Management of non variceal upper gastrointestinal bleeding: position statement of the Catalan Society of Gastroenterology

Machine Learning-Based Mortality Prediction for Acute Gastrointestinal Bleeding Patients Admitted to Intensive Care Unit

OBJECTIVE

The study aimed to develop machine learning (ML) models to predict the mortality of patients with acute gastrointestinal bleeding (AGIB) in the intensive care unit (ICU) and compared their prognostic performance with that of Acute Physiology and Chronic Health Evaluation II (APACHE-II) score.

METHODS

A total of 961 AGIB patients admitted to the ICU of Renmin Hospital of Wuhan University from January 2020 to December 2023 were enrolled. Patients were randomly divided into the training cohort (n = 768) and the validation cohort (n = 193). Clinical data were collected within the first 24 h of ICU admission. ML models were constructed using Python V.3.7 package, employing 3 different algorithms: XGBoost, Random Forest (RF) and Gradient Boosting Decision Tree (GBDT). The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the performance of different models.

RESULTS

A total of 94 patients died with an overall mortality of 9.78% (11.32% in the training cohort and 8.96% in the validation cohort). Among the 3 ML models, the GBDT algorithm demonstrated the highest predictive performance, achieving an AUC of 0.95 (95% CI 0.90-0.99), while the AUCs of XGBoost and RF models were 0.89 (95% CI 0.82-0.96) and 0.90 (95% CI 0.84-0.96), respectively. In comparison, the APACHE-II model achieved an AUC of 0.74 (95% CI 0.69-0.87), with a specificity of 70.97% (95% CI 64.07-77.01). When APACHE-II score was incorporated into the GBDT algorithm, the ensemble model achieved an AUC of 0.98 (95% CI 0.96-0.99) with a sensitivity of 85.71% and a specificity up to 95.15%.

CONCLUSIONS

The GBDT model serves as a reliable tool for accurately predicting the in-hospital mortality for AGIB patients. When integrated with the APACHE-II score, the ensemble GBDT algorithm further enhances predictive accuracy and provides valuable insights for prognostic evaluation.

Dictionary Learning-Based Ultrasound Image Combined with Gastroscope for Diagnosis of Helicobacter pylori-Caused Gastrointestinal Bleeding

The study is aimed at evaluating the application value of ultrasound combined with gastroscopy in diagnosing gastrointestinal bleeding (GIB) caused by Helicobacter pylori (HP). An ultrasound combined with a gastroscopy diagnostic model based on improved K-means Singular Value Decomposition (N-KSVD) was proposed first. 86 patients with Peptic ulcer (PU) and GIB admitted to our Hospital were selected and defined as the test group, and 86 PU patients free of GIB during the same period were selected as the control group. The two groups were observed for clinical manifestations and HP detection results. The results showed that when the noise ρ was 10, 30, 50, and 70, the Peak Signal to Noise Ratio (PSNR) values of N-KSVD dictionary after denoising were 35.55, 30.47, 27.91, and 26.08, respectively, and the structure similarity index measure (SSIM) values were 0.91, 0.827, 0.763, and 0.709, respectively. Those were greater than those of DCT dictionary and Global dictionary and showed statistically significant differences versus the DCT dictionary (P < 0.05). In the test group, there were 60 HP-positives and 26 HP-negatives, and there was significant difference in the numbers of HP-positives and HP-negatives (P < 0.05), but no significant difference in gender and age (P > 0.05). Of the subjects with abdominal pain, HP-positives accounted for 59.02% and HP-negatives accounted for 37.67%, showing significant differences (P < 0.05). Finally, the size of the ulcer lesion in HP-positives and HP-negatives was compared. It was found that 71.57% of HP-positives had ulcers with a diameter of 0-1 cm, and 28.43% had ulcers with a diameter of ≥1 cm. Compared with HP-negatives, the difference was statistically significant (P < 0.05). In conclusion, N-KSVD-based ultrasound combined with gastroscopy demonstrated good denoising effects and was effective in the diagnosis of GIB caused by HP.

Nasogastric aspiration/lavage in patients with gastrointestinal bleeding: a review of the evidence

The usefulness of nasogastric aspiration and nasogastric lavage in patients with gastrointestinal bleeding is controversial, as evidenced by conflicting recommendations, both among and within society guidelines. Areas covered: Considering these controversies, we reviewed the evidence regarding the following questions: 1) Can nasogastric lavage stop or slow down the bleeding and improve subsequent endoscopic visualization? 2) Is nasogastric aspiration helpful for the localization of bleeding? 3) Can nasogastric aspiration identify high risk patients that might benefit from earlier endoscopy? 4) Is there evidence for benefit in terms of outcomes from using nasogastric aspiration? 5) Is nasogastric intubation safe in patients with possible esophageal varices? Our review was conducted according to PRISMA guidelines. Expert commentary: Based on the available literature, nasogastric lavage or aspiration cannot be routinely recommended unless a large properly designed randomized trial (which is currently lacking) proves otherwise. It is a painful and time-consuming procedure with no demonstrated benefit for the patient in terms of outcomes. Other clinical and laboratory parameters, and risk scores, are less invasive and are effective for guiding the stratification and management of patients, while pre-endoscopic erythromycin infusion is a good if not better alternative for improving visualization of the stomach.