Composition Analysis and Identification of Ancient Glass Products Based on L1 Regularization Logistic Regression

In view of the composition analysis and identification of ancient glass products, L1 regularization, K-Means cluster analysis, elbow rule and other methods were comprehensively used to build logical regression, cluster analysis, hyper-parameter test and other models, and SPSS, Python and other tools were used to obtain the classification rules of glass products under different fluxes, sub classification under different chemical compositions, hyper-parameter K value test and rationality analysis. Research can provide theoretical support for the protection and restoration of ancient glass relics.

Comparison of dimension reduction-based logistic regression models for case-control genome-wide association study:principal components analysis vs.partial least squares

With recent advances in biotechnology, genome-wide association study （GWAS） has been widely used to identify genetic variants that underlie human complex diseases and traits. In case-control GWAS, typical statistical strategy is traditional logistical regression （LR） based on single-locus analysis. However, such a single-locus analysis leads to the well-known multiplicity problem, with a risk of inflating type I error and reducing power. Dimension reduction-based techniques, such as principal component-based logistic regression （PC-LR）, partial least squares-based logistic regression （PLS-LR）, have recently gained much attention in the analysis of high dimensional genomic data. However, the perfor- mance of these methods is still not clear, especially in GWAS. We conducted simulations and real data application to compare the type I error and power of PC-LR, PLS-LR and LR applicable to GWAS within a defined single nucleotide polymorphism （SNP） set region. We found that PC-LR and PLS can reasonably control type I error under null hypothesis. On contrast, LR, which is corrected by Bonferroni method, was more conserved in all simulation settings. In particular, we found that PC-LR and PLS-LR had comparable power and they both outperformed LR, especially when the causal SNP was in high linkage disequilibrium with genotyped ones and with a small effective size in simulation. Based on SNP set analysis, we applied all three methods to analyze non-small cell lung cancer GWAS data.

Logistics Regression Analysis between TCM Constitution Types of Patients with Hypertension,Insomnia and ABPM,PSQI

[Objectives]The research aimed to explore the distribution characteristics of TCM constitution types of patients with hypertension and insomnia,and study the clinical characteristics of patients with different constitutions,in order to provide new ideas for the treatment of patients with hypertension and insomnia.[Methods]Cross sectional observation method was used,and 420 patients with hypertension and insomnia were selected.Required information was collected,and the constitution type of traditional Chinese medicine was determined,and relevant data were recorded.SPSS and Logistic regression analysis method were used to explore the correlation between the distribution of TCM constitution types and gender,age,24 h-SBP,24 h-DBP,24 h-BPV,PSQI score,etc.[Results]Among 420 patients,the proportion of gentleness constitution was the most,and others in turn were Qi deficiency constitution>Yang deficiency constitution>phlegm dampness constitution>Qi stagnation constitution>Yin deficiency constitution>blood stasis constitution>damp heat constitution>special constitution.Among male patients,the proportion of gentleness constitution was the most.Among female patients,the proportion of Qi deficiency constitution was the most.In each constitution,the proportion of men and women was different,and the difference in gentleness constitution,Qi deficiency constitution and Yin deficiency constitution had statistical significance(P<0.05).The proportion of gentleness constitution for young and middle-aged patients was the most,while elderly patients with Qi deficiency constitution was the most.There was difference in the distribution of TCM constitution in different age groups,and the difference had statistical significance(P<0.05).Compared with the patients with gentleness constitution,the patients with Qi deficiency constitution,Yang deficiency constitution,Yin deficiency constitution,damp heat constitution,blood stasis constitution and Qi stagnation constitution had different differences in terms of age,24 h-SBP,24 h-DBP,24 h-BPV and PSQI score,and there was statistical significance(P<0.05).Except damp heat constitution,blood stasis constitution and special constitution,other constitutions had certain correlation with age,24 h-SBP,24 h-DBP,24 h-BPV and PSQI score.[Conclusions]TCM constitution types of patients with hypertension and insomnia were dominant by gentleness constitution,Qi deficiency constitution and Yang deficiency constitution.The distribution of TCM constitution in different gender and age groups was different,and different TCM constitution was related to ABPM and PSQI.

Landslide Susceptibility Assessment Using Conditional Analysis and Rare Events Logistics Regression: A Case-Study in the Antrodoco Area (Rieti, Italy)

This paper discusses some methodological aspects for the production of susceptibility maps of slope instability developed within the CARG Project (Geological Cartography of Italy at 1:50,000 scale). It describes an example of a susceptibility map in the presence of low susceptibility, using database having zero or negligible cost, with the aim to test some methodologies that can be easily reproducible to get a first estimate of the landslide susceptibility on a wide area. Two statistical approaches have been applied: the non-parametric conditional analysis and the logistic analysis for rare events. The predictive ability obtained from the two methodologies, was evaluated by the success-prediction curves for the conditional analysis, and by the Receiver Operating Characteristic curve (ROC), for the logistic model. The landslide susceptibility maps have been classified into four classes using both the Natural Breaks algorithm and the method proposed by Chung and Fabbri (2003). The paper considers the influence of these two classification methods on the quality of final results.

Usage as Complementary Correspondence Analysis and Logistic Regression in a Scientific Survey on Self Healing Methods

The aim of this study is to show complementary usage of logistic and correspondence analysis in a research subject to self-healing methodologies. Firstly, the number of the variables is reduced by logistic regression according to relationship between dependent and independent variables and then research carries on searching variables. The relationship among the behaviours of individuals and their demographic characteristics is modelled by logistic regression and shown graphically by correspondence analysis. In application, first of all, the effect of age, sex, marital status, education level, occupation and income level and present health condition, on appreciating self-health, is explained by a model. As a result of that model, it can be said that the effect of age, occupation and present health condition is reasonable. After analysing that model, the relationship between categorical variables (age, sex, occupation, preferred precautions, and worth of personal health) is shown graphically by multiple correspondence analysis.

Logistic Regression Analysis the Risk Factors of Peripherally Inserted Central Catheter Related Blood Stream Infection of Tumor Patients

Objective: Our object is to study risk factors of tumor patients’ PICC catheter-related blood stream infection. Method: a retrospective analysis of data of 586 PICC catheterized patients was implemented, a univariate analysis of general data and catheterizing data of tumor patients was then carried out, and data of single factors with statistical significance were incorporated into multi-factor Logistic regression model for analysis. Results: PICC catheter-related blood stream infection occurred to 16 patients, and occurrence rate was 2.73%. Multi-factor Logistic regression analysis results showed that number of puncturing times, positioning method and maintenance frequency were risk factors for tumor patients’ peripherally inserted central catheter catheter-related blood stream infection, and odds risk values were respectively 8.762, 9.253 and 10.324. Conclusion: for tumor patients implanted with peripherally inserted central catheters, using ECG positioning during strict sterile operation and catheterizing process to avoid repeated puncturing and increasing maintenance frequency could effectively reduce occurrence of PICC catheter-related blood stream infection.

Logistic Regression Analysis of Influencing Factors on Serum ALT and HCV RNA Changes in Patients with Chronic Hepatitis C

Objective This study was undertaken to investigate the influencing factors on serum ALT level and hepatitis C virus(HCV)RNA titer in chronic hepatitis C(CHC)patients.Methods All patients enrolled into this study were anti-HCV positive.Retrospective tracing method was applied to detect serum ALT level and HCV RNA titer and to collect general information of the patients such as genders,age groups,interferon medication history,infection pathways,height and weight.Then the multi-factor analysis was adopted with the application of binominal logistic regression mode.Results The abnormal rate of ALT level was positively correlated to HCV RNA and gender while negatively correlated to interferon medication history and age group,with Wald value of the 4 factors as 39.604,11.823,18.991 and 7.389,respectively.The positive rate of HCV RNA was negatively correlated to interferon medication history and gender while positively correlated to ALT level,with corresponding Wald value of the 3 factors as81.394,7.618 and 27.562,respectively.Conclusions The normal ALT level in HCV infected patients was associated with viral load,age,gender and interferon medication history,while the normal rate of HCV RNA titer was closely associated with gender,interferon medication history and ALT level.

A Comparative Study of Locality Preserving Projection and Principle Component Analysis on Classification Performance Using Logistic Regression

There are a variety of classification techniques such as neural network, decision tree, support vector machine and logistic regression. The problem of dimensionality is pertinent to many learning algorithms, and it denotes the drastic raise of computational complexity, however, we need to use dimensionality reduction methods. These methods include principal component analysis (PCA) and locality preserving projection (LPP). In many real-world classification problems, the local structure is more important than the global structure and dimensionality reduction techniques ignore the local structure and preserve the global structure. The objectives is to compare PCA and LPP in terms of accuracy, to develop appropriate representations of complex data by reducing the dimensions of the data and to explain the importance of using LPP with logistic regression. The results of this paper find that the proposed LPP approach provides a better representation and high accuracy than the PCA approach.

Unveiling the Predictive Capabilities of Machine Learning in Air Quality Data Analysis: A Comparative Evaluation of Different Regression Models

Air quality is a critical concern for public health and environmental regulation. The Air Quality Index (AQI), a widely adopted index by the US Environmental Protection Agency (EPA), serves as a crucial metric for reporting site-specific air pollution levels. Accurately predicting air quality, as measured by the AQI, is essential for effective air pollution management. In this study, we aim to identify the most reliable regression model among linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression, and K-nearest neighbors (KNN). We conducted four different regression analyses using a machine learning approach to determine the model with the best performance. By employing the confusion matrix and error percentages, we selected the best-performing model, which yielded prediction error rates of 22%, 23%, 20%, and 27%, respectively, for LDA, QDA, logistic regression, and KNN models. The logistic regression model outperformed the other three statistical models in predicting AQI. Understanding these models' performance can help address an existing gap in air quality research and contribute to the integration of regression techniques in AQI studies, ultimately benefiting stakeholders like environmental regulators, healthcare professionals, urban planners, and researchers.

影响结核性胸腔积液并发胸膜增厚的非条件Logistic回归分析

目的探讨影响结核性胸腔积液并发胸膜增厚的相关危险因素。方法回顾性分析2019年1月至2021年3月广州市胸科医院呼吸内科收治的123例结核性胸腔积液患者的临床资料,统计患者胸膜增厚情况,采用单因素及非条件Logistic回归法分析患者胸膜增厚的影响因素。结果胸膜未增厚与胸膜增厚患者性别、年龄、肺结核、胸水分布、用力肺活量(FVC)、第1秒用力呼气容积(FVE1)/FVC、胸水腺苷脱氨酶(ADA)、血清ADA、胸水白细胞、胸水淋巴细胞、胸水中性粒细胞和淋巴细胞比例比较差异无统计学意义;胸膜未增厚与胸膜增厚患者胸水量、胸水吸收时间、FVE1、胸水乳酸脱氢酶(LDH)、血清LDH、胸水蛋白、血清蛋白比较差异有统计学意义(P<0.05)。Logistic回归分析结果显示,胸水量(中大量)、FVE1、胸水LDH、血清LDH、胸水蛋白及血清蛋白是胸膜增厚发生的危险因素(P<0.05)。结论结核性胸腔积液并发胸膜增厚是多因素作用的结果,胸水量、FVE1、胸水吸收时间、胸水LDH、血清LDH、胸水蛋白和血清蛋白与胸膜增厚的发生密切相关,建议临床予以密切监测并积极采取针对性干预措施。

卵圆孔未闭合并偏头痛患者头痛程度影响因素的logistic回归分析

目的分析卵圆孔未闭(PFO)合并偏头痛患者头痛程度影响因素的logistic回归分析。方法选取2022年1—12月商丘市第一人民医院就诊的100例PFO合并偏头痛患者作为研究对象,采用视觉模拟评分法(VAS)分为轻度头痛组(58例)和中重度疼痛组(42例),采用多因素logistic回归分析影响PFO合并偏头痛患者头痛程度的因素。结果单因素分析显示,轻度头痛组和中重度疼痛组吸烟史、高血压史、睡眠质量、情绪变化、PFO右向左分流量、PFO直径、PFO隧道长度及有无房间隔膨出瘤差异有统计学意义(P<0.05)。logistic多因素回归分析显示,吸烟史、高血压史、睡眠质量差、情绪变化、PFO右向左分流量大、PFO直径大、PFO隧道短及房间隔膨出瘤是影响PFO合并偏头痛患者头痛程度的影响因素(P<0.05)。结论吸烟史、高血压史、睡眠质量差、情绪变化、PFO右向左分流量大、PFO直径大、PFO隧道短及房间隔膨出瘤可影响PFO合并偏头痛患者头痛程度,临床应根据上述因素进行针对性干预,以缓解偏头痛患者头痛程度。

影响阳虚体质因素的Logistic回归分析

目的:研究影响阳虚体质的主要因素。方法:采用横断面调查,收集上海中医药大学附属岳阳中西医结合医院治未病中心2020年9月至2021年11月中医体质调查数据。中医体质调查采用中医体质辨识软件V3.0实施,运用多因素Logistic回归模型分析筛选影响阳虚体质的主要因素。结果:共纳入811例研究对象,其中阳虚质211例(26.0%)。多因素Logistic回归分析结果显示,相对女性,男性阳虚质比例降低,差异具有统计学意义[OR=0.55,95%CI(0.37~0.81),P=0.002];相对年龄<50岁,年龄≥50岁者阳虚质比例较高,差异具有统计学意义[OR=1.83,95%CI(1.26~2.65),P=0.001];体质量超重、肥胖人群阳虚质比例高于体质量正常及偏低人群,差异具有统计学意义[OR=0.59,95%CI(0.38~0.91),P=0.018];高脂血症患者阳虚质比例高于血脂正常人群,差异具有统计学意义[OR=0.63,95%CI(0.44~0.90),P=0.011];脂肪性肝病患者阳虚质比例高于正常人群,差异具有统计学意义[OR=0.56,95%CI(0.37~0.87),P=0.010];功能性胃肠病阳虚质比例高于正常人群,差异具有统计学意义[OR=1.77,95%CI(1.05~2.99),P=0.032]。结论:年龄、性别、超重/肥胖、功能性胃肠病、脂肪性肝病、高脂血症是影响阳虚质的主要因素。

Logistic Regression在我国河流水系氮污染研究中的应用

对四川省岷江、沱江及嘉陵江流域和江西省的赣江流域及鄱阳湖地区共 1 70多个水文站的数据进行了相关分析和多元回归分析 .结果表明 ,河流水中硝态氮浓度与年降雨量、人口密度、氮肥施用量、牲畜饲养量、农作物及粮食作物种植面积等因素有较好的相关性 .以以上数据资料为基础 ,将河流水NO3- N的浓度划分为背景浓度 (<0 7mg/L)、受人类活动的显著影响的NO3- N浓度 (>3 0mg/L)以及中间类 (0 7— 3 0mg/L)进行LogisticRegression分析 ,两个Logistic模型的准确度分别达 82 46%和 89 1 9% .运用Logistic模型对整个长江流域河流水中NO3- N浓度进行估计 ,结果与实测值基本相符合 .

基于Logistic回归模型和PCA模型的急性缺血性脑卒中发作影响因素分析

目的应用Logistic回归模型和主成分分析(PCA)模型分析急性缺血性脑卒中(AIS)发作的影响因素。方法从大连医科大学附属第二医院医渡云科研大数据服务器系统提取2001年1月1日至2021年12月31日的数据,将医院病历系统收录的55620例AIS患者纳入病例组,将筛选后的64134例在医院体检中心体检的人群纳入对照组。收集两组的临床资料,分析AIS发作的影响因素。结果多因素分析结果显示,年龄、性别、肌酐、白细胞计数、血红蛋白、红细胞计数、血小板计数、甘油三酯(TG)、低密度脂蛋白胆固醇(LDL-C)、高密度脂蛋白胆固醇(HDL-C)、丙氨酸氨基转移酶(ALT)、总胆固醇(TC)、γ-谷氨酰转移酶(γ-GGT)水平及吸烟史、饮酒史、高血压、糖尿病、心梗、冠心病、动脉粥样硬化为AIS的影响因素(P<0.05);受试者工作特征(ROC)曲线下面积(AUC)为0.927。PCA提取8个主成分,既往病史、血脂水平风险比较大。结论既往病史及血脂水平是AIS发作的主要影响因素,有效控制原发疾病及血脂水平能够更好控制AIS发作。

COVID-19老年患者味觉功能障碍特点及相关危险因素的logistic回归分析

目的本文旨在探讨老年新型冠状病毒(COVID-19)感染者味觉功能障碍特点及相关危险因素。方法选取2022年12月至2023年8月就诊首都医科大学附属北京友谊医院口腔科的COVID-19受试者作为研究对象,以问卷星及问卷调查的方式记录感染COVID-19后味觉功能障碍的临床特点及相关情况,通过logistic回归分析方法探讨其影响因素。结果老年组味觉障碍的发生率为32.54%,中青年组达42.57%。新冠感染持续时间、发热情况、吸烟史、呼吸道症状(嗅觉功能障碍)、全身症状(肌肉、关节酸痛)、基础疾病(高血压、糖尿病、睡眠呼吸暂停综合征)等是老年感染者味觉功能障碍的独立危险因素(P<0.05)。年龄、疫苗接种情况以及心脑血管类疾病等与味觉功能改变相关性不大(P>0.05)。味觉丧失者的sQOD-NS评分显著低于正常和味觉异常者(P=0.001)。结论味觉功能障碍是COVID-19老年感染者主要的口腔临床症状之一,影响味觉功能障碍的因素很多,其危险因素包括:新冠感染持续时间、发热情况、吸烟史、嗅觉功能障碍、肌肉、关节酸痛、以及高血压、糖尿病、睡眠呼吸暂停综合征等,上述情况显著增加了COVID-19患者味觉功能障碍的风险。这些发现有助于未来对COVID-19感染的诊断、预防和治疗的研究。

基于DCE-MRI表现的logistic回归分析模型在乳腺良恶性病变诊断中的应用

目的 分析基于动态对比增强磁共振成像(DCE-M RI)表现的logistic回归分析模型在乳腺良恶性病变诊断中的应用。方法 回顾性分析2021年1月~2023年10月来我院进行乳腺检查患者161例临床资料。其中良性病变60例、恶性病变101例,分别纳入良性组(n=60)及恶性组(n=101)。分析两组DCE-MRI表现差异,进行单因素分析,利用二元Logistic回归分析构建乳腺良恶性病变诊断模型。采用受试者工作特征(ROC)曲线分析乳腺良恶性病变诊断模型的效能。结果 单因素分析显示,良性组与恶性组TIC曲线、BI-RADS分级、早期强化率、边缘形态及病灶大小比较差异有统计学意义(P<0.05);二元Logistic回归分析结果显示, TIC曲线、BI-RADS分级、早期强化率、边缘形态及病灶大小是乳腺良恶性病变危险征像;构建logistic乳腺癌良恶性病变诊断模型Y=-0.633+0.645TIC曲线+2.112×BI-RADS分级+1.142×早期强化率+1.136×边缘形态+1.136×病灶大小;ROC曲线分析显示该模型诊断效能,AUC为0.944,敏感度为83.33%,特异度为85.15%,提示该模型具有较高的诊断效能。结论 基于乳腺病变早期DCE-MRI表现的logistic诊断模型,能够筛选出对乳腺恶性病变鉴别诊断有意义的特征变量,对乳腺良恶性病变具有较高的诊断效能。

基于连续比例Logistic回归模型的贝叶斯判别分析

针对传统贝叶斯判别分析方法处理实际问题的局限性,提出一种基于连续比例Logistic回归模型的贝叶斯判别分析方法.首先基于连续比例Logistic回归模型建立半参数密度比模型,通过经验似然法估计模型的参数,并使用贝叶斯定理计算后验概率进行分类预测.然后对比新方法与传统方法的回判正确率,统计模拟表明当总体数据符合正态分布时,2者判别能力相当,否则,提出的新方法能够更好地判别不同的数据特征.最后运用新方法分析真实的数据集,验证了新方法在分类预测中的准确性和稳健性,与传统方法相比,更适用于实际应用中多元分类问题的建模和预测.

血液指标与非创伤性股骨头坏死分期及病程的关系:多元Logistic回归分析

背景:目前尚无关于血液实验室检查和非创伤性股骨头坏死分期及病程关系的文献,有必要进一步探讨分析以便更好地明确非创伤性股骨头坏死的影响因素。目的:分析血液实验室检查指标与非创伤性股骨头坏死国际骨循环研究会(ARCO)分期病程之间的关系,探讨非创伤性股骨头坏死病程的血液实验室指标影响因素。方法:采用回顾性研究的方法,从中国中医科学院望京医院数据库调取共2103例股骨头坏死患者的病历资料,根据纳入排除标准,最终纳入了1075例非创伤性股骨头坏死患者。收集患者的年龄、性别、体质量指数和血液实验室指标结果,血液实验室指标包含低密度脂蛋白、总胆固醇、三酰甘油、高密度脂蛋白、载脂蛋白β、载脂蛋白α1、尿酸、总蛋白定量、碱性磷酸酶、活化部分凝血活酶时间、凝血酶原时间、凝血酶原时间国际标准化比值、凝血酶原时间活动度、纤维蛋白原定量、凝血酶凝结时间、D-二聚体、总铁结合力、血小板计数。比较不同年龄段、不同ARCO分期患者的相关指标,并应用多元Logistic回归分析探讨股骨头坏死ARCO分期的影响因素。结果与结论:①青年组的总胆固醇、尿酸、凝血酶原时间、凝血酶原时间国际标准化比值、D-二聚体在ARCO分期组间比较差异有显著性意义(P<0.05),青年组ARCOⅡ期患者在总胆固醇水平上高于ARCOⅢ期患者(P<0.05),ARCOⅣ期的尿酸水平高于ARCOⅡ、Ⅲ期患者(P<0.05),ARCOⅡ、Ⅳ期的凝血酶原时间和凝血酶原时间国际标准化比值少于ARCOⅢ期患者(P<0.05),ARCOⅢ、Ⅳ期患者的D-二聚体高于ARCOⅡ患者(P<0.05);②中年组的高密度脂蛋白、凝血酶凝结时间、D-二聚体的ARCO分期组间比较差异有显著性意义(P<0.05),中年组ARCOⅣ期患者在高密度脂蛋白水平上高于ARCOⅡ、Ⅲ期患者(P<0.05),ARCOⅣ期凝血酶凝结时间短于ARCOⅢ期(P<0.05),ARCOⅣ期患者的D-二聚体水平高于ARCOⅡ、Ⅲ期患者(P<0.05);③老年组的尿酸、活化部分凝血活酶时间、D-二聚体、血小板计数组间比较差异有显著性意义(P<0.05),老年组ARCOⅣ期患者的尿酸水平高于ARCOⅡ、Ⅲ期患者(P<0.05),ARCOⅡ期患者的活化凝血酶原时间短于ARCOⅢ期(P<0.05),ARCOⅢ、Ⅳ期患者的D-二聚体水平高于ARCOⅡ期患者(P<0.05),ARCOⅣ期的血小板计数要低于ARCOⅢ期患者(P<0.05);④多元Logistic回归分析显示,总胆固醇和血小板计数是非创伤性股骨头坏死病程的保护性因素,D-二聚体、尿酸、超重、中青年年龄段可能是非创伤性股骨头坏死病程的危险因素;⑤提示不同ARCO分期患者的总胆固醇、高密度脂蛋白、尿酸、凝血酶原时间、凝血酶原时间国际标准化比值、D-二聚体存在统计学差异,总胆固醇、血小板计数可能是非创伤性股骨头坏死病程的保护性因素,D-二聚体、尿酸、超重、中青年年龄段可能是非创伤性股骨头坏死病程的危险因素。

基于Logistic回归分析的行为干预对肾结石病人术后复发的影响

目的:探讨基于Logistic回归分析的行为干预对肾结石病人术后复发率的影响。方法:回顾性收集2017年12月—2018年12月在我院行肾结石手术的98例病人的临床资料,通过Logistic回归模型预测肾结石病人术后复发的独立危险因素。前瞻性选取2019年1月—2022年1月在我院行手术治疗的150例肾结石病人,采用随机数字表法分成对照组与观察组,各75例。对照组进行常规干预,观察组予以基于Logistic回归分析的行为干预。比较两组术后恢复状况及术后6个月、12个月的复发率。结果:Logistic回归分析结果显示,感染性结石、术后泌尿系统感染、术后饮水量<3000 mL、术后使用腌制食物及食用动物蛋白为肾结石术后结石复发的独立危险因素(P<0.05)。观察组术后排气时间、术后排便时间、首次下床活动时间及住院时间均较对照组缩短,两组比较差异均有统计学意义(P<0.001);观察组术后6个月、术后1年肾结石复发率(8.00%、18.67%)低于对照组(20.00%、34.67%),两组比较差异均有统计学意义(P<0.05)。结论:基于Logistic回归分析的行为干预可促进肾结石术后病人恢复,降低肾结石术后复发率。

Lasso-Logistic回归模型拟合临床因素、NF-κB/NLRP3信号通路预测心肌梗死后缺血性心肌病价值

目的基于Lasso-Logistic回归分析心肌梗死后缺血性心肌病(ICM)影响因素,探讨临床因素、核因子-κB(NF-κB)/核苷酸结合寡聚结构域样受体家族3(NLRP3)信号通路及Lasso-Logistic回归模型对心肌梗死后ICM的预测价值,为本病防治提供参考。方法选取2020年9月—2023年9月秦皇岛市第一医院收治的342例心肌梗死患者为研究对象进行前瞻性研究,按照7∶3比例分为建模组239例、验证组103例,依据经皮冠状动脉介入术(PCI)术后6个月内是否发生ICM分为ICM亚组、非ICM亚组。采用Lasso筛选心肌梗死后ICM发生相关变量,以有统计学意义变量构建临床因素模型,以NF-κB/NLRP3信号通路构建NF-κB/NLRP3信号通路模型,以临床因素、NF-κB/NLRP3联合建立混合模型(Lasso-Logistic回归模型)。对比不同预测模型对心肌梗死后ICM的预测价值。结果建模组ICM发生率为27.97%,验证组ICM发生率为26.47%;Lasso筛选出5个预测变量为NF-kB mRNA、NLRP3 mRNA、Gensini评分、LVEF、饮酒,Logistic回归分析显示,Gensini评分、NLRP3 mRNA、NF-κB mRNA、饮酒是心肌梗死后ICM影响因素(P<0.05);混合模型预测心肌梗死后ICM的AUC、敏感度、特异度分别为0.921、80.30%、88.82%,临床因素模型分别为0.886、78.79%、85.29%,NF-κB/NLRP3信号通路模型分别为0.873、74.24%、87.06%,混合模型的AUC高于临床因素模型、NF-κB/NLRP3信号通路模型(P<0.05)。结论Gensini评分、NLRP3 mRNA、NF-κB mRNA、饮酒是心肌梗死后ICM危险因素,联合上述影响因素建立Lasso-Logistic回归模型,该模型对心肌梗死后ICM具有一定预测效能,有助于临床早期筛查高危人群,并予以相应干预措施,以降低ICM发生风险。