ArticleBMC nephrology2025
CKRT coagulation risk prediction and nursing feedback model based on intelligent algorithms.
Article in BMC nephrology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo construct an intelligent Continuous Kidney Replacement Therapy nursing feedback model to predict extracorporeal coagulation risk and support timely clinical decisions.
methodsAnalyzed 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.
resultsUnivariate 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.
conclusionThe 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.