Development and evaluation of a mortality risk prediction model for severe bacterial infections in children / 中华急诊医学杂志

Objective:To establish a mortality risk prediction model of severe bacterial infection in children and compare it with the pediatric early warning score (PEWS), pediatric critical illness score (PCIS) and pediatric risk of mortality score Ⅲ (PRISM Ⅲ).Methods:A total of 178 critically ill children were selected from the PICU of the Children's Hospital of Nanjing Medical University from May 2017 to June 2022. After obtaining the informed consent of the parents/guardians, basic information such as sex, age, height and weight, as well as indicators such as heart rate, systolic blood pressure and respiratory rate were collected from all children. A standard questionnaire was used to score the child 24 h after admission to the PICU. The children were divided into the survival and death groups according to their survival status at 28 d after admission. A mortality risk prediction model was constructed and nomogram was drawn. The value of the mortality risk prediction model, PEWS, PCIS and PRISM in predicting the risk of death was assessed and compared using the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC).Results:Among the 178 critically ill children, 11 cases were excluded due to severe data deficiencies and hospitalization not exceeding 24 h. A total of 167 children were included in the analysis, including 134 in the survival group and 33 in the death group. A mortality risk prediction model for children with severe bacterial infection was constructed using pupillary changes, state of consciousness, skin color, mechanical ventilation, total cholesterol and prothrombin time. ROC curve analysis showed that the AUCs of mortality risk prediction model was 0.888 ( P<0.05). The AUCs of PEWS, PCIS and PRISM Ⅲ in predicting death in children with severe bacterial infection were 0.769 ( P< 0.05), 0.575 ( P< 0.05) and 0.759 ( P< 0.05), respectively. Hosmer-Lemeshow goodness-of-fit test showed the best agreement between risk of death and PEWS predicted morbidity and mortality and actual morbidity and mortality (χ 2 = 5.180, P = 0.738; χ 2 = 4.939, P = 0.764), and the PCIS and PRISM Ⅲ predicted mortality rates fitted reasonably well with actual mortality rates (χ 2= 9.110, P= 0333; χ 2 = 8.943, P= 0.347). Conclusions:The mortality risk prediction model for predicting the death risk has better prognostic value than PEWS, PCIS and PRISM Ⅲ for children with severe bacterial infection.

Learning curve of robot-assisted L 4 and L 5 pedicle screw insertion for lumbar spondylolisthesis / 中华骨科杂志

Objective:To study the learning curve and inflection point of robot-assisted L 4 and L 5 pedicle screw insertion for lumbar spondylolisthesis. Methods:A retrospective study was conducted on the data of 43 patients with L 4 and L 5 pedicle screw insertion for lumbar spondylolisthesis from January 2016 to December 2020 using surgical robot, including 19 males and 24 females, aging 59 (48, 66) years old. According to Meyerding classification, there were 23 grade I slippage, 18 grade II slippage, and 2 grade III slippage. The screw deviation and screw accuracy grade were assessed. The operation time, intraoperative blood loss, the number of intraoperative fluoroscopies, postoperative complications, and postoperative hospital stay were recorded. Cumulative Sum (CUSUM) was used to analyze the learning curve, and the learning curve is divided into early and late learning stages according to morphology. T test and Wilcoxon rank-sum test were used for statistical analysis and comparison of indicators between early and late learning stages. Results:43 patients with lumbar spondylolisthesis successfully completed the operation, with 60 L 4 pedicle screws and 70 L 5 pedicle screws inserted. The accuracy of L 4 pedicle screw placement began to improve since the 23rd placement, and the accuracy of L 5 pedicle screw placement began to improve since the 20th placement. Using the 23rd pedicle screw (the 14th patient) to divide the learning curve as the early stage and the late stage. There was no statistically significant difference in the operation time (225.0±74.0 min vs. 207.0±81.2 min, t=0.65, P=0.521), intraoperative blood loss[200 (75, 500) ml vs. 100 (60, 200) ml, Z=1.30, P=0.195], the number of intraoperative fluoroscopies[10 (6, 10) vs. 10 (6, 10), Z=-0.37, P=0.712] and postoperative complications (8% vs. 0, P=0.302) between the early stage and late stage of learning curve. In the late stage of learning, the postoperative hospital stay was shorter[4.5 (3, 6) d vs. 6.0 (5, 9) d, Z=2.00, P=0.046]and the pedicle screw insertion accuracy was higher[L 4: 1.33 (1.06, 1.79) mm vs. 2.23 (1.12, 4.55) mm, Z=2.43, P=0.015; L 5: 1.47 (0.98, 1.87) mm vs. 3.21 (1.64, 4.87) mm, Z=3.90, P=0.001]. The accuracy of screw placement was similar between the early and late stages[L 4: 95%(21/22) vs. 97%(37/38), P=1.000; L 5: 91%(20/22) vs. 96%(46/48), P=0.585]. Conclusion:Robot-assisted L 4 and L 5 pedicle screw placement in the treatment of lumbar spondylolisthesis had a relatively obvious learning curve. Starting from the placement of the 23rd screws, the accuracy of screw placement gradually increased.

Clinical outcomes of robot-assisted transforaminal percutaneous endoscopic lumbar discectomy / 中华骨科杂志

Objective:To introduce a new TIANJI robot assisted targeted puncture technique, and discuss the feasibility and clinical effect of transforaminal percutaneous endoscopic lumbar discectomy (tPELD) using this technique.Methods:The first 14 consecutive cases of single level lumbar disc herniation who underwent robot assisted tPELD procedure were retrospectively analyzed. The mean age was 46.3±16.0 years old (ranged from 16-72). After data transferred from C-arm to robot system and automatic registration, surgeons made plans of the trajectory on robot system based on intraoperative 3-dimensional images of lumbar spine. Move robotic arm to planned position, guide an accurate puncture pathway and establish working cannula. 25 consecutive patients who underwent conventional C-arm assisted tPELD surgery during the same period of time were assessed as the controlled group. The mean age was 45.5±13.7 years old (ranged from 16-68). All patients were followed up for 12 months. Clinical effect was assessed by visual analogue scale (VAS), Oswestry disability index (ODI) and Modified Macnab criteria. Intraoperative parameters and surgery-related complications were recorded.Results:The baseline data of age, surgical level, types of herniation, preoperative VAS scores and ODI had no significant difference between two groups ( P>0.05). In robot group, one case was converted to open microdiscectomy during operation due to technical failure. The other thirteen cases had successful robot assisted tPELD surgeries and were assessed accordingly. The new technique had good clinical outcomes. The immediate post-operative VAS score 2.85±1.79 and the last follow-up VAS score 1.50±1.04 were both significantly decreased than that before surgery 7.62±0.92 ( F=69.747, P<0.01); the last follow-up ODI 18.89%±12.16% was significantly reduced from the pre-operative ODI 71.19%±12.12% ( t=15.430, P<0.01). Between two groups, the immediate post-operative VAS score ( t=0.568, P=0.574), the last follow-up VAS score ( t=0.713, P=0.481), and last follow-up ODI had no significant difference ( t=0.171, P=0.865). The excellent or good rate of modified Macnab criteria at the last follow-up was 92.30% in robot group, comparing to 84.0% in controlled group. The fluoroscopic times during surgery of robot group 8.8±5.5 was significantly lowered the in controlled group 21.3±8.3 ( P<0.01). One case in robot group and two cases in controlled group had recurrence during follow-up period (recurrence rate 7.7% vs. 8.3%). However, there was no significant complications such as nerve root injury, dura injury or increased intracranial pressure in both groups. Conclusion:This study confirmed the feasibility of this new technique. Preliminary results indicated that TIANJI robot could help to build an easy, accurate and safe procedure of tPELD surgery.

Guidelines for navigation-assisted spine surgery / 医学前沿

Spinal surgery is a technically demanding and challenging procedure because of the complicated anatomical structures of the spine and its proximity to several important tissues. Surgical landmarks and fluoroscopy have been used for pedicle screw insertion but are found to produce inaccuracies in placement. Improving the safety and accuracy of spinal surgery has increasingly become a clinical concern. Computerassisted navigation is an extension and application of precision medicine in orthopaedic surgery and has significantly improved the accuracy of spinal surgery. However, no clinical guidelines have been published for this relatively new and fast-growing technique, thus potentially limiting its adoption. In accordance with the consensus of consultant specialists, literature reviews, and our local experience, these guidelines include the basic concepts of the navigation system, workflow of navigation-assisted spinal surgery, some common pitfalls, and recommended solutions. This work helps to standardize navigation-assisted spinal surgery, improve its clinical efficiency and precision, and shorten the clinical learning curve.