Association of cognitive impairment with the interaction between chronic kidney disease and depression: findings from NHANES 2011-2014.

BACKGROUND: Cognitive impairment (CoI), chronic kidney disease (CKD), and depression are prevalent among older adults and are interrelated, imposing a significant disease burden. This study evaluates the association of CKD and depression with CoI and explores their potential interactions. METHOD: Data for this study were sourced from the 2011-2014 National Health and Nutritional Examination Survey (NHANES). Multiple binary logistic regression models assessed the relationship between CKD, depression, and CoI while controlling for confounders. The interactions were measured using the relative excess risk of interaction (RERI), the attributable proportion of interaction (AP), and the synergy index (S). RESULTS: A total of 2,666 participants (weighted n = 49,251,515) were included in the study, of which 700 (16.00%) had CoI. After adjusting for confounding factors, the risk of CoI was higher in patients with CKD compared to non-CKD participants (odds ratio [OR] = 1.49, 95% confidence interval [CI]:1.12-1.99). The risk of CoI was significantly increased in patients with depression compared to those without (OR = 2.29, 95% CI: 1.73-3.03). Furthermore, there was a significant additive interaction between CKD and depression in terms of the increased risk of CoI (adjusted RERI = 2.01, [95% CI: 0.31-3.71], adjusted AP = 0.50 [95% CI: 0.25-0.75], adjusted S = 2.97 [95% CI: 1.27-6.92]). CONCLUSION: CKD and depression synergistically affect CoI, particularly when moderate-to-severe depression co-occurs with CKD. Clinicians should be mindful of the combined impact on patients with CoI. Further research is needed to elucidate the underlying mechanisms and assess the effects specific to different CKD stages.

A nomogram to predict cognitive function impairment in patients with chronic kidney disease: A national cross-sectional survey.

Background: Cognitive function impairment (CFI) is common in patients with chronic kidney disease (CKD) and significantly impacts treatment adherence and quality of life. This study aims to create a simplified nomogram for early CFI risk detection. Methods: Data were obtained from the National Health and Nutrition Examination Survey cycles spanning from 1999 to 2002 and again from 2011 to 2014. Stepwise logistic regression was used to select variables and construct a CFI risk prediction model. Furthermore, C-statistic and Brier Score (BS) assessed model performance. Additionally, Kaplan-Meier survival curves were utilised to assess risk group-death prognosis relationships. Results: Of the 545 participants in the CKD model development cohort, a total of 146 (26.8 %) had CFI. The final model included the variables of age, race, education, annual family income, body mass index, estimated glomerular filtration rate, serum albumin and uric acid. The model had a C-statistic of 0.808 (95 % confidence interval (CI): 0.769-0.847) and a BS of 0.149. Furthermore, the 5-fold cross-validation internal C-statistic was 0.764 (interquartile range: 0.763-0.807) and BS was 0.154. Upon external validation, the model's C-statistic decreased to 0.752 (95 % CI: 0.654-0.850) and its BS increased to 0.182. The Kaplan-Meier survival curves demonstrated that intermediate-to-high-risk participants had shorter overall survival time than low-risk participants (log-rank test: p = 0.00042). Conclusions: This study established an effective nomogram for predicting CFI in patients with CKD, which can be used for the early detection of CFI and guide the treatment of patients with CKD.

Early identification of bloodstream infection in hemodialysis patients by machine learning.

Background: Bloodstream infection (BSI) is a prevalent cause of admission in hemodialysis (HD) patients and is associated with increased morbidity and mortality. This study aimed to establish a diagnostic, predictive model for the early identification of BSI in HD patients. Methods: HD patients who underwent blood culture testing between August 2018 and March 2022 were enrolled in this study. Machine learning algorithms, including stepwise logistic regression (SLR), Lasso logistic regression (LLR), support vector machine (SVM), decision tree, random forest (RF), and gradient boosting machine (XGboost), were used to predict the risk of developing BSI from the patient's clinical data. The accuracy (ACC) and area under the subject working curve (AUC) were used to evaluate the performance of such models. The Shapley Additive Explanation (SHAP) values were used to explain each feature's predictive value on the models' output. Finally, a simplified nomogram for predicting BSI was devised. Results: A total of 391 HD patients were enrolled in this study, of whom 74 (18.9%) were diagnosed with BSI. The XGboost model achieved the highest AUC (0.914, 95% confidence interval [CI]: 0.861-0.964) and ACC (86.3%) for BSI prediction. The four most significant co-variables in both the significance matrix plot of the XGboost model variables and the SHAP summary plot were body temperature, dialysis access via a non-arteriovenous fistula (non-AVF), the procalcitonin levels (PCT), and neutrophil-lymphocyte ratio (NLR). Conclusions: This study created an effective machine-learning model for predicting BSI in HD patients. The model could be used to detect BSI at an early stage and hence guide antibiotic treatment in HD patients.

The rare complication caused by thrombus shedding during arteriovenous fistula thrombolysis: a case report.

BACKGROUND: Arteriovenous fistula is the lifeline of maintenance for patients requiring hemodialysis, with thrombosis being a common complication of this procedure. Traditionally, thrombi have been removed via thrombectomy. In recent years, it has been reported that newly occurring thrombi can be treated by urokinase thrombolysis. However, the thrombus shedding in the process should be valued, which may cause the distal limb ischemia syndrome. The complication of thrombolysis is rare but serious. Patients will experience pain and numbness, and possibly even extremity necrosis may occur without diagnosed or treated timely. There are not any reports about the occurrence or treatment of distal limb ischemia syndrome caused by thrombus shedding during thrombolysis. CASE DESCRIPTION: Considering the thrombosis volume and texture of this case, we attempted to use urokinase thrombolysis to resolve the thrombus in fistula. During thrombolysis, thrombus shedding occurred in the distal limb. The patient's fingers of the limbs on the side of the internal fistula were pale, numb, and painful in the case. Fortunately, we solved the problem ultimately by continuously pumping urokinase. The heparin, urokinase, infusion pumps, ultrasound, and infrared therapy devices were obtained from the Affiliated Hospital of Chuanbei Medical College. Their usage and dosage are described in the relevant literature and China's 2020 blood purification standard operating procedures. CONCLUSIONS: In the process of thrombolysis of arteriovenous fistula, attention should be paid to thrombus shedding. Distal limb ischemia syndrome is a rare but serious complication of thrombus shedding. Continued pumping of urokinase may be effective for this complication.