基于Bevilacqua模型的乳腺癌保乳术患者术后淋巴水肿风险预测的临床研究

目的验证Bevilacqua乳腺癌术后淋巴水肿风险预测模型的临床适用性及可行性。方法回顾性分析2010年1月至2015年12月203例乳腺癌患者临床资料,临床数据分析使用统计学软件SPSS 24.0。Cox回归模型分析乳腺癌患者术后发生上肢淋巴水肿的危险因素,以P<0.05为有统计学意义;绘制ROC曲线,以曲线下面积检验模型预测效果;应用Hosmere-Lemeshow检验评估预测值与实际值的校准程度,以P>0.05为预测模型校准能力较好,预测与实际没有区别。结果所有患者随访共计62~86个月,中位随访时间70个月。术后5年内共发生上肢淋巴水肿患者45例(22.2%)。Cox回归模型分析结果显示,高身体质量指数(BMI)、接受过新辅助化疗、全腋窝淋巴结清扫、接受过放疗是上肢淋巴水肿的独立危险因素。Becilacqua上肢淋巴水肿风险预测模型ROC曲线分析结果显示,模型AUC值为0.711,95%CI(0.651~0.760),有较好的的预测效果。Hosmer-Lemeshow检验结果显示,风险预测模型预测风险与实际无明显差异(P=0.262),校准能力较好,与实际差别不大。结论Bevilacqua术后6个月淋巴水肿风险预测模型的准确性及适用性较高,可用于临床对乳腺癌保乳术后淋巴水肿的预测,可为预防淋巴水肿的发生制定干预决策提供参考。     

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。     

江苏省卫生人力资源配置的公平性及预测研究

目的 评价江苏省卫生人力资源配置的公平性,预测各类卫生人员的数量,为江苏省卫生事业的发展提供理论依据。方法 运用泰尔指数与集聚度评价资源配置的公平性,运用灰色GM (1, 1) 模型、多项式回归模型、二次指数平滑法以及组合预测模型对数据进行预测。结果 2015—2019年江苏省泰尔指数总体呈上升趋势,卫生人力资源配置差异性拉大,注册护士的资源配置差异最大。从地区来看,卫生人力资源集聚度由大到小分别为苏南＞苏中＞苏北。卫生人员灰色GM (1, 1) 模型预测效果最好,其他三类人员均是组合预测模型预测效果最好。结论 江苏省应合理配置卫生人力资源,加大卫生人员的培养力度,加强卫生人才队伍建设。     

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.     

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。     

A combination therapy with mitomycin c, etoposide, doxifluridine and medroxyprogesterone acetate as second line therapy for advanced breast cancer refractory to combination chemotherapy of cyclophosphamide, doxorubicin and 5 fluorouracil

Thirty-two patients with advanced breast cancer refractory to combination chemotherapy with cyclophosphamide (CPA), doxorubicin (ADR) and 5-fluorouracil (5-FU) (CAF) were treated with the combination of mitomycin C, etoposide, doxifluridine and medroxyprogesterone acetate as second line therapy. Observed responses included 6 patients (18.7%) with complete response (CR) and 7 (21.9%) with partial response (PR). Two (50%) out of 4 patients who had bone pain due to bone metastasis noted pain relief. CR or PR were obtained in 4 out of 12 patients who had not responded to the previous CAF therapy. While grade III myelosuppression was observed in 3 patients, other adverse effects were minimal. It is suggested that this combination therapy may be recommended for advanced breast cancer patients as a second therapy.     

Use of Drug Kinetics in Dermis to Predict in VivoBlood Concentration After Topical Application

Purpose. To use the drug kinetics in dermis to predict the in vivo blood concentration after topical administration. Methods. A two-step pharmacokinetic model was established. The first step was to calculate the drug input rate or flux from the skin to the systemic circulation using the drug kinetic parameters in dermis. These parameters include (a) distance over which the drug concentration declines by 50%, (b) drug concentration at the epidermal-dermal junction, and (c) minimal plateauing drug concentration in the muscle layer. These parameters were experimentally determined from the drug concentration-tissue depth profiles in the dermis, after the application of a topical dose of ddI (200 mg/kg) to rats. The second step was to use the drug input rate together with the systemic disposition pharmacokinetics of ddI in rats to predict the plasma concentration-time profiles. The model-predicted plasma concentration-time profiles were compared with the observed profiles, to determine the validity of the proposed pharmacokinetic model. Results. The observed steady state concentration (C_ss) in individual animals (n = 6) deviated from the predicted values by 3 to 55% with 3 of 6 rats showing a <15% deviation. The mean observed C_ss of all animals deviated from the mean predicted values by less than 15%. Conclusions. The close agreement between the observed and the model-predicted drug concentrations indicates that the systemic drug input can be calculated from the drug kinetics in the dermis.