Description synthétique des trajectoires des patients insuffisants rénaux chroniques terminaux à partir du registre REIN

Context and objectivesSince 2001, the aim of the REIN registry has been to identify patients suffering from end-stage renal disease and benefiting from replacement therapy in France. The analysis of trajectories aims to evaluate the flow of patients between the different types of treatment in order to better understand and predict patient pathways. The objective of this study was to analyse the incoming and outgoing flows at 1 year of patients prevalent in the REIN registry on 12/31/2017.MethodsFlow analysis was carried out on patients prevalent on 12/31/2017 in the REIN registry by studying the before and after treatment modalities on 12/31/2016 and 12/31/2018. This analysis was initially carried out on all patients, then in sub-groups for each of the 5 treatment modalities.ResultsThe analyses covered 85,472 patients prevalent on 12/31/2017. The overall analysis showed that more than 20% of patients had been diagnosed with end-stage renal disease the year before. Regarding inflow, there was a relative stability for patients treated with self-care hemodialysis, in-center haemodialysis, peritoneal dialysis, and graft, in contrast to patients treated with hemodialysis in a medical unit. Regarding outgoing flows, proportion of deaths at one year was 9%. Peritoneal dialysis was the modality with the highest outflow proportion at one year.ConclusionAnalysis of patients’ trajectories shows variable evolution profiles according to treatment modality and thus could be a valuable tool in the evaluation and improvement of management and care in the field of end-stage renal disease.     

Clinical outcome after surgery on schwannomas in the extremities

BACKGROUND Schwannoma is a benign, encapsulated and slowly growing tumor originating from Schwann cells and is rarely seen in the peripheral nerve system. Typical symptoms are soreness, radiating pain and sensory loss combined with a soft tissue mass.AIM To evaluate pre-and postoperative symptoms in patients operated for schwannomas in the extremities and investigate the rate of malignant transformation.METHODS In this single center retrospective study design, all patients who had surgery for a benign schwannoma in the extremities from May 1997 to January 2018 were included. The location of the tumor in the extremities was divided into five groups; forearm, arm, shoulder, thigh and leg including foot. The locations of the tumor in the nerves were also categorized as either; proximal, distal, minor or major nerve. During the pre-and postoperative clinical evaluation, symptoms were classified as paresthesia, local pain, radiating pain, swelling, impairment of mobility/strength and asymptomatic tumors that were found incidentally(with magnetic resonance imaging). The patients were evaluated after surgery using the following categories: Asymptomatic or symptomatic patients(radiating and/or local pain) and those with complications. The follow up period was from the time of surgery until last examination of the particular physician. Multivariate logistic regression analysis was performed to identify independent prognostic factors for postoperative significant symptoms at follow-up.RESULTS We identified 858 cases from the institutional pathology register. We excluded cases with duplicate diagnoses(n = 407), pathology not including schwannomas(n = 157), lesions involving the torso, spine and neck(n = 150) leaving 144 patients for further analysis. In this group 99 patients underwent surgery and there were five complications recorded: 2 infections(treated with antibiotics) and 3 nerve palsies(2 involving the radial nerve and one involving the median nerve) that recovered spontaneously. At the end of follow-up, 1.4 mo(range 0.5-76) postoperatively, we recorded a post-operative decrease in clinical symptoms: Local pain 76%(6/25), radiating pain 97%(2/45), swelling 20%(8/10). Symptoms of paresthesia increased by 2.8%(37/36) and there was no change in motor weakness before and after surgery 1%(1/1). Multivariate analysis showed that tumors located within minor nerves had a significantly higher prevalence of postoperative symptoms compared with tumors in major nerves(odds ratio: 2.63; confidence intervals: 1.22-6.42, P = 0.029). One patient with schwannoma diagnosed by needle biopsy was diagnosed to have malignant transformation diagnosed in the surgically removed tumor. No local recurrences were reported.CONCLUSION Surgery of schwannomas can be conducted with low risk of postoperative complications, acceptable decrease in clinical symptoms and risk of malignant transformation is low.     

Derivation and validation of a novel comorbidity‐based delirium risk index to predict postoperative delirium using national administrative healthcare database

ObjectiveTo derive and validate a comorbidity‐based delirium risk index (DRI) to predict postoperative delirium.Data Source/Study SettingData of 506 438 hip fracture repair surgeries from 2006 to 2016 were collected to derive DRI and perform internal validation from the Premier Healthcare Database, which provided billing information on 20‐25 percent of hospitalizations in the USA. Additionally, data of 1 130 569 knee arthroplasty surgeries were retrieved for external validation.Study DesignThirty‐six commonly seen comorbidities were evaluated by logistic regression with the outcome of postoperative delirium. The hip fracture repair surgery cohort was separated into a training dataset (60 percent) and an internal validation (40 percent) dataset. The least absolute shrinkage and selection operator (LASSO) procedure was applied for variable selection, and weights were assigned to selected comorbidities to quantify corresponding risks. The newly developed DRI was then compared to the Charlson‐Deyo Index for goodness‐of‐fit and predictive ability, using the Akaike information criterion (AIC), Bayesian information criterion (BIC), area under the ROC curve (AUC) for goodness‐of‐fit, and odds ratios for predictive performance. Additional internal validation was performed by splitting the data by four regions and in 4 randomly selected hospitals. External validation was conducted in patients with knee arthroplasty surgeries.Data CollectionHip fracture repair surgeries, knee arthroplasty surgeries, and comorbidities were identified by using ICD‐9 codes. Postoperative delirium was defined by using ICD‐9 codes and analyzing billing information for antipsychotics (specifically haloperidol, olanzapine, and quetiapine) typically recommended to treat delirium.Principal FindingsThe derived DRI includes 14 comorbidities and assigns comorbidities weights ranging from 1 to 6. The DRI outperformed the Charlson‐Deyo Comorbidity Index with better goodness‐of‐fit and predictive performance.ConclusionsDelirium risk index is a valid comorbidity index for covariate adjustment and risk prediction in the context of postoperative delirium. Future work is needed to test its performance in different patient populations and varying definitions of delirium.     

Analysis of time‐to‐event for observational studies: Guidance to the use of intensity models

This paper provides guidance for researchers with some mathematical background on the conduct of time‐to‐event analysis in observational studies based on intensity (hazard) models. Discussions of basic concepts like time axis, event definition and censoring are given. Hazard models are introduced, with special emphasis on the Cox proportional hazards regression model. We provide check lists that may be useful both when fitting the model and assessing its goodness of fit and when interpreting the results. Special attention is paid to how to avoid problems with immortal time bias by introducing time‐dependent covariates. We discuss prediction based on hazard models and difficulties when attempting to draw proper causal conclusions from such models. Finally, we present a series of examples where the methods and check lists are exemplified. Computational details and implementation using the freely available R software are documented in Supplementary Material. The paper was prepared as part of the STRATOS initiative.     

Clinical prediction models to predict the risk of multiple binary outcomes: a comparison of approaches

Clinical prediction models (CPMs) can predict clinically relevant outcomes or events. Typically, prognostic CPMs are derived to predict the risk of a single future outcome. However, there are many medical applications where two or more outcomes are of interest, meaning this should be more widely reflected in CPMs so they can accurately estimate the joint risk of multiple outcomes simultaneously. A potentially naïve approach to multi‐outcome risk prediction is to derive a CPM for each outcome separately, then multiply the predicted risks. This approach is only valid if the outcomes are conditionally independent given the covariates, and it fails to exploit the potential relationships between the outcomes. This paper outlines several approaches that could be used to develop CPMs for multiple binary outcomes. We consider four methods, ranging in complexity and conditional independence assumptions: namely, probabilistic classifier chain, multinomial logistic regression, multivariate logistic regression, and a Bayesian probit model. These are compared with methods that rely on conditional independence: separate univariate CPMs and stacked regression. Employing a simulation study and real‐world example, we illustrate that CPMs for joint risk prediction of multiple outcomes should only be derived using methods that model the residual correlation between outcomes. In such a situation, our results suggest that probabilistic classification chains, multinomial logistic regression or the Bayesian probit model are all appropriate choices. We call into question the development of CPMs for each outcome in isolation when multiple correlated or structurally related outcomes are of interest and recommend more multivariate approaches to risk prediction.     

四川省卫生总费用的结构变动及预测研究

目的 分析2008—2019年四川省卫生总费用结构变动及影响因素和预测研究,为助力“健康四川”建言献策。方法 使用四川省卫生统计年鉴的相关卫生总费用数据,在描述性分析的基础上,使用结构变动度分析卫生总费用的结构变动,灰色关联法研究卫生总费用的影响因素并进行GM(1,1)预测。结果 2008—2019年卫生总费用持续增加,政府、社会和个人卫生支出结构逐步合理;人均卫生费用水平排名靠后,社会卫生支出成为卫生总费用的主要构成单元;千人口床位、常住人口和农村居民人均可支配收入是影响卫生总费用的主要因素;预测发现,2020—2025年卫生总费用仍呈增加趋势,个人卫生支出占比进一步下降。结论 四川省卫生总费用结构渐趋合理,医疗服务水平得到大幅度改善;但仍需重点关注人均卫生费用等相对性指标。     

The Development of a Predictive Method for the Estimation of Flux Through Polydimethylsiloxane Membranes. IV. Application to a Series of Substituted Quinolines

The steady-state flux of 33 substituted quinoline derivatives was determined in polydimethylsiloxane membranes using isopropyl alcohol as the receiver solvent. These diffusants constituted a diverse group of compounds possessing a wide range of hydrophobic, steric, and electronic characteristics. Various parameters representing these physicochemical properties such as cyclohexane–water fragmental constants, molar refractivity, Hammett's constants, intramolecular hydrogen bonding ability, melting point, and mole fraction solubility were employed to develop empirical models capable of relating the rate of diffusion to these characteristics of either the substituent on the quinoline ring or the compound itself.     

《严重过敏反应急救指南》临床问题与结局指标的收集和确定

目的: 确定纳入《严重过敏反应急救指南》的临床问题与结局指标。方法: 由指南指导委员会7位临床医学、药学及护理学专家参考国际指南指导项目组起草、修订拟纳入指南的临床问题与结局指标初稿。临床问题分为背景问题与前景问题,其中背景问题在专家共识组共22位临床专家修订后直接纳入指南范围,前景问题与结局指标将通过三轮德尔菲法对共识组临床专家咨询形成终稿。研究在方法学家全程监督下完成,并通过分析积极系数、变异系数与分值频率等指标进行质量控制。结果: 问题征集初拟共纳入34个前景问题、6个背景问题及6个结局指标,背景问题经专家组修订后直接纳入指南。使用德尔菲法(Delphi method)对前景问题与结局指标的重要程度进行分级后,最终确定28个关键前景问题及6个结局指标纳入指南范围。其中,4个结局指标为关键性结局,其余2个为重要结局,纳入的28个关键前景问题分为严重过敏反应的诊断、救治准备、救治措施及救治后的管理四个部分,另有5个重要问题和1个次要问题此次未纳入指南,供指南修订或更新时参考或讨论。研究中专家响应的积极程度高,积极系数为100%;前景问题的共识程度高,4分及以上的问卷频率均≥0.75;结局指标中,病死率及疾病严重程度的共识程度高,变异系数≤15%。结论: 在全面严谨的信息收集基础上,以德尔菲法的方式确定了需要纳入《严重过敏反应急救指南》的临床问题与结局指标为该指南的后续制定工作奠定了基础。     

基于不完整数据的IHB-LightGBM心脏病预测模型

提出基于不完整数据的IHB-LightGBM(Improved Hyperband-Light Gradient Boosting Machine)心脏病预测模型。首先,在Hyperband算法超参数采样的基础上引入了权重值,并通过蓄水池法按特征权重对其进行排序,从而筛选出最优参数以提高算法的参数寻优能力;其次,针对心脏病数据样本小且属性缺失的问题,使用K近邻算法对不完整数据进行缺失值插补,再将处理得到的完整数据进行归一化,使数据映射至0～1范围内;最后,对LightGBM采用改进后的IHB优化算法进行全局参数寻优,建立IHB-LightGBM心脏病预测模型。使用UCI心脏病数据集进行实验,结果表明IHB算法的参数寻优效果优于贝叶斯、随机搜索等优化算法,IHB-LightGBM模型在各项评价指标也上明显高于随机森林、极端随机树等算法,可以获得更快的预测速度和更高的预测精度。     

Transcranial Doppler sonography in craniosynostosis

In the management of craniosynostosis, there is a need for quantitative assessment of treatment methods and outcome. Radiology and pressure studies are well documented, but so far little attention has been given to cerebral blood flow changes and their possible relevance. This paper reports our initial experience using transcranial Doppler sonography to calculate the cerebrovascular resistance and cerebral blood velocities in the major basal vessels before and after surgery for craniosynostosis. Ten patients were studied using the Scimed 2-MHz probe through the temporal and frontal windows of the skull. Measurements were taken under standard physiological conditions before, during and after surgery. Preliminary results suggest that this non-invasive technique may be helpful in predicting and assessing the outcome following surgery for craniosynostosis.