Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Oct 3:11:1474311.
doi: 10.3389/fmed.2024.1474311. eCollection 2024.

Development and validation of a web-based nomogram for acute kidney injury in acute non-variceal upper gastrointestinal bleeding patients

Chaolian Wei #  1 , Honghua Cao #  2 , Lina Huang  3 , Lu-Huai Feng  3
Affiliations

Development and validation of a web-based nomogram for acute kidney injury in acute non-variceal upper gastrointestinal bleeding patients

Chaolian Wei et al. Front Med (Lausanne). .

Abstract

Background: Acute kidney injury (AKI) is a common and serious complication in patients with acute non-variceal upper gastrointestinal bleeding (NVUGIB). Early prediction and intervention are crucial for improving patient outcomes.

Methods: Data for patients presenting with acute NVUGIB in this retrospective study were sourced from the MIMC-IV database. Patients were randomly allocated into training and validation cohorts for further analysis. Independent predictors for AKI were identified using least absolute shrinkage and selection operator regression and multivariable logistic regression analyses in the training cohort. Based on the logistic regression results, a nomogram was developed to predict early AKI onset in acute NVUGIB patients, and implemented as a web-based calculator for clinical application. The nomogram's performance was evaluated through discrimination, using the C-index, calibration curves, and decision curve analysis (DCA) to assess its clinical value.

Results: The study involved 1082 acute NVUGIB patients, with 406 developing AKI. A multivariable logistic regression identified five key AKI predictors: CKD, use of human albumin, chronic liver disease, glucose, and blood urea nitrogen. The nomogram was constructed based on independent predictors. The nomogram exhibited robust accuracy, evidenced by a C-index of 0.73 in the training cohort and 0.72 in the validation cohort. Calibration curves demonstrated satisfactory concordance between predicted and observed AKI occurrences. DCA revealed that the nomogram offered considerable clinical benefit within a threshold probability range of 7% to 54%.

Conclusion: Our nomogram is a valuable tool for predicting AKI risk in patients with acute NVUGIB, offering potential for early intervention and improved clinical outcomes.

Keywords: acute kidney injury; intensive care unit; nomogram; prediction; upper gastrointestinal bleeding.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
LASSO Regression Analysis for Predictor Selection. (A) Tuning parameter (lambda) selection in the LASSO model using 10-fold cross-validation. The x-axis represents the log(lambda), and the y-axis represents the binomial deviance. The red dots indicate the average deviance values, and the vertical bars represent the standard errors. The vertical dashed lines represent the optimal values chosen by the minimum criteria and 1 standard error of the minimum criteria (the 1-SE rule). (B) LASSO coefficient profiles of the 21 potential predictors. Each curve represents a predictor, with the y-axis displaying the coefficient values and the x-axis representing the log(lambda). The numbers at the top of the plot indicate the number of predictors included in the model at each log(lambda) value.
FIGURE 2
FIGURE 2
Nomogram for Predicting the Risk of AKI. (A) Nomogram to predict the risk of AKI in patients with acute NVUGIB. The nomogram integrates multiple predictors, including blood urea nitrogen (mmol/L), glucose (mmol/L), chronic liver disease, use of human albumin, and CKD. Each predictor has a corresponding point scale, which is used to calculate the total points. The total points are then used to determine the risk of AKI, displayed on the bottom scale. (B) Web-based calculator interface for predicting AKI risk. Users input values for blood urea nitrogen, glucose, chronic liver disease, use of human albumin, and CKD status to obtain the predicted probability of AKI. The example provided shows a current probability of AKI at 46.1%.
FIGURE 3
FIGURE 3
Calibration Curves for Nomogram Predicted Probability of AKI. These calibration plots compare the predicted probabilities of AKI against the actual observed probabilities, demonstrating the accuracy of the nomogram model. (A) Calibration curve for the training cohort. (B) Calibration curve for the validation cohort. The x-axis represents the nomogram predicted probability of AKI, while the y-axis shows the actual probability. The plot includes the apparent calibration (blue dotted line), the bias-corrected calibration (red solid line), and the ideal calibration (black dashed line). The closer the red line is to the black line, the more accurate the model.
FIGURE 4
FIGURE 4
Decision Curve Analysis for the Nomogram Predicting AKI in acute NVUGIB Patients. Decision curve analysis was conducted to compare the net benefits of the nomogram against individual predictors across a spectrum of high-risk thresholds. The x-axis denotes the high-risk threshold for predicting acute kidney injury (AKI), while the y-axis indicates the net benefit. The red line, representing the nomogram, demonstrates superior net benefit across most thresholds when compared to individual predictors, including chronic kidney disease (CKD), use of human albumin, chronic liver disease, glucose levels, and blood urea nitrogen.
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Benedeto-Stojanov D, Bjelaković M, Stojanov D, Aleksovski B. Prediction of in-hospital mortality after acute upper gastrointestinal bleeding: Cross-validation of several risk scoring systems. J Int Med Res. (2022) 50:3000605221086442. 10.1177/03000605221086442 - DOI - PMC - PubMed
    1. Hoste E, Bagshaw S, Bellomo R, Cely C, Colman R, Cruz D, et al. Epidemiology of acute kidney injury in critically ill patients: The multinational AKI-EPI study. Intensive Care Med. (2015) 41:1411–23. 10.1007/s00134-015-3934-7 - DOI - PubMed
    1. Kellum J, Romagnani P, Ashuntantang G, Ronco C, Zarbock A, Anders H. Acute kidney injury. Nat Rev Dis Prim. (2021) 7:52. - PubMed
    1. Ronco C, Bellomo R, Kellum J. Acute kidney injury. Lancet. (2019) 394:1949–64. - PubMed
    1. Cakmak U, Merhametsiz O, Gok Oguz E, Ercan Z, Haspulat A, Ozkan S, et al. Effects of acute kidney injury on clinical outcomes in patients with upper gastrointestinal bleeding. Ren Fail. (2016) 38:176–84. - PubMed

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of the article. This work was supported by grants from the Natural Science Foundation of Guangxi (NO. 2021GXNSFAA220036).

LinkOut - more resources