Objective To evaluate the clinical effect of early total care (ETC) for treatment of unstable pelvic fractures.Methods This retrospective case series study included eighteen patients with unstable pelvic fractures treated surgically from May 2013 to May 2015.There were 11 male and 7 female patients,aged (45.1 ± 13.1)years.Eleven patients were injured due to road traffic accidents and 7 due to fall from height.Tile B type pelvic fractures were seen in 8 patients and Tile C type in 10 patients.All patients were treated with ETC method and received open reduction and internal fixation within 24 to 48 hours after the primary treatment.Data were collected,such as operation time,intraoperative blood loss,hospital length of stay and perioperative complications.Majeed score and EuroQol 5 dimension (EQ-5D) were used for evaluation of clinical outcome and quality of life respectively.Excellent and good rate of operation was assessed using the Matta clinical evaluation standard.Results Three patients died and fifteen patients survived.Operation time was (120.1 ± 22.2)minutes,amount of intraoperative blood loss was (355.9 ± 56.4) ml,and hospitalization was (8.5 ± 1.6) days.Incidence of perioperative complications was 56％,including 3 patients with acute respiratory distress syndrome,1 pneumonia,2 acute lung injury,1 incisional wound infection,2 multiple organ failure and 1 diffuse intravascular coagulation.All patients were followed up for (15.1 ±2.4)months (range,12-21 months).Majeed score was decreased from preoperative (93.7 ± 6.1) points to final follow-up of (74.1 ± 9.2) points,and the EQ-5D index was decreased from preoperative 0.96 ± 0.04 to final follow-up of 0.74 ± 0.19 (both P ＜ 0.05).Excellent and good rate of operation was 80％.Conclusions ECT provides satisfactory short-term outcome for treatment of unstable pelvic fractures,but the incidence of complications is high.Doctors should choose reasonable treatment plan according to the surgical indications.

Objective:To investigate the management of pelvic injury associated with complete anterior sacroiliac joint dislocation.Methods:In the study, 6 cases of pelvic injury associated with complete an-terior sacroiliac joint dislocation treated in Beijing Jishuitan Hospital from February 2008 to June 2014 were analyzed.We described the history and severity of injury, emergency treatment, and fracture radio-logy.In all the cases, the surgical treatment and postoperative functional exercise were performed.We followed up all the cases on an average of 1.6 years, assessed the postoperative recovery and summed up the treatment experience.Results:All the 6 patients with fractures recovered without infection and nerve symptoms after surgery.Their X-rays showed good reduction of sacroiliac joints.All the cases were followed up on an average of 1.6 years.Six months after surgery, the Majeed scores were perfect in 2 cases, good in 2, fair in 1, and poor in 1.The patients with poor scores suffered persistent pain, and decreased physical activity, and when walking long distances, they needed a walking stick.The 2 patients with low scores could not resume the original work.Conclusion:Pelvic injury associated with complete anterior sacroiliac joint dislocation is a special type of the pelvic injury since the managements during the emergency phase are difficult.The surgery should be done as early as possible, and the anterior approach is available for the reduction and fixation.

OBJECTIVETo establish a preoperative deep venous thrombosis predictor score for patients with fresh lower extremity fractures by statistical analysis.

METHODSFrom January 2011 to December 2012, 1 705 patients with fresh lower extremity fractures were admitted to department of orthopaedic trauma, Beijing Jishuitan Hospital. They were randomly divided into two groups, the group 1 (n = 879) was used to screen risk factors and derived a predictive models based on logistic regression, the group 2 (n = 826) validated the models.

RESULTSAmong the patients, there were 1 106 male and 599 female patients, with an average age of (50 ± 18) years.Variables related to preoperative deep venous thrombosis were age, length of time before surgery, cause of injury, low/high-energy injury, location of injury, history of cardiovascular and cerebrovascular diseases, and D-Dimer. The scores based on OR were: age ≤ 35 years: 1 point, > 35- < 65 years: 4 points, ≥ 65 years: 6 points; length of time before surgery, < 8 days:1 point, ≥ 8 days:2 points;low-energy injury:1 point, high energy injury:3 points;location of injury, foot and ankle:1 point, calf:3 points, around the knee: 5 points, femoral diaphysis and proximal femur:7 points, pelvis and acetabulum:4 points, ≥ 2 sites:6 point;history of cardiovascular and cerebrovascular diseases, yes:2 points, no:1 point. D-Dimer < 600 µg/L:1 point, ≥ 600 µg/L:3 points. Area under receiver operating characteristic curve was 0.79, critical point 15.5 points, sensitivity was 77.00%, specificity was 68.17%.

CONCLUSIONThe score can predict the preoperative deep venous thrombosis for patients with fresh lower extremity fractures, but limited.

Aged ; Female ; Fibrin Fibrinogen Degradation Products ; Fractures, Bone ; Humans ; Leg ; Leg Injuries ; Logistic Models ; Lower Extremity ; Male ; Middle Aged ; ROC Curve ; Risk Factors ; Sensitivity and Specificity ; Venous Thrombosis

Objective:This study aims to compare the application value of Autoregressive Integrated Moving Average model (ARIMA ) and deep learning model inprediction and analysis of liposuction operation data.Methods:The patients who met inclusion criteria and underwent liposuction surgery in the Plastic Surgery Hospital of Chinese Academy of Medical Sciences from January 2019 to September 2019 were enrolled in this study. For each patient, 250~400 s operation data including kinematics and mechanical data were collected by a senior plastic surgeon, usingthe liposuction operation recording system which consists of optical tracking and force sensing equipment. After pretreatment, the collected data were divided into one liposuction reciprocating cycle as one set of data. ARIMA model and deep learning model were used to analyze the collected datafor establishing prediction models of liposuction operation. Using Matlab2017, 30 couplesofliposuction data setwereextractedby simple random sampling, andthe DTW valueofeachcoupleofdatasetswascalculated as test standard. Then, the DTW values between 30 sets of predicted data and actual data based on the ARIMA model and the deep learning model were calculated respectively and compared with the test standard to verify the prediction result of the two models. Matlab2017 was used for statistical analysis. Independent sample t-test was used to compare the two groups, and P<0. 05 indicates that the difference is statistically significant.Results:18 patients were enrolled. All patients were females at 23-49 years old, with the mean age of 36. 6 years old. Liposuction was performed in the abdomen, thighs, and waist. A total of 16 800 sets of liposuction cycle data were obtained. The mean DTW value of test standard was 0. 048±0. 028. The meanDTW value between the ARIMA model predicted data and the actual data was 0. 660±0. 577, which was statistically significant compared with the test standard ( P< 0. 05) . The meanDTW value between the deep learning model predicted data and the actual data was 0. 052±0. 030, which was no significant difference compared with the test standard ( P> 0. 05 ). Conclusions:Compared with ARIMA model, deep learning model can predict liposuction operation data more accurately, and has better adaptability and real-time performance.

Objective:This study aims to compare the applicability value of autoregressive integrated moving average model(ARIMA) and deep learning model inprediction and analysis of liposuction operation data.Methods:The patients who met inclusion criteria and underwent liposuction surgery in the Plastic Surgery Hospital of Chinese Academy of Medical Sciences from January 2019 to September 2019 were enrolled in this study. For each patient, 250-400 s operation data including kinematics and mechanical data were collected by a senior plastic surgeon, using the liposuction operation recording system which consists of optical tracking and force sensing equipment. After pretreatment, the collected data were divided into one liposuction reciprocating cycle as one set of data. ARIMA model and deep learning model were used to analyze the collected data for establishing prediction models of liposuction operation. Using Matlab 2017, 30 couples of liposuction data set were extracted by simple random sampling, and the dynamic time warping (DTW) value of each couple of data sets was calculated as test standard. Then, the DTW values between 30 sets of predicted data and actual data based on the ARIMA model and the deep learning model were calculated respectively and compared with the test standard to verify the prediction results of the two models. Matlab 2017 was used for statistical analysis. Independent sample t-test was used to compare the two groups, and P<0.05 indicated statistically significant difference. Results:Eighteen patients were enrolled. All patients were females at 23-49 years old, with the mean age of 36.6 years old. Liposuction was performed in the abdomen, thighs, and waist. A total of 16 800 sets of liposuction cycle data were obtained. The mean DTW value of test standard was 0.048±0.028. The mean DTW value between the ARIMA model predicted data and the actual data was 0.660±0.577, which was statistically significant compared with the test standard ( P<0.05). The mean DTW value between the deep learning model predicted data and the actual data was 0.052±0.030, which was not significantly different compared to the test standard ( P>0.05). Conclusions:Compared with ARIMA model, deep learning model can predict liposuction operation data more accurately, and has better adaptability and real-time performance.

Objective:This study aims to compare the application value of Autoregressive Integrated Moving Average model (ARIMA ) and deep learning model inprediction and analysis of liposuction operation data.Methods:The patients who met inclusion criteria and underwent liposuction surgery in the Plastic Surgery Hospital of Chinese Academy of Medical Sciences from January 2019 to September 2019 were enrolled in this study. For each patient, 250~400 s operation data including kinematics and mechanical data were collected by a senior plastic surgeon, usingthe liposuction operation recording system which consists of optical tracking and force sensing equipment. After pretreatment, the collected data were divided into one liposuction reciprocating cycle as one set of data. ARIMA model and deep learning model were used to analyze the collected datafor establishing prediction models of liposuction operation. Using Matlab2017, 30 couplesofliposuction data setwereextractedby simple random sampling, andthe DTW valueofeachcoupleofdatasetswascalculated as test standard. Then, the DTW values between 30 sets of predicted data and actual data based on the ARIMA model and the deep learning model were calculated respectively and compared with the test standard to verify the prediction result of the two models. Matlab2017 was used for statistical analysis. Independent sample t-test was used to compare the two groups, and P<0. 05 indicates that the difference is statistically significant.Results:18 patients were enrolled. All patients were females at 23-49 years old, with the mean age of 36. 6 years old. Liposuction was performed in the abdomen, thighs, and waist. A total of 16 800 sets of liposuction cycle data were obtained. The mean DTW value of test standard was 0. 048±0. 028. The meanDTW value between the ARIMA model predicted data and the actual data was 0. 660±0. 577, which was statistically significant compared with the test standard ( P< 0. 05) . The meanDTW value between the deep learning model predicted data and the actual data was 0. 052±0. 030, which was no significant difference compared with the test standard ( P> 0. 05 ). Conclusions:Compared with ARIMA model, deep learning model can predict liposuction operation data more accurately, and has better adaptability and real-time performance.

Objective:This study aims to compare the applicability value of autoregressive integrated moving average model(ARIMA) and deep learning model inprediction and analysis of liposuction operation data.Methods:The patients who met inclusion criteria and underwent liposuction surgery in the Plastic Surgery Hospital of Chinese Academy of Medical Sciences from January 2019 to September 2019 were enrolled in this study. For each patient, 250-400 s operation data including kinematics and mechanical data were collected by a senior plastic surgeon, using the liposuction operation recording system which consists of optical tracking and force sensing equipment. After pretreatment, the collected data were divided into one liposuction reciprocating cycle as one set of data. ARIMA model and deep learning model were used to analyze the collected data for establishing prediction models of liposuction operation. Using Matlab 2017, 30 couples of liposuction data set were extracted by simple random sampling, and the dynamic time warping (DTW) value of each couple of data sets was calculated as test standard. Then, the DTW values between 30 sets of predicted data and actual data based on the ARIMA model and the deep learning model were calculated respectively and compared with the test standard to verify the prediction results of the two models. Matlab 2017 was used for statistical analysis. Independent sample t-test was used to compare the two groups, and P<0.05 indicated statistically significant difference. Results:Eighteen patients were enrolled. All patients were females at 23-49 years old, with the mean age of 36.6 years old. Liposuction was performed in the abdomen, thighs, and waist. A total of 16 800 sets of liposuction cycle data were obtained. The mean DTW value of test standard was 0.048±0.028. The mean DTW value between the ARIMA model predicted data and the actual data was 0.660±0.577, which was statistically significant compared with the test standard ( P<0.05). The mean DTW value between the deep learning model predicted data and the actual data was 0.052±0.030, which was not significantly different compared to the test standard ( P>0.05). Conclusions:Compared with ARIMA model, deep learning model can predict liposuction operation data more accurately, and has better adaptability and real-time performance.

Objective To explore the management of postoperative infection following open reduction and internal fixation for acetabular fractures.Methods Ten patients were treated and completely followed up in Beijing Jishuitan Hospital from February 2012 to December 2016 for postoperative infection after open reduction and internal fixation for acetabular fracture.They were 9 males and one female,aged from 14 to 64 years(mean,40.6 years).According to Letournel classification,there were 2 double-column fractures,2 anterior descending transverse fractures,2 anterior column fractures,one posterior wall fracture,and one posterior wall fracture.Eight cases developed surgical regional infection within 2 weeks after internal fixation,and 2 presented with symptoms of infection more than 3 months after fracture fixation.Vacuum sealing drainage (VSD) was used to treat one case of acute superficial infection;open debridement surgery,carrier with sensitive antibiotics and intravenous antibiotics were used to treat 7 cases of acute deep infection and 2 cases of chronic deep infection.Results The infection symptoms disappeared 9 days after removal of VSD device in the one case of acute superficial infection.Normal fracture union was achieved in 6 cases after their infection was controlled;4 cases had to undergo total hip arthroplasty because their articular structure was damaged after control of infection.The 10 patients were followed up for 6 to 54 months (mean,25.7 months).Their Harris scores at the last follow-up averaged 74.8 (from 32 to 92).Conclusions Negative-pressure wound therapy is an effective management for acute superficial infection after acetabular fracture.Deep acute infection needs early repeated debridement combined with sensitive antibiotic carrier to protect joint function.For infection which is difficult to control or chronic infection associated with structural damage,repeated debridement combined with sensitive antibiotic spacer is effective for infection control at the first stage and artificial total hip arthroplasty can be carried out at the second stage when the infection is controlled.

Objective:To evaluate the emergency iliosacral screw fixation assisted by TiRobot for unstable posterior pelvic ring fracture.Methods:The 26 patients with unstable pelvic fracture were analyzed retrospectively who had undergone emergency iliosacral screw fixation at Department of Orthopedics & Traumatology, Beijing Jishuitan Hospital from June 2018 to December 2020. They were divided into 2 groups depending on whether orthopaedic TiRobot was used to assist screw insertion. In the observation group of 14 cases subjected to TiRobot-assisted insertion of iliosacral screws, there were 10 males and 4 females with an age of (45.9 ± 10.1) years; in the control group of 12 cases subjected to conventional manual insertion of iliosacral screws, there were 9 males and 3 females with an age of (49.2 ± 11.3) years. All the surgeries were conducted within 24 hours after injury. The 2 groups were compared in terms of screw insertion time, pin insertion, intraoperative blood loss, fluoroscopy time, postoperative screw position, fracture reduction and Harris hip score at the final follow-up.Results:The 2 groups were comparable because there was no significant difference between them in their preoperative general clinical data or follow-up time ( P>0.05). The screw insertion time [(16.1 ± 3.4) min] and fluoroscopy time [(8.1 ± 3.3) s] in the observation group were significantly shorter than those in the control group [(26.4 ± 5.4) min and (25.2 ± 7.4) s], and the pin insertions [1 (1, 2) times] and intraoperative blood loss [(10.5 ± 6.4) mL] in the former were significantly less than those in the latter [6 (3, 8) times and (24.8 ± 6.7) mL] (all P<0.05). Postoperatively, the sacroiliac screw position was excellent in 18 cases and good in 2 in the observation group while excellent in 14 cases, good in 2 and poor in 2 in the control group; the fracture reduction was excellent in 12 cases, good in one and fair in one in the observation group while excellent in 10 cases, good in one and fair in one in the control group, showing insignificant differences in the above comparisons ( P>0.05). There was no significant difference either in the Harris hip score at the final follow-up between the 2 groups ( P>0.05). Conclusion:Compared with conventional manual insertion of iliosacral screws, emergency iliosacral screw fixation assisted by TiRobot can effectively decrease surgical time and reduce operative invasion due to a higher accuracy rate of screw insertion.

Objective:This study aims to discuss the detection effect of a novel liposuction cannula movement recording system based on optical tracking technique and force sensor during liposuction and analyze the relationship between recorded data and liposuction efficiency.Methods:The patients who met inclusion criteria underwent liposuction from January 2019 to September 2019 were enrolled in this study and operated on by two surgeons with extensive liposuction experience. During a given procedure, one surgeon performed liposuction on one side of the targeted liposuction area, another surgeon performed liposuction on the contralateral side. The trajectory and force of liposuction cannula were recorded for approximately 300 seconds, and the volume of upper layer fat in liposuction aspirate was measured during experiment. The average amplitude, average frequency, and average forward resistance of the liposuction needle movement of the two surgeons were compared, and the liposuction efficiency was calculated and compared. The data were analyzed by paired t-test and signed rank sum test. Results:Eighteen patients were enrolled. All patients were females at 23-49 years old, with the mean age of 37 years old. Liposuction was performed in the abdomen, thighs, and waist. The movement amplitude and forward resistance of the liposuction cannula of surgeon A were (11.43±1.23) cm and (9.35±2.24) N, which were higher than those of surgeon B (10.00±2.33) cm and (8.20±3.05) N, and the differences were significant ( t=2.780, P=0.013; t=2.328, P=0.033). The frequency of liposuction cannula movement of surgeon A was (2.14±0.19) Hz, and that of surgeon B was (2.19±0.55) Hz. There was no significant difference between the two surgeons ( t=-0.366, P=0.719). The liposuction efficiency of operator A was (19.20±9.36) ml/min, and the efficiency of operator B was (15.27±8.05) ml/min. A was 3.93 ml/min higher than B. The difference was statistically significant ( t=3.736, P=0.002). Conclusions:The liposuction cannula movement recording system based on optical tracking and force sensing technology established in this study can record and analyze the operation trajectory and resistance of the liposuction cannula. When the movement amplitude and forward resistance of the cannula are larger, the liposuction efficiency is higher.