Downstaging of breast cancer primary lesions and metastatic axillary lymph nodes among patients who underwent neoadjuvant chemotherapy (NAC) has raised the new challenges and opportunities on individualized breast cancer surgical treatment. Downstaging of the primary lesion has given patients that were previously deemed inoperable or not suitable for surgery a second chance. While downstaging of the lymph nodes has made it possible for sentinel lymph node biopsy (SLNB) to safely replace axillary lymph node dissection. However, the detection rate and false negative rate of early breast cancer SLNB technique in post-NAC patients barely meet the standard of clinical practice. Therefore, it is required that SLNB in post-NAC patients to be carried out by a medical team with advanced imaging equipments and extensive experiences in SLNB. Furthermore, they should be able to precisely evaluate axillary lymph node status before and after NAC as well as mark metastatic lymph node before NAC. Indications of SLNB should be restricted to patients that are downstaged from cN0 to ycN0 or from cN1 to ycN0. Particularly, it is only safe for patients whose axillary lymph node status become negative after NAC to receive SLNB when dual tracer (blue dye and radionuclide), removing more than 2 sentinel lymph nodes and targeted axillary dissection technique are used.

Objective:To explore the risk factors of in hospital death in patients with acute circulatory failure, and to further construct the prediction model.Methods:This study retrospectively analysed clinical data of 224 shock patients admitted to Xinjiang Uygur Autonomous Region People’s Hospital from January 2014 to January 2023, and patients were eligible for shock diagnosis according to the expert consensus of emergency clinical practice in China for acute circulatory failure. Including age, gender, admission diagnosis and other basic information, as well as platelet, lactic acid, lymphocyte count, NK cell count, CD4, CD8 and other indicators completed within 24 hours of admission.They were divided into survival group and death group according to the condition at discharge.Variables with P<0.1 in the univariate analysis were included in the LASSO regression model to screen out the most important predictors of hospital death in ACF patients, and the prediction model was constructed by Logistic regression.The model differentiation was evaluated by receiver operator characteristic (ROC) curve and area under the curve (AUC). Hosmer-Lemeshow test was used to evaluate the calibration degree of the prediction model. Finally, clinical decision curve analysis (DCA) was used to test the clinical benefit and application value of the model. Results:A total of 224 ACF patients, 113 survived and 111 died. The results of the univariate analysis showed statistically significant differences between the two groups in age, mental status, type of shock, respiratory rate, APACHE score, lymphocyte count, lactate, CD4, CD8 and qsofa ( P<0.05).The Logistic regression prediction model was constructed according to the 4 predictors and outcome variables selected by LASSO method,in which increased delirium, coma, respiratory rate and APACHE score were risk factors and increased CD4 was a protective factor.The above indicators were used to construct a line graph model for predicting in-hospital death in ACF patients, with a probability cut-off value of 0.4404 for predicting in-hospital death, corresponding to a total line graph score of approximately 136.This model had an AUC of 0.830 (0.764-0.895), a sensitivity of 81.25% and a specificity of 68.83%.The Hosmer-Lemshow test for the modelling set showed χ 2=712 and P=0.624, suggesting good accuracy of the model predictions.The assessment of the DCA analysis showed that the net benefit of the model was significantly higher than the two extreme conditions and had good clinical applicability. Conclusions:Mental status, respiratory rate, APACHE score as risk factors for in-hospital mortality in patients with acute circulatory failure and CD4 as a protective factor. The predictive model constructed from this may predict the risk of in-hospital death in patients and has certain clinical application value.