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.

Chest tightness variant asthma(CTVA)is an atypical form of asthma with chest tightness as the sole or predominant symptom.The underlying receptors for chest tightness are bronchial C-fibers or rapidly adapting receptors.The nerve impulses are transmitted via the vagus nerve and processed in different regions of the cerebral cortex.Chest tightness is associated with sensory perception,and CTVA patients may have heightened ability to detect subtle changes in lung function,but such sensory perception is unrelated to respiratory muscle activity,lung hyperinflation,or mechanical loading of the respiratory system.Airway inflammation,pulmonary ventilation dysfunction(especially involving small airways),and airway hyperresponsiveness may underlie the sensation of chest tightness.CTVA patients are prone to comorbid anxiety and depression,which share similar central nervous system processing pathways with dyspnea,suggesting a possible neurological basis for the development of CTVA.This article examines the recognition and mechanisms of chest tightness,and explores the pathogenesis of CTVA,focusing on its association with airway inflammation,ventilation dysfunction,airway hyperresponsiveness,and psychosocial factors.