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.

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.