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• W3037366924 abstract "Serum albumin is a major determinant of hospitalization in patients with end-stage renal disease (ESRD). Previous reports generally use the Poisson model to evaluate the relationships between outcome and response variables. However, hospitalization data are often overdispersed, and few studies using appropriate methods exist in the literature.This retrospective cohort study included 426 patients with ESRD receiving hemodialysis treatment between 2014 and 2018. Using a negative binomial regression model with hierarchical multivariable adjustments, we investigated the relationship between serum albumin, hospital admissions, and total hospitalization days. Mean age and mean baseline serum albumin levels were 64.7 ± 11 years and 3.5 ± 0.5 g/dL, respectively. At least one hospitalization was identified in 402 (94%) patients. The incidence rate was 1.48 (95% CI, 1.41-1.56) admissions per patient-year during the follow-up period of 5 years. A negative linear association was observed between serum albumin and hospitalization frequency. Hospitalization rates (95% CI) were 1.81 (1.65-1.98), 1.44 (1.3-1.59), 1.36 (1.22-1.51), and 1.33 (1.2-1.48) per patient-year in serum albumin levels ≤3, 3.1 to ≤3.3, 3.4 to ≤3.7, and ≥3.8 g/dL, respectively. Case mix-adjusted incidence rate ratio was 0.82 (95% CI, 0.70-0.94), while it was robust to further adjustments for malnutrition and inflammation markers. Similar results were observed in hospitalization days and time to the first hospitalization. These findings, which result from the negative binomial model using overdispersed data, suggest that lower serum albumin is related to increased hospitalization rates and hospital days in incident hemodialysis patients." @default.
• W3037366924 created "2020-07-02" @default.
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• W3037366924 date "2020-08-20" @default.
• W3037366924 modified "2023-09-25" @default.
• W3037366924 title "Lower serum albumin level is associated with increased risk of hospital admission and length of stay in hospital among incident hemodialysis patients by using overdispersed model" @default.
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• W3037366924 doi "https://doi.org/10.1111/1744-9987.13552" @default.
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