个体化预测腹股沟疝无张力修补术后切口感染风险列线图模型的建立

目的 探讨个体化预测腹股沟疝无张力修补术后切口感染风险列线图模型的建立.方法 采用回顾性分析方法,收集2017年1月至2019年12月因腹股沟疝于铜陵市市立医院行无张力修补术的病人的临床资料,分别使用单因素和Logistic回归多因素分析腹股沟疝无张力修补术后切口感染的独立危险因素,并建立相关列线图预测模型.结果 经L...     

尿毒症维持性血液透析短期内死亡的风险预测列线图模型建立

目的:探讨尿毒症维持性血液透析(MHD)短期内死亡的相关危险因素,并建立列线图模型。方法:回顾性分析本院2019年01月—2021年02月收治的180例行MHD治疗的尿毒症患者的临床资料,根据MHD治疗12个月内生存情况分为存活组(155例)和死亡组(25例)。采用多因素Logistic回归分析尿毒症MHD治疗短期内死亡的相关危险因素,并据此建立列线图预测模型。结果:单因素分析结果显示,死亡组年龄≥60岁、高血压、糖尿病、血流量<250 ml/min占比及透析龄、超滤量、总胆固醇、三酰甘油和C反应蛋白水平、平均病程均高于存活组(P <0.05),死亡组血红蛋白和白蛋白水平均低于存活组(P <0.05);多因素Logistic回归结果显示,年龄≥60岁、糖尿病史、超滤量、高总胆固醇、高三酰甘油和高C反应蛋白水平均是尿毒症患者采用MHD治疗短期内死亡的独立危险因素(P <0.05),而高白蛋白水平均是尿毒症患者采用MHD治疗短期内死亡的独立保护因素(P <0.05);列线图风险模型ROC曲线结果显示Youden指数为0.475,AUC为0.815,95%CI:...     

个体化预测肝叶切除术后发生肺部感染的风险列线图模型的建立

目的 探讨个体化预测肝叶切除术后发生肺部感染的风险并建立列线图模型.方法 选取2018年1月～2019年12月我院肝叶切除术的患者120例,调取患者的临床资料,采用多因素回归分析筛选出患者肝叶切除术后发生肺部感染的独立危险因素,对筛选出的独立危险因素建立列线图预测模型,并且验证该模型的预测性及准确性.结果 对两组患者基...     

糖尿病足患者溃疡愈合风险的列线图预测模型

目的 探讨预测糖尿病足溃疡（DFU）患者溃疡愈合风险的列线图模型。方法 收集2021年1―12月于新疆医科大学第一附属医院就诊的200例DFU患者的临床资料,依据DFU是否愈合将其分为愈合组（n=57）和未愈合组（n=143）。收集患者的年龄、性别、体重指数（BMI）、糖化血红蛋白、知-信-行（KAP）模式量表评分以及患者血液中微小RNA(miRNA)-15b-5p的相对含量,采用Logistic回归模型对临床特征数据进行单因素及多因素分析,向后法筛选出相关预测因子并建立预测模型,使用增强Bootstrap验证法对模型内部进行验证,使用一致性指数（C-index）、列线图、校准图、受试者工作特征（ROC）曲线及校准曲线、决策曲线分析（DCA）进行评估。结果 通过二元Logistic回归分析及向后法筛选变量,从8个总变量中确定了3个预测因子,即年龄、miRNA-15b-5p相对含量、K评分,其构建的预测模型呈现出中等预测能力,ROC曲线下面积（AUC）为0.923。结论 年龄、K评分及miRNA-15b-5p相对含量均为DFU患者溃疡未愈合相关的预测因子,基于这些预测因子建立的预测模型具...     

预测糖尿病患者并发糖尿病肾病风险的列线图模型的建立

目的探究2型糖尿病(type 2 diabetes mellitus,T2DM)患者并发糖尿病肾病(diabetic nephropathy,DN)的危险因素,开发和验证一种辅助临床预测DN的可视化评价工具。方法选取符合标准的2型糖尿病(T2DM)患者559例,其中单纯T2DM组(对照组)280例和合并微量白蛋白尿组(DN组) 279例。收集临床资料,采用单因素分析筛选DN相关因素,将有统计学意义变量纳入多因素Logistic回归模型,分析DN危险因素;应用R软件构建预测DN风险的列线图模型,采用Bootstrap法进行验证,并绘制ROC曲线,计算C-指数评估模型预测性能。通过绘制预测结果与实际结果的校正曲线,进行一致性测试。使用Hosmer-Lemeshow检验判断模型的拟合优度,P0.05表明模型的拟合优度较好。结果年龄、糖尿病病程、中性粒细胞计数、贫血、三酰甘油、体质量指数、糖尿病性周围神经病(diabetic peripheral neuropathy,DPN)、促甲状腺激素(thyroid stimulating hormone,TSH)与DN的发生有关(均P0.05),老年人、TSH4.6 mU/L、三酰甘油≥1.7 mmol/L、糖尿病性周围神经病、糖尿病病程1年是T2DM并发DN的独立危险因素(均P0.05),将这些因素纳入并成功构建了列线图。列线图模型预测效能好,ROC曲线下面积为0.852(95%CI=0.822～0.882),内部验证C-指数为0.846。校正曲线显示预测结果与实际结果的相关性良好(P=0.178)。结论本次研究构建的个体化预测DN早期患者风险的列线图模型,具有良好区分度,临床应用价值高,对甄别DN高风险人群,制订干预对策具有指导意义。     

卒中后抑郁风险列线图预测模型的构建及验证

目的 建立早期卒中后抑郁风险列线图预测模型,为临床医护人员筛查卒中后抑郁高危患者提供工具。 方法 对建模组259例卒中患者在卒中后第7~14天收集10项相关危险因素,于卒中后8~10周采用汉密尔顿抑郁量表和蒙哥马利抑郁量表测评卒中患者抑郁状况。利用χ2检验、Lasso回归及logistic回归筛选危险因素建立列线图预测模型,对模型进行内部及外部验证（验证组有82例患者）。 结果 卒中后抑郁检出率为39.38%;性别、婚姻状况、并存疾病数目、卒中部位、神经功能受损程度、日常生活活动能力是卒中后抑郁的独立危险因素（均P＜0.05）。基于上述6个独立危险因素建立的列线图预测模型具有较好的区分度（AUC值：内部验证为0.883,外部验证为0.849）和准确度（Hosmer-Lemeshow检验：内部验证χ2=7.939,P=0.439,外部验证χ2=3.538,P=0.896）;决策曲线分析显示预测模型曲线在大于10%的阈值概率区间具有临床实用价值。 结论 卒中患者发作后8~10周即有较高的卒中后抑郁发生率,构建的列线图模型能够有效预测早期卒中后抑郁风险,利于临床给予针对性干预。     

初产妇产后抑郁的护理干预探讨

将52例产后抑郁的初产妇随机分为两组,对照组22例采用产后常规治疗与护理,观察组30例同时实施社会支持和放松训练。结果于预10 d后,采用汉密顿抑郁量表评分,观察组显著低于对照组(P<0.01)。提示采取有效的护理干预对初产妇产后抑郁的恢复具有一定的临床意义。     

骨肉瘤预后个体化预测模型列线图的建立

 目的 通过对骨肉瘤患者的临床、病理学、影像学及随访资料进行预后因素分析,建立骨肉瘤预后预测模型列线图,并验证其准确度。方法 收集1998至2008年确诊且符合入组标准的235例骨肉瘤患者组成建模组,2009年的55例骨肉瘤患者组成验证组。单因素生存分析采用Kaplan-Meier法绘制生存曲线,Log-rank法进行统计学分析;应用Cox比例风险模型进行多因素分析,确定独立预后因子;然后应用R软件建立预测模型列线图,内部验证运用Bootstrap法,外部验证运用验证组,一致性指数（C-index）用来评价模型准确度,并绘制出列线图预测和实际观察的五年生存率校准曲线。结果 建模组和验证组五年总体生存率分别为46.1%±6.7%和61.8% ±12.9%。多因素分析结果显示,病理性骨折、入院时碱性磷酸酶水平、肿瘤大小、肿瘤分期和术后化疗次数是独立预后因素。校准曲线显示列线图预测与实际观察的五年生存率有很好的一致性。列线图预测五年生存率的C-index为0.74（95%CI,0.70~0.78）,明显高于Enneking分期系统。应用验证组进行外部验证,列线图、Enneking分期和美国癌症联合委员会（AJCC）分期系统的C-index分别为0.71、0.54和0.56,提示列线图比Enneking分期及AJCC分期的预后预测准确性更高。结论 成功建立的预测骨肉瘤患者总体生存列线图能实现个体化预测,且与其他预后预测系统相比更直观、准确。     

预测胰十二指肠切除术后切口疝发病风险列线图模型的建立

目的建立预测胰十二指肠切除（PD）术后发生切口疝的风险列线图模型,为切口疝的早期干预提供依据。 方法回顾性分析2009年1月至2017年1月于邯郸市中心医院行PD术的926患者的临床资料,随访发生切口疝的患者24例（切口疝组）,未发生切口疝的按照1∶5的比例随机抽取120例为非切口疝组。分别使用单因素和Logistic回归多因素分析术后切口疝的独立危险因素,并建立相关列线图预测模型。 结果年龄≥60岁（OR=5.800,95% CI 1.530~21.984）、BMI≥24 kg/m²（OR=4.165,95% CI 1.187~14.613）、糖尿病（OR=5.321,95% CI 1.548~18.285）、呼吸系统疾病（OR=4.565,95% CI 1.225~17.007）、切口感染（OR=6.803,95% CI 1.573~29.419）及手术时间≥6 h（OR=6.934,95% CI 1.938~24.813）是PD术后发生切口疝的独立危险因素（P<0.05）。基于以上6项独立危险因素建立列线图模型,并对该模型进行验证,预测值同实测值基本一致,C-index 0.890（95% CI 0.854~0.926）,说明本研究列线图模型具有良好的精准度和区分度。 结论年龄≥60岁、BMI≥24 kg/m²、糖尿病、呼吸系统疾病、切口感染及手术时间≥6 h是PD术后发生切口疝的独立危险因素,本研究建立的列线图预测模型具有良好的精准性和区分度,有利于临床筛查PD术后发生切口疝的高风险人群和制定针对性规避措施。     

预测右半结肠癌术后胃瘫危险因素列线图模型的建立

探讨右半结肠癌术后胃瘫发生的高危因素,建立预测胃瘫发生的列线图模型,并进行评价。选取山东大学齐鲁医院德州医院右半结肠癌患者233例,根据术后胃瘫发生情况分为发生胃瘫组(n=29)及非胃瘫组(n=204)。采用单因素及多因素Logistic回归分析术后发生胃瘫的危险因素,将独立危险因素建立列线图模型,绘制受试者工作特征(ROC)曲线、一致性指数及内部Bootstrap法抽样检验等评估模型的准确性。多因素Logistic回归分析显示NRS2002营养评分、术前有无糖尿病、脉管浸润与否、有无吻合口瘘、有无腹腔感染是右半结肠癌术后胃瘫发生的高危因素(P<0.05),基于以上影响因素构建了列线图模型,预测右半结肠癌术后胃瘫发生的曲线下面积为0.980(95%CI:0.907,0.966)。一致性指数验证、Bootstrap法内部验证模型显示稳健性良好。NRS2002营养评分、术前有无糖尿病、脉管浸润与否、有无吻合口瘘、有无腹腔感染是右半结肠癌术后胃瘫发生的独立危险因素,基于以上因素构建的列线图模型具有一定的临床应用价值。     

The effect of progressive muscle relaxation on the postpartum depression risk and general comfort levels in primiparas

In the postpartum period, mode of delivery, planned pregnancy, rapid changes in estrogen and progesterone hormone concentrations, quality of care, and environmental conditions affect the postpartum depression and comfort of the women. This study was conducted to evaluate the effect of progressive muscle relaxation (PMR) on the postpartum depression risk and general comfort levels in primiparas. A quasi‐experimental design was used with pretest, post‐test, and control group. The intervention and control groups consisted of 35 women each. PMR was applied to intervention group for 8 weeks. There was a statistically significant difference between Edinburgh Postnatal Depression Scale pretest and third follow‐up scores of the intervention group (p < .05). There was a statistically significant difference between the intervention and control groups in the mean General Comfort Questionnaire scores at the first, second, and third follow‐ups (p < .05). PMR may be effective on decreasing the postpartum depression risk and increasing general comfort. PMR could be administered through home visits to help women in postpartum period cope with their mental health problems after discharge.     

产后抑郁风险预测模型的构建和验证

目的 构建产后抑郁风险预测模型,并识别预测因子。方法 选取住院分娩产妇835人为研究对象,按照时间段分为训练集722人及测试集113人,以产后6周是否发生产后抑郁为结局指标。利用logistic回归、支持向量机和随机森林3种监督学习算法建立风险预测模型,采用序列前向选择法筛选特征,通过网格搜索法调整模型参数。将训练好的模型在训练集上进行十折交叉验证,在测试集上进行外部验证。结果 产妇产后6周抑郁发生率为22.6%（189/835）。经筛选,最终纳入14个预测因子。3种监督学习模型中,随机森林模型预测性能最佳,在测试集上的受试者工作特征曲线下面积、Brier得分、准确率、精确度、召回率和F1得分分别为0.943、0.073、0.903、0.684、0.722和0.703。结论 基于随机森林的产后抑郁风险模型预测性能最佳,能够辅助医护人员识别高风险人群。     

A nomogram prediction model for sternal incision problems

Presently, the incidence and mortality rates of sternal incision problems (SIPs) after thoracotomy remain high, and no effective preventive measures are available. The data on 23 182 patients at Xinqiao Hospital, Army Medical University treated with median sternotomy from 1 August 2009 to 31 July 2019 were retrospectively reviewed. A prediction model of SIPs after median thoracotomy was established using R software and then validated using the bootstrap method. Next, the validity and accuracy of the model were tested and evaluated. In total, 15 426 cases met the requirements of the present study, among which 309 cases were diagnosed with SIPs, with an incidence rate of 2%. The body mass index (BMI), intensive care unit (ICU) time, diabetes mellitus, and revision for bleeding were identified as independent risk factors for postoperative SIPs. The nomogram model achieved good discrimination (73.9%) and accuracy (70.2%) in predicting the risk of SIPs after median thoracotomy. Receiver operating characteristic curve analysis showed that the area under curve of the model was 0.705 (95% confidence interval [CI]: 0.746‐0.803); the Hosmer‐Lemeshow test showed that χ ² = 6.987 and P = 0.538, and the fitting degree of the calibration curve was good. Additionally, the clinical decision curve showed that the net benefit of the model was greater than 0, and the clinical application value was high. The nomogram based on BMI, ICU time, diabetes mellitus, and revision for bleeding can predict the individualised risk of SIPs after median sternotomy, showing good discrimination and accuracy, and has high clinical application value. It also provides significant guidance for screening high‐risk populations and developing intervention strategies.     

社区老年慢性病患者认知衰弱风险预测模型的构建及验证

目的 探索社区老年慢性病患者发生认知衰弱的危险因素,为针对性干预提供参考.方法 对674例社区老年慢性病患者,采用一般资料调查问卷、衰弱表型、蒙特利尔认知评估量表、临床痴呆评定量表、简易营养评价精法问卷以及简版老年人抑郁量表进行调查;行单因素及多因素分析提取认知衰弱影响因素,基于主要影响因素构建认知衰弱风险列线图预测模...     

住院癌症患者衰弱风险预测模型的构建与验证

目的 调查住院癌症患者发生衰弱的危险因素,构建风险预测模型并检验预测效果。方法 选取广州市2所三级甲等医院住院癌症患者570例,分为建模集422例和验证集148例。采用一般资料调查表、Fried衰弱表型、安德森症状评估量表、医院焦虑抑郁量表、中文版失志量表、营养风险筛查2002、Barthel指数量表进行调查,同期收集患者的白细胞介素-6、C-反应蛋白等实验室指标,应用logistic回归分析筛选衰弱的独立危险因素,构建风险预测模型。结果 住院癌症患者衰弱发生率为33.7%,衰弱的影响因素分别为：造口、营养状况、抑郁、神经心理症状群、消化道症状群、D-二聚体、白细胞介素-6。建模集和验证集ROC曲线下面积为0.788、0.735;Brier得分为0.205,校准斜率为0.625,决策曲线分析表明模型具有一定的临床可用性。结论 住院癌症患者衰弱发生率较高,构建的列线图预测模型具有较好的风险预测价值,可有效识别和筛选住院癌症衰弱高危人群。     

个体化预测老年急性胆囊炎患者发生术后感染的风险列线图模型的建立

目的 探讨个体化预测老年急性胆囊炎患者发生术后感染的风险列线图模型的建立。方法 回顾性分析2018年1月至2019年12月因急性胆囊炎于眉山市人民医院行胆囊切除术治疗的233例老年患者的临床资料,分别使用单因素法和多因素Logistic回归法分析术后感染的独立危险因素。然后用筛选出的独立危险因素建立列线图预测模型,并对模型的预测性及准确度进行验证。结果 年龄≥70岁（OR 3.032,95%CI 1.289～7.132）、糖尿病（OR 3.321,95%CI 1.452～7.599）、肝硬化（OR 2.543,95%CI 1.004～6.438）、胆囊结石（OR 9.051,95%CI 1.833～44.687）、胆囊周边积液（OR 3.264,95%CI 1.428～7.459）及白细胞计数＞10×10⁹/L（OR 3.873,95%CI 1.673～8.966）是老年急性胆囊炎患者发生术后感染的独立危险因素。基于以上6项独立危险因素,建立相关列线图预测模型,并对该模型进行验证,内部验证和外部验证的C-index指数分别为0.782和0.735。校正曲线分析显示,训练集和验证集的校正曲线和理想曲线拟合均较好,预测值同实测值均基本一致。ROC曲线分析显示,训练集和验证集的曲线下面积（AUC）分别为0.852（95%CI 0.821～0.883）和0.838（95%CI 0.812～0.864）,说明本次列线图模型具有良好的预测精准度。结论 年龄≥70岁、糖尿病、肝硬化、胆囊结石、胆囊周边积液及白细胞计数＞10×10⁹/L是老年急性胆囊炎患者发生术后感染的独立危险因素,相关列线图模型的建立对临床筛查高危人群和制定针对性防治措施具有指导意义,临床应用价值较高。     

A risk prediction model for acute kidney injury in patients with pulmonary tuberculosis during anti-tuberculosis treatment

BackgroundAcute kidney injury (AKI) is not a rare complication during anti-tuberculosis treatment in some patients with pulmonary tuberculosis (PTB). We aimed to develop a risk prediction model for early recognition of patients with PTB at high risk for AKI during anti-TB treatment.MethodsThis retrospective cohort study assessed the clinical baseline, and laboratory test data of 315 inpatients with active PTB who were screened for predictive factors from January 2019 to June 2020. The elements were analyzed by logistic regression analysis. A nomogram was established by the results of the logistic regression analysis. The prediction model discrimination and calibration were evaluated by the concordance index (C-index), ROC curve, and Hosmer-Lemeshow analysis.ResultsA total of 315 patients with PTB were enrolled (67 patients with AKI and 248 patients without AKI). Seven factors, including microalbuminuria, hematuria, cystatin-C (CYS-C), albumin (ALB), creatinine-based estimated glomerular filtration rates (eGFRs), body mass index (BMI), and CA-125 were acquired to develop the predictive model. According to the logistic regression, microalbuminuria (OR = 3.038, 95%CI 1.168–7.904), hematuria (OR = 3.656, 95%CI 1.325–10.083), CYS-C (OR = 4.416, 95%CI 2.296–8.491), and CA-125 (OR = 3.93, 95%CI 1.436–10.756) were risk parameter, while ALB (OR = 0.741, 95%CI 0.650–0.844) was protective parameter. The nomogram demonstrated good prediction in estimating AKI (C-index= 0.967, AUC = 0.967, 95%CI (0.941–0.984), sensitivity = 91.04%, specificity = 93.95%, Hosmer-Lemeshow analysis SD = 0.00054, and quantile of absolute error = 0.049).ConclusionsMicroalbuminuria, hematuria, ALB reduction, elevated CYS-C, and CA-125 are predictive factors for the development of AKI in patients with PTB during anti-TB treatments. The predictive nomogram based on five predictive factors is achieved good risk prediction for AKI during anti-TB treatments.     

A nomogram prediction of pressure injury in critical ill patients: A retrospective cohort study

Pressure injury (PI) is still a significant public health problem to be solved. Accurate prediction can lead to timely prophylaxis and therapy. However, the currently used Braden score shows insufficient predictive validity. We aimed to develop a nomogram to predict PI development in critically ill patients. We extracted data from Medical Information Mart for Intensive Care‐IV v1.0. Variable selection was based on univariate logistic regression and all‐subset regression. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the nomogram and Braden score. Decision curve analysis (DCA) was performed to identify and compare the clinical usefulness between the nomogram model and Braden score. We have developed a novel and practical nomogram that accurately predicts pressure ulcers. The AUC of the new model was better than that of the Braden score (P < .001). DCA showed that the nomogram model had a better net benefit than the Braden score at any given threshold. This finding needs to be confirmed by external validation as well as multicentre prospective studies.