ArticleJournal of clinical and experimental hepatology
Early Prediction of Acute Kidney Injury Following Liver Transplantation: Development and Validation of a Clinical Risk Model.
Article in Journal of clinical and experimental hepatology. 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
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The first 48 h following liver transplantation (LT) represent a critical therapeutic window. Early identification of patients who are at high risk of developing acute kidney injury (AKI) can optimize treatment strategies and improve patient outcomes. This study aimed to develop and validate a clinical risk prediction model for AKI within 48 h following LT by utilizing preoperative and intraoperative parameters. Methods: A total of 453 adult LT recipients treated at the Beijing Tsinghua Changgung Hospital between January 2018 and October 2022 were enrolled. Patients were randomly assigned to a development cohort and a validation cohort at a 6:4 ratio. AKI was diagnosed using the 2012 Kidney Disease Improving Global Outcomes (KDIGO) criteria. Univariate and multivariate logistic regression analyses identified clinical factors associated with early AKI. A predictive model was constructed and internally validated. Additionally, stages 2 and 3 AKI, as defined by the KDIGO criteria, were classified as severe AKI. Independent risk factors for severe AKI within 48 h following LT were similarly identified using logistic regression analyses. Results: At 48 h following LT, 125 (46%) patients developed AKI. Univariate analysis identified 17 potential predictive factors for AKI, including preoperative hepatic encephalopathy (HE), a history of alcohol-associated cirrhosis, body mass index ≥28 kg/m Conclusion: The proposed predictive model could promote the identification of high-risk LT recipients immediately following surgery, enabling clinicians to intervene early to mitigate the risk of developing AKI within 48 h postoperatively. This approach has the potential to improve patient prognosis by supporting timely and targeted management strategies.
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.