ArticleBMC surgery2024
A model for predicting AKI after cardiopulmonary bypass surgery in Chinese patients with normal preoperative renal function.
Article in BMC surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Development and validation of a prediction model for acute kidney injury following cardiopulmonary bypass surgery.European journal of medical research · 2025Observational
- Procalcitonin: Infection or Maybe Something More? Noninfectious Causes of Increased Serum Procalcitonin Concentration: Updated Knowledge.Life (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo develop and validate a predictive model for acute kidney injury (AKI) after cardiopulmonary bypass (CPB) surgery in Chinese patients with normal preoperative renal function.
methodFrom January 1, 2015, to September 1, 2022, a total of 1003 patients were included in the analysis as a development cohort. We used the ratio of 7:3 to divide the patients into a training group (n = 703) and a testing group (n = 300). In addition, a total of 178 patients were collected as an external validation cohort from January 1, 2023, to May 1, 2023. In the training group, independent risk factors for postoperative AKI were identified through the least absolute shrinkage and selection operator (LASSO) regression and multifactor logistic regression analysis. A nomogram predictive model was then established. The area under the curve (AUC) of receiver operating characteristic (ROC) curve, as well as calibration curve and decision curve, were used for validation of the model.
resultsAge, body mass index (BMI), emergent surgery, CPB time, intraoperative use of adrenaline, and postoperative procalcitonin (PCT) were identified as important risk factors for AKI after CPB surgery (P < 0.05). The nomogram predictive model demonstrated good discrimination (AUC: 0.772 (95%CI: 0.735 - 0.809), 0.780 (95% CI: 0.724 - 0.835), and 0.798 (95% CI: 0.731 - 0.865)), calibration (Hosmer and Lemeshow goodness of fit test: P-value 0.6941, 0.9539, and 0.2358), and clinical utility (the threshold probability values in the decision curves are respectively > 12%, > 10%, and 16% ~ 75%) in the training, testing, and external validation groups.
conclusionThe predictive model, which was established in Chinese patients with normal preoperative renal function, has high accuracy, calibration, and clinical utility. Clinicians can utilize this model to predict and potentially reduce the incidence of AKI after CPB surgery in the Chinese population.
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.