Objective:To establish a risk prediction model for acute exacerbation of chronic obstructive pulmonary disease (AECOPD) using regression analysis and verify the model.Methods:The risk factors and acute exacerbation of 1 326 patients with chronic obstructive pulmonary disease (COPD) who entered the stable phase and followed up for 6 months in the four completed multi-center large-sample randomized controlled trials were retrospectively analyzed. Using the conversion-random number generator, about 80% of the 1 326 cases were randomly selected as the model group ( n = 1 074), and about 20% were the verification group ( n = 252). The data from the model group were selected, and Logistic regression analysis was used to screen independent risk factors for AECOPD, and an AECOPD risk prediction model was established; the model group and validation group data were substituted into the model, respectively, and the receiver operating characteristic (ROC) curve was drawn to verify the effectiveness of the risk prediction model in predicting AECOPD. Results:There were no statistically significant differences in general information (gender, smoking status, comorbidities, education level, etc.), body mass index (BMI) classification, lung function [forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), etc.], disease status (the number and duration of acute exacerbation in the past year, duration of disease, etc.), quality of life scale [COPD assessment test (CAT), etc.] and clinical symptoms (cough, chest tightness, etc.) between the model group and the validation group. It showed that the two sets of data had good homogeneity, and the cases in the validation group could be used to verify the effectiveness of the risk prediction model established through the model group data to predict AECOPD. Logistic regression analysis showed that gender [odds ratio ( OR) = 1.679, 95% confidence interval (95% CI) was 1.221-2.308, P = 0.001], BMI classification ( OR = 0.576, 95% CI was 0.331-1.000, P = 0.050), FEV1 ( OR = 0.551, 95% CI was 0.352-0.863, P = 0.009), number of acute exacerbation ( OR = 1.344, 95% CI was 1.245-1.451, P = 0.000) and duration of acute exacerbation ( OR = 1.018, 95% CI was 1.002-1.034, P = 0.024) were independent risk factors for AECOPD. A risk prediction model for AECOPD was constructed based on the results of regression analysis: probability of acute exacerbation ( P) = 1/(1+ e- x), x = -3.274 + 0.518×gender-0.552×BMI classification + 0.296×number of acute exacerbation + 0.018×duration of acute exacerbation-0.596×FEV1. The ROC curve analysis verified that the area under ROC curve (AUC) of the model group was 0.740, the AUC of the verification group was 0.688; the maximum Youden index of the model was 0.371, the corresponding best cut-off value of prediction probability was 0.197, the sensitivity was 80.1%, and the specificity was 57.0%. Conclusion:The AECOPD risk prediction model based on the regression analysis method had a moderate predictive power for the acute exacerbation risk of COPD patients, and could assist clinical diagnosis and treatment decision in a certain degree.

Objective:To investigate the anatomical safety and feasibility ofposterior occipitocervical fixation with atlan-tooccipital-clivus screw.Methods:Data of 60 patients who treated in the spinal department of our hospital with upper cervical computed tomographic scans from February 2017 to November 2019 were retrospectively collected. Occipitocervical infection, injury, tumor and deformity were excluded. The Mimics software was used to reconstruct the occiput, atlas and measure the anatomical parameters, including the height and width of the anterior edge of the clivus, the height and width of the middle part of the clivus, the thinnest distance of the soft tissue in front of the clivus, the anteroposterior diameter, transverse diameter, the angle of inside tilting in coronary plane of the occipital condyle, the distance from the hypoglossal canal to the atlantooccipital articular surface, the anteroposterior diameter and transverse diameter of the superior joint of atlas, the height of the lateral mass, and the height and transverse diameter of the inferior articular process of the superior atlas joint. The three-dimensional digital modeling was performed and the screw diameter of 3.5mm was simulated. 3-Matic software were used to measure the screw placement parameters, including the inside tilting angle in coronary plane of screw, and the angle of upper tilting in sagittal plane and length of screw. The atlanto-occipital junction was exposed at the rear of 8 cadavers. According to the above parameters, the titanium alloy screws with a diameter of 3.5 mm were transferred from the inferior articular process and posterior arch of the atlas to the clivus through the atlantooccipital. Finally, the screw path was cut along the nail path with a pendulum saw, and the track of the screw was observed to confirm the safety and effectiveness of the screw.Results:The leading edge height and width of male clivus was 16.8±2.5 mm and 20.1±3.1 mm. The middle part of the clivus was 9.7±2.3 mm and 22.4±3.7 mm. The thinnest soft tissue in front of the clivus was 5.8±1.48 mm. The anteroposterior diameter of the occipital condyle was 19.1±1.9 mm, the transverse diameter was 12.6±2.0 mm, the inside tilting angle was 33.7°±4.5°, and the vertical distance from the lowest point of the neural tube to the articular surface of the occipital condyle was 9.6±1.1 mm. The height of the lateral mass of atlas was 12.9±2.4 mm, the anteroposterior diameter of the upper joint of atlas was 21.7±1.9 mm, and the transverse diameter was 11.7±1.4 mm. The width of the inferior facet was 14.9±1.4 mm and the height of the inferior facet was 5.7±0.85 mm. The distance from the screw entry point to the vertical line of the lateral mass migration midpoint was 2.5±0.6 mm; The distance from the screw entry point to the horizontal line of the midpoint was 2.3±0.7 mm.The inside titling angle of screw was 18.4°±1.6°, the upper tilting angle was 55.6°±3.1°, the length of the screw track was 53.0±2.8 mm, the adjustment range of upper tilting angle was 15.0±2.8 mm, the adjustment range of inside tilting angle was 10.4±2.4 mm. The anatomical parameters of females were slightly smaller than those of males, and the difference was statistically significant, but there was no significant difference between left and right parameters. The screws of 8 specimens could be inserted safely and effectively.Conclusion:Atlan-tooccipital-clivus screw can be implanted without damaging the nerve and vascular structure, and it can be used as a choice for occipitocervical fixation.

Objective:To construct the risk prediction model of acute exacerbation of chronic obstructive pulmonary disease (AECOPD) and verify its effectiveness based on deep learning and back propagation algorithm neural network (BP neural network).Methods:Based on the relevant data of 1 326 patients with chronic obstructive pulmonary disease (COPD) in the team's previous clinical study, the acute exacerbation, and its risk factors during the stable period and 6 months of follow-up were recorded and analyzed. Combined with previous clinical research data and expert questionnaire results, the independent risk factors of AECOPD after screening and optimization by multivariate Logistic regression including gender, body mass index (BMI) classification, number of acute exacerbation, duration of acute exacerbation and forced expiratory volume in one second (FEV1) were used to build the BP neural network by Python 3.6 programming language and Tensorflow 1.12 deep learning framework. The patients were randomly selected according to the ratio of 4∶1 to generate the training group and the test group, of which, the training group had 1 061 sample data while the test group had 265 pieces of sample data. The training group was used to establish the prediction model of neural network, and the test group was used for back-substitution test. When using the training group data to construct the neural network model, the training group was randomly divided into training set and verification set according to the ratio of 4∶1. There were 849 training samples in the training set and 212 verification samples in the verification set. The optimal model was screened by adjusting the parameters of the neural network and combining the area under the receiver operator characteristic curve (AUC), and the sample data of the test group was substituted into the model for verification.Results:The independent risk factors including gender, BMI classification, number of acute exacerbation, duration of acute exacerbation and FEV1 were collected from the team's previous clinical research, and the AECOPD risk prediction model was constructed based on deep learning and BP neural network. After 10 000 training sessions, the accuracy of the AECOPD risk prediction model in the validation set of the training group was 83.09%. When the number of training times reached 8 000, the accuracy basically tended to be stable and the prediction ability reached the upper limit. The AECOPD risk prediction model trained for 10 000 times was used to predict the risk of the validation set data, and the receiver operator characteristic curve (ROC curve) analysis showed that the AUC was 0.803. When using this model to predict the risk of the data of the test group, the accuracy rate was 81.69%.Conclusion:The risk prediction model based on deep learning and BP neural network has a medium level of prediction efficiency for acute exacerbation within 6 months in COPD patients, which can evaluate the risk of AECOPD and assist the clinic in making accurate treatment decisions.