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logistic regression analysis of risk factors for anxiety and depression in patients with coronary heart disease and subclinical hypothyroidism

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SCIENTIFIC REPORTS 2024年第1期14卷 1-10页

作者： Meng, Limin Zhu, Qifeng Ma, Fangfang Wang, Jincheng Lu, Wenting Zheng, Mingqi Su, Guanli Liu, Gang Hebei Med Univ Hosp 1 Dept Psychiat Shijiazhuang Peoples R China Hebei Med Univ Hosp 1 Dept Cardiol Shijiazhuang Peoples R China 89 Donggang Rd Shijiazhuang 050031 Hebei Peoples R China

We sought to explore the risk factors for anxiety and depression in patients with coronary heart disease and subclinical hypothyroidism through logistic regression analysis. A retrospective analysis was conducted on 168 patients with coronary heart disease and subclinical hypothyroidism admitted to the Department of Cardiology of our hospital from February 2020 to November 2022. Patients were categorized into the control group, anxiety group, and depression group based on the Hamilton Anxiety Scale (HAMA) and Hamilton Depression Rating Scale (HAMD) scores. All participants were informed about the protocol and provided signed informed consent upon inclusion. The study examined influencing factors for anxiety and depression in patients with coronary heart disease and subclinical hypothyroidism. Collect patients' gender, age, presence or absence of chronic diseases (including Diabetes, hypertension and hyperthyroidism), sleep quality, dietary habits, psychosocial stress, living environment, social support, education level, and blood TSH levels. The linear relationship between anxiety, depression, and each influencing factor was quantified using the Pearson correlation coefficient. Blood level of TSH and free T4 were detected by chemiluminescence immunoassay. Multiple logistic regression was applied to analyze the factors influencing anxiety and depression in these patients. Various factors were identified as significant influencers of anxiety and depression in patients with coronary heart disease and subclinical hypothyroidism. For anxiety, presence or absence of chronic diseases, sleep quality, dietary habits, psychosocial pressure, living environment, and blood TSH levels were found to be influential (P < 0.05). Similarly, for depression, presence or absence of chronic diseases, sleep quality, social support, quality of life, social support, education level, and blood TSH levels were identified as significant factors (P < 0.05). The study revealed positive correlatio

关键词： Coronary heart disease combined with subclinical hypothyroidism Anxiety Depression Influencing factors logistic regression analysis

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logistic regression analysis of Conventional Ultrasound, and Contrast-Enhanced Ultrasound Characteristics Is It Helpful in Differentiating Benign and Malignant Superficial Lymph Nodes?

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JOURNAL OF ULTRASOUND IN MEDICINE 2022年第2期41卷 343-353页

作者： Wang, Bo Zhang, Meng Ke Zhou, Mao Ping Liu, Yuan Li, Nan Liu, Gang Wang, Zhi Li Chinese Peoples Liberat Army Gen Hosp Med Ctr 1 Dept Ultrasound Beijing Peoples R China Qingdao Univ Affiliated Hosp Dept Ultrasound Qingdao Peoples R China Chinese Peoples Liberat Army Gen Hosp Med Ctr 5 Dept Ultrasound Beijing Peoples R China Chinese Peoples Liberat Army Gen Hosp Med Ctr 1 Dept Radiol Beijing Peoples R China

Objectives This study aimed to screen the significant sonographic features for differentiation of benign and malignant superficial lymph nodes (LNs) by logistic regression analysis and fit a model to diagnose LNs. Methods A total of 204 pathological LNs were analyzed retrospectively. All the LNs underwent conventional ultrasound (US) and contrast-enhanced ultrasound (CEUS) examinations. A total of 16 suspicious sonographic features were used to assess LNs. All variables that were statistically related to the diagnosis of LNs were included in the logistic regression analysis in order to ascertain the significant features of diagnosing LNs, and to establish a logistic regression analysis model. Results The significant features in the logistic regression analysis model of diagnosing malignant LNs were absence of echogenic hilus, age, and absence of hilum after enhancement. According to the results of logistic regression analysis, the formula to predict whether LNs were malignant was established. The area under the receiver operating curve (ROC) was 0.908 and the accuracy, sensitivity, and specificity were 85.0%, 92.9%, and 85.3%, respectively. Conclusion The logistic regression model for the significant sonographic features of conventional US and CEUS is an effective and accurate diagnostic tool for differentiating malignant and benign LNs.

关键词： lymph nodes logistic regression analysis ultrasound features

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Two improving approaches for faulty interaction localization using logistic regression analysis

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SOFTWARE QUALITY JOURNAL 2024年第3期32卷 1039-1073页

作者： Nishiura, Kinari Choi, Eun-Hye Choi, Eunjong Mizuno, Osamu Kyoto Inst Technol Kyoto Japan Natl Inst Adv Ind Sci & Technol Ikeda Japan

Faulty Interaction Localization (FIL) is a process to identify which combination of input parameter values induced test failures in combinatorial testing. An accurate and fast FIL provides helpful information to fix defects causing the test failure. One type of conventional FIL approach, which analyzes test results of whole test cases and estimates the suspiciousness of each combination, has two main concerns;(1) the accuracy is not enough, (2) the huge time cost is sometimes needed. In this paper, we propose two novel approaches to improve those concerns. FROGa attempts to estimate suspiciousness more accurately using logistic regression analysis. FROGb attempts to estimate failure-inducing combinations at high speed by estimating the subsets of them using logistic regression analysis and exploring just their supersets. Through evaluation experiments using a large number of artificial test results based on several real software systems, we observed that FROGa has very high accuracy, and FROGb can drastically reduce time cost for targets that have been difficult to complete by the conventional method.

关键词： Software testing Combinatorial testing Faulty interaction localization logistic regression analysis

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Factors influencing resuscitation outcomes in obese out-of-hospital cardiac arrest patients: A LASSO logistic regression analysis in Wuhan, China

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NOTFALL & RETTUNGSMEDIZIN 2025年 1-7页

作者： Yu, Di-Hua Wang, Wei-Qing Wang, Ying-Lin Yu, Di-Hua Zhu, Zheng Zeng, Xiao-Xia Li, Gang Hubei Prov Hosp TCM Emergency Dept Wuhan 430074 Peoples R China Emergency Med Ctr Wuhan Wuhan Peoples R China

ObjectiveGiven limited prehospital emergency human resources, emergency transfer of obese out-of-hospital cardiac arrest (OHCA) patients is often associated with great difficulties. This study aims to identify and weigh factors influencing outcomes of emergency transfer in obese OHCA patients. MethodsWe conducted a retrospective analysis of 172 OHCA patients treated by emergency medical services (EMS). Spearman correlation analysis was used to examine the correlation between factors. Least absolute shrinkage and selection operator (LASSO) regression was employed to screen for significant factors. A multifactorial logistic risk regression model was then established. The influencing factors were evaluated using an odds ratio (OR) forest plot, and the predictive efficacy of the model was assessed using a working curve. ResultsAfter conducting Spearman correlation analysis and LASSO regression screening, body mass index (BMI), age, cardiopulmonary resuscitation (CPR) grade, timely detection and defibrillation, and bystander CPR duration were found to be correlated with return of spontaneous circulation (ROSC). logistic regression analysis indicated that obese OHCA patients with a BMI above 22.5 have a 9.25 times higher risk (95% confidence interval [CI] 1.23-82.13, p = 0.009) than those without obesity. Similarly, individuals above the age of 35 faced a 7.67 times higher risk (95%CI 1.07-98.4, p = 0.043) compared to those aged 35 or below. Additionally, the risk of low-quality CPR was 16.45 times higher (95%CI 2.45-207.39, p = 0.009) than high-quality CPR. Failing to defibrillate in a timely manner resulted in 8.15 times higher risk (95%CI 1.24-76.23, p = 0.036) compared to timely detection and defibrillation. Moreover, having a bystander CPR time exceeding 10 min carried a 2.36 times higher risk (95%CI 0.20-28.67, p = 0.047) compared to a bystander CPR time of 10 min or less. The logistic regression indicated a high predictive efficacy of the model. ConclusionThe ROSC

关键词： Wuhan, China Obesity logistic regression analysis Cardiopulmonary resuscitation Return of spontaneous circulation

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Predictive models for delay in medical decision-making among older patients with acute ischemic stroke: a comparative study using logistic regression analysis and lightGBM algorithm

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BMC PUBLIC HEALTH 2024年第1期24卷 1413页

作者： Sheng, Zhenwen Kuang, Jinke Yang, Li Wang, Guiyun Gu, Cuihong Qi, Yanxia Wang, Ruowei Han, Yuehua Li, Jiaojiao Wang, Xia Shandong Xiehe Univ Jinan Shandong Peoples R China Qingdao Univ Qingdao Shandong Peoples R China Shandong Univ Qilu Hosp Jinan Shandong Peoples R China

Objective To explore the factors affecting delayed medical decision-making in older patients with acute ischemic stroke (AIS) using logistic regression analysis and the Light Gradient Boosting Machine (LightGBM) algorithm, and compare the two predictive *** A cross-sectional study was conducted among 309 older patients aged >= 60 who underwent AIS. Demographic characteristics, stroke onset characteristics, previous stroke knowledge level, health literacy, and social network were recorded. These data were separately inputted into logistic regression analysis and the LightGBM algorithm to build the predictive models for delay in medical decision-making among older patients with AIS. Five parameters of Accuracy, Recall, F1 Score, AUC and Precision were compared between the two *** The medical decision-making delay rate in older patients with AIS was 74.76%. The factors affecting medical decision-making delay, identified through logistic regression and LightGBM algorithm, were as follows: stroke severity, stroke recognition, previous stroke knowledge, health literacy, social network (common factors), mode of onset (logistic regression model only), and reaction from others (LightGBM algorithm only). The LightGBM model demonstrated the more superior performance, achieving the higher AUC of *** This study used advanced LightGBM algorithm to enable early identification of delay in medical decision-making groups in the older patients with AIS. The identified influencing factors can provide critical insights for the development of early prevention and intervention strategies to reduce delay in medical decisions-making among older patients with AIS and promote patients' health. The LightGBM algorithm is the optimal model for predicting the delay in medical decision-making among older patients with AIS.

关键词： Older patients Stroke Acute ischemic stroke Medical decision-making delay logistic regression analysis LightGBM algorithm

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Clinical characteristics and logistic regression analysis of macrolide-resistant Mycoplasma pneumoniae pneumonia in children

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EUROPEAN JOURNAL OF CLINICAL MICROBIOLOGY & INFECTIOUS DISEASES 2024年第9期43卷 1825-1835页

作者： Yan, Mengzhen Tao, Ran Li, Shigang Xiong, Jinwen Xiang, Jinbo China Three Gorges Univ Affiliated Renhe Hosp Yichang 443001 Hubei Peoples R China

Purpose To investigate macrolide-resistant Mycobacterium pneumoniae (MRMP) pneumonia in children and construct a logistic regression model for mutations in the Mycoplasma pneumoniae drug-resistant gene. Methods Clinical data of 281 children were analyzed. Sequencing confirmed a mutation at the A2063G locus of the 23 S rRNA gene in 227 children (A2063G group);54 children showed no mutations (non-MRMP [NMRMP] group). We compared clinical features, laboratory tests, imaging, and bronchoscopy results and constructed a multifactorial logistic regression model to analyze risk and protective factors. Results The A2063G group had longer durations of fever and hospitalization before admission, a higher proportion of treatment with sodium methylprednisolone succinate (MPS)/dexamethasone, longer time to discontinue hormones, and higher probability of combined infections. Monocyte percentage was significantly higher in the A2063G group. Imaging suggested a higher incidence of infections in the right lung compared to both lungs. Univariate analysis revealed fever duration before admission, hormone dose and duration, monocyte percentage, and mixed infections as risk factors for Mycoplasma pneumoniae infection with the A2063G mutation. The logistic regression model showed that mixed infections were an independent risk factor for the A2063G locus mutation, whereas hormone dose was a protective factor. Conclusion A prevalence of macrolide resistance of 80.8% among children was observed in the region. logistic regression analysis revealed that co-infection with other respiratory pathogens is an independent risk factor for the development of resistance genes, while the use of hormone dosage acts as a protective factor.

关键词： Macrolide resistance Mycoplasma pneumoniae Glucocorticoid logistic regression analysis

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logistic regression analysis of Catheter Fixation Defects and Their Influencing Factors

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Medicinal Plant 2024年第6期15卷 63-65页

作者： Xiaoli LI Department of General Surgery Ward II Taihe HospitalHubei University of MedicineShiyan 442000China

[Objectives] To analyze the influencing factors of fixed defects in patients with catheter fixation in clinical nursing work, in order to provide the best catheter fixation nursing plan for patients.[Methods] 176 inpatients with indwelling catheter from surgical system of Taihe Hospital in Shiyan City from August 2022 to March 2023 were selected. Using a retrospective analysis method, the influencing factors of catheter fixation defects in the study subjects were divided into two categories based on objective characteristics: type I non modifiable influencing factors and type II modifiable influencing factors. Using the standard for catheter fixation defects, whether the patient had catheter fixation defects was determined. After classified and statistically analyzed item by item, binary logistic multiple regression analysis was used to identify the influencing factors.[Results] The occurrence of catheter fixation defects in patients with catheter fixation was related to factors such as whether the patient was evaluated before fixation, whether the fixation method was standardized and systematic, whether there was sufficient communication between nurses and patients, and the patient s knowledge of catheter fixation. It was also influenced by factors such as the patient s age, catheterization site, catheterization number, catheterization duration, where there was a consciousness disorder, educational level, and external environmental temperature.[Conclusions] Early attention to the key factors affecting patients with catheter fixation defects can effectively prevent adverse factors and provide patients with the best catheter fixation nursing plan to improve nursing quality.

关键词： Catheter Fixed defect Influence factor logistic regression analysis

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logistic regression analysis of risk factors for hemorrhagic fever with renal syndrome complicated with acute pancreatitis

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ANNALS OF MEDICINE 2023年第1期55卷 2232355页

作者： Wang, Wenjie Fan, Dongqing Quan, Bin Hou, Weishun Yang, Jinsun Wannan Med Coll Affiliated Hosp 1 Dept Infect Dis Wuhu Anhui Peoples R China Wannan Med Coll Dept Publ Hlth Wuhu Anhui Peoples R China Wannan Med Coll Affiliated Hosp 1 Dept Infect Dis 2 Zheshan West Rd Wuhu 241001 Anhui Peoples R China

Background Hantavirus infection is the main cause of hemorrhagic fever with renal syndrome (HFRS), which is common in Asia and Europe. There is a considerable risk of morbidity and mortality from the uncommon Hantavirus complication known as acute pancreatitis (AP). Methods Retrospective analysis of the medical records of individuals with HFRS was performed. Relevant variables were assessed by univariate analyses and the variables with a p value Results In this study, 114 individuals with HFRS in total were included, and 30 of them (26.32%) had AP. The univariate analyses showed that living in Xuancheng city (Anhui Province);an alcohol consumption history;white blood cell (WBC) count;lymphocyte (lym%) and eosinophil percentages (EO%);neutrophil (neut), eosinophil (EO), and red blood cell (RBC) counts;hemoglobin (Hb);hematocrit (HCT);proteinuria;hematuria;albumin (ALB), blood urea nitrogen (BUN), creatinine (Cr), uric acid (UA), cystatin-C (Cys-C) levels;carbon dioxide-combining power (CO2CP);fibrinogen degradation products (FDPs);and D-dimer level were significantly associated with HFRS complicated with AP (p < .05). In the multivariable regression analysis, an alcohol consumption history, lym%, proteinuria, FDPs and D-dimer level were found to be risk factors for HFRS complicated with AP (p < .05). Conclusion Our findings indicate that HFRS patients with a history of consuming alcohol, a high lym%, intense proteinuria, high levels of FDPs, and a low level of D-dimer might be more prone to the development of AP. KEY MESSAGES This is the first report employing logistic regression analysis methods for exploring the risk factors for HFRS complicated with AP in China. Many factors (most are laboratory parameters) were significantly associated with HFRS complicated with AP. We found that HFRS patients with a history of consuming alcohol, a high lym%, intense proteinuria, high levels of FDPs, and a low level of D-dimer might be more prone to the development of AP.

关键词： Hemorrhagic fever with renal syndrome acute pancreatitis risk factors logistic regression analysis

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Parameter study on characteristic pulse diagram of polycystic ovary syndrome based on logistic regression analysis

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JOURNAL OF OBSTETRICS AND GYNAECOLOGY 2022年第8期42卷 3712-3719页

作者： Wang, Weiying Zeng, Weiwei Chen, Xinmin Tu, Liping Xu, Jiatuo Yin, Xiuqi Shanghai Univ Chinese Med Shuguang Hosp Dept Gynecol & Obstet Shanghai Peoples R China Shanghai Univ Tradit Chinese Med Basic Med Coll Shanghai Peoples R China

This study aimed to explore the parameters of the independent predictive characteristic pulse diagram of polycystic ovary syndrome (PCOS) by analysing the pulse characteristics between healthy women and the PCOS group. A total of 278 women were recruited for this study. Pulse wave parameters were collected by the pulse spectrum analyser. The single-factor analysis of the pulse diagram parameters was used to identify significant indicators, and the logistic regression analysis was carried out on the above indicators with statistical differences to obtain independent predictors. According to the single-factor and multi-factor analyses, h(1), h(5), h(3)/h(1), t, t(1) and t(5) were independent predictors of PCOS diagnosis. The results showed that PCOS patients had a faster heart rate, decreased left ventricular systolic function and decreased aortic compliance compared to healthy individuals. These findings suggested that the characteristic pulse parameters screened out are valuable for the diagnosis of *** STATEMENTWhat is already known on this subject? Polycystic ovary syndrome (PCOS) is a common gynecological reproductive endocrine and metabolic disease, which is significant for screening and early intervention in the disease. However, due to the lack of pulse's diagnostic evidence of PCOS, there is still an unknown area in the research on the correlation between PCOS and pulse diagram *** do the results of this study add? This study fills the gap between the research on PCOS and pulse wave. The study also shows that the pulse characteristic parameters h(1), h(5), h(3)/h(1), t, t(1), and t(5) are independent predictors of PCOS, suggesting that the patients have a higher heart rate, lower ventricular systolic function, and aortic compliance than healthy *** are the implications of these findings for clinical practice and/or further research? Prominent risk factors for pulse parameters associated with the occurrence of PCOS facilitate e

关键词： Polycystic ovary syndrome pulse characteristics traditional Chinese medicine logistic regression analysis diagnosis cardiovascular risk

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logistic regression analysis for LncRNA-Disease Association Prediction Based on Random Forest and Clinical Stage Data

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IEEE ACCESS 2020年 8卷 35004-35017页

作者： Wang, Bo Zhang, Jing Harbin Engn Univ Coll Comp Sci & Technol Harbin 150001 Peoples R China Univ Jinan Sch Informat Sci & Engn Jinan 250022 Peoples R China Qiqihar Univ Coll Comp & Control Qiqihar 161006 Peoples R China

An increasing amount of studies have found that LncRNA plays an important role in various life processes of the body. In current prediction research on lncRNA-disease associations, correlation analysis of disease prognosis is overlooked. In this study, a logistic regression prediction model based on tumor clinical stage data and the expression quantity of lncRNA transcript is constructed. The proposed model is based on unknown human lncRNA-disease associations combining with the clinical stage data. Firstly, the importance of the characteristic variable is calculated by the proposed CVSgC-RF algorithm. Secondly, 95 lncRNAs, which are most closely related to prostate cancer, are calculated from 480 alternative lncRNAs by CASO and CVSe-CS-CF. On the basis of the above 95 lncRNAs, the CSPA-PL algorithm is used to select a further 22 lncRNAs that are most closely related to the tumor clinical stage for prostate cancer. Finally, 22 lncRNAs are used to construct a logistic regression prediction model. Additionally, this method is applied to lung cancer data;16 lncRNAs are selected to construct a logistic regression prediction model for lung cancer. Experimental results show that the best results for ROC Area, the accuracy and recall rate of the prediction model are achieved by the proposed method for prostate cancer and lung cancer, which provides a promising basis for subsequent prediction studies of lncRNA-disease associations.

关键词： Tumors Predictive models Prognostics and health management Prostate cancer logistics LncRNA-disease association random forest logistic regression analysis clinical stage data

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