Dental Caries Prediction Based on a Survey of the Oral Health Epidemiology among the Geriatric Residents of Liaoning, China

Background. Dental caries is one of the most common chronic diseases observed in elderly patients. The development of preventive strategies for dental caries in elderly individuals is vital. Objective. The objective of the present study was to construct a generalized regression neural network (GRNN) prediction model for the risk assessment of dental caries among the geriatric residents of Liaoning, China. Methods. A stratified equal-capacity random sampling method was used to randomly select 1144 elderly (65-74 years) residents (gender ratio 1 : 1) of Liaoning, China. Data for the oral assessment, including caries characteristics, and questionnaire survey from each participant were collected. Multivariate logistic regression analysis was then performed to identify the independent predictors. GRNN was applied to establish a prediction model for dental caries. The accuracy of the unconditional logistic regression and the GRNN early warning model was compared. Results. A total of 1144 patients fulfilled the requirements and completed the questionnaires. The caries rate was 68.5%, and the main associated factors were toothache history, residence area, smoking, and drinking. We randomly divided the data for the 1144 participants into a training set (915 cases) and a test set (229 cases). The optimal smoothing factor was 0.7, and the area under the receiver operating characteristic curve for the GRNN model was 0.626 (95% confidence interval, 0.544 to 0.708), with a P value of 0.002. In terms of consistency, sensitivity, and specificity, the GRNN model was better than the traditional unconditional multivariate logistic regression model. Conclusion. Geriatric (65-74 years) residents of Liaoning, China, have a high rate of dental caries. Residents with a history of toothache and smoking habits are more susceptible to the disease. The GRNN early warning model is an accurate and meaningful tool for screening, early diagnosis, and treatment planning for geriatric individuals with a high risk of caries.

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Development of an Early Warning Model for Predicting the Death Risk of Coronavirus Disease 2019 Based on Data Immediately Available on Admission

Frontiers in Medicine ◽

10.3389/fmed.2021.699243 ◽

2021 ◽

Vol 8 ◽

Author(s):

Hai Wang ◽

Haibo Ai ◽

Yunong Fu ◽

Qinglin Li ◽

Ruixia Cui ◽

...

Keyword(s):

Prediction Model ◽

Early Warning ◽

Univariate Analysis ◽

Full Model ◽

Death Risk ◽

Early Warning Model ◽

Home Based ◽

Medical Facilities ◽

Risk Patients ◽

Warning Model

Introduction: COVID-19 has overloaded worldwide medical facilities, leaving some potentially high-risk patients trapped in outpatient clinics without sufficient treatment. However, there is still a lack of a simple and effective tool to identify these patients early.Methods: A retrospective cohort study was conducted to develop an early warning model for predicting the death risk of COVID-19. Seventy-five percent of the cases were used to construct the prediction model, and the remaining 25% were used to verify the prediction model based on data immediately available on admission.Results: From March 1, 2020, to April 16, 2020, a total of 4,711 COVID-19 patients were included in our study. The average age was 63.37 ± 16.70 years, of which 1,148 (24.37%) died. Finally, age, SpO2, body temperature (T), and mean arterial pressure (MAP) were selected for constructing the model by univariate analysis, multivariate analysis, and a review of the literature. We used five common methods for constructing the model and finally found that the full model had the best specificity and higher accuracy. The area under the ROC curve (AUC), specificity, sensitivity, and accuracy of full model in train cohort were, respectively, 0.798 (0.779, 0.816), 0.804, 0.656, and 0.768, and in the validation cohort were, respectively, 0.783 (0.751, 0.815), 0.800, 0.616, and 0.755. Visualization tools of the prediction model included a nomogram and an online dynamic nomogram (https://wanghai.shinyapps.io/dynnomapp/).Conclusion: We developed a prediction model that might aid in the early identification of COVID-19 patients with a high probability of mortality on admission. However, further research is required to determine whether this tool can be applied for outpatient or home-based COVID-19 patients.

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Clinical features and risk factors analysis of bronchitis obliterans due to refractory Mycoplasma pneumoniae pneumonia in children: a nomogram prediction model

BMC Infectious Diseases ◽

10.1186/s12879-021-06783-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Qi Cheng ◽

Han Zhang ◽

Yunxiao Shang ◽

Yuetong Zhao ◽

Ye Zhang ◽

...

Keyword(s):

Logistic Regression ◽

Mycoplasma Pneumoniae ◽

Characteristic Curve ◽

Acute Infection ◽

Multivariate Logistic Regression Analysis ◽

Multivariate Logistic Regression Model ◽

Early Prediction ◽

Multivariate Logistic Regression ◽

Fiber Bronchoscopy ◽

Wbc Count

Abstract Background Early prediction of bronchitis obliterans (BO) is of great significance to the improvement of the long-term prognosis of children caused by refractory Mycoplasma pneumoniae pneumonia (RMPP). This study aimed to establish a nomogram model to predict the risk of BO in children due to RMPP. Methods A retrospective observation was conducted to study the clinical data of children with RMPP (1–14 years old) during acute infection. According to whether there is BO observed in the bronchoscope, children were divided into BO and the non-BO groups. The multivariate logistic regression model was used to construct the nomogram model. Results One hundred and forty-one children with RMPP were finally included, of which 65 (46.0%) children with RMPP were complicated by BO. According to the multivariate logistic regression analysis, WBC count, ALB level, consolidation range exceeding 2/3 of lung lobes, timing of macrolides, glucocorticoids or fiber bronchoscopy and plastic bronchitis were independent influencing factors for the occurrence of BO and were incorporated into the nomogram. The area under the receiver operating characteristic curve (AUC-ROC) value of nomogram was 0.899 (95% confidence interval [CI] 0.848–0.950). The Hosmer–Lemeshow test showed good calibration of the nomogram (p = 0.692). Conclusion A nomogram model found by seven risk factor was successfully constructed and can use to early prediction of children with BO due to RMPP.

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Adherence to Mediterranean Diet among Students from Primary and Middle School in the Province of Taranto, 2016–2018

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17155437 ◽

2020 ◽

Vol 17 (15) ◽

pp. 5437

Author(s):

Guglielmo Bonaccorsi ◽

Federica Furlan ◽

Marisa Scocuzza ◽

Chiara Lorini

Keyword(s):

Logistic Regression ◽

Nutritional Status ◽

Mediterranean Diet ◽

Eating Habits ◽

Economic Status ◽

Multivariate Logistic Regression Analysis ◽

Multivariate Logistic Regression Model ◽

Multivariate Logistic Regression ◽

Predictive Role ◽

The Mediterranean

The Mediterranean diet represents one of the healthiest dietary patterns, but nowadays it is increasingly being ignored in schools and by families. The aim of this study is to assess the adherence to the Mediterranean diet by pupils living in a small Southern Italian municipality, and whether it is a predictor of nutritional status.The degree of adherence to the Mediterranean diet, the socio-economic status and the nutritional status of 314 students (6–14 years) were tested during the 2016/2017 and 2017/2018 school years with the help of a questionnaire comprising the Mediterranean Diet Quality Index for Children and Adolescents (KIDMED) test. Multivariate logistic regression analysis was used to assess the predictive role of the KIDMED score and the other variables with respect to nutritional status. Adherence to the Mediterranean diet is high, medium and poor in, respectively, 24.8, 56.4 and 18.8% of students; it varies depending on gender and age, with females and older students showing higher values. In the multivariate logistic regression model, sex and KIDMED level are become significant predictors of nutritional status. This study highlights the need for intervention in the form of school projects—also involving families—to promote healthier eating habits in younger generations.

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Decision Tree Analysis: A Retrospective Analysis of Postoperative Recurrence of Adhesions in Patients with Moderate-to-Severe Intrauterine

BioMed Research International ◽

10.1155/2019/7391965 ◽

2019 ◽

Vol 2019 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Ru Zhu ◽

Hua Duan ◽

Sha Wang ◽

Lu Gan ◽

Qian Xu ◽

...

Keyword(s):

Logistic Regression ◽

Decision Tree ◽

Endometrial Thickness ◽

Characteristic Curve ◽

Multivariate Logistic Regression Analysis ◽

Decision Tree Model ◽

Classification And Regression Tree ◽

Multivariate Logistic Regression Model ◽

Multivariate Logistic Regression ◽

Tree Model

Objective. To establish and validate a decision tree model to predict the recurrence of intrauterine adhesions (IUAs) in patients after separation of moderate-to-severe IUAs. Design. A retrospective study. Setting. A tertiary hysteroscopic center at a teaching hospital. Population. Patients were retrospectively selected who had undergone hysteroscopic adhesion separation surgery for treatment of moderate-to-severe IUAs. Interventions. Hysteroscopic adhesion separation surgery and second-look hysteroscopy 3 months later. Measurements and Main Results. Patients’ demographics, clinical indicators, and hysteroscopy data were collected from the electronic database of the hospital. The patients were randomly apportioned to either a training or testing set (332 and 142 patients, respectively). A decision tree model of adhesion recurrence was established with a classification and regression tree algorithm and validated with reference to a multivariate logistic regression model. The decision tree model was constructed based on the training set. The classification node variables were the risk factors for recurrence of IUAs: American Fertility Society score (root node variable), isolation barrier, endometrial thickness, tubal opening, uterine volume, and menstrual volume. The accuracies of the decision tree model and multivariate logistic regression analysis model were 75.35% and 76.06%, respectively, and areas under the receiver operating characteristic curve were 0.763 (95% CI 0.681–0.846) and 0.785 (95% CI 0.702–0.868). Conclusions. The decision tree model can readily predict the recurrence of IUAs and provides a new theoretical basis upon which clinicians can make appropriate clinical decisions.

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Risk factors for peritoneal dialysis–associated peritonitis

European Journal of Inflammation ◽

10.1177/2058739218772243 ◽

2018 ◽

Vol 16 ◽

pp. 205873921877224

Author(s):

Hongyue Wang ◽

Xiangtuo Wang ◽

Haichuan Dou ◽

Chenhao Li ◽

Mingji Cui ◽

...

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Peritoneal Dialysis ◽

Serum Albumin ◽

Multivariate Logistic Regression Analysis ◽

Roc Curves ◽

Multivariate Logistic Regression Model ◽

Phosphorus Concentration ◽

Multivariate Logistic Regression ◽

Gram Positive

The purpose of this study was to summarize the pathogens that cause peritoneal dialysis (PD)-associated peritonitis and to identify risk factors for PD-associated peritonitis. This retrospective study included 115 end-stage renal disease (ESRD) patients receiving PD therapy. Patients were categorized into two groups: peritonitis group (n = 41) and non-peritonitis group (n = 74). Clinical data and laboratory tests were collected from medical records. The multivariate logistic regression model was used to evaluate associations between PD-associated peritonitis and potential risk factors. PD-associated peritonitis occurred 54 times in 41 patients. The most frequently identified pathogen was Gram-positive cocci (57.78%). Multivariate logistic regression analysis showed that serum albumin (β = –0.208, P < 0.001), blood phosphorus concentration (β = –1.732, P = 0.001), gastrointestinal disorders (β = 1.624, P = 0.043), and use of calcitriol (β = –2.239, P = 0.048) were significantly correlated with PD-associated peritonitis. Receiver operating characteristic (ROC) curves showed that the areas under the curve were 0.832 for serum albumin and 0.700 for blood phosphorus concentration with optimal cut-off values of 29.1 g/L for serum albumin and 1.795 mmol/L for blood phosphorus concentration. Gram-positive coccus is the major pathogen responsible for PD-associated peritonitis. Serum albumin <29.1 g/L, blood phosphorus concentration <1.795 mmol/L, and intestinal disorders are risk factors for PD-associated peritonitis, whereas the use of calcitriol can reduce the risk of PD-associated peritonitis.

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Prevalence and risk factors of depression and anxiety for inpatients with lung cancer in China: A hospital-based cross-sectional study

10.21203/rs.3.rs-1149458/v1 ◽

2022 ◽

Author(s):

Xueqian Wang ◽

Xuejiao Ma ◽

Mo Yang ◽

Yan Wang ◽

Yi Xie ◽

...

Keyword(s):

Risk Factors ◽

Lung Cancer ◽

Logistic Regression ◽

Risk Factor ◽

Univariate Analysis ◽

Multivariate Logistic Regression Analysis ◽

Multivariate Logistic Regression Model ◽

Depression And Anxiety ◽

Multivariate Logistic Regression ◽

Cross Sectional

Abstract Background Lung cancer was often accompanied by depression and anxiety. Nowadays, most investigations for depression and anxiety were concentrated in western medical hospitals, while few related studies have been carried out in the tradition Chinese medicine (TCM) ward. It was necessary to understand the prevalence and risk factors of depression and anxiety in the inpatients with lung cancer in TCM hospital. Methods This study adopted cross-sectional research method, which enrolled a total of 222 inpatients with lung cancer in TCM hospital. PHQ-9 and GAD-7 scales were used to assess depression and anxiety for the inpatients, respectively. Demographic and clinical data were also collected. Statistical methods of the univariate analysis and the multivariate logistic regression model were used. Results The prevalence of depression and anxiety in the inpatients with lung cancer were 58.1% and 34.2%, respectively. Multivariate logistic regression analysis prompted that the common risk factor of depression and anxiety was the symptom of insomnia. Constipation and gender were the two anther risk factors of depression. Conclusion Depression and anxiety were common for the inpatients with lung cancer in TCM hospital. Gender, insomnia and constipation were risk factors for depression, and insomnia was risk factor for anxiety. Therefore, medical workers should pay close attention to the emotional changes of these high-risk patients and intervene the symptoms as early as possible.

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A Novel Prediction Model for Long-Term SARS-CoV-2 RNA Shedding in Non-death Hospitalized Patients with COVID-19: A Retrospective Cohort Study

10.21203/rs.3.rs-940304/v1 ◽

2021 ◽

Author(s):

Xiaoli Lei ◽

Junli Wang ◽

Lijie Kou ◽

Zhigang Yang

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Prediction Model ◽

Predictive Model ◽

Goodness Of Fit ◽

Multivariate Logistic Regression Analysis ◽

Hospitalized Patients ◽

Multivariate Logistic Regression ◽

Data Set

Abstract Background: Because of the lack of compelling evidence for predicting the duration of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA shedding, the purpose of this retrospective study was to establish a predictive model for long-term SARS-CoV-2 RNA shedding in non-death hospitalized patients with coronavirus disease-19 (COVID-19).Methods: 97 non-death hospitalized patients with COVID-19 admitted to two hospitals in Henan province of China from February 3, 2020 to March 31, 2020 were retrospectively enrolled. Multivariate logistic regression was performed to identify the high risk factors associated with long-term SARS-CoV-2 RNA shedding and a predictive model was established and represented by a nomogram. Its performance was assessed with discrimination and calibration.Results: 97 patients were divided into the long-term (>21 days) group (n = 27, 27.8%) and the short-term (≤ 21 days) group (n = 70, 72.2%) based on their viral shedding duration. Multivariate logistic regression analysis showed that time from illness onset to diagnosis (OR 1.224, 95% CI 1.070-1.400, P = 0.003) and interstitial opacity in chest computerized tomography(CT) scan (OR 6.516, 95% CI 2.041-20.798, P = 0.002) were independent risk factors for long-term SARS-CoV-2 RNA shedding. A prediction model, which is presented with a nomogram, was established by incorporating the two risk factors. The goodness-of-fit statistics for the nomogram was not statistically significant (χ2 = 8.292; P = 0.406), and its area under the receiver operator characteristic curve was 0.834 (95% CI 0.731- 0.936; P < 0.001).Conclusion: The established model has a good predictive performance on the long-term viral RNA shedding in non-death hospitalized patients with COVID-19, but it still needs further validation by independent data set of large samples in the future.

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Risk factors and prediction model construction of post-operative atrial fibrillation after thoracic surgery in Chinese patients

Archives of Medical Science ◽

10.5114/aoms/131087 ◽

2021 ◽

Author(s):

Yunling Lin ◽

Jianmin Sun ◽

Xun Yuan ◽

Hui Liu

Keyword(s):

Risk Factors ◽

Atrial Fibrillation ◽

Logistic Regression ◽

Thoracic Surgery ◽

Prediction Model ◽

Univariate Analysis ◽

Multivariate Logistic Regression Analysis ◽

Chinese Patients ◽

Multivariate Logistic Regression ◽

Post Operative Atrial Fibrillation

IntroductionThe purpose of this study was to analyze the risk factors of post-operative atrial fibrillation (POAF) after thoracic surgery, and to build a predictive model for accurate preoperative identification of high-risk patients.Material and methodsIn this study, data of 2072 patients with pulmonary masses and esophageal cancer who attended our hospital in the period from January 1, 2017 to December 31, 2018 were analyzed retrospectively. According to whether AF occurred after the operation, the patients were divided into atrial fibrillation (AF) and non-AF (NAF) groups. The general information (age, sex, height, etc.), previous medical history (chronic lung disease, hypertension, etc.), medication history, preoperative ultrasound and cardiogram results, and preoperative and postoperative electrocardiogram (ECG) were collected. The operation mode, resection scope, histopathology and hospitalization were recorded. Univariate and multivariate logistic regression were used to screen out the risk factors of AF and establish a prediction model.ResultsThe incidence of POAF was 5.98%. Univariate analysis showed that sex, age, body mass index, left atrial diameter and operation organ were the risk factors of POAF. The above factors were included in the multivariate logistic regression analysis, and the results showed that male sex, age, anteroposterior diameter of left atrium and surgical organs were related to POAF. On this basis, a POAF prediction model was constructed, which had good discrimination and calibration. The area under the curve (AUC) is 0.784 with 95% CI: 0.746–0.822.ConclusionsThe prediction model of POAF based on the risk factors selected in this study can accurately predict the occurrence of AF after thoracic surgery.

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Nigral Iron Deposition Is Associated With Levodopa-Induced Dyskinesia in Parkinson’s Disease

Frontiers in Neuroscience ◽

10.3389/fnins.2021.647168 ◽

2021 ◽

Vol 15 ◽

Author(s):

Tianbin Song ◽

Jiping Li ◽

Shanshan Mei ◽

Xiaofei Jia ◽

Hongwei Yang ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Logistic Regression ◽

Multivariate Logistic Regression Analysis ◽

Iron Deposition ◽

Multivariate Logistic Regression Model ◽

Multivariate Logistic Regression ◽

Increased Risk ◽

Left And Right ◽

The Mean

ObjectiveTo investigate iron deposition in the substantia nigra (SN) of Parkinson’s disease (PD) patients associated with levodopa-induced dyskinesia (LID).MethodsSeventeen PD patients with LID, 17 PD patients without LID, and 16 healthy controls were recruited for this study. The mean QSM values of the whole, left, and right SN were compared among the three groups. A multivariate logistic regression model was constructed to determine the factors associated with increased risk of LID. The receiver operating characteristic curve of the QSM value of SN in discriminating PD with and without LID was evaluated.ResultsThe mean QSM values of the whole and right SN in the PD with LID were higher than those in the PD without LID (∗P = 0.03, ∗P = 0.03). Multivariate logistic regression analysis revealed that the QSM value of whole, left, or right SN was a predictor of the development of LID (∗P = 0.03, ∗P = 0.04, and ∗P = 0.04). The predictive accuracy of LID in adding the QSM value of the whole, left, and right SN to LID-related clinical risk factors was 70.6, 64.7, and 67.6%, respectively. The QSM cutoff values between PD with and without LID of the whole, left, and right SN were 148.3, 165.4, and 152.7 ppb, respectively.ConclusionThis study provides the evidence of higher iron deposition in the SN of PD patients with LID than those without LID, suggesting that the QSM value of the SN may be a potential early diagnostic neuroimaging biomarker for LID.

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Comparison of Clinical Characteristics and Outcomes between Respiratory s yncytial Virus and Influenza-related Pneumonia in China from 2013 to 2019

10.21203/rs.3.rs-97784/v1 ◽

2020 ◽

Author(s):

Liang Chen ◽

Xiudi Han ◽

YanLi Li ◽

Chunxiao Zhang ◽

Xiqian Xing

Keyword(s):

Logistic Regression ◽

Influenza A ◽

Clinical Characteristics ◽

Nasal Congestion ◽

Multivariate Logistic Regression Analysis ◽

Teaching Hospitals ◽

Multivariate Logistic Regression Model ◽

Influenza B ◽

Multivariate Logistic Regression ◽

Increased Risk

Abstract Background: Differences in the clinical features and outcomes between syncytial virus-related (RSV-p) and influenza-related pneumonia (Flu-p) are largely unknown. We aimed to compare clinical characteristics and severity between adults with the two conditions . Methods: A total of 127 patients with RSV-p, 693 patients with influenza A-related pneumonia (FluA-p) and 386 patients with influenza B-related pneumonia (FluB-p) were retrospectively reviewed from 2013 through 2019 in five teaching hospitals in China. Results: A multivariate logistic regression model indicated that age ≥ 50 years, cerebrovascular disease, chronic kidney disease, solid malignant tumor, nasal congestion, myalgia, sputum production, respiratory rates ≥ 30 beats/min, lymphocytes < 0.8×109/L and blood albumin < 35 g/L were predictors that differentiated RSV-p from Flu-p. After adjusting for confounders, a multivariate logistic regression analysis confirmed that, relative to RSV-p, FluA-p (OR 2.313 , 95% CI 1.377 - 3.885, p = 0.002) incurred an increased risk for severe outcomes, including invasive ventilation, ICU admission, and 30-day mortality. FluB-p (OR 1.630 , 95% CI 0.958 - 2.741, p = 0.071) was not associated with increased risk. Conclusions: The severity of RSV-p was less than that of FluA-p, but was comparable to FluB-p. Some clinical variables were useful for discriminating RSV-p from Flu-p.

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