Objective: To observe the impact of cardiac resynchronization therapy (CRT) on ventricular remodeling in patients with III°atrio-ventricular block (AVB) combining systolic dysfunction.
Methods: A total of 49 III °AVB patients received CRT in our hospital from 2009-01 to 2014-10 were studied. Echocardiography was conducted at pre-operation and 6, 12 months post-operation to measure left ventricular ejection fraction (LVEF), left ventricular end-systolic volume (LVESV), left ventricular end-diastolic volume (LVEDV), left ventricular end-diastolic diameter (LVEDD), left ventricular end-systolic diameter (LVESD) and mitral regurgitation (MR) grade in order to observe the changes of cardiac structure and function in relevant patients.
Results: Compared with pre-operative condition, at 6 months post-operation, LVEF was increased (4.92±5.24)%and at 12 months post-operation, it was further increased (5.02±6.52)%, both P<0.05;at 6 months post-operation, LVESV reduced (25.02±17.95) ml and at 12 months post-operation, it was further reduced (24.79±22.49) ml, both P<0.05. Compared with pre-operative condition, at 6 months post-operation, LVEDV dropped (25.61±24.24) ml, LVEDD dropped (3.22±2.91) mm, LVESD dropped (4.43±2.86) mm and MR grade dropped 0.49±0.76, all P<0.05. Compared with 6 months post-operation, at 12 months post-operation, LVEDV declined (28.18±22.36) ml, LVEDD declined (4.17±3.14) mm, both P<0.05, LVESD declined (4.92±4.40) mm, P<0.01 and MR grade declined (0.22±0.55), P<0.05.
Conclusion:CRT may reverse ventricular remodeling and improve cardiac function in patients with III°AVB combining systolic dysfunction.

Objective: To analyze the predictors of left ventricular reverse remodeling in patients with III? atrio-ventricular block (AVB) combining left ventricular systolic dysfunction after cardiac re-synchronization therapy (CRT). Methods: A total of 65 III? AVB patients received CRT in our hospital from 2009-01 to 2015-05 were enrolled. Clinical information before and after the operation were recorded. Left ventricular reverse remodeling was deifned by left ventricular end systolic volume (LVESV) decreased 15% or left ventricular ejection fraction (LVEF) increased≥5% at 12 months after CRT. The patients were divided into 2 groups: Reversal group,n=36 and No reversal group,n=29. Clinical condition was compared between 2 groups, predictors for CRT reversing left ventricular remodeling were evaluated by two classiifcation Logistic regression analysis. Results: The patients' average age was (62±14) years and 36/65 (55.4%) with reverse remodeling. In Reversal group, the ratios of female (P=0.011), baseline QRS width>120ms (P=0.001), inter-ventricular mechanical delay (IVMD)≥40 ms (P=0.027) and standard deviation of time-to-minimum systolic volume of 16 segments [Tmsv16-SD (%R-R)≥8.3%, (P=0.001)] were higher than those in No reversal group. Two classiifcation Logisitic regression analysis indicated that female (OR=6.228, 95%CI 1.561-24.842, P=0.01), QRS duration>120 ms (OR=7.778, 95% CI 1.996-30.769,P=0.003) and Tmsv16-SD (%R-R)≥8.3% (OR=8.134, 95% CI 2.064-32.057,P=0.003) were the independent predictors for ventricular reverse remodeling . Conclusion: Female, QRS>120ms and Tmsv16-SD (%R-R)≥8.3% could be used as the predictors for CRT reversing left ventricular remodeling in III? AVB patients combining left ventricular systolic dysfunction.

Objective:To explore the risk factors of hemothorax after rib fracture and evaluate its predictive value for hemothorax.Methods:A retrospective case control study was made on the data of 449 patients with rib fracture admitted to Jiangsu Shengze Hospital affiliated to Nanjing Medical University from January 2018 to November 2019. There were 308 males and 141 females，with the age range of 19-97 years［（57.4±14.0）years］. The hemothorax was defined as pleural effusion on chest CT or X examination on admission or within one week after admission. There were 330 patients in hemothorax group and 119 patients in non-hemothorax group. Indices were compared between the two groups，including gender，age，occupation，weight，height，underlying diseases［diabetes，chronic obstructive pulmonary disease（COPD），hypertension，hyperlipidemia］，causes of injury，imaging findings［number of rib fracture，flail chest，bilateral rib fractures，locations of rib fracture and intramural injuries（pneumothorax，pulmonary contusion，mediastinal emphysema and myocardial contusion）］，thoracic cavity drainage，injury to admission time，vital signs（blood pressure and heart rate），routine blood［white blood cell，hemoglobin（Hb），platelet，hematocrit（Hct）］，blood type，urine routine（urinary occult blood，urinary protein，urinary ketone body），biochemical examination［total cholesterol（TCHO），triglyceride（TG），high density lipoprotein（HDL-C），low-density lipoprotein（LDL-C），albumin（ALB），total bilirubin（TBIL），glutamic oxalacetic transaminase（AST），alanine transaminase（ALT），urea nitrogen（BUN），creatinine（CRE），glycosylated hemoglobin（HbA1C）］，coagulation tests［prothrombin time（PT），fibrinogen（FIB），plasma D-dimer（D-D），thrombin time（TT）］after admission，trauma score［chest wall injury score（CIS），injury severity score（ISS），new injury severity score（NISS）］and length of hospital stay. The univariate analysis was used to observe the correlation between each factor and hemothorax after rib fracture and to screen the significant correlation factors，followed by multivariate logistic regression analysis to further identify the independent risk factors. The receiver operating characteristic（ROC）curve was used to analyze the predictive value of continuous variables in independent risk factors and to calcuate the optimal threshold.Results:The two groups showed no significant differences in gender，occupation，weight，height，diabetes，COPD，hyperlipidemia，injury to admission time，blood pressure，heart rate，platelet，urine protein，urine ketone body，TCHO，HDL-C，TBIL，ALT，CRE，HbA1C or PT（ P>0.05）. The hemothorax group showed significantly decreased Hb，Hct，TG，LDL-C and TT and significantly increased age，number of rib fracture，white blood cell count，AST，FIB，D-D，trauma score（CIS，ISS，NISS）and length of hospital stay when compared to non-hemothorax group（ P<0.05）. There were significant differences in hypertension，causes of injury，flail chest，bilateral rib fractures and locations of rib fracture and urinary occult blood between the two groups（ P<0.05）. The univariate analysis showed that age，hypertension，number of rib fractures，flail chest，bilateral rib fractures，locations of rib fracture（upper chest anterolateral segment，middle chest anterolateral segment，middle chest posterolateral segment，middle chest proximal spinal segment，lower chest posterolateral segment，lower chest proximal spinal segment），pneumothorax，pulmonary contusion，myocardial contusion，thoracic cavity drainage，white blood cell count，urinary occult blood，BUN，FIB，trauma score（CIS，ISS，NISS）and length of hospital stay were significantly associated with hemothorax（ P<0.05）. The multivariate Logistic regression analysis showed that locations of rib fracture（including middle chest posterolateral segment，middle chest proximal spinal segment，lower chest posterolateral segment and lower chest proximal spinal segment），pulmonary contusion，thoracic cavity drainage，BUN and trauma score（CIS，ISS，NISS）were significantly associated with hemothorax after rib fracture（ P<0.05）. The ROC curve analysis of continous variables in independent risk factors showed BUN area under the curve（AUC）of 0.587（95% CI 0.529-0.645），CIS AUC of 0.824（95% CI 0.779-0.870），ISS AUC of 0.789（95% CI 0.739-0.840）and NISS AUC of 0.876（95% CI 0.835-0.917）（ P<0.05），and the optimal thresholds for the above variables were 5.0 mmol/L，2.5 points，15 points and 21.5 points，respectively. Conclusion:Locations of rib fracture（including the middle chest posterolateral segment，middle chest proximal spinal segment，lower chest posterolateral segment，lower chest proximal spinal segment），pulmonary contusion，thoracic cavity drainage，BUN，trauma score（CIS，ISS，NISS）are independent risk factors for hemothorax after rib fracture. BUN>5.0 mmol/L and trauma score（CIS>2.5 points，ISS>15 points，NISS>21.5 points）have significant values in predicting hemothorax.

Objective:To establish a predictive model for myocardial contusion (MC) in patients with rib fractures and evaluate its clinical application value.Methods:A retrospective case-control study was conducted to analyze the clinical data of 370 patients with rib fractures admitted to the Affiliated Jiangsu Shengze Hospital of Nanjing Medical University from January 2017 to December 2019, including 257 males and 113 females, aged 18-95 years [(56.5±14.0)years]. All the patients underwent electrocardiogram examination and myocardial biomarker test within 24 hours on admission, of whom 159 were diagnosed with MC, and 211 with non-MC (NMC). The 370 patients were divided into a training set of 264 patients (106 with MC, 158 with NMC) and a validation set of 106 patients (53 with MC, 53 with NMC) at a ratio of 7∶3 through the completely randomized method. In the training set, the MC group and NMC group were compared in terms of their demographic characteristics, vital signs on admission, types of rib fractures, number of rib fractures, locations of rib fractures, associated thoracic injuries, trauma scores, and laboratory indices. Variables of positive correlation with MC in patients with rib fractures were screened by Spearman correlation analysis, followed by univariate binary Logistic regression analysis for these variables to determine the risk factors for MC in patients with rib fractures. LASSO regression analysis and multivariate Logistic regression analysis were applied to identify the independent risk factors for MC in patients with rib fractures, and the regression equation was constructed. A nomogram prediction model was plotted based on the regression equation with R software. The receiver operating characteristic (ROC) curve was plotted to evaluate the model′s discriminability. Hosmer-Lemeshow (H-L) goodness-of-fit test and calibration curves of 1000 repeated samplings by the Bootstrap method were used to evaluate the calibration of the model. The decision curve analysis (DCA) and clinical impact curve analysis (CIC) were plotted to evaluate its clinical efficacy. A risk scoring was performed according to the assigned β coefficient of independent risk factors. Accordingly, the 370 selected patients with rib fractures were divided into low-risk subgroup of 202 patients, moderate-risk subgroup of 108 patients, high-risk subgroup of 50 patients, and extremely high-risk subgroup of 10 patients. The incidence of MC and in-hospital mortality were compared among different subgroups so as to further verify the clinical application value of the predictive model.Results:In the training set, there were significant differences between the MC group and NMC group in bilateral rib fractures, flail chest, number of rib fractures, upper chest proximal sternum segment, upper chest anterolateral segment, upper chest proximal spinal segment, middle chest anterolateral segment, middle chest proximal spinal segment, lower chest anterolateral segment, pneumothorax, mediastinal emphysema, hemothorax, sternal fractures, chest abbreviated injury scale (c-AIS), injury severity score (ISS), new injury severity score (NISS), white blood cell counts, hemoglobin, hematocrit, total cholesterol, low density lipoprotein, albumin, aspartate aminotransferase, alanine aminotransferase, and blood urea nitrogen ( P<0.05 or 0.01). Spearman correlation analysis showed that the bilateral rib fractures, flail chest, number of rib fractures, upper chest proximal sternum segment, upper chest anterolateral segment, upper chest proximal spinal segment, middle chest anterolateral segment, middle chest proximal spinal segment, lower chest anterolateral segment, pneumothorax, hemothorax, sternal fractures, c-AIS, ISS, NISS, white blood cell count, aspartate aminotransferase and blood urea nitrogen were positively correlated with MC ( P<0.05 or 0.01). Univariate binary Logistic regression analysis verified that the above variables with positive correlation were significantly correlated with MC in patients with rib fractures ( P<0.05 or 0.01). The 4 predictor variables screened by LASSO regression analysis were the upper chest anterolateral segment, middle chest proximal spinal segment, pneumothorax, and sternal fractures. Multivariate Logistic regression analysis confirmed that the aforementioned 4 predictor variables were independent risk factors for MC in patients with rib fractures ( P<0.05 or 0.01). The regression equation of the training set was established based on the above independent risk factors: P=e x/(1+e x), with the x=1.57×"upper chest anterolateral segment"+0.73×"middle chest proximal spinal segment"+1.36×"pneumothorax"+2.16×"sternal fractures"-1.10. In the predictive model for MC in patients with rib fractures established based on the equation, the area under the ROC curve (AUC) was 0.77 (95% CI 0.72, 0.83) and 0.77 (95% CI 0.71, 0.82) in the training set and validation set. The H-L goodness-of-fit test showed χ2=2.77, P=0.429 in the training set, and χ2=1.33, P=0.515 in the validation set, indicating that there was no significant difference between the predicted probability and the actual probability of the model ( P>0.05). The calibration curves showed that the bias-corrected curves of the training set and validation set were in good consistency with the actual curves and were both close to the ideal curves. The DCA of the training set and the validation set showed that within the threshold probability range of 0.2-0.8, the predictive model could obtain good net clinical benefits. The CIC of the training set and the validation set indicated that when the threshold probability was >0.4, the population identified as high-risk MC patients by the predictive model highly matched the actual MC patients. Risk scoring of subgroups found that the incidence of MC and in-hospital mortality among the patients with rib fractures were 80.0% and 6.0% in the high-risk subgroup and 90.0% and 20.0% in the extremely high-risk subgroup, significantly higher than those in the low-risk subgroup (24.8%, 1.0%) and the moderate-risk subgroup (55.6%, 1.9%) ( P<0.05). Conclusions:The predictive model for MC in patients with rib fractures constructed based on the upper chest anterolateral segment, middle chest proximal spinal segment, pneumothorax, and sternal fractures has good predictive efficacy and clinical application value.