure rates of NIV and HFNC, more infectious complications, and a higher rate of mortality in the pre-COVID period.During the COVID-19 pandemic, the overall usage of NIV and HFNC in emergency non-COVID patients decreased. Although not affecting the overall intubation rate, this change of practice could have affected some groups of patients. Therefore, treatment decisions based on a balance between the benefits to the patients and the safety of healthcare providers should be made.The online version contains supplementary material available at 10.1186/s12873-021-00491-7. Since late December 2019, a number of patients in Wuhan, China suffered from a novel coronavirus .COVID-19 is a respiratory tract infection, causing a wide range of disease severity. Although most of the infected patients present with mild or uncomplicated viral upper respiratory tract complaints, some patients who develop the severe form may present with respiratory distress and hypoxemia needing oxygen supplement and respiratory support , 4. The There have been many reports elaborating on the impact of COVID-19 on other non-COVID patients –16. MultWe conducted this retrospective analytical study to examine the characteristics, the respiratory interventions given, and outcomes of patients presenting to the ED with respiratory distress in the COVID-19 pandemic period compared with those in a pre-COVID period.This study was a retrospective cohort study including two groups from two different time periods; one from 1 March to 30 April 2019 (the pre-COVID period) and the other from 1 March to 30 April 2020 (the COVID period). The study was conducted at the ED of Siriraj Hospital, Mahidol University, which is the largest tertiary university hospital in Bangkok, Thailand. The ED has an annual visit rate of over 20,000 Emergency Severity Index triage level 1–2 patients alone. The Siriraj Institutional Review Board approved the study protocol and waived the need for informed consent. The study protocol was performed in accordance with the Declaration of Helsinki and is reported according to the STROBE guideline . This stDue to the COVID-19 outbreak, the Thai government imposed a night curfew starting on 3 March 2020 until at least 31 May 2020. Siriraj Hospital had postponed all possible outpatient appointments and elective procedures and surgery. All patients had to pass the COVID-19 triage area staffed by triage nurses before entering the hospital. Patients suspected of having COVID-19 from this primary triage were transferred to be evaluated by infectious disease (ID) specialists. The ID specialists determined which patients were classified as Patient Under Investigation (PUI) for COVID-19, and the PUIs were transferred to a designated COVID-19 ward for specimen collection. Patients who passed the initial screening were sent to the outpatient clinic or the ED according to their triage level. If an ED physician suspected a patient of having a COVID-19 infection during secondary triage in the ED, an ID specialist would be consulted to determine the patient’s PUI status.> 24 breaths/min, a pulse oximetry reading (SpO2) at room air of less than 95%, and signs of increased work of breathing, such as accessory muscle use, as recorded by the ED physicians. Pregnant patients were excluded because they differ physiologically from non-pregnant patients and are usually considered contraindicated for NIV.We included adult patients aged at least 18 years old who presented to the ED with signs and symptoms respiratory distress. This was defined as any complaints of respiratory or chest discomfort, and at least one of the following signs of respiratory distress; a respiratory rate We retrospectively assessed medical records of all ED patients for eligibility between 1 March and 30 April 2020. This was the period during which the COVID-19 outbreak was the most severe in Thailand, and we referred to this as the COVID period. We also assessed all ED patients during 1 March and 30 April 2019 (the pre-COVID period) to acquire a historic control group with the least different characteristics to the patients in the COVID period, regarding seasonal and epidemiological factors.We extracted the following data from their medical records: age, sex, initial vital signs , baseline status and underlying diseases, diagnosis, details of oxygen supplementation and respiratory interventions in the ED and during the hospital stay, complications, and outcomes. Primary diagnoses were pneumonia defined as community-, hospital-acquired or healthcare-associated pneumonia, obstructive lung conditions defined as chronic obstructive pulmonary disease (COPD) or asthma, and conditions with pulmonary edema defined as congestive heart failure and volume overload.The primary outcome was the rate of non-invasive airway equipment (NIV and HFNC) use. The secondary outcomes were intubation rate, complications such as failure of non-invasive oxygen or ventilatory support, hospital-associated pneumonia (HAP), ventilator-associated pneumonia (VAP), and septic shock that occurred after 48 h of ED admission, ED and hospital length of stay, and 30-day and in-hospital mortality. Respiratory interventions initiated within 30 min of ED arrival were considered initial intervention while those initiated after 30 min were deemed rescue interventions. Failure of NIV and HFNC was defined by any escalation to a more invasive airway procedure.2/FiO2 ratio, respiratory rate, and mean arterial pressure as the independent variables. These variables were chosen as prognostic factors of NIV and HFNC. IPTW weights were calculated to assess the average treatment effect. Balance statistics for the prognostic factors were calculated as standardized mean differences before and after IPTW weighting. Balance was considered acceptable if the standardized mean difference was > +/− 0.1. If extreme weights were detected, 99th percentile trimmed analysis would also be performed. The outcome was calculated as an adjusted odds ratio using a generalized estimating equation to account for correlated data. Risk ratios of mortality outcomes were estimated by multivariable logistic regression adjusting for age, gender, and CCI. A priori subgroup analyses of patients without do-not-intubate (DNI) status were also performed.Descriptive statistics are presented as frequency (%) for categorial variables, mean (SD) for continuous variables with parametric distribution, and median  for continuous variables with nonparametric distribution. Continuous variables were assessed for parametric distribution by histograms, Q-Q plots, and the Shapiro Wilk test. Risk ratios of respiratory intervention use during the ED visit between the pre-COVID and COVID periods were estimated by inverse probability of treatment (defined as exposure of COVID period) weighted (IPTW) analysis using a propensity score. This propensity score was calculated using a multivariable logistic regression with time period as the dependent variable with age, gender, Charlson comorbidity index (CCI), initial SpO2/FiO2 ratio was stratified by age group and diagnosis as well as by age and respiratory intervention to compare initial severity between patients in the pre-COVID and COVID periods. Endotracheal intubation and mortality rates in the subgroup of patients stratified by diagnosis were also compared between the pre-COVID and COVID periods. We performed analyses using SPSS 18.0  and R .We also performed secondary analyses. Initial severity based on respiratory rate and SpO2/FiO2 ratio in the COVID period. A similar proportion of patients in both periods had primary diagnoses of pneumonia, obstructive lung conditions, and conditions with pulmonary edema.During the COVID period, the overall ED visit rate was lower compared to the pre-COVID period, as well as the previous 10 months  compared to the pre-COVID period (55.6%), and almost half of the prescribed bronchodilators in the COVID period were delivered by metered-dose inhaler (MDI) rather than standard nebulizer . They also had a longer median duration (hour) of intubation . In addition, we found that elderly patients aged more than 70 years who were treated with HFNC in the COVID period were initially more severe, with a higher mean respiratory rate. NIV was also initiated at a lower mean SpO2/FiO2 ratio in this elderly subgroup (Table We analyzed and compared the severity at ED presentation based on the patients’ primary group of diagnoses. In the COVID period, patients with obstructive lung conditions had an overall higher mean respiratory rate but a comparable mean SpOod Table . We furtup Table .Table 32/FiO2 ratio strata and number of comorbidities (Fig. 2/FiO2 ratio strata were similar among patients in the COVID and pre-COVID period, regardless of number of co-morbidities. With similar number of comorbidities, however, a higher proportion of patients in the pre-COVID died compared to the COVID period.We further evaluated and compared mortality of patients with pneumonia, the majority of patients who died, based on age and SpOies Fig. . The dis2 and higher initial severity based on respiratory rate and SpO2/FiO2 ratio across different diagnoses in the present study. This could have been because of a generally higher severity of patients at presentation in the COVID period, despite themselves being younger with less debilitating co-morbidities.The COVID-19 outbreak has affected not only infected patients but also other non-COVID patients. There have been multiple reports of a decrease in admissions due to both critical and non-emergency conditions –15, 18. In the present study, the rate of non-invasive airway management and nebulization, which could have generated droplets/aerosols, in non-COVID infected patients has plummeted during the COVID-19 pandemic. Furthermore, ED physicians initiated non-invasive ventilatory support when patients were more severe than usual in the elderly. This could have been because of a higher threshold to initiate these interventions due to the fear of disease transmission to healthcare providers. Our results thus confirm that the avoidance of droplet/aerosol-generating procedures also occur in non-COVID patients. Although we healthcare providers typically make our treatment decisions based on the patients’ risk and benefit, our judgement may have been altered by our own risk during an infectious disease pandemic. The situation occurred in our non-COVID ED because even though the patients had passed initial screening, ED personnel were still concerned by the uncertainty of the patients’ COVID status, as 5.7% were under-triaged and considered “at-risk” during their ED visits.Although the failure rate of NIV and HFNC was lower in the COVID period, this could have been due to a more appropriate patient selection and clinical judgement before treatment initiation in the COVID period. Previous evidences have shown high failure rates in places where NIV was not appropriately selected and initiated , 20. In This change of practice has affected patients across multiple diagnoses. More patients with pulmonary edema were intubated in the COVID compared to the pre-COVID period, with a trend towards a statistical significance. These results might have been partly explained by the lower rate of treatment with NIV and HFNC in the COVID period since the benefits of both non-invasive airway measures have been extensively established in this condition , 22. AltOne clear benefit of an infectious disease pandemic found in this study was a lower rate of infectious complications, which could have led to a lower mortality rate, as seen in the present study. This lower nosocomial infection rate could have been because of the strict precaution and infectious control protocol during the pandemic. It might also have been because patients in the COVID period were younger with less severe debilitating co-morbidities, which may have also led to a lower mortality. Another possible reason for the lower mortality in the COVID period was that patients at high risk for mortality could have been treated or died at home or hospitals nearer to their residence rather than coming to a tertiary hospital in the city center during an infectious pandemic.There were several limitations to the present study. Firstly, it was a single-centered study, which may have limited its generalizability to other settings. Secondly, there were no standard criteria for when to initiate each type of airway equipment, and ED attending physicians usually make their decisions, both to initiate and to escalate to a more invasive procedure, on a case-by-case basis. However, the attending physicians working on shifts during both study periods were the same group of physicians. Consequently, different airway management between the two study periods was not confounded by different physicians. Thirdly, only a single control period from the previous year was recruited in the pre-COVID-19 era, which may not be sufficient to represent the pre-COVID population. Also, the sample size of some subgroups of patients was relatively small, limiting the validity of some of the study findings. Further larger multicentered studies with predefined respiratory protocols should be conducted.In the present study, we found that the rate of non-invasive respiratory support was lower during the COVID-19 outbreak. Although not directly affecting the overall intubation rate, some groups of patients may have lost benefits from this change of practice, particularly those with pulmonary edema and DNI status, while some patients may have benefited from lower infectious complications. This study underlines another aspect of the COVID-19 pandemic, focusing on the impact that an infectious disease outbreak might have had on patients without the disease. This information can guide physicians and help plan hospital and health policy, should a similar outbreak occur in the future, as access to care, quality of care, and safety of healthcare providers are of similar importance.Additional file 1 Table S1 Propensity score balance by standardized mean difference


Since December 2019, the coronavirus 19 (COVID-19) pandemic continues to spread worldwide and has caused millions of deaths. Although the main manifestation of the disease is acute respiratory distress syndrome, hypercoagulability resulting from hypoxemia leads to venous thromboembolism and arterial thrombosis, which have a fatal prognosis for COVID-19.We report the case of patient admitted to our unit for management of a concomitant limb ischemia, splenic and renal infarcts associated to a COVID-19 infection with favorable outcomes after thrombectomy and therapeutic doses of heparin.Systemic procoagulant manifestations related to a COVID-19 infection is a newly described phenomenon responsible of cardiovascular and neurological disorders. •COVID-19 is a complex and multi-phased disease. The occurrence of arterial and venous thromboembolic events has been described throughout literature and linked to poor prognosis.•Our case report demonstrates the likelihood of developing an arterial thrombosis even in the absence of cardiovascular comorbidities.•Thromboembolic involvement during COVID-19 should be considered, and a deeper understanding of the pathophysiology is needed for optimal and management. Otherwise, our patient reported an occasional cough over the last 4 days.The clinical examination finds a conscious patient hemodynamically stable with low oxygen saturation of 70 % on room air, physical examination revealed paleness of the right leg as well as pain and pulselessness suggesting acute limb ischemia.A nasopharyngeal swab was performed to look for the presence of SARS-CoV-2 and a total-body CT angiography was performed to investigate the cause of the acute abdominal pain and limb ischemia.The CT angiography showed a total occlusion of the right common femoral artery , splenicThe patient admitted to the operating room, he received an 80 units/kg IV induction dose of Unfractionated Heparin, and a Fogarty thrombectomy was performed. Over 30 cm sized thrombus was extracted  The patiThe patient was started on a continuous IV infusion of 18 units/kg/hr of unfractionated heparin and the evolution was favorable with complete recovery of the lower limb and the disappearance of his abdominal pain.Additional treatments included rehydration using Saline, Paracetamol, Azythromycine, Dexamethasone, Vitamins C and D, Zinc and optimization of electrolyte balance.He was gradually weaned from oxygen and was discharged from ICU after 10 days.3Today, COVID-19 is a pathology with a high mortality rate and the main cause remains respiratory failure due to coronavirus-induced lung damage, which is responsible for severe ARDS .However, cardiovascular complications can be significantly disrupted and exacerbated by SARS CoV-2 infection, even in patients without associated co-morbidities .The incidence of thromboembolic events in SARS-CoV-2 infection is common and has been widely reported in the literature ,2,5 inclIn a prospective Dutch cohort of 184 patients with COVID-19 pneumonia, the occurrence of an arterial or venous thromboembolic event was observed in 31 % of cases with predominantly venous manifestations  and only 3 arterial events (ischemic stroke) [Various physiopathological mechanisms resulting in the COVID-19 related hypercoagulability state were identified, mostly immunological and inflammatory components such as the complement cascade activation , the macAll these mechanisms, mainly target the vascular endothelium resulting in a disturbance of its anticoagulant, antithrombotic and profibrinolytic properties which are prominent, and over stimulate its procoagulant potential which is physiologically minor. This procoagulant potential involves an abnormal activity of tissue factor which amplifies significantly factors VII and X enzymatic capacity resulting in thrombin generation and clot formation. The endothelial cell also releases pre-stored von Willebrand factor (vWf) enabling platelet aggregation and thrombus assembly. In parallel the dysfunctional endothelium manufactures plasminogen activator inhibitor-1 (PAI-1) which antagonizes the endogenous fibrinolytic properties of the endothelial surface .This whole physiopathological pattern falls into the hypothesis of COVID-19 being an endothelial disease , highligThe treatment and prevention of thrombotic events in COVID- 19 are still controversial. The use of prophylactic-dose low-molecular-weight heparin (LMWH) has been shown to be associated with lower mortality in patients with severe COVID-19 or D-dimer levels more than 6 times the upper normal limit [The Guidelines recommend the use of standard prophylactic dose anticoagulants in all hospitalized patients with COVID-19 in the absence of a clear contraindication. The routine use of intermediate- or full-therapeutic doses of anticoagulation is not strongly supported by current guidelines .Our patient had simultaneous acute limb ischemia, kidney and spleen infarction. Although the patient had no respiratory symptoms on admission, a chest CT scan revealed typical COVID-19 pneumonia.The absence of lower limb pain in the presence of acute limb ischemia is dependent on the simultaneous presence of intense abdominal pain, due to a spleen infarction, which distracts attention from leg pain.Systemic inflammation and a hyper coagulant state due to SARS CoV-2 infection with severe pneumonia are probably responsible for the arterial thrombosis that occurred in our patient , even inIt is also likely that the onset of limb ischemia, amplified the coagulation cascade leading to thrombosis.4COVID-19 is a complex disease, involving viral, inflammatory and thrombotic phases. It generally affects the pulmonary system with respiratory distress syndrome, but a systemic procoagulant state may develop, leading to atypical presentations such as cardiovascular and neurological disorders.Therefor a better understanding of the pathophysiology is essential for optimal and rapid patient management.Informed consent was obtained from the patient prior to submitting this case.This work has been reported in line with the SCARE 2020 Guidelines .The authors state that they have no conflicts of interest for this report.The ethical committee approval was not required given the article type (case report).This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.Written informed consent was obtained from the patient for publication of this case report and accompanying images.Berrichi Samia: Study conception, Data collection; data analysis; writing review & editing. Bouayed Zakaria: Study conception, data analysis. Berrajaa Sara: Contributor. Bkiyar Houssam: Supervision and data validation. Housni Brahim: supervision and data validation.As this manuscript was a case report with no new medical device nor surgical techniques, not prior registration is required.BERRICHI SAMIA.BOUAYED M ZAKARIA.


The Omicron variant of concern (VOC), first detected in Italy at the end of November 2021, has since spread rapidly, despite high vaccine coverage in the Italian population, especially in healthcare workers (HCWs). This study describes an outbreak of SARS-CoV-2 Omicron infection in 15 booster-vaccinated HCWs. On 16 December 2021, two HCWs working in the same ward were infected with SARS-CoV-2. The Omicron VOC was suspected due to S gene target failure on molecular testing. Further investigation revealed that 15 (65%) of 23 HCWs attending a social gathering on 13 December were infected with Omicron, as shown by whole-genome sequencing, with a phylogenetic tree suggesting a common source of exposure. Five of these HCWs experienced mild symptoms. A patient with multiple chronic conditions hospitalized in the same ward was also infected by one of the HCWs involved in the outbreak. Despite being booster vaccinated, this patient required ICU treatment. Ten subjects achieved negativity in 10–19 days. The outbreak in booster-vaccinated subjects confirms the high transmissibility and immune evasion of the Omicron VOC. More stringent non-pharmaceutical interventions, administration of booster doses, and genomic surveillance are crucial long-term strategies to mitigate the consequences of the spread of the Omicron VOC. On 14 November 2021, a new variant of SARS-CoV-2 virus was identified in an immunocompromised patient in South Africa . The newThe immunity conferred by vaccination is likely to be lower in subjects infected with the Omicron than with the Delta VOC . MoreoveAs of 20 December 2021, Omicron has been detected in most countries, and the number of COVID-19 cases caused by this variant is increasing exponentially worldwide . The oveRegarding the disease, more data are needed to understand the severity profile of the Omicron VOC. Preliminary data on characteristics and outcomes of subjects involved in the fourth wave in South Africa showed that, compared with the previous SARS-CoV-2 waves, fewer patients presenting to the Emergency Department were admitted to the hospital and that, among hospitalized patients, the degree of disease severity and mortality rates were lower . These fIn Italy, the first cases of Omicron VOC were identified at the end of November 2021 . FollowiThe present study describes an autochthonous outbreak of SARS-CoV-2 Omicron VOC in healthcare workers (HCWs) vaccinated with three doses of BNT162b2 mRNA COVID-19 vaccine (booster-vaccinated) in the Apulia region of Southern Italy, which occurred after they attended a social gathering in early December 2021. The outbreak was investigated in depth to increase knowledge about the Omicron variant and to develop better targeted infection control measures.https://clades.nextstrain.org/, accessed on 14 February 2021). Strains closely related to those identified in the outbreak were evaluated by analysis of strains in the GISAID database [www.gisaid.org, accessed on 11 January 2021). Clade information was described using the GISAID and Nextstrain nomenclature, and lineage information was described using the Pangolin nomenclature. The sequences of 10 strains detected during the outbreak were deposited in the GenBank database .On 16 December 2021, following molecular tests that showed positivity for SARS-CoV-2 and SGTF, two HCWs working in the same ward were suspected of being infected with SARS-CoV-2 Omicron VOC. An outbreak investigation to trace other probable cases identified 15 HCWs infected with SARS-CoV-2 Omicron VOC. The clinical presentation of cases was classified according to the National Institute of Health (NIH) clinical staging of COVID-19 disease . Nasophadatabase  . All symptomatic subjects recovered after few days. The patient who required intensive care support recovered completely and was discharged on 4 January 2022. The outbreak described in this study underlines some important issues about the effectiveness of vaccines against the transmission of the Omicron VOC and the need to strengthen non-pharmaceutical interventions (NPIs) to contain its spread, especially in HCWs. These findings may also provide insight into the natural history of SARS-CoV-2 Omicron VOC infections. The cluster described here could not be linked to previous cases of Omicron VOC infection. Thus, despite travel restrictions and extended contact tracing efforts , the OmiThe subjects involved in the outbreak were all booster-vaccinated. Currently available evidence shows that the efficacy of currently available vaccines is significantly reduced against the Omicron VOC ,24, as wThe attack rate reported for the outbreak in Bari was 65%, similar to that reported for an outbreak caused by the Omicron VOC in early December 2021 in booster-vaccinated HCWs in the Faroe Islands , but lowThe incubation period for infections caused by the Omicron VOC was shorter than that for infections caused by the Delta VOC . MoreoveThe most worrisome aspect of the outbreak described in this study was the transmission of the SARS-CoV-2 Omicron VOC from an HCW involved in the outbreak to a patient hospitalized for diseases other than COVID-19. This finding confirms that vaccinated subjects can be infectious and can transmit SARS-CoV-2 virus despite the booster dose. Non-COVID-19 hospital areas should therefore be considered high-risk environments for SARS-CoV-2 outbreaks. The virus may originate from patients or HCWs who interact with others in environments inside and outside the hospital, and may especially affect vulnerable patients who are hospitalized . WearingAs suggested by the European Centre for Disease Prevention and Control , NPIs thThe Omicron VOC is characterized by very high transmissibility and a high capacity to cause outbreaks, even among booster-vaccinated subjects such as HCWs. This VOC can also cause severe disease in subjects with comorbidities. Timely reporting of the spread of this new variant is crucial, as is the importance of avoiding nonessential travel and social activities, particularly during the period of the year characterized by Christmas gatherings. Delays in implementing interventions to contain the spread of SARS-CoV-2 could result in rapid dissemination of the virus, stress on healthcare systems, and deaths . Even ifFinally, the rapid increase of cases caused by superspreading events has challenged the mitigation measures that have been introduced to contain the further spread of SARS-CoV-2 . Vaccine


In Uganda, 12% of previously treated TB cases and 1.6% of new cases have MDR-TB and require specialized treatment and care. Adherence is crucial for improving MDR-TB treatment outcomes. There is paucity of information on the extent to which these patients adhere to treatment and what the drivers of non-adherence are.We conducted a cohort study using retrospectively collected routine program data for patients treated for MDR-TB between January 2012 – May 2016 at Mulago Hospital. We extracted anonymized data on non-adherence (missing 10% or more of DOT), socio-economic, demographic, and treatment characteristics of the patients. All participants were sensitive to MDR-TB drugs after second line Drug Susceptible Testing (DST) at entry into the study. Factors associated with non-adherence to MDR-TB treatment were determined using generalized linear models for the binomial family with log link and robust standard errors. We considered a p- value less than 0.05 as statistically significant.The records of 227 MDR- TB patients met the inclusion criteria, 39.4% of whom were female, 32.6% aged between 25 – 34 years, and 54.6% living with HIV/AIDS. About 11.9% of the patients were non-adherent. The main driver for non-adherence was history of previous DR-TB treatment; previously treated DR-TB patients were 3.46  times more likely to be non-adherent.One in 10 MDR-TB patients treated at Mulago hospital is non-adherent to treatment. History of previous DRTB treatment was significantly associated with non-adherence in this study. MDR-TB program should strengthen adherence counselling, strengthen DST surveillance, and close monitoring for previously treated DR-TB patients. Multi Drug Resistant / Rifampicin Resistant TB (MDRTB/RR-TB) is one of the global public health problems. It remains a priority for the World health Organization through the End Tby 2030 strategyDaily Injectable aminoglycosides form the backbone of MDR-TB treatment regimens and their prolonged use is associated with better treatment outcomesDespite TB being a curable disease, ≥ 90% patient adherence to anti-TB treatment is required to achieve itAn understanding of the factors associated with non-adherence to MDR-TB treatment is imperative in generating evidence that can inform and guide development of programs and initiatives to improve adherence patterns, MDR-TB treatment outcomes and ending TB. In this paper, we present the prevalence and factors associated with non-adherence to MDR-TB treatment using secondary data from Mulago National Referral Hospital the largest MDR-TB treatment center in Uganda.We conducted a cohort study using secondary data from patients' records (treatment files) at Mulago National Referral Hospital (MNRH) TB center. The use of secondary data allows for the analysis of large sets of routinely collected data at low cost MNRH TB center registers between 50–70 new MDRTB patients per year under the Uganda National Tuberculosis and Leprosy Program (NTLP). It is one of the 13 MDR-TB treatment centers across the country where new MDR-TB patients are referred and treated. T/span>rained health workers and treatment supporters carry out Directly Observed Therapy (DOT) for the patients. The health worker maintains a drug record chart for each administered dose of the MDRTB drugs and provides adherence counselling to each patient. Clinical data are manually collected using the patients' treatment card and entered into three National registers; the presumptive TB register, the laboratory register, and the treatment register. It includes; the name of the patient, age, sex, HIV status, marital status, date of clinic attendance, date of sputum submission, results of sputum smear, results of the Gene Xpert test, TB diagnosis, type of treatment and treatment regimen. At a periodic basis, all the data from the 13 MDR-TB treatment centers is entered into the NTLP's electronic database hosted at the Ministry of health, while the physical records (back-up hard copies) including MDRTB patient's cards (after completion of treatment) are stored at the MNRH TB center. Before commencing of MDR-TB treatment, all patients are evaluated for sensitivity to current MDR-TB drugs after second line Drug Susceptibility Testing (DST) to rule out X-DR TB. However, due to personnel and logistical challenges, this electronic database was not current at the time of the study. We, therefore, only considered patient files from MNRH TB center for data abstraction in order to meet the objectives of our study.We estimated the sample size using the Kish Leslie (1965) formula by assuming the prevalence of non-adherence among DR-TB patients 32.7% as reported in South AfricaWe considered the records of all MDR-TB patients treated at MNRH TB unit between January 2012 and May 2016. We extracted participants' data from the start to end of their MDR-TB treatment for the period under review. The length of the records included ranged between 18- 24 months (varied according to the prescribed duration of treatment by the attending physician) of treatment. We excluded records of the patients with missing data on key independent variables like age, sex, treatment category, HIV status, previous TB treatment from the final analysis.After obtaining ethical clearance from the IRB and permission from the NTLP, the principal investigator accessed the electronic MDR-TB register, abstracted data from the charts where possible, and set up the dataset as informed by the variables under consideration in this study.A data extraction questionnaire was pretested on less than 5 % of the sample size and adjustments made before conducting final data collection. Fifteen records were ineligible because of missing data. The excluded and included records had negligible difference in non-adherence levels.In preparing for data analysis, we merged all data to create a final data set. Database access was locked on the 15th September 2016 permitting no further edits. The raw data set was imported into STATA version 14.0 for analysis . We computed means, medians, standard deviations and interquartile ranges for continuous variables; proportions and percentages for categorical variables. Non- adherence was defined as a patient missing ≥ 10 % of the total prescribed dose over the 18–24 months of treatmentWe evaluated the factors associated with non-adherence to MDR-TB treatment using generalized linear models for the binomial family with a log link and robust standard errors. Factors with p value less than 0.25 and potential confounding variables were considered for multivariable regression analysis. We formed two-way interaction terms between the variables to assess for interaction. The crude and adjusted prevalence ratios and their 95 % confidence intervals were calculated. A p-value of less than 0.05 was considered statistically significant.The School of Biomedical Sciences – Higher Degrees Research and Ethics Committee (SBS-HDREC #549), approved the study protocol and a waiver of informed consent while Mulago Hospital Research Ethics Committee (MHREC) granted administrative approval. MNRH and NTLP granted final permission to the study team to access the patient records the before final data extraction. We maintained confidentiality of the patients' data by using anonymized extracted data for further analysis.Eighty-nine (34.9%) of the participants were female, seventy-four (32.6%) were aged between 25 and 34 years of age and 80/227 (38.5%) were married. About 65.1% of the participants were employed and one hundred eighteen (52%) lived in rural settings.One hundred twenty-four (54.6%) of the participants were co-infected with HIV while one hundred fifty-one (66.5%) had had TB treatment before .Of the 227 patients with MDR-TB, 27  were non-adherent to treatment .At regression analysis, a previous history of DR-TB treatment and the treatment registration group were significantly associated with non-adherence to treatment  and 4.At multivariable regression analysis, patients who were treated previously for DR-TB were 3.46 times  as likely to be non-adherent compared to those that had not been treated for DR-TB before .This study set out with the aim of determining to what extent MDR-TB patients previously treated at MNRH under the national TB program in Uganda adhered to treatment. The results of the study showed that 11.9% of the patients are non-adherent to their treatment. This finding is lower than those of other studies done in different settings that reported non-adherence levels between 18–33%Participants who had been treated for DR-TB previously were 3.46 times more likely to be non-adherent to MDR-TB treatment. Previous studies have described the role of prior susceptible TB treatment in the development of MDR-TBThe strength of the current study is the utilization of programmatic data in assessing non-adherence and associated factors. Unlike in clinical trials where the characteristics of those participants are highly selective, routinely collected data gives us the true picture of what is happening in the real world. The inferences made from this data can be used to develop interventions that are relevant to specific contexts and environments in which the patients live on a daily basis. When further developed and analyzed from the patient's perspective it can be used to inform policy makers on how quality of care can be improved and how patient education tools can be developed to fit patient needs.This study was unable to assess the impact of factors that are not routinely collected within the National Tuberculosis treatment program. Factors such as the patient's level of education, distance from hospital, smoking and lack of awareness of the treatment duration that have been found to significantly impact on adherence in other studies could not be assessed in this study because data was not available.1 in 10 MDR-TB patients treated at the Mulago National Referral Hospital in Kampala, Uganda is non-adherent to treatment. History of previous DR-TB treatment was associated with non-adherence to TB treatment in our cohort.The National MDR-TB treatment program should strengthen adherence counselling, provide consistent second line DST during treatment and close monitoring for patients that have been previously treated for MDR-TB. Secondly, because such patients have been exposed to sub-optimal doses of the treatment regimen, they are very likely to develop Extensively Drug Resistant Tuberculosis, which is more lethal. Therefore, to protect the public, TB treatment programs should focus on promoting adherence to MDR-TB treatment and ensuring strict management for those that have been treated before.The TB enablers program that is currently being used to defray catastrophic costs for MDR-TB patients should also incorporate aspects of adherence, patients that adhere to treatment should be given extra incentives in order to encourage the non-adherent ones to adhere more.The National MDR-TB program and the Ministry of health should take up the shorter treatment regimens that have been found to be equally effective. Though there is currently no evidence that the shorter treatment regimens will improve on adherence, reducing the treatment episode length will give patients more confidence that they can complete treatment, reduce the cost associated with accessing treatment and possibly the side effects associated with prolonged treatment periods.