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.     

重症患者再喂养综合征危险因素的Meta分析

目的 分析重症患者营养支持期间发生再喂养综合征的危险因素，为早期识别及预防再喂养综合征提供参考。方法 检索中国知网、万方数据库、中华医学期刊全文数据库、中国生物医学文献数据库、维普网、PubMed、Embase、Cochrane Library、Elsevier关于重症患者再喂养综合征危险因素的文献，检索时间为各数据库建库至2021年12月，采用RevMan5.3进行Meta分析。结果 纳入13篇文献，包括2 519例患者，其中病例组793例，对照组1 726例；Meta分析合并效应值显示年龄(WMD=4.69)、APACHEⅡ评分(WMD=2.50)、BMI(WMD=-0.94)、白蛋白水平(OR=4.61)、前白蛋白水平(WMD=-53.46)、基线血镁水平(WMD=-0.05)、基线血钾水平(WMD=0.18)、基线血磷水平(WMD=-0.07)是重症患者发生再喂养综合征的危险因素(P<0.05,P<0.01)。结论 基于现有证据，重症患者再喂养综合征的危险因素包括年龄、APACHEⅡ评分、营养状况及电解质水平，医护人员需密切关注重症患者营养支持期间能量、蛋白质及电解...     

Comparison study of patient demographics and patient-related risk factors for peri-prosthetic joint infections following primary total shoulder arthroplasty

BackgroundWhile studies have demonstrated favorable outcomes in utilization of primary total shoulder arthroplasty (TSA) for the treatment of glenohumeral osteoarthritis (OA), adverse events such as infections can still occur. Periprosthetic joint infections (PJIs) are associated with worse outcomes and patient morbidity. The purpose of this study was to: (1) compare patient demographics amongst TSA patients with and without PJIs following primary TSA; and (2) identify patient-related risk factors for PJIs following primary TSA.MethodsPatients undergoing primary TSA for the treatment of glenohumeral OA were identified using the Mariner administrative claims database by CPT code 23,472. Laterality modifiers were utilized to ensure PJIs were developing in the correct laterality as those patients undergoing primary TSA. Inclusion for the study group consisted of patients who developed PJIs within 2-years after the index procedure, whereas patients who did not develop PJIs served as the comparison cohort. Primary outcomes analyzed included patient demographics and patient-related risk factors for PJIs following primary TSA. A stepwise backwards elimination multivariate binomial logistic regression analyses was performed to determine the odds (OR) of PJIs in patients undergoing primary TSA. A P value less than .05 was considered statistically significant.ResultsThe query yielded 15,396 patients who underwent primary TSA for glenohumeral OA, of which 191 patients developed PJIs and 15,205 did not develop PJIs. The study found statistically significant differences amongst patients who did and did not develop PJIs following primary TSA with respect to age, sex, and presence of comorbid conditions. Risk factors associated with developing PJIs following primary TSA included: pathologic weight loss (OR: 2.06, P < .0001), obesity (OR: 1.56, P = .0001), male sex (OR: 1.52, P = .007), and peripheral vascular disease (OR: 1.46, P = .022).ConclusionAs the number of primary TSAs for the treatment of glenohumeral OA increase worldwide, identifying modifiable risk-factors to reduce the incidence of infection is critical. The study found various modifiable and non-modifiable risk factors associated with developing PJIs following primary TSA. This study is valuable to orthopedists in order to identify and risk-stratify patients with regard to PJI in the setting of primary TSA for OA.Level of EvidenceLevel III; Case-Control Study     

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.