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 analyze the influencing factors on postoperative complications and mortality of gastric cancer after total gastrectomy. Methods The clinical data of 622 patients with gastric cancer received total gastrectomy were collected. According to the extent of lymph node dissection, the patients were divided into 2 groups: D0/D1 group (n=35) and D2/D3 group (n=587). The risk factors influencing postoperative morbidity and mortality were determined by logistic multiple regression analysis. Results The total postoperative complication morbidity and mortality for all patients were 9.81% (61/622) and 2.89% (18/622), respectively. The postoperative complication morbidity was 8.57% (3/35) and 9.88% (58/587) in the two groups, the postoperative mortality was 2.86% (1/35) and 2.90% (17/587) in the two groups, there were no significant differences between the two groups (Pgt;0.05). The most common postoperative complication was intestinal obstruction (18.03%, 11/61). Multivariate analysis revealed that risk factors on the postoperative complications and mortality were age ≥ 70 years, TNM stage Ⅳ, preoperative complication, palliative excision, merely manual or mechanical anastomosis, and multivisceral resection (Plt;0.05), however, the extent of lymph node was not influencing factor (Pgt;0.05). Conclusions Patients with advanced gastric cancer have a high risk of postoperative complications and mortality. Multiple organ resection should be avoided for patients with gastric cancer of TNM stage Ⅳ.
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%.
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.
Objective To investigate the effect factors of knee function recovery after operation in distal femoral fractures. Methods From January 2001 to May 2007, 92 cases of distal femoral fracture were treated. There were 50 males and 42 females, aged 20-77 years old (average 46.7 years old). Fracture was caused by traffic accident in 48 cases, by fall ing fromheight in 26 cases, by bruise in 12 cases and by tumble in 6 cases. According to Müller’s Fracture classification, there were 29 cases of type A, 12 cases of type B and 51 cases of type C. According to American Society of Anesthesiologists (ASA) classification, there were 21 cases of grade I, 39 cases of grade II, 24 cases of grade III, and 8 cases of grade IV. The time from injury to operation was 4 hours to 24 days with an average of 7 days. Anatomical plate was used in 43 cases, retrograde interlocking intramedullary nail in 37 cases, and bone screws, bolts and internal fixation with Kirschner pins in 12 cases. After operation, the HSS knee function score was used to evaluate efficacy. Ten related factors were appl ied for statistical analysis, to knee function recovery after operation in distal femoral fractures, such as age, sex, preoperative ASA classification, injury to surgery time, fracture type, treatment, reduction qual ity, functional exercise after operation, whether or not CPM functional training and postoperative compl ications. Results Wound healed by first intention in 88 cases, infection occurred in 4 cases. All patients followed up 16-32 months with an average of 23.1 months. Cl inical union of fracture was achieved within 3-7 months after operation. Extensor device adhesions and the scope of activities of lt; 80° occurred in 29 cases, traumatic arthritis in 25 cases, postoperative fracture displacement in 6 cases, mild knee varus or valgus in 7 cases and implant loosening in 6 cases. According to HSS knee function score, the results were excellent in 52 cases, good in 15 cases, fair in 10 cases and poor in 15 cases with an excellent andgood rate of 72.83%. Single factor analysis showed that age, preoperative ASA classification, fracture type, reduction qual ity, whether or not CPM functional exercise, and postoperative compl ications were significantly in knee function recovery (P lt; 0.05). logistic regression analysis showed that the fracture type, qual ity of reduction, whether or not CPM functional exercise, and age were major factors in the knee joint function recovery. Conclusion Age, preoperative ASA classification, fracture type, reduction qual ity, and whether or not CPM functional training, postoperative compl ications factors may affect the knee joint function recovery. Adjustment to the patient’s preoperative physical status, fractures anatomic reduction and firm fixation, early postoperative active and passive functional exercises, less postoperative compl ications can maximize the restoration of knee joint function.
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.
ObjectiveTo investigate the quality of life (QOL) and its influencing factors of patients with human epidermal growth factor receptor 2 (HER2) positive breast cancer returning to social life after treatment.MethodsFunctional assessment of cancer therapy-breast scale (FACT-B Scale) was adopted to investigate the QOL of the HER2 positive breast cancer survivors, who were admitted and treated during January 2015 and October 2019 in Fujian Provincial Hospital. The demographic, social and economic data, as well as the clinical information of the responded survivors were collected. Logistic regression model was adopted to analyze factors associated with the QOL of the responded survivors.ResultsA total of 117 responded survivors were included. The median of the FACT-B scale was 106.0 (91.0, 121.3) points out of 148 points (71.6%). With the control of the demographic, social and economic status of the responded survivors, as well as the time from diagnosis and treatment to responding to the follow-up, we found that "having other chronic conditions" was the risk factor for the HER2 positive breast cancer survivors to have higher QOL in the social life after treatment (OR=4.17, 95%CI 1.33 to 15.37, P=0.01).ConclusionsThe overall QOL of the HER2 positive breast cancer survivors in the social life after treatment was low. "Having other chronic conditions" was the risk factor for the HER2 positive breast cancer survivors to have higher QOL in the social life after treatment.
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.
ObjectiveTo explore the risk factors for postoperative respiratory failure (RF) in patients with esophageal cancer, construct a predictive model based on the least absolute shrinkage and selection operator (LASSO)-logistic regression, and visualize the constructed model. MethodsA retrospective analysis was conducted on patients with esophageal cancer who underwent surgical treatment in the Department of Thoracic Surgery, Sun Yat-sen University Cancer Center Gansu Hospital from 2020 to 2023. Patients were divided into a RF group and a non-RF (NRF) group according to whether RF occurred after surgery. Clinical data of the two groups were collected, and LASSO-logistic regression was used to optimize feature selection and construct the predictive model. The model was internally validated by repeated sampling 1000 times based on the Bootstrap method. ResultsA total of 217 patients were included, among which 24 were in the RF group, including 22 males and 2 females, with an average age of (63.33±9.10) years; 193 were in the NRF group, including 161 males and 32 females, with an average age of (62.14±8.44) years. LASSO-logistic regression analysis showed that the percentage of forced expiratory volume in one second/forced vital capacity (FEV1/FVC) to predicted value (FEV1/FVC%pred) [OR=0.944, 95%CI (0.897, 0.993), P=0.026], postoperative anastomotic fistula [OR=4.106, 95%CI (1.457, 11.575), P=0.008], and postoperative lung infection [OR=3.776, 95%CI (1.373, 10.388), P=0.010] were risk factors for postoperative RF in patients with esophageal cancer. Based on the above risk factors, a predictive model was constructed, with an area under the receiver operating characteristic curve of 0.819 [95%CI (0.737, 0.901)]. The Hosmer-Lemeshow test for the calibration curve showed that the model had good goodness of fit (P=0.527). The decision curve showed that the model had good clinical net benefit when the threshold probability was between 5% and 50%. Conclusion FEV1/FVC%pred, postoperative anastomotic fistula, and postoperative lung infection are risk factors for postoperative RF in patients with esophageal cancer. The predictive model constructed based on LASSO-logistic regression analysis is expected to help medical staff screen high-risk patients for early individualized intervention.
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.