Trial reportClinical and experimental nephrology2022
Development and validation of a predictive model for acute kidney injury in patients with moderately severe and severe acute pancreatitis.
Trial report in Clinical and experimental nephrology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review of machine learning-based prognostic models for acute pancreatitis: Towards improving methods and reporting quality.PLoS medicine · 2025Pooled it
- Predictive models for acute kidney injury in acute pancreatitis: a systematic review and meta-analysis.Frontiers in medicine · 2025Pooled it
- Predicting the Initiation of Continuous Venovenous Hemofiltration in Acute Pancreatitis Patients: The Role of Plasma and Urinary Neutrophil Gelatinase-Associated Lipocalin.Journal of clinical medicine · 2026Article
- Acute kidney injury is a major mediator of intra-abdominal pressure-related mortality in critically ill patients: a longitudinal analysis.Journal of anesthesia, analgesia and critical care · 2026Article
- Relationship between intra-bladder pressure and acute kidney injury in patients with acute pancreatitis: interpretable machine learning approach.BMC nephrology · 2025Article
- AI and Machine Learning for Precision Medicine in Acute Pancreatitis: A Narrative Review.Medicina (Kaunas, Lithuania) · 2025Review
- Establishment and validation of a prediction model for acute kidney injury in moderate severe and severe acute pancreatitis patients.European journal of medical research · 2025Article
- Article
- Risk factors for acute kidney injury in patients with acute pancreatitis and construction of nomogram model: a single-center study and external validation.Frontiers in medicine · 2025Article
- Predictive model of acute kidney injury in critically ill patients with acute pancreatitis: a machine learning approach using the MIMIC-IV database.Renal failure · 2024Article
- Association between hematocrit-to-albumin ratio and acute kidney injury in patients with acute pancreatitis: a retrospective cohort study.Scientific reports · 2024Article
- Machine learning models for mortality prediction in critically ill patients with acute pancreatitis-associated acute kidney injury.Clinical kidney journal · 2024Article
- Highly Efficient Photoelectrochemical Detection of Cystatin C Based on a Core-Shell MOF Nanocomposite with Biomimetic-Catalysis Amplification.ACS omega · 2024Article
- Different timing for abdominal paracentesis catheter placement and drainage in severe acute pancreatitis complicated by intra-abdominal fluid accumulation.World journal of gastrointestinal surgery · 2024Article
- Automated machine learning for early prediction of acute kidney injury in acute pancreatitis.BMC medical informatics and decision making · 2024Article
- Predictive Value of C-Reactive Protein/Albumin Ratio for Acute Kidney Injury in Patients with Acute Pancreatitis.Journal of inflammation research · 2024Article
- Advances in the clinical application of machine learning in acute pancreatitis: a review.Frontiers in medicine · 2024Review
- Article
- Development and validation of a prediction model for the early occurrence of acute kidney injury in patients with acute pancreatitis.Renal failure · 2023Article
- Prediction of acute pancreatitis complications using routine blood parameters during early admission.Immunity, inflammation and disease · 2022Article
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
Abstract
backgroundAcute kidney injury is a serious complication of moderately severe and severe acute pancreatitis, which significantly increases mortality. There are currently no reliable tools for early identification of AKI especially severe AKI in these patients. We aim to develop a predictive model so that physicians can assess the risk of AKI and severe AKI, thus take further preventive measures.
methodsPatients with a diagnosis of MSAP and SAP admitted to our hospital from January 2018 to December 2021 were retrospectively included in the study. The participants were divided into the training and validation cohorts randomly, in a 2:1 ratio. A clinical signature was built based on reproducible features, using the least absolute shrinkage and selection operator method and machine learning. Multivariate logistic regression analysis was used to develop the prediction model. Nomogram performance was determined by its discrimination, calibration, and clinical usefulness.
resultsA total of 996 eligible patients were enrolled. 698 patients were allocated in the training cohort and 298 in the validation cohort. AKI occurred in 148 patients (21%) in the training cohort and 54 (18%) in the validation cohort, respectively. The clinical features, including C-reactive protein, intra-abdominal pressure and serum cysC, were significantly associated with AKI as well as severe AKI. The nomogram showed favorable discrimination, calibration and clinical usefulness.
conclusionsThe novel risk score model has good performance for predicting AKI and severe AKI in MSAP and SAP patients. Application of this model can help clinicians stratify patients for primary prevention, surveillance and early therapeutic intervention to improve care and prognosis.
Indexed as
Identifiers
35430680What 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.