Examining the relationship between severe persistent mental illness and surgical outcomes in women undergoing mastectomy for breast cancer.

BACKGROUND: Severe persistent mental illness (SPMI) is associated with worse outcomes in cancer patients. Less is known about the relationship between SPMI and surgical outcomes after mastectomy for breast cancer. METHODS: We selected patients with breast cancer and SPMI from the National Inpatient Sample (2016-2018) and used propensity score matching. We then used multivariate analysis, Kruskal-Wallis tests, and conditional logistic regression to compare demographics and outcomes. RESULTS: The study sample consisted of 670 patients: 536 without SPMI and 134 with SPMI. SPMI was associated with bilateral mastectomy (bilateral: 53% vs. unilateral: 42.7%, p = 0.033) and decreased frequency of breast reconstruction (p < 0.001). SPMI was associated with more extended hospitalization (4 days vs. 2 days, p < 0.001) and increased risk of developing post-procedural infection and sepsis (OR 2.909). CONCLUSIONS: SPMI is associated with bilateral mastectomy, more extended hospitalization, and increased risk for post-procedural infection and sepsis - suggesting the need for increased use of standardized screening tools to identify SPMI in patients and inform perioperative management correctly.

PopInf: An Approach for Reproducibly Visualizing and Assigning Population Affiliation in Genomic Samples of Uncertain Origin.

Germline genetic variation contributes to cancer etiology, but self-reported race is not always consistent with genetic ancestry, and samples may not have identifying ancestry information. In this study, we describe a flexible computational pipeline, PopInf, to visualize principal component analysis output and assign ancestry to samples with unknown genetic ancestry, given a reference population panel of known origins. PopInf is implemented as a reproducible workflow in Snakemake with a tutorial on GitHub. We provide a preprocessed reference population panel that can be quickly and efficiently implemented in cancer genetics studies. We ran PopInf on The Cancer Genome Atlas (TCGA) liver cancer data and identify discrepancies between reported race and inferred genetic ancestry. The PopInf pipeline facilitates visualization and identification of genetic ancestry across samples, so that this ancestry can be accounted for in studies of disease risk.