Performance of risk prediction models for post-operative mortality in patients undergoing liver resection

BackgroundLiver resection is commonly performed for hepatic tumors, however preoperative risk stratification remains challenging. We evaluated the performance of contemporary prediction models for short-term mortality after liver resection in patients with and without cirrhosis.MethodsThis retrospective cohort study examined National Surgical Quality Improvement Program data. We included patients who underwent liver resections from 2014 to 2019. VOCAL-Penn, MELD, MELD-Na, ALBI, and Mayo risk scores were evaluated in terms of model discrimination and calibration for 30-day post-operative mortality.ResultsA total 15,198 patients underwent liver resection, of whom 249 (1.6%) experienced 30-day post-operative mortality. The VOCAL-Penn score had the highest discrimination (area under the ROC curve [AUC] 0.74) compared to all other models. The VOCAL-Penn score similarly outperformed other models in patients with (AUC 0.70) and without (AUC 0.74) cirrhosis.ConclusionThe VOCAL-Penn score demonstrated superior predictive performance for 30-day post-operative mortality after liver resection as compared to existing clinical standards.     

Bayesian consensus clustering for multivariate longitudinal data

In clinical and epidemiological studies, there is a growing interest in studying the heterogeneity among patients based on longitudinal characteristics to identify subtypes of the study population. Compared to clustering a single longitudinal marker, simultaneously clustering multiple longitudinal markers allow additional information to be incorporated into the clustering process, which reveals co-existing longitudinal patterns and generates deeper biological insight. In the current study, we propose a Bayesian consensus clustering (BCC) model for multivariate longitudinal data. Instead of arriving at a single overall clustering, the proposed model allows each marker to follow marker-specific local clustering and these local clusterings are aggregated to find a global (consensus) clustering. To estimate the posterior distribution of model parameters, a Gibbs sampling algorithm is proposed. We apply our proposed model to the primary biliary cirrhosis study to identify patient subtypes that may be associated with their prognosis. We also perform simulation studies to compare the clustering performance between the proposed model and existing models under several scenarios. The results demonstrate that the proposed BCC model serves as a useful tool for clustering multivariate longitudinal data.     

Prolyl and lysyl hydroxylases in collagen synthesis

Collagens are the most abundant proteins in the extracellular matrix. They provide a framework to build organs and tissues and give structural support to make them resistant to mechanical load and forces. Several intra‐ and extracellular modifications are needed to make functional collagen molecules, intracellular post‐translational modifications of proline and lysine residues having key roles in this. In this article, we provide a review on the enzymes responsible for the proline and lysine modifications, that is collagen prolyl 4‐hydroxylases, 3‐hydroxylases and lysyl hydroxylases, and discuss their biological functions and involvement in diseases.     

COVID-19 convalescent plasma: current status,lessons from the past and future perspectives

When the COVID-19 pandemic hit, blood transfusion services worldwide started collection of convalescent plasma as early as possible, as exemplified by the response in Norway. There were challenges related to donor selection, donor safety, testing for relevant antibodies and indications for and dosing of the convalescent plasma. As more knowledge became available, the product quality was more standardised. Multiple case reports, observational studies and some randomized studies were published during the pandemic, as well as laboratory studies reporting different approaches to antibody testing. The results were conflicting and the importance of convalescent plasma was disputed.Even though there has been strong international collaboration with involvement of many key organisations, we may better prepare for the next pandemic. An even stronger, more formalised collaboration between these organisations could provide more clear evidence of the importance of convalescent plasma, based on the principles of passive immunisation.     

我国家庭医生签约服务政策执行的制约因素与优化路径：基于史密斯政策执行过程模型

深化家庭医生签约服务是深化医药卫生体制改革、强化基层医疗卫生服务、实现"健康中国"战略目标的重要选择，也是当前更好维护人民群众健康的重要途径。为有效推进签约服务工作，国家陆续推出各项政策，全国各地也在积极进行实践探索，成效明显。但是，签约服务仍面临诸多问题，其中"执行难"是签约服务深度推进的一大困境。通过史密斯政策执行过程模型，结合签约服务政策执行过程，发现签约服务仍存在法治性不足、政策执行人员水平不高、激励不足、政策环境影响等诸多制约因素。因此，需要从法律和制度方面进行顶层设计、提升执行人员素质和职业认同、建立医患互信、优化政策执行环境等角度进行政策创新，探索家庭医生签约服务可持续发展的路径。     

Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy

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鸟巢式护理模式对早产儿生长发育及肠胃功能的影响

目的 探讨鸟巢式护理模式在早产儿护理中的应用价值。方法 选取2016年6月—2020年5月本院收治的早产儿94例,采用随机数表法将其分为两组,每组各47例。对照组给予早产儿采取常规护理,观察组在早产儿常规护理上实施鸟巢式护理模式。比较两组临床效果、护理前后生长发育情况、肠胃功能及家属护理满意度。结果 观察组体温波动幅度低于对照组,睡眠时间长于对照组,血氧饱和度高于对照组,出暖箱时间短于对照组,差异有统计学意义(P<0.05)。护理前,两组头围、身高、体质量比较,差异无统计学意义(t=0.381,P=0.704、t=0.211,P=0.833、t=1.472,P=0.144);护理后2周,观察组头围(28.44±1.35)cm、身高(44.86±2.05)cm、体质量(2 336.93±12.47)g,高于对照组的(27.76±1.87)cm、(42.46±2.43)cm、(2 310.14±13.56)g,差异有统计学意义(t=2.021,P=0.046、t=5.175,P<0.001、t=9.970,P<0.001);观察组首次胎便时间、胎便转黄时间分别为(21.74±7.35)h、(58.91±12.44)h,短于对照组的(25.02±8.13)h、(63.87±13.74)h,排便次数及进奶量的增加分别为(5.67±1.32)次/d、(3.38±1.43)mL/d,高于对照组的(4.21±1.84)次/d、(2.13±0.27)mL/d,差异有统计学意义(t=2.052,P=0.043、t=4.420,P<0.001、t=2.205,P=0.030、t=5.889,P<0.001);观察组护理总满意度高于对照组,差异有统计学意义(P<0.05)。结论 鸟巢式护理模式可减少早产儿体温波动幅度,提供适宜的恒温环境,促进早产儿生长发育,效果确切,值得广泛应用。     

Predicting Survival for Chimeric Antigen Receptor T-Cell Therapy: A Validation of Survival Models Using Follow-Up Data From ZUMA-1

ObjectivesSurvival extrapolation for chimeric antigen receptor T-cell therapies is challenging, owing to their unique mechanistic properties that translate to complex hazard functions. Axicabtagene ciloleucel is indicated for the treatment of relapse or refractory diffuse large B-cell lymphoma after 2 or more lines of therapy based on the ZUMA-1 trial. Four data snapshots are available, with minimum follow-up of 12, 24, 36, and 48 months. This analysis explores how survival extrapolations for axicabtagene ciloleucel using ZUMA-1 data can be validated and compared.MethodsThree different parametric modeling approaches were applied: standard parametric, spline-based, and cure-based models. Models were compared using a range of metrics, across the 4 data snapshot, including visual fit, plausibility of long-term estimates, statistical goodness of fit, inspection of hazard plots, point-estimate accuracy, and conditional survival estimates.ResultsStandard and spline-based parametric extrapolations were generally incapable of fitting the ZUMA-1 data well. Cure-based models provided the best fit based on the earliest data snapshot, with extrapolations remaining consistent as data matured. At 48 months, the maximum survival overestimate was 8.3% (Gompertz mixture-cure model) versus the maximum underestimate of 33.5% (Weibull standard parametric model).ConclusionsWhere a plateau in the survival curve is clinically plausible, cure-based models may be helpful in making accurate predictions based on immature data. The ability to reliably extrapolate from maturing data may reduce delays in patient access to potentially lifesaving treatments. Additional research is required to understand how models compare in broader contexts, including different treatments and therapeutic areas.