Outcomes of emergency general surgery admissions in patients experiencing homelessness: A matched cohort study.

BACKGROUND: Housing status impacts outcomes after elective and emergent operations but has not been well studied in the emergency general surgery population. This study investigates the impact of housing status on complications and 30-day follow-up, emergency department visits, and readmissions after emergency general surgery admission. METHODS: We conducted a retrospective matched cohort study of adult patients admitted with an emergency general surgery diagnosis at an urban, safety net hospital from 2014 to 2021. Patients were matched 1 to 2 on the basis of age, sex, Charlson Comorbidity Index, diagnosis, and operative status. The primary exposure was unhoused status. The primary outcome was in-hospital complications. Secondary outcomes included intensive care unit admission, extended length of stay, follow-up attendance, and emergency department visit or unplanned readmission within 30 days. Multivariable conditional logistic regression was used to determine the association between housing status and the outcomes of interest. RESULTS: The study included 531 patients (177 unhoused, 354 housed). There were no significant differences in complications, intensive care unit admissions, or extended length of stay. Unhoused patients had lower odds of outpatient follow-up (odds ratio, 0.54; 95% confidence interval, 0.35-0.85, P = .008) and higher odds of emergency department utilization (odds ratio, 2.72; 95% confidence interval, 1.78-4.14, P < .001) and readmission (odds ratio, 1.87; 95% confidence interval, 1.09-3.19, P = .02). CONCLUSION: Compared with housed patients, unhoused patients with emergency general surgery conditions have lower rates of outpatient follow-up and greater odds of using the emergency department and being readmitted within 30 days of discharge. This points to a need for dedicated posthospitalization care and creative methods of engaging with this population.

Association of Race, Ethnicity, Language, and Insurance with Time to Treatment Initiation Among Women with Breast Cancer at an Urban, Academic, Safety-Net Hospital.

INTRODUCTION: Initial treatment for nonmetastatic breast cancer is resection or neoadjuvant systemic therapy, depending on tumor biology and patient factors. Delays in treatment have been shown to impact survival and quality of life. Little has been published on the performance of safety-net hospitals in delivering timely care for all patients. METHODS: We conducted a retrospective study of patients with invasive ductal or lobular breast cancer, diagnosed and treated between 2009 and 2019 at an academic, safety-net hospital. Time to treatment initiation was calculated for all patients. Consistent with a recently published Committee on Cancer timeliness metric, a treatment delay was defined as time from tissue diagnosis to treatment of greater than 60 days. RESULTS: A total of 799 eligible women with stage 1-3 breast cancer met study criteria. Median age was 60 years, 55.7% were non-white, 35.5% were non-English-speaking, 18.9% were Hispanic, and 49.4% were Medicaid/uninsured. Median time to treatment was 41 days (IQR 27-56 days), while 81.1% of patients initiated treatment within 60 days. The frequency of treatment delays did not vary by race, ethnicity, insurance, or language. Diagnosis year was inversely associated with the occurrence of a treatment delay (OR: 0.944, 95% CI 0.893-0.997, p value: 0.039). CONCLUSION: At our institution, race, ethnicity, insurance, and language were not associated with treatment delay. Additional research is needed to determine how our safety-net hospital delivered timely care to all patients with breast cancer, as reducing delays in care may be one mechanism by which health systems can mitigate disparities in the treatment of breast cancer.

LigGrep: a tool for filtering docked poses to improve virtual-screening hit rates.

Structure-based virtual screening (VS) uses computer docking to prioritize candidate small-molecule ligands for subsequent experimental testing. Docking programs evaluate molecular binding in part by predicting the geometry with which a given compound might bind a target receptor (e.g., the docked "pose" relative to a protein target). Candidate ligands predicted to participate in the same intermolecular interactions typical of known ligands (or ligands that bind related proteins) are arguably more likely to be true binders. Some docking programs allow users to apply constraints during the docking process with the goal of prioritizing these critical interactions. But these programs often have restrictive and/or expensive licenses, and many popular open-source docking programs (e.g., AutoDock Vina) lack this important functionality. We present LigGrep, a free, open-source program that addresses this limitation. As input, LigGrep accepts a protein receptor file, a directory containing many docked-compound files, and a list of user-specified filters describing critical receptor/ligand interactions. LigGrep evaluates each docked pose and outputs the names of the compounds with poses that pass all filters. To demonstrate utility, we show that LigGrep can improve the hit rates of test VS targeting H. sapiens poly(ADPribose) polymerase 1 (HsPARP1), H. sapiens peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 (HsPin1p), and S. cerevisiae hexokinase-2 (ScHxk2p). We hope that LigGrep will be a useful tool for the computational biology community. A copy is available free of charge at http://durrantlab.com/liggrep/ .