上海市嘉定区社区老年人的失能现状及其影响因素

目的  了解上海市嘉定区老年人的失能现状并分析其影响因素,为老年人失能防控和健康老龄化建设提供科学依据。方法  采用多阶段随机抽样方法,选取≥60岁的嘉定区户籍老年人群作为调查对象,采用世界卫生组织研发的自报健康调查量表测量老年人失能情况;应用多因素Logistic回归分析模型及列线图分析老年人失能的影响因素。结果  共调查老年人4 773人,其中失能老人495人,失能率为11.4%,女性的失能率(13.6%)高于男性(8.9%)。老年人自我评价在认知记忆、视力辨认、疼痛不适、睡眠及活动行走这五方面存在更多的失能,失能率分别为31.7%、23.0%、21.6%、20.4%和13.6%。多因素分析结果显示,年龄大、自评健康较差、患2种及以上慢性病的老年人失能率较高(均有P＜0.05);每周体育锻炼、饮酒的老年人失能率较低(均有P＜0.05)。结论  嘉定区社区老年人失能率较高。应加强慢性病管理,关注老年人的不同照护需求,开展针对性的早期预防和干预。     

青年结直肠黏液腺癌预后列线图的建立与验证

目的 利用美国监测、流行病学和最终结果数据库(SEER)建立青年结直肠黏液腺癌(MAC)预后列线图并对其进行验证.方法 收集SEER数据库中2004—2015年936例青年结直肠MAC的资料,利用R软件将其随机分为建模组(n=656)和验证组(n=280).通过COX比例风险回归模型筛选建模组人群的预后因素,并建立列线...     

心房颤动患者预后评分系统的构建和比较

目的  构建心房颤动人群预后预测工具, 并对其预测能力进行比较评估。 方法  连续性纳入275例新发心房颤动患者, 随访终点包括卒中和全因死亡。收集相关基线资料, 检测患者基线血浆N末端B型利钠肽原(N-terminal pro B-type natriuretic peptide, NT-proBNP)、高敏肌钙蛋白T(high-sensitivity cardiac troponin T, hs-cTnT)、生长分化因子15(growth differentiation factor-15, GDF-15)浓度。运用Cox比例风险模型构建卒中和死亡风险评分系统。应用C-统计量和校准图比较评分系统的预测能力。 结果  多因素Cox回归显示, 糖尿病、短暂性脑缺血发作(transient ischemic attack, TIA)、卒中史、血浆NT-proBNP浓度与心房颤动患者卒中风险独立相关; 年龄、心衰史、血浆hs-cTnT和GDF-15浓度与心房颤动患者全因死亡风险独立相关。我们构建的卒中风险评分系统预测能力与国外年龄、生物标志物和临床病史(age, biomarker, clinical history, ABC)卒中评分以及CHA2DS2-VASc评分相当, 死亡风险评分系统与国外ABC死亡评分相当, 优于CHA2DS2-VASc评分。 结论  本研究构建的心房颤动患者卒中和死亡风险预测评分系统表现出较好的预测性能, 此评分系统的列线图可望作为临床决策的辅助工具。     

基于SEER数据库脊索瘤临床预测模型的建立及验证

目的 构建脊索瘤患者的预测模型并进行验证.方法 从SEER数据库(2004～2015年)中鉴定和收集597例脊索瘤患者.Nomogram是基于建模组420例拥有完整数据的患者建立的.C指数(C-index)和校正曲线确定Nomogram的预测精度和判别能力.结果 建立了基于年龄、种族、原发部位及数量、肿瘤分期(TNM)...     

Explorations on risk profiles for overweight and obesity in 9501 preschool-aged children

BackgroundChildhood obesity places a major burden on global public health. We aimed to identify and characterize potential factors, both individually and jointly, in association with overweight and obesity in Chinese preschool-aged children.MethodsWe cross-sectionally recruited 9501 preschool-aged children from 30 kindergartens in Beijing and Tangshan. Overweight and obesity are defined according to the World Health Organization (WHO), International Obesity Task Force (IOTF), and China criteria.ResultsAfter multivariable adjustment, eating speed, sleep duration, birthweight, and paternal body mass index (BMI) were consistently and significantly associated with childhood overweight and obesity under three growth criteria at a significance level of 5%. Additional fast food intake frequency, maternal BMI, gestational weight gain (GWG) and maternal pre-pregnancy BMI were significant factors for overweight (WHO criteria) and obesity (both IOTF and China criteria). Importantly, there were significant interactions between parental obesity and eating speed for childhood obesity. Finally, for practical reasons, risk nomogram models were constructed for childhood overweight and obesity based on significant factors under each criterion, with good prediction accuracy.ConclusionOur findings indicated a synergistic association of lifestyle, fetal and neonatal, and family-related factors with the risk of experiencing overweight and obesity among preschool-aged children.     

新型冠状病毒肺炎患者死亡风险列线图模型的构建与验证

目的构建列线图模型以预测新型冠状病毒病2019(COVID-19)的死亡风险,以早期筛选死亡高危患者。 方法收集2020年1月至2020年4月武汉大学人民医院(东院)和2022年4月至2022年5月上海市第九人民医院(北院)收治COVID-19患者的临床资料。以武汉大学人民医院患者(166例)作为训练集,上海市第九人民医院患者(52例)作为验证集。采用先单因素后多因素Logistic回归分析确定死亡的独立危险因素,应用R语言构建列线图模型。采用受试者工作特征曲线(ROC)、C指数及校准曲线评估列线图模型的预测准确性及判断能力,决策曲线分析评估模型的临床应用价值。通过验证集对模型进行外部验证。 结果本研究共纳入重型/危重型COVID-19患者218例,其中67例(30.73%)死亡,多因素Logistic回归分析显示,≥3种基础疾病、APACHE Ⅱ评分(5~40分)、中性粒细胞/淋巴细胞(0~90)、乳酸(0~16mmol/L)均是死亡的独立危险因素。ROC曲线分析显示,训练集的曲线下面积(AUC)为0.869(95%CI：0.811~0.927),验证集AUC为0.797(95%CI：0.671~0.924),训练集与验证集校准曲线经Hosmer-Lemeshow拟合优度检验(P＝0.473,P＝0.421)。临床决策曲线分析表明,该列线图预测模型的临床应用价值高。 结论本研究构建COVID-19患者死亡风险列线图模型预测效能良好,可个体化、可视化、图形化预测,有助于医师早期做出合适临床决策及诊疗。     

Nomograms as a Tool in Predicting Prostate Cancer Prognosis

Prostate cancer (PCa) is a heterogeneous disease with different disease states. Clinical judgment has significant biases. Nomograms are graphical calculating tools that use several clinical variables to determine a specific clinical outcome. Nomograms as prediction tools use multiple variables and continuous variables continuously, and their characteristics include patient populations, clinical end points, accuracy, and whether internal and/or external validations were performed. Numerous nomograms are available for most PCa stages, and their availability and accuracy make them powerful tools for the improvement of clinical prediction and important aids in personalised medicine for both patients and physicians.     

Development and internal validation of a nomogram predicting the probability of prostate cancer Gleason sum upgrading between biopsy and radical prostatectomy pathology

OBJECTIVE: Previous reports indicate that as many as 43% of men with low grade PCa at biopsy will be diagnosed with high-grade PCa at RP. We explored the rate of upgrading from biopsy to RP specimen in our contemporary cohort, and developed a model capable of predicting the probability of biopsy Gleason sum upgrading. MATERIALS AND METHODS: The study cohort consisted of 2982 men treated with RP, with available clinical stage, serum prostate specific antigen and biopsy Gleason scores. These clinical data were used as predictors in multivariate logistic regression models (LRM) addressing the rate of Gleason sum upgrading between biopsy and RP pathology. LRM regression coefficients were used to develop a nomogram predicting the probability of Gleason sum upgrading and was subjected to 200 bootstrap resamples for internal validation and to reduce overfit bias. RESULTS: Overall, 875 patients were upgraded (29.3%). In multivariate LRMs, all predictors were highly significant (all p values <0.0001). Bootstrap-corrected predictive accuracy of the nomogram predicting the probability of Gleason sum upgrading between biopsy and RP was 0.804. CONCLUSION: We developed a highly accurate clinical aid for treatment decision-making. It may prove useful when the possibility of a more aggressive Gleason variant may change the treatment options.     

Pre-treatment nomogram for disease-specific survival of patients with chemotherapy-naive androgen independent prostate cancer

OBJECTIVE: Our objective was to develop a nomogram that predicts the probability of cancer-specific survival in men with untreated androgen-independent prostate cancer (AIPC). METHODS: AIPC was diagnosed in 129 consecutive patients between 1989 and 2002. No patient received cytotoxic chemotherapy. Univariate and multivariate Cox regression models were used to test the association between prostate-specific antigen (PSA) level at initiation of androgen deprivation, PSA doubling time (PSADT), PSA nadir on androgen deprivation therapy (ADT), time from ADT to AIPC, and AIPC-specific mortality. Multivariate regression coefficients were then used to develop a nomogram predicting AIPC-specific survival at 12-60 mo after AIPC diagnosis. Two-hundred bootstrap resamples were used to internally validate the nomogram. RESULTS: AIPC-specific mortality was recorded in 74 of 129 patients (57.4%). Other-cause mortality was recorded in 7 men (5.4%). Median overall survival was 52.0 mo (mean, 36.0 mo) and median AIPC-specific survival was 54.0 mo (mean, 35.0 mo). In univariate regression models, all variables were significant predictors of AIPC-specific survival (p < or = 0.02). In multivariate models, PSADT and time from androgen deprivation to AIPC remained statistically significant (p < or = 0.004). Bootstrap-corrected predictive accuracy of the nomogram was 80.9% versus 74.9% for our previous model. CONCLUSIONS: A nomogram predicting AIPC-specific survival is between 13% and 14% more accurate than previous nomograms and 6% more accurate than tree regression-based predictions obtained from the same data. Moreover, a nomogram approach combines several advantages, such as user-friendly interface and precise estimation of individual recurrence probability at several time points after AIPC diagnosis, which all patients deserve to know and all treating physicians need to know.