Rectal cancer, which accounts for approximately 40% of colorectal cancers, remains a major clinical concern. Recent advances in diagnostic imaging, surgical techniques, radiotherapy, and systemic treatment have steadily improved rectal cancer outcomes. Considering this, the Korean Rectal Cancer Multidisciplinary (KRCM) Committee has aimed to provide clinicians and policymakers with up-to-date, evidence-based clinical practice guidelines to support optimal decision-making, reflecting current evidence, the Korean healthcare context, and patient values and preferences. The Clinical Practice Guidelines for Rectal Cancer version 2.0 were developed through multidisciplinary collaboration with related academic societies, building upon and updating the KRCM Clinical Practice Guidelines version 1.0 (titled “Multidisciplinary guidelines for the management of rectal cancer”). These consensus guidelines of the KRCM were established based on a comprehensive literature review, evidence synthesis, with recommendation development guided by the GRADE (Grading of Recommendations Assessment, Development and Evaluation) methodology, and consideration of applicability in real-world clinical practice under the national health insurance system. Each recommendation has been presented with its strength and level of evidence.

Background/Aims: Recent advances in computer-aided diagnosis (CADx) systems have demonstrated expert-level accuracy in the optical diagnosis of colorectal polyps. High-confidence (HC) diagnoses have been defined as those made within 3 seconds without hesitation, and these systems have been shown to improve diagnostic accuracy. We aimed to evaluate the performance of endoscopists with varying levels of experience in diagnosing colorectal polyps with the assistance of a new CADx system applying the 3-second rule and without artificial intelligence assistance. Methods: In this multicenter ex vivo study, 35 endoscopists assessed 100 polyps (51 adenomas, 39 hyperplastic polyps, 10 sessile serrated lesions) using narrow-band imaging video clips on an online platform. Assessments consisted of individual endoscopist diagnosis and CADx-assisted diagnosis. HC assignments followed the 3-second rule in both phases. Performance metrics included HC accuracy, HC rate, and adherence to the Preservation and Incorporation of Valuable Endoscopic Innovations (PIVI) and Simple Optical Diagnosis Accuracy (SODA) thresholds. Results: HC diagnostic accuracy improved from 78.3% (95% confidence interval [CI], 76.6% to 80.0%) to 89.8% (95% CI, 88.6% to 90.9%) with CADx assistance (p<0.001). The proportion of HC predictions increased from 64.2% to 75.4% (p<0.001). Novice endoscopists showed marked improvement with CADx (74.1% vs 88.8%; p<0.001). CADx-assisted diagnoses nearly met SODA and PIVI thresholds under the 3-second rule. Additional analysis demonstrated that CADx assistance significantly improved interobserver agreement and ground truth, particularly for novices (κ=0.37 to κ=0.65; p<0.001). Conclusions Integrating CADx with the 3-second rule significantly enhances the performance of endoscopists in the optical diagnosis of colorectal polyps, with the greatest benefit observed among novice endoscopists.

Allergen immunotherapy (AIT) has been used for over a century and has been demonstrated to be effective in treating patients with various allergic diseases. AIT allergens can be administered through various routes, including subcutaneous, sublingual, intralymphatic, oral, or epicutaneous routes. Sublingual immunotherapy (SLIT) has recently gained clinical interest, and it is considered an alternative treatment for allergic rhinitis (AR) and asthma. This review provides an overview of the current evidence-based studies that address the use of SLIT for treating AR, including (1) mechanisms of action, (2) appropriate patient selection for SLIT, (3) the current available SLIT products in Korea, and (4) updated information on its efficacy and safety. Finally, this guideline aims to provide the clinician with practical considerations for SLIT.

Allergen immunotherapy (AIT) is a causative treatment of allergic diseases in which allergen extracts are regularly administered in a gradually escalated doses, leading to immune tolerance and consequent alleviation of allergic diseases. The need for uniform practice guidelines in AIT is continuously growing as the number of potential candidates for AIT increases and new therapeutic approaches are tried. This updated version of the Korean Academy of Asthma Allergy and Clinical Immunology recommendations for AIT, published in 2010, proposes an expert opinion by specialists in allergy, pediatrics, and otorhinolaryngology. This guideline deals with the basic knowledge of AIT, including mechanisms, clinical efficacy, allergen standardization, important allergens in Korea, and special consideration in pediatrics. The article also covers the methodological aspects of AIT, including patient selection, allergen selection, schedule and doses, follow-up care, efficacy measurements, and management of adverse reactions. Although this guideline suggests the optimal dosing schedule, an individualized approach and modifications are recommended considering the situation for each patient and clinic.

Background: Worldwide, sepsis is the leading cause of death in hospitals. If mortality rates in patients with sepsis can be predicted early, medical resources can be allocated efficiently. We constructed machine learning (ML) models to predict the mortality of patients with sepsis in a hospital emergency department. Methods: This study prospectively collected nationwide data from an ongoing multicenter cohort of patients with sepsis identified in the emergency department. Patients were enrolled from 19 hospitals between September 2019 and December 2020. For acquired data from 3,657 survivors and 1,455 deaths, six ML models (logistic regression, support vector machine, random forest, extreme gradient boosting [XGBoost], light gradient boosting machine, and categorical boosting [CatBoost]) were constructed using fivefold cross-validation to predict mortality. Through these models, 44 clinical variables measured on the day of admission were compared with six sequential organ failure assessment (SOFA) components (PaO 2 /FIO 2 [PF], platelets (PLT), bilirubin, cardiovascular, Glasgow Coma Scale score, and creatinine).The confidence interval (CI) was obtained by performing 10,000 repeated measurements via random sampling of the test dataset. All results were explained and interpreted using Shapley’s additive explanations (SHAP). Results: Of the 5,112 participants, CatBoost exhibited the highest area under the curve (AUC) of 0.800 (95% CI, 0.756–0.840) using clinical variables. Using the SOFA components for the same patient, XGBoost exhibited the highest AUC of 0.678 (95% CI, 0.626–0.730). As interpreted by SHAP, albumin, lactate, blood urea nitrogen, and international normalization ratio were determined to significantly affect the results. Additionally, PF and PLTs in the SOFA component significantly influenced the prediction results. Conclusion Newly established ML-based models achieved good prediction of mortality in patients with sepsis. Using several clinical variables acquired at the baseline can provide more accurate results for early predictions than using SOFA components. Additionally, the impact of each variable was identified.

Although major countries, such as South Korea, have developed and disseminated national smoking cessation guidelines, these efforts have been limited to developing individual societies or specialized institution-based recommendations. Therefore, evidence-based clinical guidelines are essential for developing smoking cessation interventions and promoting effective smoking cessation treatments. This guideline targets frontline clinical practitioners involved in a smoking cessation treatment support program implemented in 2015 with the support of the National Health Insurance Service. The Guideline Development Group of 10 multidisciplinary smoking cessation experts employed the Grading of Recommendations Assessment, Development, and Evaluation (GRADE)-ADOLOPMENT approach to review recent domestic and international research and guidelines and to determine evidence levels using the GRADE methodology. The guideline panel formulated six strong recommendations and one conditional recommendation regarding pharmacotherapy choices among general and special populations (mental disorders and chronic obstructive lung disease [COPD]). Strong recommendations favor varenicline rather than a nicotine patch or bupropion, using varenicline even if they are not ready to quit, using extended pharmacotherapy (>12 weeks) rather than standard treatment (8–12 weeks), or using pharmacotherapy for individuals with mental disorders or COPD. The conditional recommendation suggests combining varenicline with a nicotine patch instead of using varenicline alone. Aligned with the Korean Society of Medicine’s clinical guideline development process, this is South Korea’s first domestic smoking cessation treatment guideline that follows standardized guidelines. Primarily focusing on pharmacotherapy, it can serve as a foundation for comprehensive future smoking cessation clinical guidelines, encompassing broader treatment topics beyond medications.

Background: Surgical resection is the standard treatment for early-stage lung cancer. Since postoperative lung function is related to mortality, predicted postoperative lung function is used to determine the treatment modality. The aim of this study was to evaluate the predictive performance of linear regression and machine learning models. Methods: We extracted data from the Clinical Data Warehouse and developed three sets: set I, the linear regression model; set II, machine learning models omitting the missing data: and set III, machine learning models imputing the missing data. Six machine learning models, the least absolute shrinkage and selection operator (LASSO), Ridge regression, ElasticNet, Random Forest, eXtreme gradient boosting (XGBoost), and the light gradient boosting machine (LightGBM) were implemented. The forced expiratory volume in 1 second measured 6 months after surgery was defined as the outcome. Five-fold cross-validation was performed for hyperparameter tuning of the machine learning models. The dataset was split into training and test datasets at a 70:30 ratio. Implementation was done after dataset splitting in set III. Predictive performance was evaluated by R2 and mean squared error (MSE) in the three sets. Results: A total of 1,487 patients were included in sets I and III and 896 patients were included in set II. In set I, the R2 value was 0.27 and in set II, LightGBM was the best model with the highest R2 value of 0.5 and the lowest MSE of 154.95. In set III, LightGBM was the best model with the highest R2 value of 0.56 and the lowest MSE of 174.07. Conclusion The LightGBM model showed the best performance in predicting postoperative lung function.

Purpose: Although osimertinib is the standard-of-care treatment of epidermal growth factor receptor (EGFR) T790M mutation–positive non–small cell lung cancer, real-world evidence on the efficacy of osimertinib is not enough to reflect the complexity of the entire course of treatment. Herein, we report on the use of osimertinib in patients with EGFR T790M mutation–positive non–small cell lung cancer who had previously received EGFR tyrosine kinase inhibitor (TKI) treatment in Korea. Materials and Methods: Patients with confirmed EGFR T790M after disease progression of prior EGFR-TKI were enrolled and administered osimertinib 80 mg daily. The primary effectiveness outcome was progression-free survival, with time-to-treatment discontinuation, treatment and adverse effects leading to treatment discontinuation, and overall survival being the secondary endpoints. Results: A total of 558 individuals were enrolled, and 55.2% had investigator-assessed responses. The median progression-free survival was 14.2 months (95% confidence interval [CI], 13.0 to 16.4), and the median time-to-treatment discontinuation was 15.0 months (95% CI, 14.1 to 15.9). The median overall survival was 36.7 months (95% CI, 30.9 to not reached). The benefit with osimertinib was consistent regardless of the age, sex, smoking history, and primary EGFR mutation subtype. However, hepatic metastases at the time of diagnosis, the presence of plasma EGFR T790M, and the shorter duration of prior EGFR-TKI treatment were poor predictors of osimertinib treatment. Ten patients (1.8%), including three with pneumonitis, had to discontinue osimertinib due to severe adverse effects. Conclusion Osimertinib demonstrated its clinical effectiveness and survival benefit for EGFR T790M mutation–positive in Korean patients with no new safety signals.

Background/Aims: Gastrointestinal (GI) bezoars are relatively rare diseases with clinical characteristics and treatment modalities that depend on the location of the bezoars. This study evaluated the clinical characteristics and treatment outcomes in patients with GI bezoars. Methods: Seventy-five patients diagnosed with GI bezoars were enrolled in this study. Data were collected on the demographic and clinical characteristics and the characteristics of the bezoars, such as type, size, location, treatment modality, and clinical outcomes. Results: Among the 75 patients (mean age 71.2 years, 38 males), 32 (42.6%) had a history of intra-abdominal surgery. Hypertension (43%) and diabetes (30%) were common morbidities. The common location of the bezoars was the stomach in 33 (44%) and the small intestine in 33 (44%). Non-surgical management, including adequate hydration, chemical dissolution, and endoscopic removal, was successful in 2/2 patients with esophageal bezoars, 26/33 patients with gastric bezoars, 7/9 patients with duodenal bezoars, and 20/33 patients with small intestinal bezoars. The remaining patients had undergone surgical management. Conclusions The management of GI bezoars requires multidisciplinary approaches, including the appropriate correction of fluid and electrolyte imbalances, chemical dissolution, and endoscopic and surgical treatments. (Korean J Gastroenterol 2023;81:253-258)