ABSTRACT
BACKGROUND: Optimal timing for initiating maintenance dialysis in patients with chronic kidney disease (CKD) stages 3-5 is challenging. This study aimed to develop and validate a machine learning (ML) model for early personalised prediction of maintenance dialysis initiation within 1-year and 3-year timeframes among patients with CKD stages 3-5. METHODS: Retrospective electronic health record data from the Taipei Medical University clinical research database were used. Newly diagnosed patients with CKD stages 3-5 between 2008 and 2017 were identified. The observation period spanned from the diagnosis of CKD stages 3-5 until the maintenance dialysis initiation or a maximum follow-up of 3 years. Predictive models were developed using patient demographics, comorbidities, laboratory data and medications. The dataset was divided into training and testing sets to ensure robust model performance. Model evaluation metrics, including area under the curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value and F1 score, were employed. RESULTS: A total of 6123 and 5279 patients were included for 1 year and 3 years of the model development. The artificial neural network demonstrated better performance in predicting maintenance dialysis initiation within 1 year and 3 years, with AUC values of 0.96 and 0.92, respectively. Important features such as baseline estimated glomerular filtration rate and albuminuria significantly contributed to the predictive model. CONCLUSION: This study demonstrates the efficacy of an ML approach in developing a highly predictive model for estimating the timing of maintenance dialysis initiation in patients with CKD stages 3-5. These findings have important implications for personalised treatment strategies, enabling improved clinical decision-making and potentially enhancing patient outcomes.
Subject(s)
Machine Learning , Renal Dialysis , Renal Insufficiency, Chronic , Humans , Female , Male , Retrospective Studies , Renal Insufficiency, Chronic/therapy , Middle Aged , Aged , Electronic Health Records , Taiwan , Precision MedicineABSTRACT
Objective: To evaluate the value of contrast volume/glomerular filtration ratio (Vc/eGFR ratio) and urine Neutrophil Gelatinase-Associated Lipocalin (uNGAL) in predicting the progression contract associated-acute kidney injury (CA-AKI) to chronic kidney disease (CKD) in planned percutaneous coronary intervention (PCI) patients. Patients and Methods: We examined 387 adult patients who had undergone planned percutaneous coronary intervention (PCI). We determined acute kidney injury (AKI) and chronic kidney disease (CKD) using the criteria set by the Kidney Disease: Improving Global Outcomes (KDIGO). We calculated the estimated glomerular filtration rate (eGFR) using the CKD-EPI formula based on serum creatinine levels. To determine the Vc/eGFR ratio, we considered the contrast medium volume and eGFR for each patient. Additionally, we measured urine NGAL levels using the ELISA method. Results: The percentage of CA-AKI patients who developed CKD after planned PCI was 36.36%. Within the CA-AKI to CKD group, the Vc/eGFR ratio was 2.82, and uNGAL levels were significantly higher at 72.74 ng/mL compared to 1.93 ng/mL for Vc/eGFR ratio and 46.57 ng/mL for uNGAL in the recovery CA-AKI group. This difference was statistically significant (p<0.001). Diabetic mellitus, urine NGAL concentration, and Vc/eGFR ratio were found to be independent factors in the progression of CA-AKI to CKD. The Vc/eGFR ratio and uNGAL showed predictive capabilities for progressing CA-AKI to CKD with an AUC of 0.884 and 0.878, respectively. The sensitivity was 81.3% for both, while the specificity was 89.3% for Vc/eGFR ratio and 85.7% for uNGAL. Conclusion: The Vc/eGFR ratio and uNGAL were good predictors for CA-AKI to CKD in planned PCI patients.
ABSTRACT
BACKGROUND: Lupus nephritis is a common complication of systemic lupus erythematosus (SLE, OMIM #15200) in the Asian population and a main contributor to mortality and morbidity. In this study, we evaluate the variants on three genes STAT4, CDKN1A, and IRF5 and their association with lupus nephritis. METHOD: One hundred fifty-two SLE patients with confirmed lupus nephritis (through biopsy) and 76 healthy controls were recruited. Genotyping of SNPs on three gene STAT4, CDKN1A, and IRF5, phenotypic, and laboratory assessment were performed; renal biopsy and classification were carried out for the patient group. RESULTS: Carriers of rs7582694 C alleles on STAT4 have higher risk of lupus nephritis (OR 2.0; 95% CI [1.14, 3.19]; p = 0.015), at higher risk of hematuria and higher serum level of dsDNA antibodies compared to controls (p < 0.05) and were more likely to have nephrotic histopathology grading of class III or higher. No association was observed for CDKN1A; and no variation was observed for the IRF5 gene in both the study and control group. CONCLUSION: This study investigates the relationship between STAT4, CDKN1A, and IRF5 gene and SLE in a Vietnamese patient population. Patients with the C allele (STAT4) in rs7582694 were associated with a more severe disease phenotype.
