A rapid preliminary prediction model for intestinal necrosis in acute mesenteric ischemia: a retrospective study.

BACKGROUND: Acute mesenteric ischemia (AMI) is a life-threatening condition. However, there is no accurate method to predict intestinal necrosis in AMI patients that may facilitate early surgical intervention. This study thus aimed to explore a simple and accurate model to predict intestinal necrosis in patients with AMI. METHODS: A single-center retrospective study was performed on the data of 132 AMI patients treated between October 2011 and June 2020. The patients were divided into the intestinal necrosis and non-intestinal necrosis groups. The clinical characteristics and laboratory data were analyzed by univariate analysis, and the variables with statistical significance were further analyzed by multivariate logistic regression analysis. The independent predictors of intestinal necrosis were determined and a logistic prediction model was established. Finally, the accuracy, sensitivity, and specificity of the model in predicting intestinal necrosis were evaluated. RESULTS: Univariate analysis showed that white blood cell (WBC) count, blood urea nitrogen (BUN) level, neutrophil ratio, prothrombin time (PT), and LnD-dimer were associated with intestinal necrosis. According to logistic regression multivariate analysis, WBC count, BUN level and LnD-dimer were independent predictors of intestinal necrosis. These parameters were used to establish a clinical prediction model of intestinal necrosis (CPMIN) as follows: model score = 0.349 × BUN (mmol/L) + 0.109 × WBC × 109 (109/L) + 0.394 × LnD - Dimer (ug/L) - 7.883. The area under the receiver operating characteristics (ROC) curve of the model was 0.889 (95% confidence interval: 0.833-0.944). Model scores greater than - 0.1992 predicted the onset of intestinal necrosis. The accuracy, specificity, and sensitivity of the model were 82.6%, 78.2%, and 88.3%, respectively. The proportion of intestinal necrosis in the high-risk patient group (CPMIN score ≥ - 0.1992) was much greater than that in the low-risk patient group (CPMIN score < - 0.1992; 82.7% vs. 15.0%, p < 0.001). CONCLUSION: The CPMIN can effectively predict intestinal necrosis and guide early surgical intervention to improve patient prognosis. Patients with AMI who are classified as high-risk should be promptly treated with surgery to avoid the potential complications caused by delayed operation. Patients classified as low-risk group can receive non-surgical treatment. This model may help to lower the morbidity and mortality from AMI. However, this model's accuracy should be validated by larger sample size studies in the future.

MicroRNAs as potential biomarkers for the diagnosis of Traumatic Brain Injury: A systematic review and meta-analysis.

Background: Traumatic brain injury (TBI) is a sudden trauma on the head, in which severe TBI (sTBI) is usually associated with death and long-term disability. MicroRNAs (miRNAs) are potential biomarkers of diverse diseases, including TBI. However, few systematic reviews and meta-analyses have been conducted to determine the clinical value of miRNAs expression in TBI patients. Methods: We conducted this systematic review and meta-analysis study according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched PubMed, Embase, the Cochrane Library, Web of Science, from inception to August 26, 2020. We included articles written in English that have reported on the diagnostic value of miRNAs expression in TBI patients. We excluded studies that did not provided sufficient information to construct the 2×2 contingency table. Results: Eight studies investigating the diagnostic value of miRNA in TBI were analyzed in this study. The overall sensitivity, specificity and area under the curve (AUC) of miRNAs in diagnosis of TBI were 89% [95% confidence interval (CI): 0.84-0.93], 92% (95% CI 0.82-0.97) and 95% (95% CI 0.93-0.97). We found that panels of multiple miRNAs could improve the diagnostic accuracy of TBI. Samples from blood and brain tissue have significantly enhanced diagnostic accuracy, when compared with saliva. The AUC of miRNAs in severe TBI was 0.97, with 91% sensitivity and 92% specificity. Conclusion: This systematic review and meta-analysis demonstrated that miRNAs could be potential diagnostic markers in TBI patients. MiRNAs detected in blood and brain tissue display high accuracy for TBI diagnosis.