A Nomogram Model for Prediction of Mortality Risk of Patients with Dangerous Upper Gastrointestinal Bleeding: A Two-center Retrospective Study.

OBJECTIVE: This study aimed to establish a nomogram model to predict the mortality risk of patients with dangerous upper gastrointestinal bleeding (DUGIB), and identify high-risk patients who require emergent therapy. METHODS: From January 2020 to April 2022, the clinical data of 256 DUGIB patients who received treatments in the intensive care unit (ICU) were retrospectively collected from Renmin Hospital of Wuhan University (n=179) and the Eastern Campus of Renmin Hospital of Wuhan University (n=77). The 179 patients were treated as the training cohort, and 77 patients as the validation cohort. Logistic regression analysis was used to calculate the independent risk factors, and R packages were used to construct the nomogram model. The prediction accuracy and identification ability were evaluated by the receiver operating characteristic (ROC) curve, C index and calibration curve. The nomogram model was also simultaneously externally validated. Decision curve analysis (DCA) was then used to demonstrate the clinical value of the model. RESULTS: Logistic regression analysis showed that hematemesis, urea nitrogen level, emergency endoscopy, AIMS65, Glasgow Blatchford score and Rockall score were all independent risk factors for DUGIB. The ROC curve analysis indicated the area under curve (AUC) of the training cohort was 0.980 (95%CI: 0.962-0.997), while the AUC of the validation cohort was 0.790 (95%CI:0.685-0.895). The calibration curves were tested for Hosmer-Lemeshow goodness of fit for both training and validation cohorts (P=0.778, P=0.516). CONCLUSION: The developed nomogram is an effective tool for risk stratification, early identification and intervention for DUGIB patients.

A Nomogram Model for Predicting Type-2 Myocardial Infarction Induced by Acute Upper Gastrointestinal Bleeding.

OBJECTIVE: To examine the independent risk factors of type-2 myocardial infarction (T2MI) elicited by acute upper gastrointestinal bleeding (AUGIB), and to establish a nomogram model for the prediction of AUGIB-induced T2MI. METHODS: A nomogram model was established on the basis of a retrospective study that involved 533 patients who suffered from AUGIB in the Department of Critical Care Medicine (CCM) or Emergency Intensive Care Unit (EICU) of Renmin Hospital of Wuhan University, Wuhan, China, from January 2017 to December 2020. The predictive accuracy and discriminative power of the nomogram were initially evaluated by internal validation, which involved drawing the receiver operating characteristic (ROC) curve, calculating the area under the curve (AUC), plotting the calibration curve derived from 1000 resampled bootstrap data sets, and computing the root mean square error (RMSE). The predictive ability of the nomogram was further validated through the prospective and multicenter study conducted by the investigators, which enrolled 240 AUGIB patients [including 88 cases from Renmin Hospital of Wuhan University, 73 cases from Qilu Hospital of Shandong University (Qingdao), and 79 cases from Northern Jiangsu People's Hospital)], who were admitted to the Department of CCM or EICU, from February 2021 to July 2021. RESULTS: Among the 533 patients in the training cohort, 78 (14.6%) patients were assigned to the T2MI group and 455 (85.4%) patients were assigned to the non-T2MI group. The multivariate analysis revealed that age >65, hemorrhagic shock, cerebral stroke, heart failure, chronic kidney disease, increased blood urea nitrogen, decreased hematocrit, and elevated D-Dimer were independent risk factors for AUGIB-induced T2MI. All these factors were incorporated into the nomogram model. The AUC for the nomogram for predicting T2MI was 0.829 (95% CI, 0.783-0.875) in the internal validation cohort and 0.848 (95% CI, 0.794-0.902) in the external validation cohort. The calibration curve for the risk of T2MI exhibited good consistency between the prediction by the nomogram and the actual clinical observation in both the internal validation (RMSE=0.016) and external validation (RMSE=0.020). CONCLUSION: The nomogram was proven to be a useful tool for the risk stratification of T2MI in AUGIB patients, and is helpful for the early identification of AUGIB patients who are prone to T2MI for early intervention, especially in emergency departments and intensive care units.

Magnetic resonance imaging features of hippocampus and mechanism of neurocognitive dysfunction for antiepileptic drugs in treatment of depression rats.

To explore the effects of antiepileptic drug sodium valproate on magnetic resonance imaging (MRI) images, neurological cognition, and JAK1/STAT3 pathway in hippocampus of rats with depression, 30 Sprague Dawley (SD) rats were included. The depression model (DM) was prepared through the chronic stress restraint test. Some model rats were injected with 10 mg/kg sodium valproate into abdominal cavity before modeling (RT group)), and healthy rats were selected as controls (healthy control (HC) group). Depth of split brain was greatly increased in DM group, and nitrogen-acetyl aspartic acid (NAA)/creatine (Cr), glutamic acid (Glu)/Cr, and choline (Cho)/Cr ratios were greatly reduced (P < 0.05). Behavioral test results showed that sugar water preference rate, escape latency, and divergence index in DM group were greatly reduced (P < 0.05), and cumulative immobility time, target quadrant stay time, and number of crossings in forced swimming and tail suspension were prolonged dramatically (P < 0.05), with no difference between the two groups (P > 0.05). Expression levels of interleukin 1ß (IL-1ß) and interleukin 6 (IL-6) in hippocampus of DM group were obviously increased (P < 0.05), and expression levels of JAK1 and STAT3 were decreased visibly (P < 0.05), with no difference between the two (P > 0.05). In summary, anti-epileptic drug sodium valproate effectively improves hippocampal volume characteristics and memory and neurocognitive dysfunction of depression models.