ABSTRACT
Objective:To analyze the prognostic factors and evaluate the accuracy of existing survival prediction models in patients with lung cancer-derived spinal metastases who have undergone open surgery.Methods:According to the inclusion criteria, the data of 76 patients with spinal metastasis of lung cancer who underwent open surgery in the department of Orthopedics in Guangdong Provincial People's Hospital were collected from January 2019 to November 2021. The relationship between the number of bone metastasis, pathological type, visceral metastasis, epidermal growth factor receptor mutation, serum alkaline phosphatase (ALP), hemoglobin (Hb), Frankel grade and postoperative survival time in 76 cases was analyzed by Cox logical regression analysis and Kaplan-Meier method to determine the potential prognostic factors. The accuracy of Tomita score, Tokuhashi revised score, Katagiri New score, New England Spinal Metastasis Score score (NESMS) and Skeletal Oncology Research Group (SORG) machine learning algorithm in predicting postoperative survival time was verified by drawing receiver operating characteristic (ROC) curve.Results:The median follow-up time of the patients was 18.0 months (2.3-36.0 months). The median survival time was 12.6 months [95% CI (10.8, 14.4)]. The survival rates at 6 and 12 months after operation were 71.6% and 52.0%, respectively. Multivariate regression analysis showed that ALP [ HR=0.23, 95% CI (0.11, 0.48), P<0.001], Hb [ HR=4.48, 95% CI (2.07, 9.70), P< 0.001] and EGFR mutation [ HR=2.22, 95% CI (1.04, 4.76), P=0.040] were independent predictors of prognosis. The accuracy of Tomita score, Tokuhashi revised score (2005), Katagiri New score and NESMS score in predicting 1-year mortality was 58.7%, 65.7%, 70.5% and 65% respectively, and the accuracy in predicting 6-month mortality was 63.7%, 62.2%, 61.2% and 56.8% respectively. The accuracy of SORG machine learning algorithm in predicting 1-year and 90 d mortality was 81.1%, 67.5%, respectively. Conclusion:No EGFR mutation, ALP>164 U/L and Hb≤125 g/L were risk factors affecting the survival of patients with spinal metastasis of lung cancer. SORG machine learning algorithm has good accuracy in predicting the postoperative survival rate of patients with lung cancer spinal metastasis.
ABSTRACT
Objective:To characterize the knee gait maps of ordinary people, athletes and patients with anterior cruciate ligament (ACL) injury when walking on a level ground in order to identify potential kinematic indicators for early identification of ACL injury.Methods:From December 2021 to March 2022, 39 ordinary college students (normal group) and 39 college athletes (athlete group) were recruited in Southern Medical University, and 26 patients with ACL injury (patient group) were recruited at the Department of Orthopedics, Guangdong Provincial People＇s Hospital. The normal group consisted of 20 males and 19 females with a median age of 19 (18, 21) years; the athlete group consisted of 22 males and 17 females with a median age of 19 (18, 20) years; the patient group consisted of 23 males and 3 females with a median age of 20 (19, 20) years. A portable knee joint motion capture system was used to collect the knee gait maps of the subjects walking at a speed of 3 km/h on a treadmill. The knee varus and valgus angles, internal and external rotation angles, flexion and extension angles during the movement, and anteroposterior, medial-lateral superior-inferior displacements of the tibia relative to the femur were compared between the 3 groups.Results:There was no significant difference in the general data among the 3 groups except for gender, showing they were comparable ( P>0.05). There were significant differences in the varus and valgus angles during the whole gait cycle (1% to 100%), internal and external rotation angles during the weight-bearing response period (9% to 10%), flexion and extension angles during the stance phase and swing phase (1% to 27%, 29% to 100%), anteroposterior displacements during the weight-bearing reaction phase (1% to 3%) and at the end of the swing phase (96% to 98%), superior-inferior displacements at the middle support phase (15% to 19%), the end of the support phase (29% to 33%, 36% to 43%) and the swing phase (68% to 94%), and medial-lateral displacements at the middle stance phase and the middle swing phase (12% to 82%) among the 3 groups ( P<0.05). The maximum varus and valgus angles (-10.89°±4.55°, -12.20°±4.38°) of the subjects in the normal group and the athlete group were significantly greater than those in the patient group (-5.44°±3.72°) ( P<0.05). The medial-lateral displacement at the middle support phase [3.69 (0.13, 7.25) mm] of the subjects in the normal group was significantly larger than those in the athlete group and the patient group [-0.59 (-6.65, 5.24) mm, 0.96 (-1.54, 3.89) mm] ( P<0.05). Conclusions:The gait of college athletes is significantly different from that of ordinary college students and that of patients with ACL injury. Indexes like the varus and valgus angles and the medial-lateral displacement may be used as potential indictors for early identification of ACL injury.