Sun Yuan-Hui, Song Yun-Yun, Sha Sha, Sun Qi, Huang Deng-Chao, Gao Lan, Li Hao, Shi Qin-Dong
Department of Critical Care Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, Shaanxi Province, China.
Shaanxi Province Key Laboratory of Sepsis in Critical Care Medical, Xi'an 710061, Shaanxi Province, China.
World J Gastrointest Surg. 2024 Dec 27;16(12):3818-3834. doi: 10.4240/wjgs.v16.i12.3818.
Acute gastrointestinal injury (AGI) is common in intensive care unit (ICU) and worsens the prognosis of critically ill patients. The four-point grading system proposed by the European Society of Intensive Care Medicine is subjective and lacks specificity. Therefore, a more objective method is required to evaluate and determine the grade of gastrointestinal dysfunction in this patient population. Digital continuous monitoring of bowel sounds and some biomarkers can change in gastrointestinal injuries. We aimed to develop a model of AGI using continuous monitoring of bowel sounds and biomarkers.
To develop a model to discriminate AGI by monitoring bowel sounds and biomarker indicators.
We conducted a prospective observational study with 75 patients in an ICU of a tertiary-care hospital to create a diagnostic model for AGI. We recorded their bowel sounds, assessed AGI grading, collected clinical data, and measured biomarkers. We evaluated the model using misjudgment probability and leave-one-out cross-validation.
Mean bowel sound rate and citrulline level are independent risk factors for AGI. Gastrin was identified as a risk factor for the severity of AGI. Other factors that correlated with AGI include mean bowel sound rate, amplitude, interval time, Sequential Organ Failure Assessment score, Acute Physiology and Chronic Health Evaluation II score, platelet count, total protein level, blood gas potential of hydrogen (pH), and bicarbonate (HCO ) level. Two discriminant models were constructed with a misclassification probability of < 0.1. Leave-one-out cross-validation correctly classified 69.8% of the cases.
Our AGI diagnostic model represents a potentially effective approach for clinical AGI grading and holds promise as an objective diagnostic standard for AGI.
急性胃肠损伤（AGI）在重症监护病房（ICU）中很常见，会使危重症患者的预后恶化。欧洲重症监护医学学会提出的四点分级系统具有主观性且缺乏特异性。因此，需要一种更客观的方法来评估和确定该患者群体的胃肠功能障碍等级。对肠鸣音进行数字连续监测以及一些生物标志物会在胃肠损伤时发生变化。我们旨在通过对肠鸣音和生物标志物的连续监测来建立AGI模型。
通过监测肠鸣音和生物标志物指标建立一个区分AGI的模型。
我们在一家三级医院的ICU对75例患者进行了一项前瞻性观察研究，以创建AGI的诊断模型。我们记录了他们的肠鸣音，评估AGI分级，收集临床数据并测量生物标志物。我们使用误判概率和留一法交叉验证来评估该模型。
平均肠鸣音频率和瓜氨酸水平是AGI的独立危险因素。胃泌素被确定为AGI严重程度的危险因素。与AGI相关的其他因素包括平均肠鸣音频率、振幅、间隔时间、序贯器官衰竭评估评分、急性生理与慢性健康状况评估II评分、血小板计数、总蛋白水平、血气酸碱度（pH）和碳酸氢盐（HCO₃⁻）水平。构建了两个误分类概率<0.1的判别模型。留一法交叉验证正确分类了69.8%的病例。
我们的AGI诊断模型是临床AGI分级的一种潜在有效方法，有望成为AGI的客观诊断标准。
