Objective To discuss the application of 3D printing technology in the adjuvant treatment of complex Stanford type B aortic dissection(SBAD)and abdominal aortic aneurysm.Methods The clinical data of 64 patients with complex SBAD and 64 patients with abdominal aortic aneurysm,who were admitted to the Mianyang No.404 Hospital of China from January 2022 to January 2024,were retrospectively analyzed.Of the 64 patients with complex SBAD,33 received preoperative 3D printing adjuvant treatment(observation group Ⅰ)and 31 received preoperative routine examination(control group Ⅰ).Of the 64 patients with abdominal aortic aneurysm,32 received preoperative 3D printing adjuvant treatment(observation group Ⅱ)and 32 received preoperative routine examination(control group Ⅱ).The changes in left-right diameter(LR)and anterior-posterior diameter(AP)of anatomical structure in observation group Ⅰ and observation group Ⅱ were analyzed.The perioperative situations were compared between observation group Ⅰ and control group Ⅰ,as well as between observation groupⅡ and control group Ⅱ.Results In patients with complex SBAD,LR of descending aorta diaphragm in S2(STL model)was significantly higher than that in S1(CTA image)and S3(plastic model,P＜0.05),and AP of descending aorta diaphragm in S2 was higher than that in S3(P＜0.05).LR of brachiocephalic trunk in S3 was significantly lower than that in S1 and S2(P＜0.05),AP of brachiocephalic trunk in S3 stage was significantly higher than that in S1 and S2(P＜0.05),and AP of brachiocephalic trunk in S2 was higher than that in S1(P＜0.05).LR of left common carotid artery in S3 was significantly higher than that in S1 and S2(P＜0.05),LR of left common carotid artery in S2 was higher than that in S1(P＜0.05),and AP of left common carotid artery in S3 was lower than that in S1(P＜0.05).LR and AP of left subclavian artery in S3 were significantly higher than those in S1 and S2(P＜0.05).In patients with abdominal aortic aneurysm,LR and AP of tumor neck in S3 were significantly higher than those in S1(P＜0.05),and AP of aneurysm neck in S3 was significantly higher than that in S2(P＜0.05).LR and AP of aneurysm in S3 and S2 were significantly higher than those in S1(P＜0.05),and LR and AP of aneurysm in S3 were significantly higher than those in S2(P＜0.05).LR of abdominal aortic bifurcation in S3 and S2 was significantly higher than that in S1(P＜0.05),LR of abdominal aortic bifurcation in S3 was significantly higher than that in S2(P＜0.05),and AP of abdominal aortic bifurcation in S3 was significantly lower than that in S1(P＜0.05).AP of left common iliac artery in S3 was significantly lower than that in S1(P＜0.05).In the observation group Ⅰ,the operation time,endovascular operation time and length of hospital stay were significantly shorter than those in the control group Ⅰ(P＜0.05),and the intraoperative blood loss and used dosage of contrast agent were lower than those in the control group Ⅰ(P＜0.05).In observation group Ⅱ,the operation time,endovascular operation time and length of hospital stay were significantly shorter than those in the control group Ⅱ(P＜0.05),and the intraoperative blood loss and used dosage of contrast agent were lower than those in the control group Ⅱ(P＜0.05).In patients with complex SBAD or abdominal aortic aneurysm,there was no internal leakage or stent displacement at 6 months after surgery.Conclusion Adjuvant treatment with 3D printing technology is helpful for improving anatomical structure measurement of lesion sites in patients with complex SBAD and abdominal aortic aneurysm.Preoperative 3D plastic model preview surgery is helpful for shortening the operation time and length of hospital stay and reducing the used dosage of contrast agent without affecting surgical treatment effect.

Objective:To establish an acute kidney injury (AKI) prediction model in patients after cardiac surgery by extreme gradient boosting (XGBoost) machine learning model, and to explore the risk and protective factors for AKI in patients after cardiac surgery.Methods:All patients who underwent cardiac surgery in Medical Information Mart for Intensive Care-Ⅲ (MIMIC-Ⅲ) database were enrolled, and they were divided into AKI group and non-AKI group according to whether AKI developed within 14 days after cardiac surgery. Their clinical characteristics were compared. Based on five-fold cross-validation, XGBoost and Logistic regression were used to establish the prediction model of AKI after cardiac surgery. And the area under the receiver operator characteristic curve (AUC) of the models was compared. The output model of XGBoost was interpreted by Shapley additive explanations (SHAP).Results:A total of 6 912 patients were included, of which 5 681 (82.2%) developed AKI within 14 days after the operation, and 1 231 (17.8%) did not. Compared with the non-AKI group, the main characteristics of AKI group included older age [years: 68.0 (59.0, 76.0) vs. 62.0 (52.0, 71.0)], higher incidence of emergency admission and complicated with obesity and diabetes (52.4% vs. 47.8%, 9.0% vs. 4.0%, 32.0% vs. 22.2%), lower respiratory rate [RR; bpm: times/min: 17.0 (14.0, 20.0) vs. 19.0 (15.0, 22.0)], lower heart rate [HR; bpm: 80.0 (67.0, 89.0) vs. 82.0 (71.5, 93.0)], higher blood pressure [mmHg (1 mmHg ≈ 0.133 kPa): 80.0 (70.7, 90.0) vs. 78.0 (70.0, 88.0)], higher hemoglobin (Hb), blood glucose, blood K + level and serum creatinine [SCr; Hb (g/L): 122.0 (109.0, 136.0) vs. 120.0 (106.0, 135.0), blood glucose (mmol/L): 7.3 (6.1, 8.9) vs. 6.8 (5.7, 8.5), blood K + level (mmol/L): 4.2 (3.9, 4.7) vs. 4.2 (3.8, 4.6), SCr (μmol/L): 88.4 (70.7, 106.1) vs. 79.6 (70.7, 97.2)], lower albumin (ALB) and triacylglycerol [TG; ALB (g/L): 38.0 (35.0, 41.0) vs. 39.0 (37.0, 42.0), TG (mmol/L): 1.4 (1.0, 2.0) vs. 1.5 (1.0, 2.2)] as well as higher incidence of multiple organ dysfunction syndrome (MODS) and sepsis (30.6% vs. 16.2%, 3.3% vs. 1.9%), with significant differences (all P < 0.05). In the output model of Logistic regression, important predictors were lactic acid [Lac; odds ratio ( OR) = 1.062, 95% confidence interval (95% CI) was 1.030-1.100, P = 0.005], obesity ( OR = 2.234, 95% CI was 1.900-2.640, P < 0.001), male ( OR = 0.858, 95% CI was 0.794-0.928, P = 0.049), diabetes ( OR = 1.820, 95% CI was 1.680-1.980, P < 0.001) and emergency admission ( OR = 1.278, 95% CI was 1.190-1.380, P < 0.001). Receiver operator characteristic curve (ROC curve) analysis showed that the AUC of the Logistic regression model for predicting AKI after cardiac surgery was 0.62 (95% CI was 0.61-0.67). After optimizing the XGBoost model parameters by grid search combined with five-fold cross-validation, the model was trained well with no overfitting or overfitting. ROC analysis showed that the AUC of XGBoost model for predicting AKI after cardiac surgery was 0.77 (95% CI was 0.75-0.80), which was significantly higher than that of Logistic regression model ( P < 0.01). After SHAP treatment, in the output model of XGBoost, age and ALB were the most important predictors of the final outcome, where age was the risk factor (average |SHAP value| was 0.434), and ALB was the protective factor (average |SHAP value| was 0.221). Conclusions:Age is an important risk factor for AKI after cardiac surgery, and ALB is a protective factor. The performance of machine learning in predicting cardiac and vascular surgery-associated AKI is better than the traditional Logistic regression. XGBoost can analyze the more complex relationship between variables and outcomes, and can predict the risk of postoperative AKI more accurately and individually.

Objective:To survey the awareness of common primary care clinical guidelines among primary care providers in Qingdao city.Methods:A questionnaire survey on the awareness of common clinical guidelines in primary care was conducted in August 2020 among 659 providers (293 males and 366 females) from 81 primary medical institutions in Qingdao city. The contents of the questionnaire included the general information of primary physicians, the frequency of using the guidelines, the level of understanding of the guidelines, the effect of guidelines in clinical work, the attitudes towards promoting the guidelines, and the interest in learning the guidelines and the way of learning.Results:A total of 659 valid questionnaires were recovered with a recovery rate of 100.0%. The results showed that 14.7% (97/659) practitioners applied the guidelines in most cases, and 31.6% (208/659) frequently used. There were significant differences in use frequency among providers with different practice types, professional titles, education background, practice locations and institutions ( P<0.01). Among participants, 15.2% (100/659) did not know about primary care guidelines, 63.3% (417/659) knew but did not learned guidelines, 21.5% (142/659) knew and studied guidelines carefully. The top five of the learned guidelines (114, 80.3%) were bronchial asthma (113, 79.6%), type 2 diabetes (108, 76.1%), chronic obstructive pulmonary disease (101, 71.1%), and chronic cor pulmonale (83, 58.5%). Most participants who learnt the guidelines viewed the guidelines as quite helpful or very helpful, the score of usefulness for professional knowledge was the highest (4.44±0.60). Most of participants (94.2%, 621/659) thought it was necessary to develop primary care guidelines; and the major factors affecting the promotion were lack of training (79.8%, 526/659), difficulties in access (46.7%, 308/659) and ignorance or neglect (23.2%, 153/659).Most practitioners (93.8%, 618/659) were interested in learning primary care clinical guidelines, and the expected ways of training were online teaching (70.0%, 420/618), training courses(58.3%, 360/618) and special lectures (55.2%, 341/618). Conclusion:The current situation of learning clinical guidelines among primary care providers in Qingdao is not satisfactory, but they are willing to learn the guidelines for improving clinical practice. We should strengthen the training and promotion of primary care guidelines among primary care providers in the future.