Somatic hypermutation defects in two adult hyper immunoglobulin M patients

Immunologic Research - Hyper immunoglobulin M (HIGM) syndrome is a rare disorder of the immune system with impaired antibody functions. The clinical picture of the patients varies according to the...     

Measuring Psychological Inflexibility of Suicidal Thoughts: The Acceptance and Action Questionnaire for Suicidal Ideation (AAQ-SI)

Cognitive Therapy and Research - Despite interest in psychological inflexibility as a marker of suicide risk, no measure of psychological inflexibility specific to SI exists. The present study...     

The magnitude and progress of lean body mass,fat-free mass,and skeletal muscle mass loss following bariatric surgery: A systematic review and meta-analysis

Postbariatric loss of muscle tissue could negatively affect long-term health due to its role in various bodily processes, such as metabolism and functional capacity. This meta-analysis aimed to unravel time-dependent changes in the magnitude and progress of lean body mass (LBM), fat-free mass (FFM), and skeletal muscle mass (SMM) loss following bariatric surgery. A systematic literature search was conducted in Pubmed, Embase, and Web of Science. Fifty-nine studies assessed LBM (n = 37), FFM (n = 20), or SMM (n = 3) preoperatively and ≥1 time points postsurgery. Random-effects meta-analyses were performed to determine pooled loss per outcome parameter and follow-up time point. At 12-month postsurgery, pooled LBM loss was ?8.13 kg [95%CI ?9.01; ?7.26]. FFM loss and SMM loss were ?8.23 kg [95%CI ?10.74; ?5.73] and ?3.18 kg [95%CI ?5.64; ?0.71], respectively. About 55% of 12-month LBM loss occurred within 3-month postsurgery, followed by a more gradual decrease up to 12 months. Similar patterns were seen for FFM and SMM. In conclusion, >8 kg of LBM and FFM loss was observed within 1-year postsurgery. LBM, FFM, and SMM were predominantly lost within 3-month postsurgery, highlighting that interventions to mitigate such losses should be implemented perioperatively.     

Diagnostic difficulty,delayed diagnosis,and increased tendencies of surgical treatment in fibromyalgia syndrome

Clinical Rheumatology - Aimed to evaluate the time elapsed between the onset of early symptoms and the diagnosis of fibromyalgia syndrome (FMS), the delays in diagnosis and frequent physician...     

PREDICT-GTN 1: Can we improve the FIGO scoring system in gestational trophoblastic neoplasia?

Gestational trophoblastic neoplasia (GTN) patients are treated according to the eight-variable International Federation of Gynaecology and Obstetrics (FIGO) scoring system, that aims to predict first-line single-agent chemotherapy resistance. FIGO is imperfect with one-third of low-risk patients developing disease resistance to first-line single-agent chemotherapy. We aimed to generate simplified models that improve upon FIGO. Logistic regression (LR) and multilayer perceptron (MLP) modelling (n = 4191) generated six models (M1-6). M1, all eight FIGO variables (scored data); M2, all eight FIGO variables (scored and raw data); M3, nonimaging variables (scored data); M4, nonimaging variables (scored and raw data); M5, imaging variables (scored data); and M6, pretreatment hCG (raw data) + imaging variables (scored data). Performance was compared to FIGO using true and false positive rates, positive and negative predictive values, diagnostic odds ratio, receiver operating characteristic (ROC) curves, Bland-Altman calibration plots, decision curve analysis and contingency tables. M1-6 were calibrated and outperformed FIGO on true positive rate and positive predictive value. Using LR and MLP, M1, M2 and M4 generated small improvements to the ROC curve and decision curve analysis. M3, M5 and M6 matched FIGO or performed less well. Compared to FIGO, most (excluding LR M4 and MLP M5) had significant discordance in patient classification (McNemar's test P < .05); 55-112 undertreated, 46-206 overtreated. Statistical modelling yielded only small gains over FIGO performance, arising through recategorisation of treatment-resistant patients, with a significant proportion of under/overtreatment as the available data have been used a priori to allocate primary chemotherapy. Streamlining FIGO should now be the focus.     

Peri-operative tobacco cessation interventions: a systematic review and meta-analysis

Tobacco smoking is associated with a substantially increased risk of postoperative complications. The peri-operative period offers a unique opportunity to support patients to stop tobacco smoking, avoid complications and improve long-term health. This systematic review provides an up-to-date summary of the evidence for tobacco cessation interventions in surgical patients. We conducted a systematic search of randomised controlled trials of tobacco cessation interventions in the peri-operative period. Quantitative synthesis of the abstinence outcomes data was by random-effects meta-analysis. The primary outcome of the meta-analysis was abstinence at the time of surgery, and the secondary outcome was abstinence at 12 months. Thirty-eight studies are included in the review (7310 randomised participants) and 26 studies are included in the meta-analysis (5969 randomised participants). Studies were pooled for subgroup analysis in two ways: by the timing of intervention delivery within the peri-operative period and by the intensity of the intervention protocol. We judged the quality of evidence as moderate, reflecting the degree of heterogeneity and the high risk of bias. Overall, peri-operative tobacco cessation interventions increased successful abstinence both at the time of surgery, risk ratio (95%CI) 1.48 (1.20–1.83), number needed to treat 7; and 12 months after surgery, risk ratio (95%CI) 1.62 (1.29–2.03), number needed to treat 9. More work is needed to inform the design and optimal delivery of interventions that are acceptable to patients and that can be incorporated into contemporary elective and urgent surgical pathways. Future trials should use standardised outcome measures.