Erythemato-squamous diseases are a general designation of six common skin diseases, of which the differential diagnosis is a difficult problem in dermatology. This paper presents a new method based on virtual coding for qualitative variables and multinomial logistic regression penalized via elastic net. Considering the attributes of variables, a virtual coding is applied and contributes to avoid the irrationality of calculating nominal values directly. Multinomial logistic regression model penalized via elastic net is thence used to fit the correlation between the features and classification of diseases. At last, parameter estimations can be attained through coordinate descent. This method reached accuracy rate of 98.34%±0.0027% using 10-fold cross validation in the experiments. Our method attained equivalent accuracy rate compared to the results of other methods, but steps are simpler and stability is higher.
ObjectiveTo investigate the association of preoperative serum uric acid (UA) levels with postoperative prolonged mechanical ventilation (PMV) in patients undergoing mechanical heart valve replacement.MethodsClinical data of 311 patients undergoing mechanical heart valve replacement in The First Affiliated Hospital of Anhui Medical University from January 2017 to December 2017 were retrospectively analyzed. There were 164 males at age of 55.6±11.4 years and 147 females at age of 54.2±9.8 years. The patients were divided into a PMV group (>48 h) and a control group according to whether the duration of PMV was longer than 48 hours. Spearman's rank correlation coefficient and logistic regression analysis were conducted to evaluate the relationship between preoperative UA and postoperative PMV. The predictive value of UA for PMV was undertaken using the receiver operating characteristic (ROC) curve..ResultsAmong 311 patients, 38 (12.2%) developed postoperative PMV. Preoperative serum UA level mean values were 6.11±1.94 mg/dl, while the mean UA concentration in the PMV group was significantly higher than that in the control group (7.48±2.24 mg/dl vs. 5.92±1.82 mg/dl, P<0.001). Rank correlation analysis showed that UA was positively correlated with postoperative PMV (rs=0.205, P<0.001). Multivariate logistic regression analysis demonstrated that preoperative elevated UA was associated independently with postoperative PMV with odds ratio (OR)=1.44 and confidence interval (CI) 1.15–1.81 (P=0.002). The area under the ROC curve of UA predicting PMV was 0.72, 95% CI0.635–0.806, 6.40 mg/dl was the optimal cut-off value, and the sensitivity and specificity was 76.3% and 63.0% at this time, respectively.ConclusionPreoperative elevated serum UA is an independent risk factor for postoperative PMV in patients undergoing mechanical heart valve replacement and has a good predictive value.
ObjectiveTo evaluate the predictors of generalized anxiety disorder (GAD) among teachers in 3 months after Lushan earthquake. MethodsA prospective cohort study was conducted to diagnostically evaluate the psychological sequelae and GAD during 14-20 days and 85-95 days after the earthquake. The possible predictive factors of psychological sequelae were assessed by a self-made questionnaire and the GAD was assessed by the GAD symptom criterion of M.I.N.I. in 3 months. The univariate and multivariate logistic regression analysis (ULRA, MLRA) were applied to analyze the predictors of GAD after the two-staged assessments. ResultsThere were a total of 319 teachers completed the two-staged assessments. The total response rate was 51.3%. Seventy teachers were diagnosed as GAD and the prevalence of GAD in 3 months was 21.9%. The predictive factors by ULRA included:male, older than 35 years old, having unlivable house, living in tents, sleeping difficulties, easy to feel sad, physical discomfort, loss of appetite, feeling short of social support, unable to calm down for working, feeling difficult for teaching, observing more inattention of students, and wanting to ask for a leave. The independent predictors by MLRA included:male, having unlivable house, feeling short of social support, and feeling difficult for teaching. ConclusionThe teachers have a higher likelihood of GAD after earthquake. It is essential to pay more attention to those male teachers, who feel short of social support and don't have a livable house thus to prevent the GAD at the early stage of post-earthquake.
Objective To explore the independent risk factors for hospital infections in tertiary hospitals in Gansu Province, and establish and validate a prediction model. Methods A total of 690 patients hospitalized with hospital infections in Gansu Provincial Hospital between January and December 2021 were selected as the infection group; matched with admission department and age at a 1∶1 ratio, 690 patients who were hospitalized during the same period without hospital infections were selected as the control group. The information including underlying diseases, endoscopic operations, blood transfusion and immunosuppressant use of the two groups were compared, the factors influencing hospital infections in hospitalized patients were analyzed through multiple logistic regression, and the logistic prediction model was established. Eighty percent of the data from Gansu Provincial Hospital were used as the training set of the model, and the remaining 20% were used as the test set for internal validation. Case data from other three hospitals in Gansu Province were used for external validation. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were used to evaluate the model effectiveness. Results Multiple logistic regression analysis showed that endoscopic therapeutic manipulation [odds ratio (OR)=3.360, 95% confidence interval (CI) (2.496, 4.523)], indwelling catheter [OR=3.100, 95%CI (2.352, 4.085)], organ transplantation/artifact implantation [OR=3.133, 95%CI (1.780, 5.516)], blood or blood product transfusions [OR=3.412, 95%CI (2.626, 4.434)], glucocorticoids [OR=2.253, 95%CI (1.608, 3.157)], the number of underlying diseases [OR=1.197, 95%CI (1.068, 1.342)], and the number of surgical procedures performed during hospitalization [OR=1.221, 95%CI (1.096, 1.361)] were risk factors for hospital infections. The regression equation of the prediction model was: logit(P)=–2.208+1.212×endoscopic therapeutic operations+1.131×indwelling urinary catheters+1.142×organ transplantation/artifact implantation+1.227×transfusion of blood or blood products+0.812×glucocorticosteroids+0.180×number of underlying diseases+0.200×number of surgical procedures performed during the hospitalization. The internal validation set model had a sensitivity of 72.857%, a specificity of 77.206%, an accuracy of 76.692%, and an AUC value of 0.817. The external validation model had a sensitivity of 63.705%, a specificity of 70.934%, an accuracy of 68.669%, and an AUC value of 0.726. Conclusions Endoscopic treatment operation, indwelling catheter, organ transplantation/artifact implantation, blood or blood product transfusion, glucocorticoid, number of underlying diseases, and number of surgical cases during hospitalization are influencing factors of hospital infections. The model can effectively predict the occurrence of hospital infections and guide the clinic to take preventive measures to reduce the occurrence of hospital infections.
Features and interaction between features of liver disease is of great significance for the classification of liver disease. Based on least absolute shrinkage and selection operator (LASSO) and interaction LASSO, the generalized interaction LASSO model is proposed in this paper for liver disease classification and compared with other methods. Firstly, the generalized interaction logistic classification model was constructed and the LASSO penalty constraints were added to the interactive model parameters. Then the model parameters were solved by an efficient alternating directions method of multipliers (ADMM) algorithm. The solutions of model parameters were sparse. Finally, the test samples were fed to the model and the classification results were obtained by the largest statistical probability. The experimental results of liver disorder dataset and India liver dataset obtained by the proposed methods showed that the coefficients of interaction features of the model were not zero, indicating that interaction features were contributive to classification. The accuracy of the generalized interaction LASSO method is better than that of the interaction LASSO method, and it is also better than that of traditional pattern recognition methods. The generalized interaction LASSO method can also be popularized to other disease classification areas.
Objective To investigate the association between environmental factors and nonsyndromic cleft lip and palate (NSCLP), and to explore the interaction of main risk factors in Chinese Guangdong population. Methods A hospital-based case-control study was used. NSCLP children were selected from Cleft Lip amp; Palate Treatment Centre of Second Affil iated Hospital of Medical College of Shantou University between September 2009 and March 2010 as cases. And controlswere chosen from other departments in the same hospital during the same period. The parents of cases and controls were inquired regarding the risk factors and the answers were filled in a unification questionnaire by physicians. These data were analysed with chi-square test and multivariate unconditional logistic regression analysis. Results A total of 105 cases and 110 controls with a mean age of 2.2 years and 3.0 years, respectively, were enrolled. Multivariate logistic regression analysis revealed that genetic family history (OR=4.210, P=0.039), mothers’ abnormal reproductive history (OR=2.494, P=0.033), early pregnancy medication (OR=3.488, P=0.000), and maternal stress (OR=3.416, P=0.011) were risk factors. There were positve interactions between genetic family history and mothers’ abnormal reproductive history as well as early pregnancy medication. Conclusion Certain influencing factors including genetic family history, mothers’ abnormal reproductive history, early pregnancy medication, and maternal stress are associated with NSCLP among Chinese Guangdong population. This study suggests that it may reduce the incidence rate of NSCLP through environmental intervention.
Atrial fibrillation (AF) is the most common arrhythmia in clinic, which can cause hemodynamic changes, heart failure and stroke, and seriously affect human life and health. As a self-promoting disease, the treatment of AF can become more and more difficult with the deterioration of the disease, and the early prediction and intervention of AF is the key to curbing the deterioration of the disease. Based on this, in this study, by controlling the dose of acetylcholine, we changed the AF vulnerability of five mongrel dogs and tried to assess it by analyzing the electrophysiology of atrial epicardium under different states of sinus rhythm. Here, indices from four aspects were proposed to study the atrial activation rule. They are the variability of atrial activation rhythm, the change of the earliest atrial activation, the change of atrial activation delay and the left-right atrial dyssynchrony. By using binary logistic regression analysis, multiple indices above were transformed into the AF inducibility, which were used to classify the signals during sinus rhythm. The sensitivity, specificity and accuracy of classification reached 85.7%, 95.8% and 91.7%, respectively. As the experimental results show, the proposed method has the ability to assess the AF vulnerability of atrium, which is of great clinical significance for the early prediction and intervention of AF.
ObjectiveTo examine statistical performance of different rare-event meta-analyses methods.MethodsUsing Monte-Carlo simulation, we set a variety of scenarios to evaluate the performance of various rare-event meta-analysis methods. The performance measures included absolute percentage error, root mean square error and interval coverage.ResultsAcross different scenarios, the absolute percentage error and root mean square error were similar for Bayesian logistic regression model, generalized mixed linear effects model and continuity correction, but the interval coverage was higher with Bayesian logistic regression model. The statistical performances with Mantel-Haenszel method and Peto method were consistently suboptimal across different scenarios.ConclusionsBayesian logistic regression model may be recommended as a preferred approach for rare-event meta-analysis.
Objective To investigate the feasibility of diagnosis of potential chronic obstructive pulmonary disease (COPD) patients who cannot finish the pulmonary function test via biphasic CT scan. Methods Sixty-seven male individuals aged 43 to 74 (57.0±5.9) years were divided into a COPD group (n=26) and a control group (n=41). All individuals underwent biphasic quantitative CT scan for calculating the proportion of emphysema, functional small airway disease, and normal component of the whole lung and each lobe. Results Based on principle component analysis, two principal components “imaging feature function 1 and imaging feature function 2” were calculated and analyzed by logistic regression, which found that imaging feature function 1 was an independent risk factor of COPD (odds ratio=8.749, P<0.001), and imaging features function 1 could be used to assist the diagnosis of COPD (area under receiver operating characteristic curve=0.843, P<0.001). Conclusion Imaging features function 1 is an independent risk factor for COPD and can assist the diagnosis of COPD.
Objective To evaluate the risk factors for postoperative in-hospital mortality in elderly patients receiving cardiac valvular surgery, and develop a new prediction models using the least absolute shrinkage and selection operator (LASSO)-logistic regression. Methods The patients≥65 years who underwent cardiac valvular surgery from 2016 to 2018 were collected from the Chinese Cardiac Surgery Registry (CCSR). The patients who received the surgery from January 2016 to June 2018 were allocated to a training set, and the patients who received the surgery from July to December 2018 were allocated to a testing set. The risk factors for postoperative mortality were analyzed and a LASSO-logistic regression prediction model was developed and compared with the EuroSCOREⅡ. Results A total of 7 163 patients were collected in this study, including 3 939 males and 3 224 females, with a mean age of 69.8±4.5 years. There were 5 774 patients in the training set and 1389 patients in the testing set. Overall, the in-hospital mortality was 4.0% (290/7163). The final LASSO-logistic regression model included 7 risk factors: age, preoperative left ventricular ejection fraction, combined coronary artery bypass grafting, creatinine clearance rate, cardiopulmonary bypass time, New York Heart Association cardiac classification. LASSO-logistic regression had a satisfying discrimination and calibration in both training [area under the curve (AUC)=0.785, 0.627] and testing cohorts (AUC=0.739, 0.642), which was superior to EuroSCOREⅡ. Conclusion The mortality rate for elderly patients undergoing cardiac valvular surgery is relatively high. LASSO-logistic regression model can predict the risk of in-hospital mortality in elderly patients receiving cardiac valvular surgery.