Hemodynamics and cerebral oxygenation during acute exercise in moderate normobaric hypoxia and with concurrent cognitive task in young healthy males.

The present investigation aimed to study the cardiovascular responses and the cerebral oxygenation (Cox) during exercise in acute hypoxia (AH) and with contemporary mental stress. Fifteen physically active, healthy males (age 29.0 ± 5.9 years) completed a cardiopulmonary test on a cycle ergometer to determine the workload at their gas exchange threshold (GET). On a separate day, participants performed two randomly assigned exercise tests pedalling for six minutes at a workload corresponding to 80% of the GET: 1) during normoxia (NORMO), and 2) during acute, normobaric hypoxia at 13.5% inspired oxygen (HYPO). During the last three minutes of the exercise, they also performed a mental task (MT). Hemodynamics were assessed with impedance cardiography, and peripheral arterial oxygen saturation and Cox were continuously measured by near infrared spectroscopy. The main results were that both in NORMO and HYPO conditions, the MT caused a significant increase in the heart rate and ventricular filling rate. Moreover, MT significantly reduced (74.8 ± 5.5 vs. 62.0 ± 5.2 A.U.) COX while the Reaction Time (RT) increased (813.3 ± 110.2 vs. 868.2 ± 118.1 ms ) during the HYPO test without affecting the correctness of the answers. We conclude that in young, healthy males, adding a mental task during mild intensity exercise in both normoxia and acute moderate (normobaric) hypoxia induces a similar hemodynamic response. However, mental task and exercise in HYPO causes a decrease in COX and an impairment in RT.

Analyzing quality of life among people with opioid use disorder from the National Institute on Drug Abuse Data Share initiative: implications for decision making.

PURPOSE: We aimed to estimate health state utility values (HSUVs) for the key health states found in opioid use disorder (OUD) cost-effectiveness models in the published literature. METHODS: Data obtained from six trials representing 1,777 individuals with OUD. We implemented mapping algorithms to harmonize data from different measures of quality of life (the SF-12 Versions 1 and 2 and the EQ-5D-3 L). We performed a regression analysis to quantify the relationship between HSUVs and the following variables: days of extra-medical opioid use in the past 30 days, injecting behaviors, treatment with medications for OUD, HIV status, and age. A secondary analysis explored the impact of opioid withdrawal symptoms. RESULTS: There were statistically significant reductions in HSUVs associated with extra-medical opioid use (-0.002 (95% CI [-0.003,-0.0001]) to -0.003 (95% CI [-0.005,-0.002]) per additional day of heroin or other opiate use, respectively), drug injecting compared to not injecting (-0.043 (95% CI [-0.079,-0.006])), HIV-positive diagnosis compared to no diagnosis (-0.074 (95% CI [-0.143,-0.005])), and age (-0.001 per year (95% CI [-0.003,-0.0002])). Parameters associated with medications for OUD treatment were not statistically significant after controlling for extra-medical opioid use (0.0131 (95% CI [-0.0479,0.0769])), in line with prior studies. The secondary analysis revealed that withdrawal symptoms are a fundamental driver of HSUVs, with predictions of 0.817 (95% CI [0.768, 0.858]), 0.705 (95% CI [0.607, 0.786]), and 0.367 (95% CI [0.180, 0.575]) for moderate, severe, and worst level of symptoms, respectively. CONCLUSION: We observed HSUVs for OUD that were higher than those from previous studies that had been conducted without input from people living with the condition.

Thus far, health-related quality of life estimates for patients with opioid use disorder in the United States are limited, and importantly, they were not generated from studies among people living with the condition. This study extracted data from six clinical trials providing data among 1,777 people with opioid use disorder, made publicly available by the National Institutes of Health, to produce estimates of health-related quality of life. Our study found higher health-related quality of life estimates as compared to previous studies, modest impact of medications for opioid use disorder and strong impact of withdrawal symptoms on this outcome. These higher values among people with opioid use disorder might reflect the very negative perception of this condition among members of the general population (among whom these estimates have been generated previously). However, these relatively high estimates could also reflect an adaptation to the condition or a lack of awareness of associated-health damage in the context of dependence. The low number of observations providing data on medications for opioid use disorder led to high uncertainty around related estimates of health-related quality of life, but our findings could also reflect real experiences by patients in the absence of the positive effects of non-medication opioids, which deserve more attention in clinical practice. Our study suggests that systematically measuring withdrawal symptoms and representing these in health economic models might provide a more accurate representation of health-related quality of life among people with opioid use disorder and therefore of the impact and cost-effectiveness of interventions.

Machine Learning Methods to Estimate Individualized Treatment Effects for Use in Health Technology Assessment.

BACKGROUND: Recent developments in causal inference and machine learning (ML) allow for the estimation of individualized treatment effects (ITEs), which reveal whether treatment effectiveness varies according to patients' observed covariates. ITEs can be used to stratify health policy decisions according to individual characteristics and potentially achieve greater population health. Little is known about the appropriateness of available ML methods for use in health technology assessment. METHODS: In this scoping review, we evaluate ML methods available for estimating ITEs, aiming to help practitioners assess their suitability in health technology assessment. We present a taxonomy of ML approaches, categorized by key challenges in health technology assessment using observational data, including handling time-varying confounding and time-to event data and quantifying uncertainty. RESULTS: We found a wide range of algorithms for simpler settings with baseline confounding and continuous or binary outcomes. Not many ML algorithms can handle time-varying or unobserved confounding, and at the time of writing, no ML algorithm was capable of estimating ITEs for time-to-event outcomes while accounting for time-varying confounding. Many of the ML algorithms that estimate ITEs in longitudinal settings do not formally quantify uncertainty around the point estimates. LIMITATIONS: This scoping review may not cover all relevant ML methods and algorithms as they are continuously evolving. CONCLUSIONS: Existing ML methods available for ITE estimation are limited in handling important challenges posed by observational data when used for cost-effectiveness analysis, such as time-to-event outcomes, time-varying and hidden confounding, or the need to estimate sampling uncertainty around the estimates. IMPLICATIONS: ML methods are promising but need further development before they can be used to estimate ITEs for health technology assessments. HIGHLIGHTS: Estimating individualized treatment effects (ITEs) using observational data and machine learning (ML) can support personalized treatment advice and help deliver more customized information on the effectiveness and cost-effectiveness of health technologies.ML methods for ITE estimation are mostly designed for handling confounding at baseline but not time-varying or unobserved confounding. The few models that account for time-varying confounding are designed for continuous or binary outcomes, not time-to-event outcomes.Not all ML methods for estimating ITEs can quantify the uncertainty of their predictions.Future work on developing ML that addresses the concerns summarized in this review is needed before these methods can be widely used in clinical and health technology assessment-like decision making.

Clinical and cost-effectiveness of individualised (early) patient-directed rehabilitation versus standard rehabilitation after surgical repair of the rotator cuff of the shoulder: protocol for a multicentre, randomised controlled trial with integrated Quintet Recruitment Intervention (RaCeR 2).

INTRODUCTION: Despite the high number of operations and surgical advancement, rehabilitation after rotator cuff repair has not progressed for over 20 years. The traditional cautious approach might be contributing to suboptimal outcomes. Our aim is to assess whether individualised (early) patient-directed rehabilitation results in less shoulder pain and disability at 12 weeks after surgical repair of full-thickness tears of the rotator cuff compared with current standard (delayed) rehabilitation. METHODS AND ANALYSIS: The rehabilitation after rotator cuff repair (RaCeR 2) study is a pragmatic multicentre, open-label, randomised controlled trial with internal pilot phase. It has a parallel group design with 1:1 allocation ratio, full health economic evaluation and quintet recruitment intervention. Adults awaiting arthroscopic surgical repair of a full-thickness tear are eligible to participate. On completion of surgery, 638 participants will be randomised. The intervention (individualised early patient-directed rehabilitation) includes advice to the patient to remove their sling as soon as they feel able, gradually begin using their arm as they feel able and a specific exercise programme. Sling removal and movement is progressed by the patient over time according to agreed goals and within their own pain and tolerance. The comparator (standard rehabilitation) includes advice to the patient to wear the sling for at least 4 weeks and only to remove while eating, washing, dressing or performing specific exercises. Progression is according to specific timeframes rather than as the patient feels able. The primary outcome measure is the Shoulder Pain and Disability Index total score at 12-week postrandomisation. The trial timeline is 56 months in total, from September 2022. TRIAL REGISTRATION NUMBER: ISRCTN11499185.

Modelling COVID-19 Vaccination in the UK: Impact of the Autumn 2022 and Spring 2023 Booster Campaigns.

INTRODUCTION: The delivery of COVID-19 vaccines was successful in reducing hospitalizations and mortality. However, emergence of the Omicron variant resulted in increased virus transmissibility. Consequently, booster vaccination programs were initiated to decrease the risk of severe disease and death among vulnerable members of the population. This study aimed to estimate the effects of the booster program and alternative vaccination strategies on morbidity and mortality due to COVID-19 in the UK. METHOD: A Susceptible-Exposed-Infectious-Recovered (SEIR) model was used to assess the impact of several vaccination strategies on severe outcomes associated with COVID-19, including hospitalizations, mortality, National Health Service (NHS) capacity quantified by hospital general ward and intensive care unit (ICU) bed days, and patient productivity. The model accounted for age-, risk- and immunity-based stratification of the UK population. Outcomes were evaluated over a 48-week time horizon from September 2022 to August 2023 considering the actual UK autumn 2022/spring 2023 booster campaigns and six counterfactual strategies. RESULTS: The model estimated that the autumn 2022/spring 2023 booster campaign resulted in a reduction of 18,921 hospitalizations and 1463 deaths, compared with a no booster scenario. Utilization of hospital bed days due to COVID-19 decreased after the autumn 2022/spring 2023 booster campaign. Expanding the booster eligibility criteria and improving uptake improved all outcomes, including averting twice as many ICU admissions, preventing more than 20% additional deaths, and a sevenfold reduction in long COVID, compared with the autumn 2022/spring 2023 booster campaign. The number of productive days lost was reduced by fivefold indicating that vaccinating a wider population has a beneficial impact on the morbidities associated with COVID-19. CONCLUSION: Our modelling demonstrates that the autumn 2022/spring 2023 booster campaign reduced COVID-19-associated morbidity and mortality. Booster campaigns with alternative eligibility criteria warrant consideration in the UK, given their potential to further reduce morbidity and mortality as future variants emerge.

Combining randomized and non-randomized data to predict heterogeneous effects of competing treatments.

Some patients benefit from a treatment while others may do so less or do not benefit at all. We have previously developed a two-stage network meta-regression prediction model that synthesized randomized trials and evaluates how treatment effects vary across patient characteristics. In this article, we extended this model to combine different sources of types in different formats: aggregate data (AD) and individual participant data (IPD) from randomized and non-randomized evidence. In the first stage, a prognostic model is developed to predict the baseline risk of the outcome using a large cohort study. In the second stage, we recalibrated this prognostic model to improve our predictions for patients enrolled in randomized trials. In the third stage, we used the baseline risk as effect modifier in a network meta-regression model combining AD, IPD randomized clinical trial to estimate heterogeneous treatment effects. We illustrated the approach in the re-analysis of a network of studies comparing three drugs for relapsing-remitting multiple sclerosis. Several patient characteristics influence the baseline risk of relapse, which in turn modifies the effect of the drugs. The proposed model makes personalized predictions for health outcomes under several treatment options and encompasses all relevant randomized and non-randomized evidence.

Applying Team Science to Collaborative Digital Health Research: Learnings from the Wearable Clinic.

Collaboration across disciplinary boundaries is vital to address the complex challenges and opportunities in Digital Health. We present findings and experiences of applying the principles of Team Science to a digital health research project called 'The Wearable Clinic'. Challenges faced were a lack of shared understanding of key terminology and concepts, and differences in publication cultures between disciplines. We also encountered more profound discrepancies, relating to definitions of "success" in a research project. We recommend that collaborative digital health research projects select a formal Team Science methodology from the outset.

Flexibility and Strength Effects of Adapted Nordic Walking and Myofascial Exercises Practice in Breast Cancer Survivors and Analysis of Differences.

Breast cancer treatments can elicit negative kinesiological side effects concerning both the posture and functional status of breast cancer survivors. As our body is functionally organized in myofascial meridians, physical exercise practice should favor a whole-body approach rather than a local one. The aim of the study was to investigate and compare the effects of two whole-body disciplines, i.e., adapted Nordic Walking and myofascial exercise, on the flexibility and strength performances in BCS. One hundred and sixty breast cancer survivors were trained three times per week for 12 weeks through adapted Nordic Walking or myofascial exercise. Handgrip, sit and reach, back scratch, and single leg back bridge tests and body composition were assessed at the beginning and completion of the training period. Linear mixed models showed no significant changes in body composition, whereas flexibility (p < 0.001), strength (p < 0.001), and muscle quality index (p = 0.003) changed independently from the treatment. When data modification has been analyzed according to sub-sample membership, no significant differences have been observed. Age, radiation therapy, and chemotherapy seem to have independent effects on several investigated variables. Twelve weeks of adapted myofascial exercise and Nordic Walking led to significant changes in flexibility, strength, and muscle quality in breast cancer survivors, with no apparent superiority of one approach over the other.

Physiological adaptations of active postmenopausal women and matched men to the multi-day Sardinia Selvaggio Blu wild trek: a gender-comparative pilot study.

PURPOSE: To evaluate the effects of wild trekking by examining, in postmenopausal women, the physiological adaptations to an intensive 5-day wild trek and comparing their responses to those displayed by a group of men of comparable age, training status and mountaineering skills. METHODS: Six healthy, active postmenopausal women in their sixth decade of life participated in the study. Six men of comparable age and training status were also enrolled for gender-based comparisons. The participants traversed the Selvaggio Blu wild trek (Sardinia, Italy) completing a total of 56 km, for an overall height differential of 14,301 m. During all 5-day trek, subjects were supervised by two alpine guides. Changes in body composition, cardiorespiratory fitness, and metabolic patterns of energy expenditure were evaluated before and after the intervention. RESULTS: Total energy expenditure during the trek was significantly higher (p = 0.03) in women (12.88 ± 3.37 kcal/h/kg) than men (9.27 ± 0.89 kcal/h/kg). Extracellular (ECW) and intracellular water (ICW) increased significantly following the trek only in women (ECW: - 3.8%; p = 0.01; ICW: + 3.4%; p = 0.01). The same applied to fat-free mass (+ 5.6%; p = 0.006), fat mass (- 20.4%; p = 0.006), skeletal muscle mass (+ 9.5%; p = 0.007), and appendicular muscle mass (+ 7.3%; p = 0.002). Peak VO2/kg (+ 9.4%; p = 0.05) and fat oxidation (at 80 W: + 26.96%; p = 0.04; at 100 W: + 40.95%; p = 0.02; at 120 W: + 83.02%; p = 0.01) were found increased only in women, although no concurrent changes in partial pressure of end-tidal CO2 (PETCO2) was observed. CONCLUSIONS: In postmenopausal women, a 5-day, intensive and physically/technically demanding outdoor trekking activity led to significant and potentially relevant changes in body composition, energy balance and metabolism that are generally attained following quite longer periods of training.

Impact of information technology supported serious leisure gardening on the wellbeing of older adults: The Turntable project.

OBJECTIVE: The study presented in this paper aimed to assess the effect of an Information Technology enabled community gardening program for older adults, developed by an international consortium. METHODS: We have executed a quantitative, pre- and post-test field trial with older adult volunteers to test the proposed programme in two European countries, Italy and Belgium (n=98). We used standardized and ad hoc questionnaires to measure changes in the volunteers' mental and psychological state during the trial. The statistical data analysis sought for differences in the pre- and post-test values of the key scores related to the perceived quality of life and benefits of gardening via paired-samples t-tests, and also tried to identify the important factors of significant changes via logistic regression. RESULTS: We found significant improvements in the perceived benefits of gardening and also in the scores computed from the WHO Quality of Life instruments, especially in the social sub-domains. The improvements were associated with the country, age, marital state and education of the volunteers. Higher age or being widow, divorced or single increased the odds of a significant improvement in the scores in more than one sub-domains. CONCLUSION: Though the two trial settings were different in some aspects, the observed significant improvements generally confirmed the positive effects of gardening concerning the perceived quality of life and benefits of gardening.

Physiotherapist-led exercise versus usual care (waiting-list) control for patients awaiting rotator cuff repair surgery: A pilot randomised controlled trial (POWER).

BACKGROUND: Once a decision to undergo rotator cuff repair surgery is made, patients are placed on the waiting list. It can take weeks or months to receive surgery. There has been a call to move from waiting lists to 'preparation' lists to better prepare patients for surgery and to ensure it remains an appropriate treatment option for them. OBJECTIVE: To evaluate the feasibility, as measured by recruitment rates, treatment fidelity and follow-up rates, of a future multi-centre randomised controlled trial to compare the clinical and cost-effectiveness of undertaking a physiotherapist-led exercise programme while waiting for surgery versus usual care (waiting-list control). DESIGN: Two-arm, multi-centre pilot randomised controlled trial with feasibility objectives in six NHS hospitals in England. METHOD: Adults (n = 76) awaiting rotator cuff repair surgery were recruited and randomly allocated to a programme of physiotherapist-led exercise (n = 38) or usual care control (n = 38). RESULTS: Of 302 eligible patients, 76 (25%) were randomised. Of 38 participants randomised to physiotherapist-led exercise, 28 (74%) received the exercise programme as intended. 51/76 (67%) Shoulder Pain and Disability Index questionnaires were returned at 6-months. Of 76 participants, 32 had not received surgery after 6-months (42%). Of those 32, 20 were allocated to physiotherapist-led exercise; 12 to usual care control. CONCLUSIONS: A future multi-centre randomised controlled trial is feasible but would require planning for variable recruitment rates between sites, measures to improve treatment fidelity and opportunity for surgical exit, and optimisation of follow-up. A fully powered, randomised controlled trial is now needed to robustly inform clinical decision-making.

Reducing Opioid Use for Chronic Pain With a Group-Based Intervention: A Randomized Clinical Trial.

Importance: Opioid use for chronic nonmalignant pain can be harmful. Objective: To test whether a multicomponent, group-based, self-management intervention reduced opioid use and improved pain-related disability compared with usual care. Design, Setting, and Participants: Multicentered, randomized clinical trial of 608 adults taking strong opioids (buprenorphine, dipipanone, morphine, diamorphine, fentanyl, hydromorphone, methadone, oxycodone, papaveretum, pentazocine, pethidine, tapentadol, and tramadol) to treat chronic nonmalignant pain. The study was conducted in 191 primary care centers in England between May 17, 2017, and January 30, 2019. Final follow-up occurred March 18, 2020. Intervention: Participants were randomized 1:1 to either usual care or 3-day-long group sessions that emphasized skill-based learning and education, supplemented by 1-on-1 support delivered by a nurse and lay person for 12 months. Main Outcomes and Measures: The 2 primary outcomes were Patient-Reported Outcomes Measurement Information System Pain Interference Short Form 8a (PROMIS-PI-SF-8a) score (T-score range, 40.7-77; 77 indicates worst pain interference; minimal clinically important difference, 3.5) and the proportion of participants who discontinued opioids at 12 months, measured by self-report. Results: Of 608 participants randomized (mean age, 61 years; 362 female [60%]; median daily morphine equivalent dose, 46 mg [IQR, 25 to 79]), 440 (72%) completed 12-month follow-up. There was no statistically significant difference in PROMIS-PI-SF-8a scores between the 2 groups at 12-month follow-up (-4.1 in the intervention and -3.17 in the usual care groups; between-group difference: mean difference, -0.52 [95% CI, -1.94 to 0.89]; P = .15). At 12 months, opioid discontinuation occurred in 65 of 225 participants (29%) in the intervention group and 15 of 208 participants (7%) in the usual care group (odds ratio, 5.55 [95% CI, 2.80 to 10.99]; absolute difference, 21.7% [95% CI, 14.8% to 28.6%]; P < .001). Serious adverse events occurred in 8% (25/305) of the participants in the intervention group and 5% (16/303) of the participants in the usual care group. The most common serious adverse events were gastrointestinal (2% in the intervention group and 0% in the usual care group) and locomotor/musculoskeletal (2% in the intervention group and 1% in the usual care group). Four people (1%) in the intervention group received additional medical care for possible or probable symptoms of opioid withdrawal (shortness of breath, hot flushes, fever and pain, small intestinal bleed, and an overdose suicide attempt). Conclusions and Relevance: In people with chronic pain due to nonmalignant causes, compared with usual care, a group-based educational intervention that included group and individual support and skill-based learning significantly reduced patient-reported use of opioids, but had no effect on perceived pain interference with daily life activities. Trial Registration: isrctn.org Identifier: ISRCTN49470934.

Smartphone accelerometry for quantifying core stability and developing exercise training progressions in people with multiple sclerosis.

BACKGROUND: Core stability exercise programs have become popular in recent years for preserving balance and functional independence in people with multiple sclerosis (PwMS); however, their real impact is not well-known as the main intervention target (i.e., core stability) theoretically responsible for balance or functional improvements is not measured. The objective of this study was to test the reliability of accelerometers integrated into smartphones for quantifying core stability and developing exercise progressions in PwMS. METHODS: Twenty participants with MS [age: 47.5±8.0 years; height: 1.62±0.07 m; mass: 63.4±10.9 kg; EDSS: 3.0 (1.5-6)] participated voluntarily in this study. CS was assessed in different variations of the front, side, and back bridges and bird-dog exercises by measuring the mean lumbopelvic acceleration in two testing sessions, separated by one week. Relative and absolute reliability of lumbopelvic acceleration of those exercise variations performed by more than 60% of the participants was analyzed by the intraclass correlation coefficient (ICC3,1), and the standard error of measurement (SEM) and the minimal detectable change (MDC), respectively. Repeated measures ANOVAs were performed to detect a potential learning effect between test-retest assessments. Statistical significance was set at p < 0.05. RESULTS: Reliability analyses revealed that good to excellent relative and absolute scores (0.850.05). CONCLUSION: Smartphone accelerometry seems a low cost, portable and easy-to-use tool to objectively and reliably track core stability changes in PwMS through. However, in spite of the popularity of bridging and bird-dog exercises, only the short and long bridges and the three-point bird-dog positions proved feasible for most participants. Overall, this study provides useful information to evaluate and guide the prescription of core stability exercise programs in PwMS with mild-to-moderate impairment.

A Thematic Survey on the Reporting Quality of Randomized Controlled Trials in Rehabilitation: The Case of Multiple Sclerosis.

BACKGROUND AND PURPOSE: Optimal reporting is a critical element of scholarly communications. Several initiatives, such as the EQUATOR checklists, have raised authors' awareness about the importance of adequate research reports. On these premises, we aimed at appraising the reporting quality of published randomized controlled trials (RCTs) dealing with rehabilitation interventions. Given the breadth of such literature, we focused on rehabilitation for multiple sclerosis (MS), which was taken as a model of a challenging condition for all the rehabilitation professionals.A thematic methodological survey was performed to critically examine rehabilitative RCTs published in the last 2 decades in MS populations according to 3 main reporting themes: (1) basic methodological and statistical aspects; (2) reproducibility and responsiveness of measurements; and (3) clinical meaningfulness of the change. SUMMARY OF KEY POINTS: Of the initial 526 RCTs retrieved, 370 satisfied the inclusion criteria and were included in the analysis. The survey revealed several sources of weakness affecting all the predefined themes: among these, 25.7% of the studies complemented the P values with the confidence interval of the change; 46.8% reported the effect size of the observed differences; 40.0% conducted power analyses to establish the sample size; 4.3% performed retest procedures to determine the outcomes' reproducibility and responsiveness; and 5.9% appraised the observed differences against thresholds for clinically meaningful change, for example, the minimal important change. RECOMMENDATIONS FOR CLINICAL PRACTICE: The RCTs dealing with MS rehabilitation still suffer from incomplete reporting. Adherence to evidence-based checklists and attention to measurement issues and their impact on data interpretation can improve study design and reporting in order to truly advance the field of rehabilitation in people with MS.Video Abstract available for more insights from the authors (see the Video, Supplemental Digital Content 1 available at: http://links.lww.com/JNPT/A424 ).

Synthesizing cross-design evidence and cross-format data using network meta-regression.

In network meta-analysis (NMA), we synthesize all relevant evidence about health outcomes with competing treatments. The evidence may come from randomized clinical trials (RCT) or non-randomized studies (NRS) as individual participant data (IPD) or as aggregate data (AD). We present a suite of Bayesian NMA and network meta-regression (NMR) models allowing for cross-design and cross-format synthesis. The models integrate a three-level hierarchical model for synthesizing IPD and AD into four approaches. The four approaches account for differences in the design and risk of bias (RoB) in the RCT and NRS evidence. These four approaches variously ignoring differences in RoB, using NRS to construct penalized treatment effect priors and bias-adjustment models that control the contribution of information from high RoB studies in two different ways. We illustrate the methods in a network of three pharmacological interventions and placebo for patients with relapsing-remitting multiple sclerosis. The estimated relative treatment effects do not change much when we accounted for differences in design and RoB. Conducting network meta-regression showed that intervention efficacy decreases with increasing participant age. We also re-analysed a network of 431 RCT comparing 21 antidepressants, and we did not observe material changes in intervention efficacy when adjusting for studies' high RoB. We re-analysed both case studies accounting for different study RoB. In summary, the described suite of NMA/NMR models enables the inclusion of all relevant evidence while incorporating information on the within-study bias in both observational and experimental data and enabling estimation of individualized treatment effects through the inclusion of participant characteristics.

Face emotional expressions influence interhemispheric inhibition.

The processing of face expressions is a key ability to perform social interactions. Recently, it has been demonstrated that the excitability of the hand primary motor cortex (M1) increases following the view of negative faces expressions. Interhemispheric interactions and sensory-motor integration are cortical processes involving M1, which are known to be modulated by emotional and social behaviors. Whether these processes may mediate the effects of face emotional expressions on M1 excitability is unknown. Therefore, the aim of this study was to investigate the influence of the passive viewing of face emotional expressions on M1 interhemispheric connections and sensory-motor integration using standardized transcranial magnetic stimulation (TMS) protocols. Nineteen healthy subjects participated in the study. Interhemispheric inhibition (IHI) and short-afferent inhibition (SAI) were probed in the right first dorsal interosseous (FDI) muscle 300 ms after the randomized presentation of seven different face expressions (neutral, sadness, fear, disgust, surprise and happiness). Results showed a significantly reduced IHI following the passive viewing of fearful faces compared to neutral (p = .001) and happy (p = .035) faces and following the view of sad faces compared to neutral faces (p = .008). No effect of emotional faces was detected on SAI. Data suggest that sensory-motor integration process does not mediate the increased excitability of M1 induced by the view of negative face expressions. By contrast, it may be underpinned by a depression of IHI, which from a functional point of view may promote symmetrical avoiding movements of the hands in response to aversive stimuli.

Role of Economic Evaluations on Pricing of Medicines Reimbursed by the Italian National Health Service.

OBJECTIVE: The main objective of this study was to explore the extent to which the incremental cost-effectiveness ratio (ICER), alongside other factors, predicts the final outcome of medicine price negotiation in Italy. The second objective was to depict the mean ICER of medicines obtained after negotiation. METHODS: Data were extracted from company dossiers submitted to the Italian Medicines Agency (AIFA) from October 2016 to January 2021 and AIFA's internal database. Beta-based regression analyses were used to test the effect of ICER and other variables on the outcome of price negotiation (ΔP), defined as the percentage difference between the list price requested by manufacturers and the final price paid by the Italian National Health Service (INHS). RESULTS: In our dataset of 48 pricing and reimbursement procedures, the ICER before negotiation was one of the variables with a major impact on the outcome of negotiation when ≥ 40,000€/QALY. As resulting from multiple regression analyses, the effect of the ICER on ΔP seemed driven by medicines for non-onco-immunological and non-rare diseases. Overall, the negotiation process granted mean incremental costs of €64,688 and mean incremental QALYs of 1.96, yielding an average ICER of €33,004/QALY. CONCLUSIONS: This study provides support on the influence of cost-effectiveness analysis on price negotiation in the Italian context, providing an estimate of the mean ICER of reimbursed medicines, calculated using net confidential prices charged by the INHS. The role and use of economic evaluations in medicines pricing should be further improved to get the best value for money.

Decision Curve Analysis for Personalized Treatment Choice between Multiple Options.

BACKGROUND: Decision curve analysis can be used to determine whether a personalized model for treatment benefit would lead to better clinical decisions. Decision curve analysis methods have been described to estimate treatment benefit using data from a single randomized controlled trial. OBJECTIVES: Our main objective is to extend the decision curve analysis methodology to the scenario in which several treatment options exist and evidence about their effects comes from a set of trials, synthesized using network meta-analysis (NMA). METHODS: We describe the steps needed to estimate the net benefit of a prediction model using evidence from studies synthesized in an NMA. We show how to compare personalized versus one-size-fit-all treatment decision-making strategies, such as "treat none" or "treat all patients with a specific treatment" strategies. First, threshold values for each included treatment need to be defined (i.e., the minimum risk difference compared with control that renders a treatment worth taking). The net benefit per strategy can then be plotted for a plausible range of threshold values to reveal the most clinically useful strategy. We applied our methodology to an NMA prediction model for relapsing-remitting multiple sclerosis, which can be used to choose between natalizumab, dimethyl fumarate, glatiramer acetate, and placebo. RESULTS: We illustrated the extended decision curve analysis methodology using several threshold value combinations for each available treatment. For the examined threshold values, the "treat patients according to the prediction model" strategy performs either better than or close to the one-size-fit-all treatment strategies. However, even small differences may be important in clinical decision making. As the advantage of the personalized model was not consistent across all thresholds, improving the existing model (by including, for example, predictors that will increase discrimination) is needed before advocating its clinical usefulness. CONCLUSIONS: This novel extension of decision curve analysis can be applied to NMA-based prediction models to evaluate their use to aid treatment decision making. HIGHLIGHTS: Decision curve analysis is extended into a (network) meta-analysis framework.Personalized models predicting treatment benefit are evaluated when several treatment options are available and evidence about their effects comes from a set of trials.Detailed steps to compare personalized versus one-size-fit-all treatment decision-making strategies are outlined.This extension of decision curve analysis can be applied to (network) meta-analysis-based prediction models to evaluate their use to aid treatment decision making.

A Rare Case of Hodgkin Lymphoma of the Maxillary Sinus.

Hodgkin lymphoma (HL) is an uncommon B-cell malignant disease. It usually presents with mediastinal and/or laterocervical lymph node localization, while primary extranodal HL is a rare entity giving rise to diagnostic and therapeutic challenges. It rarely presents as just extranodal localization, so its presence within the maxillary sinus without any lymphadenopathy is exceptional. Given the rarity of this localization, there is no standard treatment for maxillary sinus HL. We present a case of a patient with extranodal HL of the right maxillary sinus treated with primary surgery followed by adjuvant sequential chemoradiation therapy.