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INTRODUCTION: Cognitive decline is prevalent in maintenance hemodialysis patients. The blood-brain barrier has been implicated in cognitive decline. In this prospective cohort study, we investigated the associations between endothelium-related biomarkers and steeper cognitive decline in this population. METHODS: Cognitive function was assessed using the Portuguese-adapted Cambridge Cognitive Examination (CAMCOG) with items of the Mini-Mental State Examination (MMSE). Endothelium-related biomarkers included syndecan-1, ICAM-1, VCAM-1 and angiopoietin-2 (AGPT2). Patients were followed up for 4 years, and cognitive assessments were repeated. Multinomial regression analyses were performed to evaluate associations between biomarkers and cognitive decline. RESULTS: A total of 216 patients completed the test battery at baseline. After 4 years, 102 patients had follow-up data. There was a significant decrease in cognitive function according to the CAMCOG and MMSE scores: a change of -0.39 (95% CI -0.27 to -0.51) and -0.51 (95% CI -0.27 to -0.76) standard deviation (SD) of the baseline scores. Additionally, executive function but not memory significantly decreased. Syndecan-1 level was independently associated with steeper cognitive decline; each increase in the SD of the syndecan-1 level was associated with a decrease in the CAMCOG of 0.20 (95% CI 0.07-0.33) SD from baseline. Syndecan-1 was associated with a steeper decline in MMSE score (ß 0.54, 95% CI 0.28-0.81) and executive function (ß 0.17, 95% CI 0.02-0.32). Syndecan-1 predicted severe cognitive impairment with an area under the curve for receiver operating characteristic curves of 0.75 (95% CI 0.64-0.83). CONCLUSION: Our findings highlight the potential of syndecan-1, a biomarker of endothelium glycocalyx derangement, as a predictor of steeper cognitive decline in prevalent hemodialysis patients.
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INTRODUCTION: The current definition of acute kidney injury (AKI) includes increased serum creatinine (sCr) concentration and decreased urinary output (UO). Recent studies suggest that the standard UO threshold of 0.5 ml/kg/h may be suboptimal. This study aimed to develop and validate a novel UO-based AKI classification system that improves mortality prediction and patient stratification. METHODS: Data were obtained from the MIMIC-IV and eICU databases. The development process included (1) evaluating UO as a continuous variable over 3-, 6-, 12-, and 24-h periods; (2) identifying 3 optimal UO cutoff points for each time window (stages 1, 2, and 3); (3) comparing sensitivity and specificity to develop a unified staging system; (4) assessing average versus persistent reduced UO hourly; (5) comparing the new UO-AKI system to the KDIGO UO-AKI system; (6) integrating sCr criteria with both systems and comparing them; and (7) validating the new classification with an independent cohort. In all these steps, the outcome was hospital mortality. Another analyzed outcome was 90-day mortality. The analyses included ROC curve analysis, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and logistic and Cox regression analyses. RESULTS: From the MIMIC-IV database, 35,845 patients were included in the development cohort. After comparing the sensitivity and specificity of 12 different lowest UO thresholds across four time frames, 3 cutoff points were selected to compose the proposed UO-AKI classification: stage 1 (0.2-0.3 mL/kg/h), stage 2 (0.1-0.2 mL/kg/h), and stage 3 (< 0.1 mL/kg/h) over 6 h. The proposed classification had better discrimination when the average was used than when the persistent method was used. The adjusted odds ratio demonstrated a significant stepwise increase in hospital mortality with advancing UO-AKI stage. The proposed classification combined or not with the sCr criterion outperformed the KDIGO criteria in terms of predictive accuracy-AUC-ROC 0.75 (0.74-0.76) vs. 0.69 (0.68-0.70); NRI: 25.4% (95% CI: 23.3-27.6); and IDI: 4.0% (95% CI: 3.6-4.5). External validation with the eICU database confirmed the superior performance of the new classification system. CONCLUSION: The proposed UO-AKI classification enhances mortality prediction and patient stratification in critically ill patients, offering a more accurate and practical approach than the current KDIGO criteria.