共查询到18条相似文献,搜索用时 187 毫秒
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《中国循证心血管医学杂志》2021,(7)
目的分析影响急性Stanford A型主动脉夹层(ATAAD)术后早期死亡的危险因素,建立列线图预测模型。方法回顾性分析2015年1月至2020年4月于河南省安阳市第六人民医院收治的304例ATAAD患者临床资料,统计术后30 d死亡率,按是否死亡分为死亡组与存活组,采用单因素及多因素分析筛选影响ATAAD术后早期死亡的危险因素,将各危险因素引入R软件建立列线图预测模型,评估模型的准确度与一致性。结果 ATAAD患者早期死亡率为18.42%(56/304);Logistic回归分析显示,年龄、同期行冠状动脉旁路移植术(CABG)、肾功能不全、体外循环时间、辅助通气时间是影响早期死亡率的独立影响因素(P0.05);基于影响ATAAD患者早期死亡风险的危险因素,使用R软件建立列线图预测模型,ROC曲线下面积为0.801,校准曲线斜率近似于1,Hosmer-Lemeshow拟合优度检验χ2=6.494,P=0.592。结论基于影响ATAAD患者早期死亡风险的危险因素构建的列线图预测模型具有良好准确度与一致性,可为降低ATAAD患者早期死亡率提供一定指导价值。 相似文献
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目的 探讨急性脑梗死患者并发急性肾损伤(AKI)的影响因素,构建其风险预测列线图模型并进行验证。方法 回顾性选取2019年1月至2023年1月连云港市第二人民医院收治的急性脑梗死患者100例为研究对象,收集患者的临床资料,记录患者发病3个月内并发AKI情况,并将其分为无AKI组和并发AKI组。采用多因素Logistic回归分析探讨急性脑梗死患者并发AKI的影响因素;采用R 3.6.3软件构建急性脑梗死患者并发AKI的风险预测列线图模型;采用Bootstrap法(重复抽样1 000次)予以内部验证,计算一致性指数,采用Hosmer-Lemeshoe拟合优度检验和校准曲线评价该列线图模型的拟合程度;采用ROC曲线分析该列线图模型对急性脑梗死患者并发AKI的预测价值。结果100例急性脑梗死患者中,31例并发AKI,AKI发生率为31.0%。并发AKI组患者年龄≥60岁、合并糖尿病、合并高血压、合并高脂血症、超敏C反应蛋白(hs-CRP)≥15.8 mg/L者占比高于无AKI组(P<0.05)。多因素Logistic回归分析结果显示,年龄、合并高血压、hs-CRP是急性脑梗死患者并发AK... 相似文献
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杨赓 《中国实用内科杂志》2018,(7)
目的通过分析急性心力衰竭(AHF)患者并发急性肾损伤(AKI)的影响因素,建立个体化预测AHF患者并发AKI风险的列线图模型。方法纳入2015年1月至2017年6月于首都医科大学附属北京安贞医院诊治的AHF患者625例作为建模组,另纳入2017年7月至2017年11月诊治的AHF患者130例作为验证组,收集临床资料。应用单因素及多因素Logistic回归模型,分析AHF患者并发AKI的影响因素。应用R软件建立预测AHF患者并发AKI风险的列线图模型,并进行验证。结果 Logistic回归分析显示,年龄、糖尿病、高敏C反应蛋白(hs-CRP)、肾小球滤过率(e GFR)及纽约心脏协会(NYHA)心功能Ⅳ级是AHF患者并发AKI的独立影响因素。对列线图模型进行验证,其在建模组与验证组的C-index分别为0.846和0.812,校准曲线示该模型具有良好的区分度与精准度;ROC曲线示该模型在建模组与验证组预测AHF患者并发AKI风险的曲线下面积分别为0.830和0.808。结论本研究基于年龄、糖尿病、hs-CRP、e GFR及NYHA心功能Ⅳ级这5项AHF患者并发AKI的独立影响因素,建立的个体化预测AHF患者并发AKI风险的列线图模型,具有良好的区分度与精准度,临床应用价值高。 相似文献
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杨赓 《中国实用内科杂志》2018,(7):633-637
目的通过分析急性心力衰竭(AHF)患者并发急性肾损伤(AKI)的影响因素,建立个体化预测AHF患者并发AKI风险的列线图模型。方法纳入2015年1月至2017年6月于首都医科大学附属北京安贞医院诊治的AHF患者625例作为建模组,另纳入2017年7月至2017年11月诊治的AHF患者130例作为验证组,收集临床资料。应用单因素及多因素Logistic回归模型,分析AHF患者并发AKI的影响因素。应用R软件建立预测AHF患者并发AKI风险的列线图模型,并进行验证。结果 Logistic回归分析显示,年龄、糖尿病、高敏C反应蛋白(hs-CRP)、肾小球滤过率(e GFR)及纽约心脏协会(NYHA)心功能Ⅳ级是AHF患者并发AKI的独立影响因素。对列线图模型进行验证,其在建模组与验证组的C-index分别为0.846和0.812,校准曲线示该模型具有良好的区分度与精准度;ROC曲线示该模型在建模组与验证组预测AHF患者并发AKI风险的曲线下面积分别为0.830和0.808。结论本研究基于年龄、糖尿病、hs-CRP、e GFR及NYHA心功能Ⅳ级这5项AHF患者并发AKI的独立影响因素,建立的个体化预测AHF患者并发AKI风险的列线图模型,具有良好的区分度与精准度,临床应用价值高。 相似文献
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杨赓 《中国实用内科杂志》2018,(7):633-637
目的通过分析急性心力衰竭(AHF)患者并发急性肾损伤(AKI)的影响因素,建立个体化预测AHF患者并发AKI风险的列线图模型。方法纳入2015年1月至2017年6月于首都医科大学附属北京安贞医院诊治的AHF患者625例作为建模组,另纳入2017年7月至2017年11月诊治的AHF患者130例作为验证组,收集临床资料。应用单因素及多因素Logistic回归模型,分析AHF患者并发AKI的影响因素。应用R软件建立预测AHF患者并发AKI风险的列线图模型,并进行验证。结果 Logistic回归分析显示,年龄、糖尿病、高敏C反应蛋白(hs-CRP)、肾小球滤过率(e GFR)及纽约心脏协会(NYHA)心功能Ⅳ级是AHF患者并发AKI的独立影响因素。对列线图模型进行验证,其在建模组与验证组的C-index分别为0.846和0.812,校准曲线示该模型具有良好的区分度与精准度;ROC曲线示该模型在建模组与验证组预测AHF患者并发AKI风险的曲线下面积分别为0.830和0.808。结论本研究基于年龄、糖尿病、hs-CRP、e GFR及NYHA心功能Ⅳ级这5项AHF患者并发AKI的独立影响因素,建立的个体化预测AHF患者并发AKI风险的列线图模型,具有良好的区分度与精准度,临床应用价值高。 相似文献
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《中国循环杂志》2021,(7)
目的:建立一个使用简便而准确的列线图预测模型,用于预测心功能不全患者冠状动脉旁路移植术(CABG)后急性肾损伤(AKI)的发生率。方法:连续入选单中心自2012年到2017年间1 208例术前左心室射血分数(LVEF)小于50%的心功能不全并行单纯CABG的患者。根据2012年KDIGO共识对于术后AKI的诊断定义,建立一个预测AKI的列线图模型,根据相同的入组标准,从中国心力衰竭外科数据库(China Heart Failure Surgery Registry, China-HFSR)中收集了同期国内几个大心脏中心的患者资料(n=540)进行新模型的外部验证。将新列线图模型与另外3个临床常用的心脏手术术后肾衰竭评分系统(克里夫兰ARF评分、Mehta评分以及SRI评分)进行对比。结果:建模组共1 208例患者,其中术后发生AKI的患者共90例(7.5%),新列线图模型纳入了性别、术前血肌酐2 mg/dl、LVEF35%、既往心肌梗死病史、高血压、体外循环使用和围术期输血等7个独立的危险因素。其ROC曲线下面积(AUC)为0.738,较其他3个常用模型都高。通过对比各模型的校准曲线(calibration curve),新列线图显示出更好校准度。验证组共540例患者,其中术后发生AKI共104例(19.3%),新列线图模型比其他3个模型显示出更好的区分度和校准度。结论:新列线图模型对于心功能不全患者CABG术后AKI的预测具有更好的准确性和便利度。 相似文献
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目的 分析影响妊娠合并系统性红斑狼疮(SLE)患者发生不良妊娠结局的相关因素,并构建列线图预测模型。方法 选择妊娠合并SLE患者314例,使用电脑随机分组法按照7∶3的比例,将220例患者作为建模组,剩余94例作为验证组,根据建模组妊娠合并SLE患者的妊娠结局分为妊娠结局不良组(73例)和妊娠结局良好组(147例)。采用Logistic回归模型分析影响妊娠合并SLE患者妊娠结局的影响因素,再利用R软件构建列线图预测模型,使用受试者工作特征(ROC)曲线、校准曲线、H-L拟合优度检验对已构建列线图预测模型的预测效能进行验证。结果 多因素Logistic回归分析结果显示,狼疮性肾炎、子痫前期、雷诺现象、抗SSA抗体+、SLE疾病活动评分(SLEDAI)低、血小板减少、肾功能不全为妊娠合并SLE患者发生不良妊娠结局的危险因素(P均<0.05)。以此7项影响因素构建列线图模型,建模组验证列线图模型的ROC曲线下面积为0.893(95%CI:0.844~0.930),校准曲线斜率接近1,区分度良好,H-L检验χ2=8.877,P=0.342;验证组ROC曲线下面积为0.831(95%CI:... 相似文献
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目的:探讨冠状动脉旁路移植术(coronary artery bypass grafting,CABG)后应用主动脉内球囊反搏(intra-aortic balloon pump,IABP)的危险因素,构建预测CABG术后应用IABP风险的列线图模型。方法:回顾性收集2020年3月至2022年9月,在河南省人民医院心脏中心行CABG手术的161例冠心病患者的临床资料,根据CABG术后是否应用IABP将患者分为IABP组(58例)和非IABP组(103例)。通过单因素及多因素Logistic回归分析筛选影响CABG术后应用IABP的危险因素,应用R软件建立预测IABP应用风险的列线图模型,利用ROC曲线下面积(AUC)、校准曲线和Hosmer-Lemeshow拟合优度检验评估列线图预测模型。结果:多因素Logistic回归分析显示:术前NT-proBNP(OR=1.288,95%CI:1.063~1.560,P=0.010)升高、术前肌钙蛋白T(TnT)(OR=2.460,95%CI:1.079~5.383,P=0.032)升高、术前LVEF(OR=0.873,95%CI:0.830~0... 相似文献
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目的 构建基于随机森林算法的心胸外科术后患者急性肾损伤(acute kindey injury,AKI)的预测模型,并分析其预测价值。方法 选取广元市第一人民医院2018年1月~2020年12月接受心胸外科手术治疗的212例患者为研究对象,采用随机数字表法按2:1的比例建立训练集和测试集。采用随机森林算法对心胸外科患者术后AKI的预测指标的重要性进行排序。根据袋外数据误差,赤池信息量准则和贝叶斯信息量准则对排序指标进行筛选并构建预测模型,多维标度法(multidimensional scaling,MDS)观察预测模型对心胸外科术后患者AKI的预测能力;采用内部验证法验证模型对心胸外科患者术后AKI的预测能力。结果 212例患者中,148例未发生AKI的为未发生组,64例发生AKI为发生组;16个指标根据平均准确度下降程度和平均基尼指数下降程度进行重要性排序。用袋外数据误差,赤池信息量准则和贝叶斯信息量准则筛选出术后中性粒细胞明胶酶相关脂质运载蛋白(NGAL)、术后金属蛋白酶组织抑制剂2(TIMP 2)、术后胰岛素样生长因子结合蛋白7(IGFBP7)及术后血浆肌酐(pCr)4个变量(P... 相似文献
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Skin cancer is a common malignant tumor in human beings. At present, the construction of clinical prediction models mainly focuses on malignant melanoma and no researchers have constructed clinical prediction models for all kind of skin cancer to predict the prognosis of skin cancer. We used patient data collected from the surveillance, epidemiology, and end results program database to construct and validate our model for clinical prediction of skin cancer, hoping to provide a reference for clinical treatment of skin cancer.R software was used for univariate and multivariate Cox regression analysis of variables to screen out factors that have an impact on the survival of skin cancer patients. Then the prognostic model of skin cancer patients was constructed and the nomogram was drawn. Concordance Index (C-index), receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the clinical prediction model.A total of 3180 skin cancer patients were included in this study. We constructed nomogram, a 3-year and 5-year clinical prediction model for skin cancer patients. We used C-index to evaluate the accuracy of nomogram model, and the result of C-index was 0.728, 95%CI (0.703–0.753). The nomogram model was evaluated by ROC curve. The area under the curve values of the ROC curve for 3-year survival rate and 5-year survival rate were 0.732 and 0.768 respectively. The model calibration diagram of the modeling group also shows that the model exhibits high accuracy.The nomogram model of postoperative survival of patients with skin cancer, based on the surveillance, epidemiology, and end results program database of patients with skin cancer, has shown good stability and accuracy in multi-method validation. 相似文献
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Chaoyi Ye Tingjun Wang Jin Gong Xiaoqi Cai Guili Lian Li Luo Huajun Wang Liangdi Xie 《Journal of clinical hypertension (Greenwich, Conn.)》2021,23(6):1176
Left ventricular hypertrophy (LVH) is an important risk factor for cardiovascular morbidity and mortality in hypertensives. Therefore, early identification of at‐risk patients is necessary. The objective of this study was to estimate the risk of LVH among Chinese hypertensives by designing a nomogram. 832 hypertensives were divided into two groups based on the presence of LVH. The least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression were successively applied for optimal variable selection and nomogram construction. Discrimination power, calibration, and clinical usefulness were evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis. Internal validation was performed using the bootstrap method. The nomogram included five predictors, namely gender, duration of hypertension, age, body mass index (BMI), and systolic blood pressure. The area under the ROC curve (AUC) was 0.724 (95% CI: 0.687‐0.761), indicating moderate discrimination. The calibration curve showed an excellent agreement between the predicted LVH and the actual LVH probability. The risk threshold between 5% and 72% according to the decision curve analysis, and the nomogram is clinically beneficial. Internal validation by bootstrapping with 1000 samples showed a good C‐index of 0.715, which suggested that the predictive abilities for the training set and testing set were in consistency. Our study proposed a nomogram that can be utilized to assess the LVH risk rapidly for Chinese hypertensives. This tool could be useful in identifying patients at high risk for LVH. Further studies are required to ascertain the stability and applicability of this nomogram. 相似文献
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Li Qin Yiwen Zhang Xiaoqian Yang Han Wang 《Journal of clinical hypertension (Greenwich, Conn.)》2021,23(8):1556
Cardiac involvement is an important cause of morbidity and mortality in patients with idiopathic inflammatory myopathies (IIMs). Hypertension, an important cardiovascular risk factor for the general population, has a crucial role in heart involvement. However, few studies have focused on the hypertension associated with IIMs. This study aimed to develop and assess the prediction model for incident hypertension in patients with IIMs. A retrospective cohort study was performed on 362 patients with IIMs, of whom 54 (14.9%) were given a diagnosis of new‐onset hypertension from January 2008 to December 2018. The predictors of hypertension in IIMs were selected by least absolute shrinkage and selection operator (LASSO) regression, multivariable logistic regression, and clinically relevance, and then these predictors were used to draw the nomogram. Discrimination, calibration and clinical usefulness of the model were evaluated using the C‐index, calibration plot, and decision curve analysis, respectively. The predicting model was validated by the bootstrapping validation. The nomogram mainly included predictors such as age, diabetes mellitus, triglyceride, low‐density lipoprotein‐cholesterol (LDL‐C), antinuclear antibodies (ANA), and smoking. This prediction model demonstrated good discrimination with a C‐index of 0.754 (95%CI, 0.684 to 0.824) and good calibration. The C‐index of internal validation was 0.728, and decision curve analysis demonstrated that this nomogram was clinically useful. Clinicians can use this prediction model to assess the risk of hypertension in IIMs patients, and early preventive measures should be taken to reduce the incidence of hypertension in high‐risk patients. 相似文献