Objective To compare the clinical effect of continuous renal replacement therapy (CRRT) and intermittent hemodialysis (IHD) in the treatment of severe acute renal failure (ARF). Methods A hundred patients with severe ARF treated between May 2011 and December 2014 were chosen to be the study subjects. According to the order of admission, they were divided into control group and observation group with 50 patients in each. Patients of the control group underwent IHD, while those in the observation group underwent CRRT. Serum creatinine (Scr), blood urea nitrogen (BUN), endogenous creatinine clearance rate (Ccr), treatment effective rate and survival rate were compared between the two groups before and after the treatment. Results Scr, BUN and Ccr were all improved after treatment in both the two groups. However, Scr, BUN and Ccr in the observation group [(225.1±162.7) μmol/L, (14.2±9.3) mmol/L, (23.4±10.5) mL/min] were significantly better than those in the control group [(588.4±183.6) μmol/L, (29.1±10.4) mmol/L, (15.9±8.2) mL/min]. The treatment effective rate and patients’ survival rate in the observation group were respectively 60% and 70%, both significantly higher than those in the control group (40% and 52%) All the differences were significant (P<0.05). Conclusion CRRT is superior in the treatment of severe ARF with a higher survival rate of the patients, which is worthy of clinical promotion.
Objective To identify and analyze risk factors for acute renal failure (ARF) following lung transplantation and to develop a predictive model. Methods Data for this study were obtained from the United Network for Organ Sharing (UNOS) database, encompassing patients who underwent unilateral or bilateral lung transplantation between 2015 and 2022. We analyzed both preoperative and postoperative clinical characteristics of the patients. A combined approach utilizing random forest and least absolute shrinkage and selection operator (LASSO) regression was employed to identify key factors associated with the incidence of ARF post-transplantation, based on which a nomogram model was developed. The predictive performance of the constructed model was evaluated in both training and validation sets, using receiver operating characteristic (ROC) curves and area under the curve (AUC) metrics to verify and compare model effectiveness. ResultsA total of 15 110 lung transplantation patients were included in the study, consisting of6 041 males and 9 069 females, with a median age of 62.00 years (interquartile range: 54.00 to 67.00). The analysis revealed statistically significant differences between postoperative renal dialysis and non-dialysis patients regarding preoperative lung diagnosis, estimated glomerular filtration rate (eGFR), mechanical ventilation, preoperative ICU treatment, extracorporeal membrane oxygenation (ECMO) support, infections occurring within two weeks prior to transplantation, Karnofsky Performance Status (KPS) score, waitlist duration, double-lung transplantation, and ischemia time (P<0.05). Five key variables associated with ARF after lung transplantation were identified through random forest and LASSO regression: recipients’ eGFR, preoperative ICU treatment, ECMO support, bilateral lung transplantation, and ischemia time. A nomogram model was subsequently established. Model evaluation demonstrated that the constructed predictive model achieved high accuracy in both training and validation sets, with favorable AUC values, confirming its validity and reliability. ConclusionThis study identifies common risk factors for ARF following lung transplantation and introduces an effective predictive model with potential clinical applications.