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
. 2025 Aug;40(11):2643-2651.
doi: 10.1007/s11606-025-09573-9. Epub 2025 May 8.

Construction and Validation of a Risk Prediction Model for Acute Gastrointestinal Injury in Non-ICU Elderly Critically Ill Patients

Jiajia Xu #  1 , Shan Li #  2 , Yue Hu  2 , Dan Liu  3 , Jianghong Zhang  3 , Binrong Zhang  3 , Sisi Yuan  3 , Xiaohong Zhang  4
Affiliations

Construction and Validation of a Risk Prediction Model for Acute Gastrointestinal Injury in Non-ICU Elderly Critically Ill Patients

Jiajia Xu et al. J Gen Intern Med. 2025 Aug.

Abstract

Background: Acute gastrointestinal injury (AGI) has a relatively high prevalence among elderly critically ill patients in non-intensive care units (non-ICUs), and significantly influences their clinical outcomes. Therefore, it is important to identify people at risk for AGI and take preventive measures as early as possible.

Objective: We aimed to construct and validate a risk prediction model for AGI in non-ICU elderly critically ill patients.

Design: Case-control study.

Participants: In total, 538 elderly critically ill patients admitted to the general medical department of a tertiary hospital in Shanxi from April 2021 to May 2024.

Main measures: Influential factors for AGI were determined using univariate and multifactorial logistic regression analyses. We constructed a risk prediction model and created a nomogram. The bootstrap resampling method was utilized for internal validation. A total of 151 patients from different time periods were selected for the external validation.

Key results: The multifactorial logistic regression analysis revealed that the independent predictors for AGI were the duration of antibiotic use, number of vasoactive drugs, delayed enteral nutrition, age-corrected Charlson comorbidity index, and white blood cell count, all of which were included in the model and created a nomogram. The Omnibus test showed that the overall efficacy of the model was good (P < 0.001). The area under the receiver operating characteristic curve (AUC) was 0.807, the corrected AUC was 0.806, and the AUC was 0.796 for external validation, indicating good model discrimination. The calibration curves and Hosmer-Lemeshow tests revealed that the model was well calibrated (P = 0.627, Brier = 0.172 in internal validation; and P = 0.366, Brier = 0.182 in external validation). The clinical decision curves showed that the model had good clinical utility.

Conclusions: AGI is common in non-ICU elderly critically ill patients. This AGI risk prediction model can be used as a screening tool to identify high-risk patients for AGI and assist clinical decision making.

Keywords: acute gastrointestinal injury; critical illness; elderly; nomogram; non-ICU; risk prediction model.

PubMed Disclaimer

Conflict of interest statement

Declarations. Ethics Approval: The study protocol was reviewed and approved by the Ethics Committee of the Third Hospital of Shanxi Medical University. The present study constitutes a retrospective study into health services and for improving the quality of care. Informed Consent: Informed consent for participation in the study was therefore not considered necessary. Conflict of Interest: The authors declare that they do not have a conflict of interest.

Figures

Figure 1
Figure 1
Nomogram predicting the probability of AGI in non-ICU elderly critically ill patients.
Figure 2
Figure 2
Receiver operating characteristic curve for the risk prediction model of AGI in the derivation and validation cohort.
Figure 3
Figure 3
Calibration curve for the risk prediction model of AGI. The derivation cohort is on the left and the validation cohort is on the right.
Figure 4
Figure 4
Clinical decision curve for the risk prediction model of AGI in the derivation and validation cohort.
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Man W, Wang S, Yang H. Exploring the spatial-temporal distribution and evolution of population aging and social-economic indicators in China. BMC Public Health. 2021;21(1):966. - PMC - PubMed
    1. Chinese Geriatric S, Critical Care Medicine Division Of Chinese Geriatric S. Expert consensus on enteral nutrition support for elderly patients with critical illness in China (2022). Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2022;34(4):337–342. - PubMed
    1. Magnan C, Lancry T, Salipante F, et al. Role of gut microbiota and bacterial translocation in acute intestinal injury and mortality in patients admitted in ICU for septic shock. Front Cell Infect Microbiol. 2023;13:1330900. - PMC - PubMed
    1. Xu PL, Pan AH, Hong JF, et al. Practice of a graded physical restraint regimen in severe neurologic patients. J Nurs Sci. 2023,38(21):67-71.
    1. Chang BL, Rubenstein LV, Keeler EB, Miura LN, Kahn KL. The validity of a nursing assessment and monitoring of signs and symptoms scale in ICU and non-ICU patients. Am J Crit Care. 1996;5(4):298-303. - PubMed

Publication types

LinkOut - more resources