Articles: mortality.
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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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Investigated the association of multiple cardiometabolic comorbidities with total/major cause-specific mortality and evaluate if this association might be modified by race among predominantly low-income Black and White participants. ⋯ Cardiometabolic comorbidities were associated with increases in all-cause and major cause-specific mortality, particularly Black Americans. This study calls for effective measures to prevent cardiometabolic comorbidities to reduce premature deaths in underserved Americans.
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The effectiveness and safety of mineralocorticoid receptor antagonists (MRA) in acute heart failure (HF) is uncertain. We sought to describe the prescription of spironolactone during acute HF and whether early treatment is effective and safe in a real-world setting. ⋯ Early treatment with spironolactone at discharge for new-onset HFrEF in a real-world setting did not reduce the risk of HF readmission or mortality in the first year after discharge. The risk of hyperkalemia was increased.
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J Clin Monit Comput · Apr 2024
Early prediction of mortality at sepsis diagnosis time in critically ill patients by using interpretable machine learning.
This study applied machine learning for the early prediction of 30-day mortality at sepsis diagnosis time in critically ill patients. Retrospective study using data collected from the Medical Information Mart for Intensive Care IV database. The data of the patient cohort was divided on the basis of the year of hospitalization, into training (2008-2013), validation (2014-2016), and testing (2017-2019) datasets. 24,377 patients with the sepsis diagnosis time < 24 h after intensive care unit (ICU) admission were included. ⋯ The calibration plot for the model revealed a slope of 1.03 (95% CI 0.94-1.12) and intercept of 0.14 (95% CI 0.04-0.25). The SHAP revealed the top three most significant features, namely age, increased red blood cell distribution width, and respiratory rate. Our study demonstrated the feasibility of using the interpretable machine learning model to predict mortality at sepsis diagnosis time.
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Background: Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. There is currently no simple immune-imbalance-driven indicator for patients with sepsis. Methods: This study was conducted in Peking Union Medical College Hospital. ⋯ In trend analysis, as the trend of D1-D3-D7 IL-6/LY# decreases, the morality rate is lower than increase or fluctuate group (42.1% vs. 58.3%, 37.9% vs. 43.8%, 37.5% vs. 38.5% in high, moderate, and low D1 IL-6/LY# group separately). Conclusion: IL-6/LY# examined on first day in intensive care unit can be used as an immune-imbalance alert to identify sepsis patients with higher risk of 28-day mortality. Decreasing trend of IL-6/LY# suggests a lower 28-day mortality rate of sepsis patients.