CKRT coagulation risk prediction and nursing feedback model based on intelligent algorithms.

OBJECTIVE: To construct an intelligent Continuous Kidney Replacement Therapy nursing feedback model to predict extracorporeal coagulation risk and support timely clinical decisions. METHODS: Analyzed 1,354 treatment records from 143 patients to identify new data features relevant to Continuous Kidney Replacement Therapy nursing. The data was preprocessed, and new variables were derived to serve as model inputs. A hybrid machine learning algorithm was applied to predict the timing of Continuous Kidney Replacement Therapy initiation for patients with acute kidney injury. Extensive numerical experiments were conducted to optimize model parameters and evaluate performance, ensuring high accuracy, stability, and superior AUC values for reliable predictions. RESULTS: Univariate analysis identified eight factors significantly affecting coagulation risk, including treatment mode, anticoagulation method, blood pump stoppage, and insufficient blood flow (P < 0.001). Logistic regression analysis indicated that treatment mode and anticoagulation method were key factors influencing extracorporeal coagulation during Continuous Kidney Replacement Therapy, with the highest regression coefficient observed for heparin-free anticoagulation (β = 2.209). The prediction model achieved an AUC of 0.87 (P < 0.001) and an accuracy rate of 99.21%, significantly outperforming the performance of other models. CONCLUSION: The intelligent Continuous Kidney Replacement Therapy nursing feedback model improves prediction accuracy while reducing redundant information. This model helps mitigate the risk of missing urgent conditions in patients under limited healthcare resources and lowers the frequency of extracorporeal coagulation events during Continuous Kidney Replacement Therapy. CLINICAL TRIAL NUMBER: Not applicable.