BMC Nephrology最新文献

Background: Commercial high-sensitivity cardiac troponin T (hs-cTnT) assays measure both intact and degraded cTnT forms (i.e. total cTnT) and values are often elevated in chronic kidney disease (CKD) patients. The measurement of long cTnT forms has recently shown improved specificity for acute myocardial infarction compared to total cTnT. However, the associations between long cTnT and adverse long-term outcomes in CKD are unknown.

Methods: Altogether, 136 CKD stage 4-5 patients not on dialysis were included in this prospective cohort study. Long cTnT and total cTnT levels before dialysis initiation were measured using investigational in-house immunoassays. The associations between cTnT measurements and all-cause mortality, incident major adverse cardiovascular or cerebrovascular events (MACCE), new-onset atrial fibrillation (NOAF) and a composite adverse outcome (all-cause mortality or MACCE) were assessed.

Results: Mean age was 61 (±13) years, 47 (34.6%) were female and median values for long cTnT and total cTnT were 1.9 (1.3-3.0) ng/L and 37 (23-66) ng/L, respectively. After a median follow-up of 6.2 (4.6-7.7) years, 62 (45.6%) patients had died, 36 (26.5%) had experienced MACCE, 28 (23.3%) NOAF and 76 (55.9%) a composite adverse outcome. In multivariable Cox models adjusted for age, sex and coronary artery disease (CAD), long cTnT and total cTnT were independently associated with all-cause mortality, NOAF and the composite adverse outcome, while only total cTnT was associated with MACCE. Replacing the adjustment for CAD with kidney transplantation in the multivariable models weakened the significance of the associations.

Conclusions: We describe for the first time associations between long cTnT and long-term cardiovascular adverse outcomes and all-cause mortality in a prospective cohort of CKD stage 4-5 patients.

Trial registration: https://www.

Clinicaltrials: gov NCT04223726. Retrospectively registered in December 10, 2019.

Elderly patients with acute kidney injury (AKI) face a significantly increased mortality risk. Recent advances in machine learning technology have made it possible to predict the risk of death in patients at an early stage, which help to enable timely clinical intervention, optimize treatment strategies, and allocate hospital resources reasonably. We conducted a retrospective analysis of elderly patients admitted to the People's Liberation Army General Hospital (PLAGH) between 2008 and 2018. This study included data on demographic characteristics, comorbidities, and laboratory test results. We employed five machine learning algorithms, including L2-regularized logistic regression (L2-logistic), Least Absolute Shrinkage and Selection Operator (LASSO), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Multi-layer Perceptron (MLP). To address the class imbalance issue , we employed oversampling techniques. Model performance was primarily evaluated using the area under the receiver operating characteristic curve (AUC), and SHapley Additive exPlanations (SHAP) values were introduced to enhance the interpretability of the prediction models. A total of 1290 AKI patients were enrolled in the study, with a 28-day mortality rate of 25.43%. Through data oversampling, the XGBoost model with random oversampling was identified as the optimal predictive model. The model achieved an AUC of 0.8659 in the validation cohort. Furthermore, external validation was performed using the eICU Collaborative Research Database (eICU-CRD), yielding an AUC of 0.6317. SHAP analysis revealed that Mechanical Ventilation, Peak serum creatinine within 7 days, Stage of AKI, urine protein and sreum albumin levels were the top five predictive factors for 28-day mortality in elderly patients with AKI. This comprehensive approach demonstrates how predictive healthcare analytics can enhance clinical decision-making and ultimately improve patient outcomes.