中国10个地区成年人慢性肾脏病流行情况及其生活方式影响因素

目的描述中国慢性病前瞻性研究(CKB)项目人群的慢性肾脏病(CKD)分布, 分析生活方式与CKD发病风险的前瞻性关联。方法数据来源于CKB基线调查及随访监测(截至2018年12月31日), 描述CKD发病的地区和人群分布差异, 使用Cox比例风险回归模型分析生活方式因素与CKD的关联。结果研究纳入505 147名研究对象, 平均随访11.26年, 期间共有4 920例发病病例, 发病率为83.43/10万人年, 肾小球肾病为最主要亚型。CKD发病率在城市、男性、年龄≥60岁人群中更高, 分别为87.83/10万人年、86.37/10万人年、132.06/10万人年。相比于从不或偶尔吸烟者, 当前吸烟男性CKD风险增加(HR=1.18, 95%CI:1.05～1.31)。以非肥胖人群为参照组, 由BMI判定的全身性肥胖(HR=1.19, 95%CI:1.10～1.29)和腰围判定的中心性肥胖(HR=1.27, 95%CI:1.19～1.35)均与更高的CKD发病风险相关。结论 CKB项目人群CKD发病率存在明显的地区和人群差异, 且其发病风险受到生活方式多因素的影响。     

中国台湾省35～74岁健检人群慢性肾病5年发病风险预测模型

目的构建中国台湾省35～74岁健检人群慢性肾病(CKD)5年发病风险(个体化)预测模型。方法选择1996～2006年初次参加台湾省美兆健检的35～74岁人群19 987人,将其中基线未患CKD者18 275人分为建模队列(用于建立5年发病预测模型)和验证队列(用于评估模型外部效度)。采用logistic回归构建预测模型,以ROC曲线下面积(AUC)评价拟合优度,并将人群的预测风险概率进行风险等级划分。结果去除基线患者后CKD5年发病率为5.24%(958/18275)。纳入5年发病风险预测模型的变量包括年龄、教育程度、糖尿病、高血压、低密度脂蛋白胆固醇、甘油三酯和血肌酐水平7个指标。建模队列建立的预测模型的ROC曲线下面积(AUC)约为0.734(95%CI:0.714～0.754),验证队列外部效度验证结果AUC=0.768(95%CI:0.734～0.801)。将建模队列划分为4个风险等级后,显示中危(占14.1%)和高危(占5.4%)的个体5年内发生CKD的危险分别比一般人群高2.0倍和4.8倍。结论利用中国台湾省美兆健检纵向数据建立的CKD 5年个体风险预测模型效应与信度均较高,且简单实用,对于今后CKD风险个体评价及群体监测均有较大的应用价值。     

上海市松江区成年人高尿酸血症与慢性肾病关系的队列研究

目的 分析社区成年人高尿酸血症（HUA）与慢性肾病（CKD）发病风险的关系。方法 基于华东区域自然人群队列,对7 276例基线时未患CKD的上海市松江区20~74岁常住居民进行基于社区的前瞻性队列研究。CKD的诊断根据美国肾脏基金会慢性肾脏病实践指南的标准,HUA定义为血清尿酸（SUA）>420 μmol/L（男性）或>360 μmol/L（女性）。采用Cox比例风险模型分析HUA与CKD发病风险的关联。结果 经过中位时间2.65年的随访,队列人群新发CKD病例301例,累积发病率为4.14%,发病密度为16.01/1 000人年（95% CI：14.20~17.82）。CKD发病人群HUA的基线患病率显著高于非CKD发病人群。多因素Cox回归分析显示,HUA与CKD发病风险呈显著正关联,HUA患者的CKD发病风险HR值为1.92（95% CI：1.46~2.53）。在对性别、年龄、BMI、2型糖尿病、高血压等因素分层后,HUA与CKD发病风险仍呈显著正关联;在相加模型中,年龄与HUA存在交互作用,交互作用指数为1.78（95% CI：1.18~2.68）。结论 上海市松江区成年人CKD的发病率较高,HUA为CKD发病的独立危险因素。     

急性心力衰竭再入院及死亡风险预测模型研究进展

心力衰竭是各种心脏疾病的严重和终末期表现,具有高住院率、高病死率等特点,已成为重要的公共卫生问题。急性心力衰竭患者出院后再入院率及死亡率是评价心力衰竭医疗质量的重要指标,基于此开展急性心力衰竭患者疾病预后风险预测研究,对量化疾病风险、落实分层管理、优化临床决策、提高生存质量、改善患者预后、全面提升我国急性心力衰竭医疗质量至关重要。近20年来,国外学者已开发出数十个急性心力衰竭再入院及死亡风险预测模型,我国学者也开发出了近十个基于中国人群的预测模型,但目前国内指南中尚无推荐使用的急性心力衰竭预后风险预测模型。本文旨在通过介绍国内外主要急性心力衰竭再入院和死亡风险预测模型,重点概述现有模型局限性及今后发展方向,包括整合多源数据、挖掘新兴生物标志物、构建多基因风险评分、优化机器学习方法、推进模型适用性调整及拓宽应用渠道等,以期为国内急性心力衰竭再入院和死亡风险预测模型相关研究提供思路。     

中国8个长寿地区65岁及以上老年人慢性肾脏病的6年发生风险预测

目的建立适用于中国≥65岁老年人慢性肾脏病(CKD)的发生风险预测工具。方法基于前瞻性队列研究,收集来自"中国老年健康影响因素跟踪调查"子队列-"老年健康生物标志物队列研究"2008/2009年至2014年和2012年至2017/2018年3742名研究对象的数据,对基线未患CKD的1055人追踪2次肾功能结局,使用Lasso方法筛选预测因素,采用Cox比例风险回归模型构建CKD发生风险预测模型并通过列线图工具实现模型可视化。采取bootstrap 1000次重复抽样的方法进行内部验证,并使用C指数和校准曲线评估模型的性能。结果研究对象年龄为(80.8±11.4)岁。经过4797人年随访,262例(24.8%)研究对象发生CKD。年龄、BMI、性别、文化程度、婚姻状况、退休金或保险、高血压、血尿酸、血尿素氮、TC和基线肾小球滤过率因素纳入6年CKD发生风险预测模型,模型校正C指数为0.766。校准曲线显示预测CKD发生概率和实际发生概率在高风险组中一致性较高,但在低风险组人群中一致性相对较差。结论本研究建立的CKD发生风险预测模型性能较好,列线图可作为可视化工具用于中国≥65岁老年人的6年CKD发生风险预测。     

疾病发病风险预测模型的应用与建立

<正>在医学研究领域中,"预测模型"常被用来预测某种疾病未来的发病情况。具体来说就是以疾病的多病因为基础,建立统计模型,用来预测具有某些特征的人群未来某种结局事件发生的概率。最早应用疾病风险预测模型(以下简称"预测模型")的领域是弗明翰心脏病研究(Framingham heart study)[1-2]。这些模型使得多危险因素和未来心血管病的发生之间确定为一种定量关系,通过多危险因素的水平来预测某一个体未     

中国10个地区成年人慢性肾脏病流行情况及其生活方式影响因素

目的 描述中国慢性病前瞻性研究（CKB）项目人群的慢性肾脏病（CKD）分布,分析生活方式与CKD发病风险的前瞻性关联。方法 数据来源于CKB基线调查及随访监测（截至2018年12月31日）,描述CKD发病的地区和人群分布差异,使用Cox比例风险回归模型分析生活方式因素与CKD的关联。结果 研究纳入505 147名研究对象,平均随访11.26年,期间共有4 920例发病病例,发病率为83.43/10万人年,肾小球肾病为最主要亚型。CKD发病率在城市、男性、年龄≥60岁人群中更高,分别为87.83/10万人年、86.37/10万人年、132.06/10万人年。相比于从不或偶尔吸烟者,当前吸烟男性CKD风险增加（HR=1.18,95%CI：1.05~1.31）。以非肥胖人群为参照组,由BMI判定的全身性肥胖（HR=1.19,95%CI：1.10~1.29）和腰围判定的中心性肥胖（HR=1.27,95%CI：1.19~1.35）均与更高的CKD发病风险相关。结论 CKB项目人群CKD发病率存在明显的地区和人群差异,且其发病风险受到生活方式多因素的影响。     

老年人群心血管疾病风险预测模型研究进展

风险预测模型（模型）对于老年人群心血管疾病（CVD）的一级预防具有重要意义。国内外针对老年人群构建的CVD模型共检索到15篇文献。模型的结局定义差异较大;10个模型报告时缺少方法、结果的重要信息;10个模型存在高偏倚风险;13个模型在内部验证时仅表现出中等区分度;仅有4个模型经过外部验证。老年人群CVD模型在模型算法、预测因子与结局的关联强度方面与一般人群模型存在差异,且老年人群模型的预测能力有所下降。未来仍需补充高质量的外部验证研究证据,并探索增加新的预测因子、采用竞争风险模型算法、机器学习算法、联合模型算法、改变预测时间范围等途径对模型进行优化。     

ICU患者再喂养综合征风险预测模型的研究进展

再喂养综合征(refeeding syndrome, RFS)在重症患者中发生率较高,严重影响患者的康复和预后。通过对再喂养综合征的风险因素和风险预测模型进行综述,发现其风险因素包括患者相关因素、治疗相关因素和疾病相关因素三个方面;风险预测模型包括风险分层模型、风险评分模型和Logistic回归模型。预防RFS的发生重点在于早期评估,但目前尚缺乏预测效能良好的RFS风险预测模型。关注营养和血清学指标等多方面因素对RFS的预防有着重要意义,未来需开展前瞻性、多中心研究,以构建预测效能良好的ICU患者RFS风险预测模型,为RFS高危人群的早期评估和早期干预提供参考。     

基于循证医学的出生缺陷发病风险预测模型

  目的  在循证医学的基础上利用Rothman-Keller模型建立出生缺陷发病风险预测模型, 为中国有针对性的制定干预措施提供依据。  方法  通过评价Meta分析的文献获得出生缺陷危险因素及相应比值比(odds ratio, OR)值, 构建Rothman-Keller模型的危险评分表, 再利用模拟数据构建模型, 确定风险预测危险程度划分界值, 并采用实际数据验证。  结果  通过17篇文献收集了先天畸形家族史和居住地有污染源等20个出生缺陷的主要危险因素。在山西省的实际数据中, Rothman-Keller模型筛选出的高危人群实际发病率为10.9%, 并与其他组有统计学差异(χ2=147.58, P < 0.001)。除此之外, Rothman-Keller模型能将先天畸形家族史的患者100%识别到高危人群。  结论  通过中国出生缺陷的Meta分析文献, 找出出生缺陷主要危险因素, 构建风险预测模型, 可用于预测出生缺陷的发病风险, 帮助筛选高危人群。同时为预测其他疾病的发病风险提供了思路。     

A systematic review finds prediction models for chronic kidney disease were poorly reported and often developed using inappropriate methods

BackgroundChronic kidney disease (CKD) is a global health concern that is increasing mainly as the result of increasing incidences of diabetes and hypertension. Furthermore, if left untreated, individuals with CKD may progress to end-stage kidney failure. Identifying individuals with undiagnosed CKD or those who are at an increased risk of developing CKD or progressing to end-stage kidney disease (ESKD) is therefore an important challenge. We sought to systematically review and critically assess the conduct and reporting of methods used to develop risk prediction models for predicting the risk of having undiagnosed (prevalent) or future risk of developing (incident) CKD or end-stage kidney failure in adults.MethodsWe conducted a systematic search of PubMed database to identify studies published up until September 2011 that describe the development of models combining two or more variables to predict the risk of prevalent or incident CKD or ESKD. We extracted key information that describes aspects of developing a prediction model, including the study design, data quality, sample size and number of events, outcome definition, risk predictor selection and coding, missing data, model-building strategies, and aspects of performance.ResultsEleven studies describing the development of 14 prediction models were included. Eight studies reported the development of 11 models to predict incident CKD or ESKD, whereas 3 studies developed models for prevalent CKD. A total of 97 candidate risk predictors were considered, and 43 different risk predictors featured in the 14 prediction models. A method, not recommended to select risk predictors for inclusion in the multivariate model, using statistical significance from univariate screening was carried out in six studies. Missing data were frequently poorly handled and reported with no mention of missing data in four studies; 4 studies explicitly excluded individuals with missing data, and only 2 studies used multiple imputation to replace missing values.ConclusionWe found that prediction models for chronic kidney were often developed using inappropriate methods and were generally poorly reported. Using poor methods can affect the predictive ability of the models, whereas inadequate reporting hinders an objective evaluation of the potential usefulness of the model.     

The DASH Diet and Cardiometabolic Health and Chronic Kidney Disease: A Narrative Review of the Evidence in East Asian Countries

The rising incidence of cardiometabolic diseases and chronic kidney disease (CKD) is a leading public health problem in East Asia. Diet is an important modifiable risk factor; thus, adopting a healthy diet such as the Dietary Approaches to Stop Hypertension (DASH) diet may help combat these chronic diseases. The DASH diet was originally developed in a U.S. population, and East Asia is demographically and culturally different from the U.S. Therefore, it is important to examine the evidence regarding the DASH diet and chronic disease in this unique population. This narrative review summarizes the evidence on the DASH diet and cardiometabolic health and CKD in East Asia. Culturally-modified DASH diets have been developed in some East Asian countries. Studies suggest the DASH diet is effective at lowering blood pressure in this population, though the long-term benefits remain unclear. Evidence also suggests the DASH diet may reduce the risk of type 2 diabetes and metabolic syndrome. Further research indicates the DASH diet and its components may reduce CKD risk. However, recommending the DASH diet in those who already have CKD is controversial, as it conflicts with current CKD dietary guidelines, especially in advanced CKD. Notably, current intakes in the general population differ from the DASH dietary pattern, suggesting public health efforts would be needed to encourage adoption of the DASH diet.     

我国慢性肾脏病防治政策及策略研究——基于卫生经济学视角

慢性肾脏病(chronic kidney disease,CKD)是世界范围内公共卫生领域的研究热点。流行病学研究证实了我国多个地区的高CKD患病率,但目前我国关于CKD防治的公共卫生政策及策略研究还处于缺失的状态。文章遵循卫生经济学的基本原理,根据我国CKD流行病学研究结果,结合我国国情并借鉴发达国家成熟的做法,从合理使用CKD防治的卫生资源;做好CKD的一级预防;依托分级诊疗模式,强化全科医师在CKD防治一线的作用,逐步充实肾科专科医师队伍;发挥政策协调作用,逐步控制部分地区的高CKD患病率;加强卫生信息系统建设,实现CKD防治的宏观管理和科学决策等方面,探索建立具有中国特色的CKD防治政策及策略。     

Modeling risk-adjusted capitation rates for Umbria,Italy

We studied the potential effect of refining per capita financing in Italy by risk adjustment using severity of illness as well as age and gender. Data were drawn from hospital, pharmaceutical, and demographic files for the entire population of the Umbrian region of Italy in 1997 and 1998. Hospitalization data from 1997 were used to classify patients into severity of illness categories which were hypothesized to be at risk for higher health services costs in 1998. Data on costs in 1998 were developed from hospital and pharmaceutical administrative data. Coefficients from 1997 models were used to develop predicted 1998 costs. Predicted costs in 1998 were compared to observed costs. Disease Staging models identified 155 unique clinical risk adjustment categories. These categories included 5.3% of the Umbrian population in 1997, who accounted for 21.6% of costs in the next year. In prediction models of future year costs using Umbrian data, R² values for Disease Staging models were 0.16, compared to values of 0.07 for a risk adjustment model used by Medicare. By identifying groups within the overall population who were more severely ill and who used more resources, these models can be used to assist health care planners estimate health care resources such as facilities, manpower, and programs.     

A Multiclass Likelihood Ratio Approach for Genetic Risk Prediction Allowing for Phenotypic Heterogeneity

The translation of human genome discoveries into health practice is one of the major challenges in the coming decades. The use of emerging genetic knowledge for early disease prediction, prevention, and pharmacogenetics will advance genome medicine and lead to more effective prevention/treatment strategies. For this reason, studies to assess the combined role of genetic and environmental discoveries in early disease prediction represent high priority research projects, as manifested in the multiple risk prediction studies now underway. However, the risk prediction models formed to date lack sufficient accuracy for clinical use. Converging evidence suggests that diseases with the same or similar clinical manifestations could have different pathophysiological and etiological processes. When heterogeneous subphenotypes are treated as a single entity, the effect size of predictors can be reduced substantially, leading to a low‐accuracy risk prediction model. The use of more refined subphenotypes facilitates the identification of new predictors and leads to improved risk prediction models. To account for the phenotypic heterogeneity, we have developed a multiclass likelihood‐ratio approach, which simultaneously determines the optimum number of subphenotype groups and builds a risk prediction model for each group. Simulation results demonstrated that the new approach had more accurate and robust performance than existing approaches under various underlying disease models. The empirical study of type II diabetes (T2D) by using data from the Genes and Environment Initiatives suggested heterogeneous etiology underlying obese and nonobese T2D patients. Considering phenotypic heterogeneity in the analysis leads to improved risk prediction models for both obese and nonobese T2D subjects.     

Several methods to assess improvement in risk prediction models: Extension to survival analysis

Risk prediction models have been widely applied for the prediction of long‐term incidence of disease. Several parameters have been identified and estimators developed to quantify the predictive ability of models and to compare new models with traditional models. These estimators have not generally accounted for censoring in the survival data normally available for fitting the models. This paper remedies that problem. The primary parameters considered are net reclassification improvement (NRI) and integrated discrimination improvement (IDI). We have previously similarly considered a primary measure of concordance, area under the ROC curve (AUC), also called the c‐statistic. We also include here consideration of population attributable risk (PAR) and ratio of predicted risk in the top quintile of risk to that in the bottom quintile. We evaluated estimators of these various parameters both with simulation studies and also as applied to a prospective study of coronary heart disease (CHD). Our simulation studies showed that in general our estimators had little bias, and less bias and smaller variances than the traditional estimators. We have applied our methods to assessing improvement in risk prediction for each traditional CHD risk factor compared to a model without that factor. These traditional risk factors are considered valuable, yet when adding any of them to a risk prediction model that has omitted the one factor, the improvement is generally small for any of the parameters. This experience should prepare us to not expect large values of the risk prediction improvement evaluation parameters for any new risk factor to be discovered. Copyright © 2010 John Wiley & Sons, Ltd.     

某医院体检人群糖尿病预测模型研究

目的 探讨logistic回归和随机森林在体检人群糖尿病患病风险预测中的应用。 方法 选择2006年1月-2015年12月在北京航天总医院体检中心参加体检的非糖尿病者11 769例次,随机选取70%样本,以性别、年龄、BMI、吸烟史、饮酒史、高血压既往史、高血压家族史、糖尿病家族史、收缩压、舒张压、空腹血糖、总胆固醇、甘油三酯、脂肪肝等14个因素作为自变量,以5年内是否罹患糖尿病作为因变量,基于logistic回归和随机森林分别建立糖尿病预测模型。将预测模型应用于剩余30%样本,根据所得受试者工作特征曲线的曲线下面积(AUC)评价模型的预测效果。 结果 Logistic回归预测模型和随机森林预测模型的AUC分别为0.912(95%CI:0.898~0.927)和0.919(95%CI:0.906~0.932),在最佳临界点,Logistic回归预测模型的灵敏度和特异度分别为80.8%和87.3%,随机森林预测模型的灵敏度和特异度分别为84.1%和85.3%。 结论 Logistic回归预测模型和随机森林预测模型对体检人群的糖尿病患病风险均具有良好的预测能力。     

Phosphorus and the Kidney: What Is Known and What Is Needed

A major role of the kidneys is to maintain phosphorus homeostasis. High serum phosphorus has been linked to all-cause and cardiovascular mortality in chronic kidney disease (CKD) both before and after initiation of renal replacement therapy. Considering the clinical implications of uncontrolled hyperphosphatemia, maintenance of phosphorus concentrations within an optimum range is standard of care in this patient population. Recently, the epidemiologic associations between serum phosphorus and worse outcome have been extended to the general population. This becomes even more important in view of the increasing dietary phosphorus intake in the American diet due in large part to the greater consumption of foods processed with phosphate additives. A greater understanding of mechanisms and epidemiology of altered phosphorus metabolism and disease in CKD may help clarify the possible role of excess dietary phosphorus as a health risk factor in the general population.