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Background: While some studies suggest a link between blood urea nitrogen (BUN) levels and adverse outcomes in hemorrhagic stroke (HS) patients, the prognostic value of longitudinal BUN changes remains unclear.
Objective: To evaluate the association between longitudinal BUN trajectories and 30-day mortality risk in HS patients.
Methods: We analyzed HS patients from the MIMIC-IV database diagnosed within 24 hours of hospitalization. Group-based trajectory modeling (GBTM) was used to identify BUN trajectories. Kaplan-Meier survival curves and Cox proportional hazards models were employed to assess mortality risk, while ROC curves evaluated BUN's predictive accuracy.
Results: Among 1,172 HS patients, three distinct BUN trajectories were identified. Patients with rising BUN trends (Class 2 and 3) had significantly higher 30-day mortality risks compared to those with stable BUN levels (Class 1) (HR > 1, < 0.001), with Class 3 patients exhibiting the worst outcomes. ROC analysis demonstrated strong predictive accuracy for mortality, with AUC values of 0.866, 0.841, and 0.841 at 7, 14, and 30 days, respectively, after adjusting for confounders.
Conclusion: Persistently elevated BUN trajectories are independently associated with increased 30-day mortality in HS patients. This study highlights the heterogeneity of BUN trajectories in HS, providing insights beyond baseline BUN measurements and enhancing understanding of HS progression.
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http://dx.doi.org/10.1080/01616412.2025.2551091 | DOI Listing |
Neurol Res
September 2025
Department of Neurosurgery, Longyan First Affiliated Hospital of Fujian Medical University, Fujian, China.
Background: While some studies suggest a link between blood urea nitrogen (BUN) levels and adverse outcomes in hemorrhagic stroke (HS) patients, the prognostic value of longitudinal BUN changes remains unclear.
Objective: To evaluate the association between longitudinal BUN trajectories and 30-day mortality risk in HS patients.
Methods: We analyzed HS patients from the MIMIC-IV database diagnosed within 24 hours of hospitalization.
Clin Chim Acta
August 2025
Department of Clinical Microbiology Laboratory, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang, China.
Objective: To investigate the predictive value of reduced platelet count for progression to severe sepsis and/or septic shock in patients with Klebsiella pneumoniae bloodstream infection.
Methods: The clinical data of 205 patients with K. pneumoniae bloodstream infection admitted to Taizhou Hospital, Zhejiang Province, from January 2023 to December 2024 were retrospectively analyzed.
PLoS One
July 2025
Department of Nephrology, Faculty of Medicine, Erciyes University, Kayseri, Turkey.
This study investigates mortality risk prediction in peritoneal dialysis (PD) patients through longitudinal biomarker analysis, comparing traditional and advanced statistical approaches. A retrospective cohort of 417 PD patients followed up between 1995 and 2016 at Erciyes University was analyzed, with serum albumin, creatinine, calcium, blood urea nitrogen (BUN), and phosphorus assessed as predictors of all-cause mortality. Statistical methods included Cox proportional hazards models, time-dependent covariates, and joint modeling (univariate and multivariate) for longitudinal-survival data integration.
View Article and Find Full Text PDFInt J Surg
July 2025
Department of Colorectal Cancer Surgery, the Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
Background: The efficacy and safety of the Kangduo robotic system in colorectal cancer (CRC) surgery are well established. However, its short-term impact on surgical stress response has not been evaluated.
Materials And Methods: This study conducted a post-hoc analysis of a previous non-inferiority randomized controlled trial.
Ren Fail
December 2025
Department of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Background: Little is known about acute kidney injury (AKI) and acute kidney disease (AKD) in patients with chronic obstructive pulmonary disease (COPD) and COPD mortality based on the acute/subacute renal injury. This study develops machine learning models to predict AKI, AKD, and mortality in COPD patients, utilizing web applications for clinical decisions.
Methods: We included 2,829 inpatients from January 2016 to December 2018.