Objective:To screen the risk factors of acute kidney injury (AKI) in patients with multiple trauma, construct a prediction model accordingly, and evaluate its predictive value.Methods:A retrospective cohort study was performed to analyze the clinical data of 560 multiple trauma patients who were admitted to while Affiliated Hospital of Jiangsu University from January 2017 to June 2023, including 424 males and 136 females, aged 18-91 years [(55.5±15.0)years]. The patients were randomly divided into a training set ( n=392) and validation set ( n=168) with a ratio of 7∶3. Of all, 77 patients were combined with AKI in the training set, while 33 patients combined with AKI in the validation set. The AKI group and non-AKI group in the training set were compared in terms of gender, age, hypertension, diabetes, cause of injury, abbreviated injury scale (AIS) score of head and neck injury, AIS score of maxillofacial injury, AIS score of chest injury, AIS score of abdominal injury, AIS score of extremities and pelvic injury, AIS score of body surface injury, systolic blood pressure, diastolic blood pressure, heart rate, respiratory rate, body temperature, red blood cell and plasma transfusion volume within 24 hours following admission, emergency surgery, mechanical ventilation, vasoactive drug therapy, Glasgow coma score (GCS) on admission, revised trauma score (RTS) on admission, acute physiology and chronic health assessment II (APACHE II) on admission, injury severity score (ISS) on admission, and laboratory test results on admission including white blood cell count, neutrophil count, lymphocyte count, C-reactive protein, hemoglobin, platelet count, activated partial thromboplastin time (APTT), prothrombin (PT), fibrinogen (FIB), thrombin time (TT), international normalized ratio (INR), D-dimer, blood lactate, base excess, total bilirubin, alanine aminotransferase (ALT), aspartate aminotransferase (AST), albumin, globulin, urea nitrogen, serum creatinine, blood glucose, potassium, sodium and chloronium. In the training set, univariate analysis and Lasso regression analysis were used to screen the risk factors of AKI in patients with multiple trauma, which were then included into multivariate logistic regression analysis to identify the independent risk factors. A nomogram prediction model was constructed using the R software based on the above independent risk factors. Hosmer-Lemeshow (H-L) goodness-of-fit test was performed to evaluate the fitting degree of the prediction model in the training set and the validation set, and the receiver operating characteristic (ROC) curve, calibration curve and clinical decision curve (DCA) were plotted in the training set and the validation set to evaluate the predictive performance of the prediction model. Results:There were statistically significant differences in AIS score of abdominal injury, heart rate, body temperature, red blood cell and plasma transfusion volume within 24 hours following admission, emergency surgery, mechanical ventilation, vasoactive drug therapy, GCS on admission, RTS on admission, APACHE II on admission, ISS on admission as well as hemoglobin, platelet count, APTT, PT, FIB, TT, INR, blood lactate, base excess, AST, albumin, globulin, urea nitrogen, serum creatinine, blood glucose and sodium on admission between the AKI group and the non-AKI group ( P<0.05 or 0.01). The characteristic variables screened by Lasso regression analysis included AIS score of abdominal injury, red blood cell transfusion volume within 24 hours following admission, mechanical ventilation, vasoactive drugs therapy, blood lactate on admission, blood creatinine on admission, AST on admission, and blood sodium on admission. Multivariate logistic regression analysis showed that red blood cell transfusion volume within 24 hour following admission ( OR=1.09, 95% CI 1.01, 1.18), mechanical ventilation ( OR=2.49, 95% CI 1.06, 5.85), vasoactive drug therapy ( OR=2.04, 95% CI 1.03, 4.03), blood lactate on admission ( OR=1.10, 95% CI 1.01, 1.21) and serum creatinine on admission ( OR=1.02, 95% CI 1.01, 1.03) were independent risk factors for AKI in patients with multiple trauma ( P<0.05). The regression equation was constructed: Logit[ P/(1- P)]=0.086 2×"red blood cell transfusion volume within 24 hour following admission"+0.912 7×"mechanical ventilation"+0.713 2×"vasoactive drug therapy"+0.098 9×"blood lactate on admission"+0.019 2×"serum creatinine on admission" -4.822 3. H-L goodness-of-fit test showed χ2 value of 9.50 in the training set ( P>0.05) and 6.43 in the validation set ( P>0.05). The results of the ROC curve indicated that the area under the curve (AUC) was 0.84 (95% CI 0.78, 0.89) in the training set and 0.80 (95% CI 0.72, 0.88) in the validation set. The calibration curves showed good agreement with the actual curves, with the predicted probability consistent with the actual probability in both training set and validation set. DCA analysis showed that the threshold probability ranged from 2% to 70% with the net benefit rate of the prediction model greater than 0 in the training set, while the threshold probability ranged from 3% to 69% with the net benefit rate of the prediction model greater than 0 in the validation set. Conclusions:Red blood cell transfusion volume within 24 hours following admission, mechanical ventilation, vasoactive drug therapy, lactate and serum creatinine on admission are independent risk factors for AKI in patients with multiple trauma. The nomogram prediction model based on the above 5 predictive variables of AKI in patients with multiple trauma shows good predictive efficacy and clinical application value.

Objective:To screen the risk factors of acute kidney injury (AKI) in patients with multiple trauma, construct a prediction model accordingly, and evaluate its predictive value.Methods:A retrospective cohort study was performed to analyze the clinical data of 560 multiple trauma patients who were admitted to while Affiliated Hospital of Jiangsu University from January 2017 to June 2023, including 424 males and 136 females, aged 18-91 years [(55.5±15.0)years]. The patients were randomly divided into a training set ( n=392) and validation set ( n=168) with a ratio of 7∶3. Of all, 77 patients were combined with AKI in the training set, while 33 patients combined with AKI in the validation set. The AKI group and non-AKI group in the training set were compared in terms of gender, age, hypertension, diabetes, cause of injury, abbreviated injury scale (AIS) score of head and neck injury, AIS score of maxillofacial injury, AIS score of chest injury, AIS score of abdominal injury, AIS score of extremities and pelvic injury, AIS score of body surface injury, systolic blood pressure, diastolic blood pressure, heart rate, respiratory rate, body temperature, red blood cell and plasma transfusion volume within 24 hours following admission, emergency surgery, mechanical ventilation, vasoactive drug therapy, Glasgow coma score (GCS) on admission, revised trauma score (RTS) on admission, acute physiology and chronic health assessment II (APACHE II) on admission, injury severity score (ISS) on admission, and laboratory test results on admission including white blood cell count, neutrophil count, lymphocyte count, C-reactive protein, hemoglobin, platelet count, activated partial thromboplastin time (APTT), prothrombin (PT), fibrinogen (FIB), thrombin time (TT), international normalized ratio (INR), D-dimer, blood lactate, base excess, total bilirubin, alanine aminotransferase (ALT), aspartate aminotransferase (AST), albumin, globulin, urea nitrogen, serum creatinine, blood glucose, potassium, sodium and chloronium. In the training set, univariate analysis and Lasso regression analysis were used to screen the risk factors of AKI in patients with multiple trauma, which were then included into multivariate logistic regression analysis to identify the independent risk factors. A nomogram prediction model was constructed using the R software based on the above independent risk factors. Hosmer-Lemeshow (H-L) goodness-of-fit test was performed to evaluate the fitting degree of the prediction model in the training set and the validation set, and the receiver operating characteristic (ROC) curve, calibration curve and clinical decision curve (DCA) were plotted in the training set and the validation set to evaluate the predictive performance of the prediction model. Results:There were statistically significant differences in AIS score of abdominal injury, heart rate, body temperature, red blood cell and plasma transfusion volume within 24 hours following admission, emergency surgery, mechanical ventilation, vasoactive drug therapy, GCS on admission, RTS on admission, APACHE II on admission, ISS on admission as well as hemoglobin, platelet count, APTT, PT, FIB, TT, INR, blood lactate, base excess, AST, albumin, globulin, urea nitrogen, serum creatinine, blood glucose and sodium on admission between the AKI group and the non-AKI group ( P<0.05 or 0.01). The characteristic variables screened by Lasso regression analysis included AIS score of abdominal injury, red blood cell transfusion volume within 24 hours following admission, mechanical ventilation, vasoactive drugs therapy, blood lactate on admission, blood creatinine on admission, AST on admission, and blood sodium on admission. Multivariate logistic regression analysis showed that red blood cell transfusion volume within 24 hour following admission ( OR=1.09, 95% CI 1.01, 1.18), mechanical ventilation ( OR=2.49, 95% CI 1.06, 5.85), vasoactive drug therapy ( OR=2.04, 95% CI 1.03, 4.03), blood lactate on admission ( OR=1.10, 95% CI 1.01, 1.21) and serum creatinine on admission ( OR=1.02, 95% CI 1.01, 1.03) were independent risk factors for AKI in patients with multiple trauma ( P<0.05). The regression equation was constructed: Logit[ P/(1- P)]=0.086 2×"red blood cell transfusion volume within 24 hour following admission"+0.912 7×"mechanical ventilation"+0.713 2×"vasoactive drug therapy"+0.098 9×"blood lactate on admission"+0.019 2×"serum creatinine on admission" -4.822 3. H-L goodness-of-fit test showed χ2 value of 9.50 in the training set ( P>0.05) and 6.43 in the validation set ( P>0.05). The results of the ROC curve indicated that the area under the curve (AUC) was 0.84 (95% CI 0.78, 0.89) in the training set and 0.80 (95% CI 0.72, 0.88) in the validation set. The calibration curves showed good agreement with the actual curves, with the predicted probability consistent with the actual probability in both training set and validation set. DCA analysis showed that the threshold probability ranged from 2% to 70% with the net benefit rate of the prediction model greater than 0 in the training set, while the threshold probability ranged from 3% to 69% with the net benefit rate of the prediction model greater than 0 in the validation set. Conclusions:Red blood cell transfusion volume within 24 hours following admission, mechanical ventilation, vasoactive drug therapy, lactate and serum creatinine on admission are independent risk factors for AKI in patients with multiple trauma. The nomogram prediction model based on the above 5 predictive variables of AKI in patients with multiple trauma shows good predictive efficacy and clinical application value.

Objective To investigate the effect of early combined functional exercise on lower limb rehabilitation of vessel bridge taken in coronary artery bypass surgery. Methods From January to October 2017,90 patients undergoing coronary artery bypass surgery were randomly divided into control group and experimental group by coin toss, with 45 patients in each group. The control group was given routine nursing care, and the experimental group was given early combined functional exercise (foot dorsiflexion, ring rotation exercise and straight leg raising exercise) on the basis of routine nursing care. The difference of correlation index between two groups was compared. Results In the experimental group, the number of swelling in the thigh, crus and ankle were 20, 25 and 35 respectively; the control group was 32, 35, 43, the difference was statistically significant(χ2=6.55, 5.00, 8.38, P<0.05). The remission of limb swelling in the experimental group was also better than that in the control group(P<0.01). The incidence of limb wound complications: 6 cases in the experimental group, 18 cases in the control group(χ2=11.79,P<0.01));limb muscle strength grade Lovett:experimental group 0 case with grade 3,19 cases with grade 4,26 cases with grade 5,control group 9 cases with grade 3,26 cases with grade 4,10 cases with grade 5, there were significant differences between two groups (χ2=17.20, P<0.05). The limb comfort score,postoperative bed time,postoperative hospital stay was(7.11±1.15)points,(5.28±1.07)d, (11.04±2.01)d in the experimental group and(5.18±1.13)points,(6.97±1.21)d,(12.67±1.59)d in the control group,the differences were significant(t=-8.02,6.97,4.23,P<0.01). Conclusions To carry out early functional exercise can not only take angiogenesis effectively reduce limb swelling rate after coronary artery bypass grafting,relieve the severity of limb swelling,but also can reduce the incidence of incision complications,promote the recovery of limb muscle strength,shorten the postoperative recovery time and postoperative hospitalization time,is worthy of promotion and use in clinical nursing work.

OBJECTIVE To assess the treatment reliability of covered stent for carotid artery blowout after head and neck tumors resection. METHODS Five cases with postoperative rupture of carotid artery invaded by the head and neck tumor were reviewed. They presented with life threatening massive neck or oral bleedings. All of them were treated with self expanding covered stents through intervention therapy approach. RESULTS The covered stent were successfully deployed in the target arteries in all cases,the instant isolation effect was quite satisfactory. After treatment, angiography showed successful occlusion of the pseudoaneurysm, patency of carotid artery lumen, and significant improvement of clinical symptoms without neurologic dysfunction. Following up 2 to 36 months, 3 patients were alive with no disease, two patients died of recurrence. CONCLUSION For the treatment of carotid blowout, endovascular occlusion with covered stent is a minimally-invasive, safe and reliable methods.

Objective To assess the influence between managements in emergency room(ER) andoutcome of severe traumatic brain injury (TBI),in order to provide inference for treatment.Methods A retrospective analysis was performed in severe TBI patients and recorded next indexes.(1) The managements in ER,including endotracheal intubation and oxygenation,fluid resuscitation,and mannitol intake.(2) The vital signs arriving at ICU,including systolic pressure and blood oxygen saturation.(3) Prognostic indicators including inhospital mortality and days during ICU,the scores of Glasgow outcome scale (GOS) at discharge and 6 months after injury.Results In 140 severe TBI patients,65 patients (46.4％) died during ICU.The mortality of patients with endotracheal intubation [65.0％ (39/60)] was significantly higher than that without endotracheal intubation [32.5％(26/80)](P＜ 0.01).The mortality in whether fluid resuscitation and using mannitol had no significant difference [44.7％ (46/103) vs.51.4％ (19/37),49.2％ (31/63) vs.44.2％ (34/77)] (P ＞0.05).In days during ICU,there was no significant difference among the three treatment measures (P＞ 0.05).In GOS grade at discharge and 6 months after injury,the proportion of 4 and 5 grade were 8.3％ (5/60) and 25.0％ (15/60) in patients with endotracheal intubation,while 27.5％ (22/80) and 52.5％ (42/80) in patients without endotraeheal intubation (P ＜ 0.01).In fluid resuscitation and using mannitol patients,there were no significant difference(P ＞ 0.05).Conclusion Treating severe TBI patients in ER,endotracheal intubation should be carefully chosen,fluid resuscitation and mannitol may not be given.