Social media group support for antidepressant deprescribing: a mixed-methods survey of patient experiences.

Background Antidepressant use has continually increased in recent decades and although they are an effective treatment for moderate-to-severe depression, when there is no longer a clinical benefit, deprescribing should occur. Currently, routine deprescribing is not part of clinical practice and research shows that there has been an increase in antidepressant users seeking informal support online. This small scoping exercise used a mixed-methods online survey to investigate the motives antidepressant users have for joining social media deprescribing support groups, and what elements of the groups are most valuable to them. Methods Thirty members of two antidepressant deprescribing Facebook groups completed an online survey with quantitative and open-text response questions to determine participant characteristics and motivation for group membership. Quantitative data were analysed using descriptive statistics, and open-text responses were analysed thematically through NVivo. Results Two overarching themes were evident: first, clinician expertise , where participants repeatedly reported a perceived lack of skills around deprescribing by their clinician, not being included in shared decision-making about their treatment, and symptoms of withdrawal during deprescribing going unaddressed. Motivated by the lack of clinical support, peer support developed as the second theme. Here, people sought help online where they received education, knowledge sharing and lived experience guidance for tapering. The Facebook groups also provided validation and peer support, which motivated people to continue engaging with the group. Conclusions Antidepressant users who wish to cease their medication are increasingly subscribing to specialised online support groups due to the lack of information and support from clinicians. This study highlights the ongoing need for such support groups. Improved clinician understanding about the complexities of antidepressant deprescribing is needed to enable them to effectively engage in shared decision-making with their patients.

Application of short-term analysis of skin temperature variability in prediction of survival in patients with cirrhosis.

Background: Liver cirrhosis is a complex disorder, involving several different organ systems and physiological network disruption. Various physiological markers have been developed for survival modelling in patients with cirrhosis. Reduction in heart rate variability and skin temperature variability have been shown to predict mortality in cirrhosis, with the potential to aid clinical prognostication. We have recently reported that short-term skin temperature variability analysis can predict survival independently of the severity of liver failure in cirrhosis. However, in previous reports, 24-h skin temperature recordings were used, which are often not feasible in the context of routine clinical practice. The purpose of this study was to determine the shortest length of time from 24-h proximal temperature recordings that can accurately and independently predict 12-month survival post-recording in patients with cirrhosis. Methods: Forty individuals diagnosed with cirrhosis participated in this study and wireless temperature sensors (iButtons) were used to record patients' proximal skin temperature. From 24-h temperature recordings, different length of recordings (30 min, 1, 2, 3 and 6 h) were extracted sequentially for temperature variability analysis using the Extended Poincaré plot to quantify both short-term (SD1) and long-term (SD2) variability. These patients were then subsequently followed for a period of 12 months, during which data was gathered concerning any cases of mortality. Results: Cirrhosis was associated with significantly decreased proximal skin temperature fluctuations among individuals who did not survive, across all durations of daytime temperature recordings lasting 1 hour or more. Survival analysis showcased 1-h daytime proximal skin temperature time-series to be significant predictors of survival in cirrhosis, whereby SD2, was found to be independent to the Model for End-Stage Liver Disease (MELD) score and thus, the extent of disease severity. As expected, longer durations of time-series were also predictors of mortality for the majority of the temperature variability indices. Conclusion: Crucially, this study suggests that 1-h proximal skin temperature recordings are sufficient in length to accurately predict 12-month survival in patients with cirrhosis, independent from current prognostic indicators used in the clinic such as MELD.

The mechanistic and prognostic implications of heart rate variability analysis in patients with cirrhosis.

Chronic liver damage leads to scarring of the liver tissue and ultimately a systemic illness known as cirrhosis. Patients with cirrhosis exhibit multi-organ dysfunction and high mortality. Reduced heart rate variability (HRV) is a hallmark of cirrhosis, reflecting a state of defective cardiovascular control and physiological network disruption. Several lines of evidence have revealed that decreased HRV holds prognostic information and can predict survival of patients independent of the severity of liver disease. Thus, the aim of this review is to shed light on the mechanistic and prognostic implications of HRV analysis in patients with cirrhosis. Notably, several studies have extensively highlighted the critical role systemic inflammation elicits in conferring the reduction in patients' HRV. It appears that IL-6 is likely to play a central mechanistic role, whereby its levels also correlate with manifestations, such as autonomic neuropathy and hence the partial uncoupling of the cardiac pacemaker from autonomic control. Reduced HRV has also been reported to be highly correlated with the severity of hepatic encephalopathy, potentially through systemic inflammation affecting specific brain regions, involved in both cognitive function and autonomic regulation. In general, the prognostic ability of HRV analysis holds immense potential in improving survival rates for patients with cirrhosis, as it may indeed be added to current prognostic indicators, to ultimately increase the accuracy of selecting the recipient most in need of liver transplantation. However, a network physiology approach in the future is critical to delineate the exact mechanistic basis by which decreased HRV confers poor prognosis.

Effect of deep breathing exercises in healthy smokers: A pilot study.

OBJECTIVE: To determine the effect of deep breathing exercises on lung functions in apparently healthy smokers. METHODS: The pilot study was conducted at Isra University, Islamabad, Pakistan, from May to December, 2017, and comprised apparently healthy smokers aged 20-30 years with a minimum smoking history of 5 years. The participants were divided into control and experimental groups. The control group was not given any treatment, while the experimental group was trained on deep breathing exercise techniques. Baseline data was compared after two weeks of intervention. SPSS 21 was used for data analysis. RESULTS: Of the 30 subjects, there were 15(50%) in each of the two groups. In the experimental group, significant changes were found in vital capacity, inspiratory capacity, tidal volume, expiratory reserve volume, force expiratory volume, and forced vital capacity (p<0.05). Post-intervention, all parameters improved significantly in the experimental group (p<0.05). CONCLUSIONS: Deep breathing exercise techniques were found to be useful in healthy smokers for improving lung functions and delaying the development of chronic obstructive lung complications.

Skin temperature variability is an independent predictor of survival in patients with cirrhosis.

BACKGROUND: Cirrhosis is a disease with multisystem involvement. It has been documented that patients with cirrhosis exhibit abnormal patterns of fluctuation in their body temperature. However, the clinical significance of this phenomenon is not well understood. The aim of this study was to determine if temperature variability analysis can predict survival in patients with cirrhosis. METHODS: Thirty eight inpatients with cirrhosis were enrolled in the study. Wireless temperature sensors were used to record patients' proximal skin temperature for 24 hr. The pattern of proximal temperature fluctuation was assessed using the extended Poincaré plot to measure short-term and long-term proximal temperature variability (PTV). Patients were followed up for 12 months, and information was collected on the occurrence of death/liver transplantation. RESULTS: During the follow-up period, 15 patients (39%) died or underwent transplantation for hepatic decompensation. Basal proximal skin temperature absolute values were comparable in survivors and nonsurvivors. However, nonsurvivors showed a significant reduction in both short-term and long-term HRV indices. Cox regression analysis showed that both short-term and long-term PTV indices could predict survival in these patients. However, only measures of short-term PTV were shown to be independent of the severity of hepatic failure in predicting survival. Finally, the prognostic value of short-term PTV was also independent of heart rate variability, that is, a measure of autonomic dysfunction. CONCLUSION: Changes in the pattern of patients' temperature fluctuations, rather than their absolute values, hold key prognostic information, suggesting that impaired thermoregulation may play an important role in the pathophysiology of cirrhosis.

Corrigendum: The Application of the Extended Poincaré Plot in the Analysis of Physiological Variabilities.

[This corrects the article DOI: 10.3389/fphys.2019.00116.].

The Application of the Extended Poincaré Plot in the Analysis of Physiological Variabilities.

The Poincaré plot is a geometrical technique used to visualize and quantify the correlation between two consecutive data points in a time-series. Since the dynamics of fluctuations in physiological rhythms exhibit long-term correlation and memory, this study aimed to extend the Poincaré plot by calculating the correlation between sequential data points in a time-series, rather than between two consecutive points. By incorporating this so-called lag, we hope to integrate a temporal aspect into quantifying the correlation, to depict whether a physiological system holds prolonged association between events separated by time. In doing so, it attempts to instantaneously characterize the intrinsic behavior of a complex system. We tested this hypothesis on three different physiological time-series: heart rate variability in patients with liver cirrhosis, respiratory rhythm in asthma and body temperature fluctuation in patients with cirrhosis, to evaluate the potential application of the extended Poincaré method in clinical practice. When studying the cardiac inter-beat intervals, the extended Poincaré plot revealed a stronger autocorrelation for patients with decompensated liver cirrhosis compared to less severe cases using Pearson's correlation coefficient. In addition, long-term variability (known as SD2 in the extended Poincaré plot) appeared as an independent prognostic variable. This holds significance by acting as a non-invasive tool to evaluate patients with chronic liver disease and potentially facilitate transplant selection as an adjuvant to traditional criteria. For asthmatics, employing the extended Poincaré plot allowed for a non-invasive tool to differentially diagnose various classifications of respiratory disease. In the respiratory inter-breath interval analysis, the receiver operating characteristic (ROC) curve provided evidence that the extension of the Poincaré plot holds a greater advantage in the classification of asthmatic patients, over the traditional Poincaré plot. Lastly, the analysis of body temperature from patients using the extended Poincaré plot helped identify inpatients from outpatients with cirrhosis. Through these analyses, the extended Poincaré plot provided unique and additional information which could potentially make a difference in clinical practice. Conclusively, the potential use of our work lies in its possible application of predicting mortality for the organ allocation procedure in patients with cirrhosis and non-invasively distinguish between atopic and non-atopic asthma.

Gait Type Analysis Using Dynamic Bayesian Networks.

This paper focuses on gait abnormality type identification-specifically, recognizing antalgic gait. Through experimentation, we demonstrate that detecting an individual's gait type is a viable biometric that can be used along with other common biometrics for applications such as forensics. To classify gait, the gait data is represented by coordinates that reflect the body joint coordinates obtained using a Microsoft Kinect v2 system. Features such as cadence, stride length, and other various joint angles are extracted from the input data. Using approaches such as the dynamic Bayesian network, the obtained features are used to model as well as perform gait type classification. The proposed approach is compared with other classification techniques and experimental results reveal that it is capable of obtaining a 88.68% recognition rate. The results illustrate the potential of using a dynamic Bayesian network for gait abnormality classification.

Atraumatic Splenic Rupture after Myocardial Infarction.

Atraumatic splenic rupture is a rare but potentially life-threatening event. It mostly happens when the spleen is already diseased; however, sometimes it can be drug induced in a previously normal spleen. Although anticoagulation has been attributed to spontaneous splenic rupture quite frequently, the role of dual antiplatelet therapy is underestimated. We report a case of an 80-year-old woman who developed spontaneous splenic rupture 4 weeks after starting dual antiplatelet therapy. LEARNING POINTS: Atraumatic or spontaneous splenic rupture can be life threatening.Various drugs, including granulocyte colony-stimulating factors (GCSF) and anticoagulants, can result in atraumatic splenic rupture in a previously normal spleen.Dual antiplatelet therapy can also cause splenic rupture in a previously normal spleen. It can occur as early as a few weeks after initiation of treatment.

Development of a voice activity controlled noise canceller.

In this paper, a variable threshold voice activity detector (VAD) is developed to control the operation of a two-sensor adaptive noise canceller (ANC). The VAD prohibits the reference input of the ANC from containing some strength of actual speech signal during adaptation periods. The novelty of this approach resides in using the residual output from the noise canceller to control the decisions made by the VAD. Thresholds of full-band energy and zero-crossing features are adjusted according to the residual output of the adaptive filter. Performance evaluation of the proposed approach is quoted in terms of signal to noise ratio improvements as well mean square error (MSE) convergence of the ANC. The new approach showed an improved noise cancellation performance when tested under several types of environmental noise. Furthermore, the computational power of the adaptive process is reduced since the output of the adaptive filter is efficiently calculated only during non-speech periods.