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Primary aldosteronism (PA) has two major subtypes: unilateral (uPA) and bilateral (bPA). Although several diagnostic models for subtype classification have been reported, the optimal combination of algorithms and clinical features remains unclear. This study aimed to identify machine learning models and clinical features that contribute to PA subtype prediction. A total of 274 PA patients who underwent successful adrenal venous sampling (AVS) at a single center were analyzed. Overall, 196 endocrine features were comprehensively collected and classified into four categories: A, PA-related features; B, challenge tests; C, general biochemistry; and D, urinary steroid profile. Five machine learning algorithms were applied; predictive performance of the models as well as predictive contribution of features and categories were evaluated. Among the models, the random forest (RF) model achieved the highest predictive accuracy (91.3%). The most contributing feature in the RF model was plasma aldosterone concentration after the captopril challenge test (CCT90-PAC). Category B made the greatest contribution to RF, followed by Categories A, D, and C. Combining Categories A and B improved predictive performance. These findings indicate that machine learning models, particularly RF, are effective for PA subtype prediction, with challenge test-related features in Category B making a major contribution.

Purpose Patients after metabolic bariatric surgery (MBS) require attention to maintain energy balance and avoid weight regain. Predictive equations for resting energy expenditure (REE) and total energy expenditure (TEE) are needed since gold standard methods like calorimetry and doubly labeled water are rarely available in routine clinical practice. This study aimed to determine which predictive equation for REE and TEE has the lowest bias in subjects after MBS. Methods MEDLINE, Embase, Web of Science, and CENTRAL searches were performed. Meta-analyses were performed with the data calculated by the predictive equations and measured by the gold standard methods for those equations that had at least two studies with these data. The DerSimonian and Laird random-effects model and the I 2 statistic were used to quantify heterogeneity in the quantitative analyses. The risk of bias was assessed using the Joanna Briggs Institute critical appraisal checklist. Results Seven studies were included. The present study found that the Mifflin St. Jeor (1990) equation had the lowest bias (mean difference =  − 39.71 kcal [95%CI =  − 128.97; 49.55]) for calculating REE in post-BS individuals. The Harris-Benedict (1919) equation also yielded satisfactory results (mean difference =  − 54.60 kcal [95%CI =  − 87.92; − 21.28]). Conclusion The predictive equation of Mifflin St. Jeor (1990) was the one that showed the lowest bias for calculating the REE of patients following MBS. Graphical Abstract

Excessive use of online computer games which leads to functional impairment and distress has recently been included as Internet Gaming Disorder (IGD) in Section III of the DSM-5. Although nosological classification of this phenomenon is still a matter of debate, it is argued that IGD might be described best as a non-substance-related addiction. Epidemiological surveys reveal that it affects up to 3 % of adolescents and seems to be related to heightened psychosocial symptoms. However, there has been no study of prevalence of IGD on a multi-national level relying on a representative sample including standardized psychometric measures. The research project EU NET ADB was conducted to assess prevalence and psychopathological correlates of IGD in seven European countries based on a representative sample of 12,938 adolescents between 14 and 17 years. 1.6 % of the adolescents meet full criteria for IGD, with further 5.1 % being at risk for IGD by fulfilling up to four criteria. The prevalence rates are slightly varying across the participating countries. IGD is closely associated with psychopathological symptoms, especially concerning aggressive and rule-breaking behavior and social problems. This survey demonstrated that IGD is a frequently occurring phenomenon among European adolescents and is related to psychosocial problems. The need for youth-specific prevention and treatment programs becomes evident.

Purpose Despite the importance of follow-up and multidisciplinary care after bariatric surgery, many patients do not attend postoperative appointments, particularly those with the medical team. The present study aimed to identify factors associated with loss to follow-up after bariatric surgery. Materials and Methods We recruited patients who underwent bariatric surgery between 01/01/2012 and 31/12/2013. Data were collected on demographic and socioeconomic information and comorbidities. Ten baseline psychological evaluations were blindly reviewed to evaluate the relationship between emotions and compliance with follow-up. During the 3-year postoperative period, we defined frequent attendees as those who attended at least two visits, whereas non-attendees were those who attended one visit or none . We evaluated baseline variables associated with non-adherence with follow-up schedules. Results Among 92 patients, 41 patients (44.6%) attended at least two postoperative appointments, while 51 (55.4%) were classified as non-attendees. Among the non-attendees, significantly more were younger than 45 years compared with attendees. No other statistically significant differences were found in terms of socioeconomic variables. Multivariate logistic regression revealed male gender and psychological issued related to obesity to be independent predictors of poor compliance with follow-up. Blinded psychological evaluation of ten patients did not suggest that psychological factors are predictive of follow-up attendance. Conclusion Identifying factors associated with loss to follow-up after bariatric surgery is challenging. However, this is important in order to enable the design of personalized follow-up plans, especially for younger patients and those with psychological issues.

Introduction Roux-en-Y gastric bypass (RYGB) is widely performed as treatment of morbid obesity. Long-term weight loss, effects on co-morbidities, and quality of life after RYGB have been well addressed. Other long-term outcomes are less elucidated. The aim of this study was to evaluate the prevalence, symptom characteristics, and possible predictors of chronic abdominal pain and gastrointestinal symptoms during consultations 5 years after RYGB. Methods A 5-year follow-up study of patients operated with RYGB 2008–2009 was performed. The patients completed questionnaires regarding chronic abdominal pain, the Gastrointestinal Symptom Rating Scale (GSRS), the ROME III questionnaire, the Hospital Anxiety and Depression Scale, Pain Catastrophing Scale (PCS), the Brief Pain Inventory, and SF-36. Uni- and multivariable logistic regression analyses of characteristics associated with chronic abdominal pain were performed. Results A total of 165/234 (71%) patients met to the follow-up, 160 of these accepted study inclusion. The mean follow-up was 64 (SD 4.2) months. The mean age was 42.5 (SD 8.7) years and 59% were females. The mean total weight loss was 23.9% (SD 11.2). Chronic abdominal pain was reported by 33.8%. Female gender, average strength of bodily pain, and the PCS sum score were associated with chronic abdominal pain. Symptoms of indigestion and irritable bowel syndrome were reported by 48.8% and 29.1%, respectively. Chronic abdominal pain was associated with reduced health related quality of life. Conclusion A substantial proportion of patients experienced chronic abdominal pain and symptoms 5 years after RYGB. Abdominal pain should be addressed at follow-up consultations after RYGB.

In an online community, users can interact with fellow community members by voluntarily contributing to existing discussion threads or by starting new threads. In practice, however, the vast majority of a community’s users (∼90%) remain inactive (lurk), simply observing contributions made by intermittent (∼9%) and heavy (∼1%) contributors. Our research examines increases and decreases of types of user engagement in online communities, characterizing user engagement based on trace user activity or lack of activity. Some lurkers later become workers (i.e., engaged in the community), but some will not. Differentiating lurkers who can be engaged from those who cannot enables managers to anticipate and proactively direct their resources toward the users who are most likely to become or remain workers (i.e., heavy contributors), thereby promoting community welfare. Our research, based on analysis of 533,714 posts from an online diabetes community, can thus guide managerial interventions to increase online community welfare. In an online community, users can interact with fellow community members by voluntarily contributing to existing discussion threads or by starting new threads. In practice, however, the vast majority of a community’s users (≈90%) remain inactive (lurk), simply observing contributions made by intermittent (≈9%) and heavy (≈1%) contributors. Our research examines increases and decreases of types of user engagement in online communities using hidden Markov models. These models characterize latent states of user engagement from trace user activity or lack of activity. The resulting framework then differentiates lurkers who can later become workers (i.e., engaged in the community) from those who will not. Differentiating lurkers who can be engaged from those who cannot enables managers to anticipate and proactively direct their resources toward the users who are most likely to become or remain workers (i.e., heavy contributors), thereby promoting community welfare. Analysis of 533,714 posts from an online diabetes community shows that incorporating latent user engagement variables can significantly improve the accuracy of welfare prediction models and guide managerial interventions. Application of our framework to five additional communities of various contexts demonstrates its generalizability.

Background Laparoscopic Roux-en-Y gastric bypass (LRYGB) is an effective treatment for morbid obesity resulting in approx. 70% excess weight loss (EWL) at 1–2 years. The aim of this study was to identify factors predictive of inadequate EWL following primary LRYGB. Methods Data on consecutive patients who underwent primary LRYGB between September 2009 and March 2013 were collected prospectively. The effects of age, gender, baseline body mass index (BMI), preoperative EWL, length of time between initial consultation and surgery (TtS), presence of diabetes mellitus (DM), arthritis, obstructive sleep apnea (OSA) and postoperative length of hospital stay (LOS) on EWL at 12 months were studied. General linear regression models were used to evaluate group differences in EWL and to assess independent associations between baseline variables and EWL at 12 months. Stepwise regression analyses were used to estimate individual contributions of independent variables to the variance in EWL at 12 months. In this study, inadequate EWL was defined as <50% EWL at 12 months. Results LRYGB was performed in 227 patients with a mean ± SD age and BMI of 48.6 ± 11 years and 53.6 ± 7.1 kg/m 2 , respectively. Female to male ratio was 3:1. EWL at 12 months had an inverse correlation with age ( p  = 0.01), baseline BMI ( p  < 0.001), TtS ( p  = 0.001), OSA ( p  = 0.039) and DM ( p  = 0.039). Conversely, there was a significant positive association between preoperative EWL and that at 12 months ( p  = 0.009). There was no effect of gender, arthritis or LOS on EWL at 12 months. Multiple regression analysis demonstrated inadequate EWL at 12 months to be predicted by older age (>60 years), patients with diabetes, higher baseline BMI (>60), those who gained weight preoperatively and in patients who waited longer than 18 months for surgery ( p  = 0.027). Conclusions Preoperative factors that predict inadequate EWL at 12 months following primary LRYGB include higher initial BMI, older age, presence of DM and preoperative weight gain. Identification of these factors preoperatively should aid in providing intensive support to these at-risk patient groups.

In the selection and design of ionic liquids (ILs) for various applications, including heat transfer fluids, thermal energy storage materials, fuel cells, and solvents for chemical processes, heat capacity is a key thermodynamic property. While several attempts have been made to develop predictive models for the estimation of the heat capacity of ILs in their liquid phase, none so far have been reported for the ILs’ solid crystal phase. This is particularly important for applications where ILs will be used for thermal energy storage in the solid phase. For the first time, a model has been developed and used for the prediction of crystal phase heat capacity based on extending and modifying a previously developed hybrid group contribution model (GCM) for liquid phase heat capacity. A comprehensive database of over 5000 data points with 71 unique crystal phase ILs, comprising 42 different cations and 23 different anions, was used for parameterization and testing. This hybrid model takes into account the effect of the anion core, cation core, and subgroups within cations and anions, in addition to the derived indirect parameters that reflect the effects of branching and distribution around the core of the IL. According to the results, the developed GCM can reliably predict the crystal phase heat capacity with a mean absolute percentage error of 6.78%. This study aims to fill this current gap in the literature and to enable the design of ILs for thermal energy storage and other solid phase applications.

BackgroundAverage percent excess weight loss data is commonly discussed preoperatively to guide patient expectations following surgery. However, there is a wide range and variation in weight loss following vertical sleeve gastrectomy (SG). Unfortunately, most surgeons and even fewer patients have heard of using predictive models to help guide their decisions on procedure choice. We have developed a predictive model for SG to help patient choice prior to this major life-changing decision.ObjectivePredict weight loss results for SG patients at 1 year using preoperative data.SettingPrivate practice.MethodsThree hundred and seventy-one SG patients met the criteria for our study. These patients underwent surgery between October 2008 and June 2016. Non-linear regressions were performed to interpolate individual patient weights at 1 year. Multivariate analysis was used to find factors that affected weight loss. A model was constructed to predict weight loss performance.ResultsVariables that affect weight loss were found to be preoperative body mass index (BMI), age, hypertension, and diabetes. Diabetes and hypertension together were found to significantly affect weight loss.ConclusionPatient weight loss can be accurately predicted by simple preoperative factors. These findings should be used to help patients and surgeons decide if the SG is an appropriate surgery for each patient. Using this model, most patients can avoid failure by choosing an appropriate surgical approach for their personal circumstances.

Background Although a few studies have been reported on predictive factors of postoperative diabetes remission, the conclusions remain inconsistent. This meta-analysis aimed to assess the preoperative clinical factors for type 2 diabetes mellitus (T2DM) remission after bariatric surgery. Methods The Cochrane Library, PubMed, MEDLINE, Embase, and CINAHL databases were searched. All human studies published in English between 1 January 1992 and 1 September 2013 reporting on the parameters of interest were included. Results In total, 15 studies involving 1,753 bariatric surgery patients were selected. Analyses were performed separately for the parameters of interest. T2DM remission was observed to be negatively correlated with age, diabetes duration, insulin use, and HbA1c levels. Baseline body mass index (BMI) and C-peptide levels were positively associated with the remission rate in Asian patients. However, there was no significant association between gender and remission rate. Conclusions Patients with younger age, short diabetes duration, better glucose control, and better β cell function were more likely to achieve T2DM remission after bariatric surgery. However, further randomized controlled trials with uniform remission criteria should be conducted to provide more reliable evidence.