Construction of An Early Warning Model for Upper Gastrointestinal Bleeding in Patients with Cirrhotic Portal Hypertension Based on Serum miR-140 and miR-335-5p Combined with Clinical Indicators

Li YOU; Ning LI; Yunjun GAO; Yongsheng LI; Yafeng WU; Jiansheng LI

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Li YOU, Ning LI, Yunjun GAO, Yongsheng LI, Yafeng WU, Jiansheng LI. Construction of An Early Warning Model for Upper Gastrointestinal Bleeding in Patients with Cirrhotic Portal Hypertension Based on Serum miR-140 and miR-335-5p Combined with Clinical Indicators[J]. Journal of Kunming Medical University.

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Construction of An Early Warning Model for Upper Gastrointestinal Bleeding in Patients with Cirrhotic Portal Hypertension Based on Serum miR-140 and miR-335-5p Combined with Clinical Indicators

Department of Gastroenterology，Taiyuan Central Hospital，Taiyuan Shanxi 030009，China

Received Date: 2025-12-12
Available Online: 2026-04-10

Abstract

Abstract

Objective To construct an early warning model of upper gastrointestinal bleeding （UGIB） in patients with cirrhosis and portal hypertension （CPH） based on serum microRNA-140 （miR-140） and microRNA-335-5p （miR-335-5p） combined with clinical indicators, and to evaluate its predictive efficacy. Methods CPH patients in Taiyuan Central Hospital from March 2021 to August 2024 were selected as the research subjects and divided into training and validation sets in a 7:3 ratio. Patients were followed up for one year, with UGIB occurrence recorded. Clinical data were collected and serum levels of miR-140 and miR-335-5p were measured. Lasso-Logistic regression analysis was used to identify independent predictive factors for UGBI in CPH patients. An early warning model was constructed and validated using calibration curves, receiver operating characteristic （ROC） curves, and decision curve analysis （DCA）. Results A total of 289 CPH patients were selected and divided into training set （203 cases） and validation set （86 cases） in a 7∶3 ratio. After one-year follow-up, in the training set, 2 patients were lost to follow-up, 125 did not develop UGIB, and 76 developed UGIB. In the validation set, 1 was lost to follow-up, 54 did not develop UGIB, and 31 developed UGIB. Lasso-Logistic regression analysis identified that Child-Pugh grade, esophageal varices severity, portal vein diameter, red cell distribution width （RDW）, hemoglobin （Hb）, miR-140 and miR-335-5p as independent factors for UGIB in CPH patients （P < 0.05）. An M1 model was constructed based on five clinical indicators （Child-Pugh grade, esophageal varices severity, portal vein diameter, RDW, and Hb）, while an M2 model incorporated miR-140 and miR-335-5p in addition to these indicators. Both models demonstrated good fit （P > 0.05）. Calibration curves in both training and validation sets showed that the M1 model had concordance indices of 0.802 and 0.806 with calibration slopes of 0.800 and 0.801, while the M2 model had concordance indices of 0.867 and 0.871 with calibration slopes of 0.859 and 0.862, demonstrating superior discriminative ability and higher predictive accuracy for the M2 model. ROC curve analysis revealed that the areas under the curve （AUC） for M2 and M1 models in predicting UGIB in CPH were 0.905 （95%CI: 0.862-0.947） and 0.898 （95%CI: 0.827-0.969） in the training set, with sensitivities of 88.16% and 83.87%, and specificities of 86.40% and 81.48%, respectively. DeLong test demonstrated that the M2 model's predictive value was significantly superior to the M1 model （Z = 2.192, 1.986; P = 0.035, 0.041）. DCA curves indicated that both M1 and M2 models demonstrated significant and stable positive net clinical benefit across a wide threshold probability range of 10%-100% in both datasets, with the M2 model exhibiting superior clinical utility. Conclusion The early warning model constructed based on serum miR-140 and miR-335-5p combined with clinical indicators is an effective tool for predicting UGIB in CPH patients, enabling early risk stratification and optimization of clinical decision-making.
- Cirrhotic portal hypertension,
- Upper gastrointestinal bleeding,
- microRNA-140,
- microRNA-335-5p,
- Clinical indicators,
- Early warning model,
- Prediction efficiency

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References(25)

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