目的 分析汶川大地震伤员的放射学表现特点。 方法 收集2008年5•12汶川大地震发生后至5月31日间213例地震伤员的完整放射学资料，着重分析伤员的X线、CT、MRI检查的影像学表现特点。 结果 213例中，同时行X线、CT、MRI检查10例（5%），X线、MRI检查7例（3%），X线、CT检查9例（4%），仅行CT检查5例（2%），仅行X线检查182例（85%）。MRI检查17例（8%）中，同时行胸椎和腰椎检查2例、颈椎和胸椎1例，颈椎1例，膝关节2例，上腹部1例，骨盆1例，腰椎9例；CT检查24例（11%）中，头部9例，胸部6例，腹部1例，脊柱6例，骨盆2例；X线检查208例中，单部位检查64例（31%），多部位检查144例（69%），仅有软组织受伤38例（18%），单纯肺挫伤6例（3%），骨折164例（79%）。 结论 地震伤员影像学检查以常规X线为主，头颅、五官受伤者首选CT，CT、MRI检查作为胸部、脊柱、关节等部位的补充检查。地震伤员以单纯性骨折为主，骨折合并脏器外伤较少。
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.