The use of immune checkpoint inhibitors (ICIs) for the treatment of various malignancies is increasing. Immune-related adverse events can occur after ICI administration, with gastrointestinal adverse events constituting a significant proportion of these events. When ICI-related diarrhea/colitis is suspected, endoscopic evaluation is recommended to differentiate it from other etiologies and assess the severity of colitis. The distribution of intestinal inflammation in ICI-related colitis demonstrates a high frequency of extensive colitis (23–86%). However, isolated right-sided colitis (3–8%) and ileitis (2–16%) are less prevalent. Endoscopic findings vary and predominantly encompass features indicative of inflammatory bowel disease, including aphthae, ulcers, diffuse or patchy erythema, mucosal edema, loss of vascular pattern, and friability. The presence of ulcers and extensive intestinal inflammation are associated with a reduced response to treatment. Microscopic inflammation can be observed even in endoscopically normal mucosa, underscoring the need for biopsies of seemingly normal mucosa. Histological findings present with acute/chronic inflammation and occasionally exhibit characteristics observed in inflammatory bowel disease, microscopic colitis, or ischemic colitis. The first-line therapeutic choice for ICI-related diarrhea/colitis with a common terminology criteria for adverse events grade of 2 or above is corticosteroids, whereas infliximab and vedolizumab are recommended for refractory cases.
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Background /Aims: Sedation has become a standard practice for patients undergoing gastrointestinal (GI) endoscopy. However, considering the serious cardiopulmonary adverse events associated with sedatives, it is important to identify patients at high risk. Machine learning can generate reasonable prediction for a wide range of medical conditions. This study aimed to evaluate the risk factors associated with sedation during GI endoscopy and develop a predictive model for hypoxia during endoscopy under sedation.
Methods This prospective observational study enrolled 446 patients who underwent sedative endoscopy at the Korea University Ansan Hospital. Clinical data were used as predictor variables to construct predictive models using the random forest method that is a machine learning algorithm.
Results Seventy-two of the 446 patients (16.1%) experienced life-threatening hypoxia requiring immediate medical intervention. Patients who developed hypoxia had higher body weight, body mass index (BMI), neck circumference, and Mallampati scores. Propofol alone and higher initial and total dose of propofol were significantly associated with hypoxia during sedative endoscopy. Among these variables, high BMI, neck circumference, and Mallampati score were independent risk factors for hypoxia. The area under the receiver operating characteristic curve for the random forest-based predictive model for hypoxia during sedative endoscopy was 0.82 (95% confidence interval, 0.79–0.86) and displayed a moderate discriminatory power.
Conclusions High BMI, neck circumference, and Mallampati score were independently associated with hypoxia during sedative endoscopy. We constructed a model with acceptable performance for predicting hypoxia during sedative endoscopy.
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