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Objective:To systematically compare the safety and reliability of remodeling and reimplantation in aortic root valve preservation surgery for acute Stanford type A aortic dissection.Methods:We searched the databases of CNKI, VIP, Wanfang, CBM, Pubmed, EMBASE, Cochrane Central Register of Controlled Trials ( CENTRAL ) to find the clinical controlled research literature on acute type A aortic dissection remodeling and replantation. The relevant outcome indicators were analyzed by Review Manager 5.3 combined with Stata15.0 statistical software.Results:Seven studies involving 356 patients were included. Remodeling surgery versus replantation surgery. There was a higher incidence of postoperative grade Ⅱ or Ⅲ aortic regurgitation( OR=5.56, 95% CI: 1.89-16.41, P<0.05 ), higher 5-year reoperation rate ( OR=7.50, 95% CI: 2.11-26.65, P<0.05 ), shorter cardiopulmonary bypass time ( MD=-20.81, 95% CI: -35.08-6.54, P< 0.05 ), and longer aortic occlusion time ( MD=35.23, 95% CI: 21.21-49.26, P<0.05 ). The 30-day/in-hospital mortality( OR=1.09, 95% CI: 0.56-2.13, P>0.05) , postoperateive secondary thoracotomy for hemostasis( OR=2.91, 95% CI: 0.34-24.99, P>0.05), the rate of reoperation 1 year after surgery( OR=1.22, 95% CI: 0.20-7.56, P> 0.05) and 5-year mortality( OR=7.50, 95% CI: 2.11-26.65, P>0.05), were no significant difference between remodeling surgery group and replantation surgery group. Conclusion:Compared with replantation surgery, remodeling surgery in patients with acute type A aortic dissection has a higher incidence of grade Ⅱ or Ⅲ aortic insufficiency, a higher rate of reoperation 5 years after surgery, a shorter duration of cardiopulmonary bypass, and a longer duration of aortic occlusion. There were no statistically significant differences in postoperative hospitalization/30-day mortality, postoperative secondary thoracotomy, reoperation rate 1 year after surgery, and late mortality using remodeling and replantation techniques, which could be selected according to the actual situation of the aortic root and the experience of the surgeon.
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Objective To investigate the potential clinical prognostic factors of severe traumatic brain injury.Methods Two hundred and thirteen severe traumatic brain injury patients,admitted to our hospital from May 2013 to July 2015,were chosen in our study.Their clinical data were retrospectively analyzed;prognostic factors,including age,gender,mGCS scores (movement GCS scores),improved CT scores,brain hernia,volume of intracranial hematoma,range of cerebral contusion and laceration,and location of intracranial hematoma,were estimated using Chi square test,and after the Chi square test,the factors enjoying statistical significance were analyzed by COX proportional hazards regression model.Results The mortality rate was 15.9％ (34/213).Death occurred within 30 days of admission,and death mostly occurred within 10 days of admission (1-10 d:23 patients;11-20 d:9 patients;20-30 d:2 patients).Cox proportional hazards regression analysis indicated that age,GCS scores (movement GCS scores),improved CT scores,and brain hernia were the prognostic risk factors in patients with severe traumatic brain injury,while gender,volume of intracranial hematoma,range of cerebral contusion and laceration,and location of intracranial hematoma were not.Conclusion Age,mGCS scores (movement GCS scores),improved CT scores,and brain hernia dare important factors affecting the prognosis of patients with severe traumatic brain injury.
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Objective To explore the awakening probabilistic prediction models of coma patients with traumatic brain injury on admission and six months after treatment,and develop and apply the software of the models.Methods Clinical data of 190 coma patients with traumatic brain injury,admitted to our hospital from September 2010 to October 2012,were analyzed retrospectively.Potential predictive factors at admission and after awakening were analyzed by binary Logistic regression analysis; based on these factors,the awakening probabilistic prediction models of coma patients with traumatic brain injury were established; C++ language was used to write the computer software that could predict the awakening probability of 103 patients with traumatic brain injury.Results Multinomial Logistic regression analysis showed that 6 factors,including age,pupillary light reflex,movement Glasgow coma scale (mGCS) scores,morphology changes of mesencephalon surrounding cisterna,eye opening time after treatment,and percentages of ischemic brain volume in CT images,were independent factors to predict the awakening probability of coma patients with traumatic brain injury.Model A and B owned high performance (C statistics of models:0.955 and 0.975; accept rate of models:90.5％ and 94.0％).The established software based on models was easy to use with reliable results (the accept rate of 103 patients were 87.3％ and 93.2％).Conclusion The established models can timely and accurately predict the awakening probability of coma patients with traumatic brain injury; the software named sober probabilistic prediction for coma patients with traumatic brain injury can help in decision-making in clinics.
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Objective To investigate the chemical constituents of Baoyuan Decoction.Methods Many chromatographic techniques and spectral analysis means were employed for isolation and identification of the constituents.Results Fifteen flavonoids were isolated from Baoyuan Decoction and their structures were identified as licuraside(1),formononetin-7-O-?-D-glucoside(2),formononetin(3),isomucronulatol(4),davidigenin(5),2',4'-dimethoxy-3'-hydroxyisoflavan-6-O-?-D-glucoside(6),(6aR,11aR)9,10-dimethoxypterocarpan-3-O-?-D-glucoside(7),calycosin(8),liquiritigenin(9),5-dehydroxykaempferol(10),liquiritin(11),isoliquiritin(12),isoliquiritigenin(13),7,3'-dihydroxy-5'-methoxyisoflavone(14),and odoriflavene(15).Conclusion All fifteen compounds are isolated from Baoyuan Decoction for the first time.