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.
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.
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.
Objective To investigate the adverse pregnant outcomes of hospitalized pregnant women in Lanzhou city, and analyze the corresponding risk factors and provide basis for the further research on better child-bearing and child-rearing. Methods In two provincial-level hospitals and one provincial-level specialized hospital, the method of cluster random sampling was applied to extract 6 825 medical records from January 2004 to December 2005. The relevant information was abstracted and correlative analyses were undertaken. Results The incidence of adverse pregnancy outcomes for the hospitalized pregnant women in Lanzhou city was 14.65%. Single-factor unconditional logistic regression analyses displayed that the variables with statistical significance were the number of previous pregnancies, the number of previous child delivery, abortion history, abnormal gestation history, and past medical history, whereas multi-factor unconditional logistic regression analyses revealed that the adverse pregnancy outcomes were positively correlated with abnormal gestation history and the number of previous pregnancies with statistical significance. Conclusion The incidence of adverse pregnancy outcomes for the hospitalized pregnant women in Lanzhou city is quite high. Abnormal gestation history and the number of previous pregnancies are the main risk factors for the adverse pregnancy outcomes.
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.
Abstract: Objective To investigate the method of improving effect, by investigating and analyzing the possible risk factors affecting shortterm outcome after total correction of tetralogy of Fallot (TOF). Methods Data of 219 patients who received total correction of TOF were divided into two groups according to the length of postoperative stay in hospital and recovery of heart function in the near future. Group A(n=110): patients had good recovery of heart function classified as gradeⅠorⅡ（NYHA classification）, and could smoothly be discharged from the hospital within two weeks without serious complications. The left ventricular ejection fraction (LVEF) had to exceed to 0.50 during 6 months followup visit. Group B(n=109): patients had worse recovery of heart function classified as grade Ⅱ or Ⅲ, and could not be discharged within two weeks with severe complications. LVEF was less than 0.50 during 6 months followup visit. The clinical data of two groups were compared, and risk factors affecting shortterm outcome after total correction of TOF operation were analyzed by logistic regression and model selection. Results There were good recovery of heart function classified as gradeⅠorⅡ(NYHA classification）in discharge, no death, and LVEF all exceeded to 0.50 in group A; there were 8 deaths in group B (7.34 %), and recovery of heart function was worse classified as grade Ⅱ or Ⅲ, with LVEF being less than 0.50（Plt;0.01）. Amount of postoperative daily thoracic drainage, assisted respiration time, time of inotropic agent stabilizing circulation, and the average length of postoperative stay in group A were all less or short than those in group B（Plt;0.01）. But the bypass and clamping time of group B were exceeded group A. The ratio of patching astride annulus in group B was greater than that in group A, and Nakata index was less than that in group A(Plt;0.01). The results of logistic regression and model selection indicate: age at repair (OR=0.69), oxygen saturation(OR=0.98), haematocrit before operation (OR=0.94), and patching astride annulus (OR=46.86), Nakata index (OR=16.90), amount of postoperative daily thoracic drainage (OR=0.84), presence of arrhythmia(OR=0.87), and wound infection(OR=63.57) have significant effect with shortterm outcome after total correction of TOF operation. Conclusions The probable methods to improving effect of shortterm outcome after total correction of TOF are an earlier age at repair, decreasing haematocrit, rising oxygen saturation before surgery, performing a palliative operation facilitating development of arteriae pulmonalis in earlier time, improving the surgical technique, and strengthening the perioperative care.
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.
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.
Signal classification is a key of brain-computer interface (BCI). In this paper, we present a new method for classifying the electroencephalogram (EEG) signals of which the features are heterogeneous. This method is called wrapped elastic net feature selection and classification. Firstly, we used the joint application of time-domain statistic, power spectral density (PSD), common spatial pattern (CSP) and autoregressive (AR) model to extract high-dimensional fused features of the preprocessed EEG signals. Then we used the wrapped method for feature selection. We fitted the logistic regression model penalized with elastic net on the training data, and obtained the parameter estimation by coordinate descent method. Then we selected best feature subset by using 10-fold cross-validation. Finally, we classified the test sample using the trained model. Data used in the experiment were the EEG data from international BCI Competition Ⅳ. The results showed that the method proposed was suitable for fused feature selection with high-dimension. For identifying EEG signals, it is more effective and faster, and can single out a more relevant subset to obtain a relatively simple model. The average test accuracy reached 81.78%.
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.