Breast-conserving surgery is associated with a lower incidence of suicide among females with breast cancer in the United States: a population-based retrospective cohort study.

BACKGROUND: With comparable overall survival and local recurrence rates with mastectomy, breast-conserving surgery (BCS) has become the cornerstone of therapy for breast cancer; however, the difference in the incidence of suicide between BCS and mastectomy among breast cancer survivors remains unclear. This study evaluated the mortality risk from suicide among breast cancer survivors and compared suicide risk between BCS and mastectomy using a population-based cohort. MATERIALS AND METHODS: Female patients newly diagnosed with first primary breast cancer, recorded in the Surveillance, Epidemiology and End Results database, were included. Standardized mortality ratio (SMR) and cumulative mortality rate from suicide among those who underwent BCS and mastectomy were compared. RESULTS: A total of 1 190 991 patients with newly diagnosed first primary breast cancer were included in the study, of whom 56.5% underwent BCS and 36.1% underwent mastectomy. During the follow-up period, 667 suicides were recorded. Patients who underwent mastectomy exhibited significantly higher suicide mortality than the general population [mortality rate, 8.16 per 100 000 person-years; SMR 1.18 (95% CI 1.05-1.33)], while there was no significant difference in suicide rate between patients who underwent BCS and the general population [SMR 0.92 (95% CI 0.83-1.02)]. Multivariate Cox analysis revealed that BCS, compared with mastectomy, was associated with a significantly decreased risk of suicide among females with breast cancer [hazard ratio 0.80 (95% CI 0.68-0.95); P = 0.009]. CONCLUSION: BCS was associated with a significantly lower incidence of suicide among females with breast cancer. BCS offers a compelling option for improving the quality of life and self-esteem of patients with cancer and provides a novel perspective on cancer management.

Analysis of a machine learning-based risk stratification scheme for acute kidney injury in vancomycin.

Vancomycin-associated acute kidney injury (AKI) continues to pose a major challenge to both patients and healthcare providers. The purpose of this study is to construct a machine learning framework for stratified predicting and interpreting vancomycin-associated AKI. Our study is a retrospective analysis of medical records of 724 patients who have received vancomycin therapy from 1 January 2015 through 30 September 2020. The basic clinical information, vancomycin dosage and days, comorbidities and medication, laboratory indicators of the patients were recorded. Machine learning algorithm of XGBoost was used to construct a series risk prediction model for vancomycin-associated AKI in different underlying diseases. The vast majority of sub-model performed best on the corresponding sub-dataset. Additionally, the aim of this study was to explain each model and to explore the influence of clinical variables on prediction. As the results of the analysis showed that in addition to the common indicators (serum creatinine and creatinine clearance rate), some other underappreciated indicators such as serum cystatin and cumulative days of vancomycin administration, weight and age, neutrophils and hemoglobin were the risk factors for cancer, diabetes mellitus, heptic insufficiency respectively. Stratified analysis of the comorbidities in patients with vancomycin-associated AKI further confirmed the necessity for different patient populations to be studied.