RESUMEN
BACKGROUND: The quality of food service is vital to patients' experiences in care and recovery in hospitals. This study aimed to identify opportunities for improving hospital food services to enhance overall patient experiences and outcomes. METHODS: This retrospective cross-sectional study uses the Adult Admitted Patient Survey in 2019. Adult patients discharged from acute or rehabilitation care across 75 public hospitals were surveyed about their in-hospital experiences, including ratings of hospital food services, overall ratings of hospital care, complications acquired, and delayed discharge due to feeling unwell. Population weighting was applied in descriptive and multivariable logistic regression analyses. We used adjusted odds ratios (AORs) and 95% confidence intervals (CIs) to estimate the association between hospital food service and the overall rating of hospital care and two recovery outcomes. RESULTS: Eight in ten participants (weighted, 16,919/21,900) consumed food in a hospital [mean age: 60.6 years (SE:0.5; SD: 18.3), 53% female]. Compared to a fair rating, adults who rated "poor/very poor" of hospital food service were 2.7 times more likely to report dissatisfaction with overall care in the hospital [Adjusted Odds Ratio (AOR) (95% CI): 2.73 (1.49, 4.99)], 1.4 times more likely to report complications [AOR:1.43 (1.11, 1.83)] and 1.9 times more likely to report delayed discharge [AOR 1.85 (1.30, 2.62)]. More moderate ratings were associated with attenuation of risk for these outcomes. Furthermore, the magnitude of the effect for these associations was more substantial among patients from non-English-speaking backgrounds (n = 1,759) after controlling for patient characteristics. Food service attributes, including received food as ordered, food delivered within reach, the taste of the meals, and meal interruption, were significant factors for the outcomes assessed. CONCLUSION: These findings underscore the importance of patients' positive experiences of hospital food service in recovery outcomes and identify several food service indicators that can be used to monitor and improve patient experiences and recovery outcomes in hospitals.
RESUMEN
We previously described an approach called RealSeqS to evaluate aneuploidy in plasma cell-free DNA through the amplification of ~350,000 repeated elements with a single primer. We hypothesized that an unbiased evaluation of the large amount of sequencing data obtained with RealSeqS might reveal other differences between plasma samples from patients with and without cancer. This hypothesis was tested through the development of a machine learning approach called Alu Profile Learning Using Sequencing (A-PLUS) and its application to 7615 samples from 5178 individuals, 2073 with solid cancer and the remainder without cancer. Samples from patients with cancer and controls were prespecified into four cohorts used for model training, analyte integration, and threshold determination, validation, and reproducibility. A-PLUS alone provided a sensitivity of 40.5% across 11 different cancer types in the validation cohort, at a specificity of 98.5%. Combining A-PLUS with aneuploidy and eight common protein biomarkers detected 51% of the cancers at 98.9% specificity. We found that part of the power of A-PLUS could be ascribed to a single feature-the global reduction of AluS subfamily elements in the circulating DNA of patients with solid cancer. We confirmed this reduction through the analysis of another independent dataset obtained with a different approach (whole-genome sequencing). The evaluation of Alu elements may therefore have the potential to enhance the performance of several methods designed for the earlier detection of cancer.