Articles: cations.
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Race and socioeconomic status incompletely identify patients with colorectal cancer (CRC) at the highest risk for screening, treatment, and mortality disparities. Social vulnerability index (SVI) was designed to delineate neighborhoods requiring greater support after external health stressors, summarizing socioeconomic, household, and transportation barriers by census tract. SVI is implicated in lower cancer center use and increased complications after colectomy, but its influence on long-term prognosis is unknown. Herein, we characterized relationships between SVI and CRC survival. ⋯ High SVI was independently associated with poorer prognosis after CRC resection beyond the perioperative period. Acknowledging needs for multi-institutional evaluation and elaborating causal mechanisms, neighborhood-level vulnerability may inform targeted outreach in CRC care.
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Purpose: Cystatin C (CysC) has been linked to the prognosis of corona virus disease 2019 (COVID-19). The study aims to investigate a predictor correlated with CysC screening for poor prognosis in COVID-19 patients combined with skeletal muscle (SKM) impairment and rhabdomyolysis (RM). Methods: A single-center retrospective cohort analysis was carried out. ⋯ LDH*CysC and AST*CysC had better predictive values than CysC and the best prediction for RM, with an AUC of 0.880 (0.796,0.964) for LDH*CysC ( P < 0.05, vs CysC) and 0.925 (0.878,0.972) for AST*CysC ( P < 0.05, vs CysC). Conclusion: CysC is an essential evaluation indicator for COVID-19 patients' prognosis. AST*CysC and LDH*CysC have superior predictive value to CysC for SKM, RM, and death, and optimal classification for RM.
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As incidence of operative spinal pathology continues to grow, so do the rates of lumbar spinal fusion procedures. Comorbidity indices can be used preoperatively to predict potential complications. However, there is a paucity of research defining the optimal comorbidity indices in patients undergoing spinal fusion surgery. We aimed to use modeling strategies to evaluate the predictive validity of various comorbidity indices and combinations thereof. ⋯ This investigation is the first to use big data and modeling strategies to delineate the relative predictive utility of the ECI and Johns Hopkins Adjusted Clinical Groups comorbidity indices for the prognostication of patients undergoing lumbar fusion surgery. With the knowledge gained from our models, spine surgeons, payers, and hospitals may be able to identify vulnerable patients more effectively within their practice who may require a higher degree of resource utilization.