Use of vasopressor for dialysis-related hypotension is a risk factor for death in hemodialysis patients: Nationwide cohort study

U-shaped relationship found between fibrinogen-to-albumin ratio and systemic inflammation response index in osteoporotic fracture patients

The relationship between the Systemic Inflammatory Response Index (SIRI) and the Fibrinogen-to-albumin ratio (FAR) has not been extensively investigated. The objective of this study was to determine the independent relationship between FAR and SIRI in people with osteoporotic fractures (OPF). A cross-sectional study was conducted using retrospective data from 3431 hospitalized OPF patients. The exposure variable in this study was the baseline FAR, while the outcome variable was the SIRI. Covariates, including age, gender, BMI, and other clinical and laboratory factors, were adjusted. Cross-correlation analysis and linear regression models were applied. The generalized additive model (GAM) investigated non-linear relationships. Adjusted analysis revealed an independent negative association between FAR and SIRI in OPF patients (β = - 0.114, p = 0.00064, 95% CI - 0.180, - 0.049). A substantial U-shaped association between FAR and SIRI was shown using GAM analysis (p < 0.001). FAR and SIRI indicated a negative association for FAR below 6.344% and a positive correlation for FAR over 6.344%. The results of our study revealed a U-shaped relationship between SIRI and FAR. The lowest conceivable FAR for a bone-loose inflammatory disease might be 6.344%, suggesting that this has particular significance for the medical diagnosis and therapy of persons with OPF. Consequently, the term "inflammatory trough" is proposed. These results offer fresh perspectives on controlling inflammation in individuals with OPF and preventing inflammatory osteoporosis.

Continuous renal replacement therapy for lithium toxicity: A worthy treatment to avoid intradialytic hypotension and vasopressors

While intermittent hemodialysis (HD) is the most efficient method of removing lithium in patients with lithium toxicity, continuous renal replacement therapy is an acceptable alternative in the setting of intradialytic hypotension. This form of dialysis can reduce the need for vasopressors during HD, which increases mortality.

Tumor metabolic volume by 18F-FDG-PET as a prognostic predictor of first-line pembrolizumab for NSCLC patients with PD-L1 ≥ 50

There is a lack of markers for predicting favorable outcomes after pembrolizumab therapy in patients with non-small cell lung cancer (NSCLC) with programmed death ligand-1 (PD-L1) expression ≥ 50%. This retrospective study examined the prognostic significance of 2-deoxy-2-[18F] fluoro-d-glucose (¹⁸F-FDG) uptake as a predictive marker of first-line pembrolizumab. Forty-eight patients with previously untreated NSCLC and PD-L1 expression levels ≥ 50% who underwent ¹⁸F-FDG-positron emission tomography (PET) just before administration of pembrolizumab monotherapy were eligible and underwent assessment of metabolic tumor volume (MTV), total lesion glycolysis (TLG), and maximum of standardized uptake value (SUV_max) on ¹⁸F-FDG uptake. The objective response rate, median progression-free survival, and median overall survival were 51.1%, 7.1 months, and 18.6 months, respectively. In univariate survival analyses, high MTV was barely a significant prognostic predictor and was confirmed as an independent factor linked to worse outcomes in multivariate analysis, predominantly in patients with a histological diagnosis of adenocarcinoma. A high MTV was significantly associated with distant metastases (especially bone metastasis), C-reactive protein (CRP) level, and PD-L1 expression ≥ 75%. Metabolic tumor activity assessed as MTV from ¹⁸F-FDG uptake predicted the prognosis after first-line pembrolizumab treatment in patients with NSCLC and PD-L1 expression ≥ 50%, especially for adenocarcinoma.

Application of explainable ensemble artificial intelligence model to categorization of hemodialysis-patient and treatment using nationwide-real-world data in Japan

BACKGROUND

Although dialysis patients are at a high risk of death, it is difficult for medical practitioners to simultaneously evaluate many inter-related risk factors. In this study, we evaluated the characteristics of hemodialysis patients using machine learning model, and its usefulness for screening hemodialysis patients at a high risk of one-year death using the nation-wide database of the Japanese Society for Dialysis Therapy.

MATERIALS AND METHODS

The patients were separated into two datasets (n = 39,930, 39,930, respectively). We categorized hemodialysis patients in Japan into new clusters generated by the K-means clustering method using the development dataset. The association between a cluster and the risk of death was evaluated using multivariate Cox proportional hazards models. Then, we developed an ensemble model composed of the clusters and support vector machine models in the model development phase, and compared the accuracy of the prediction of mortality between the machine learning models in the model validation phase.

RESULTS

Average age of the subjects was 65.7±12.2 years; 32.7% had diabetes mellitus. The five clusters clearly distinguished the groups on the basis of their characteristics: Cluster 1, young male, and chronic glomerulonephritis; Cluster 2, female, and chronic glomerulonephritis; Cluster 3, diabetes mellitus; Cluster 4, elderly and nephrosclerosis; Cluster 5, elderly and protein energy wasting. These clusters were associated with the risk of death; Cluster 5 compared with Cluster 1, hazard ratio 8.86 (95% CI 7.68, 10.21). The accuracy of the ensemble model for the prediction of 1-year death was 0.948 and higher than those of logistic regression model (0.938), support vector machine model (0.937), and deep learning model (0.936).

CONCLUSIONS

The clusters clearly categorized patient on their characteristics, and reflected their prognosis. Our real-world-data-based machine learning system is applicable to identifying high-risk hemodialysis patients in clinical settings, and has a strong potential to guide treatments and improve their prognosis.