PURPOSE: The aims of this study were to evaluate the magnitude of distress after breast cancer diagnosis and to investigate factors associated with distress, as well as to determine the effectiveness of psychological intervention. METHODS: This study was performed retrospectively, reviewing 264 patients who underwent breast cancer surgery at Seoul National University Bundang Hospital between November 2011 and May 2014. Distress was measured using the distress thermometer (DT) and Center for Epidemiological Studies-Depression scale (CES-D) questionnaires before, as well as at 3 and 6 months postsurgery. Psychological intervention was recommended to high risk patients (DT score ≥5 or CES-D score ≥16). RESULTS: In total, 149 patients (56.4%) were classified as high risk in the initial assessment. In the following assessments, the proportion of those in the high risk group was 38.5% and 25.0% at 3 and 6 months postsurgery, respectively. Mastectomy was significantly associated with high levels of distress compared to breast-conserving surgery in the univariate (p=0.048) and multivariate analyses (p=0.014). However, there was no significant relationship between any of the various socioeconomic factors and distress. Distress level was reduced over time in both scales. Of the 149 high risk patients, only 21 received the psychological intervention. Using linear mixed models, the psychological intervention resulted in marginally significant reductions in DT (p=0.051) and CES-D (p=0.077) scores. CONCLUSION: More than half of patients experienced distress upon initial diagnosis, and the determined surgery type was an important factor associated with high distress level. It is important to identify high risk patients and to manage distress during the initial phase.
Breast Neoplasms* ; Breast* ; Diagnosis* ; Humans ; Mass Screening* ; Mastectomy ; Mastectomy, Segmental ; Multivariate Analysis ; Retrospective Studies ; Seoul ; Socioeconomic Factors ; Thermometers ; Weights and Measures

Hypernatremia is a common electrolyte disorder in children and elderly people and has high short-term mortality. However, no high-quality studies have examined the correction rate of hypernatremia and the amount of fluid required for correction. Therefore, in this study, we will compare the efficacy and safety of rapid intermittent bolus (RIB) and slow continuous infusion (SCI) of electrolyte-free solution in hypernatremia treatment. Methods: This is a prospective, investigator-initiated, multicenter, open-label, randomized controlled study with two experimental groups. A total of 166 participants with severe hypernatremia will be enrolled and divided into two randomized groups; both the RIB and SCI groups will be managed with electrolyte-free water. We plan to infuse the same amount of fluid to both groups, for 1 hour in the RIB group and continuously in the SCI group. The primary outcome is a rapid decrease in serum sodium levels within 24 hours. The secondary outcomes will further compare the efficacy and safety of the two treatment protocols. Conclusion: This is the first randomized controlled trial to evaluate the efficacy and safety of RIB correction compared with SCI in adult patients with severe hypernatremia.

PURPOSE: This study was conducted to investigate the relationship of time interval from intubation to return of spontaneous circulation (ROSC) in out-of-hospital cardiac arrest (OHCA) patients according to the presence or absence of intracranial hemorrhage (ICH). METHODS: This retrospective study used data from a prospectively collected OHCA registry for patients treated from January 2008 to December 2016. Non-traumatic adult OHCA patients who underwent brain computed tomography were included, while patients who achieved a prehospital ROSC or required advanced airway management were excluded. Utstein variables, initial blood gas analysis, electrolyte levels, and the time interval from intubation to ROSC were used to compare the ICH and non-ICH groups. RESULTS: A total of 448 patients were analyzed. The ICH group was younger and had more females than the non-ICH group. The time interval from intubation to ROSC was significantly shorter in the ICH group than the non-ICH group. The median time and interquartile range were 3 (2 to 7) minutes in the ICH group and 6 (3 to 10) minutes in the non-ICH group. The patient age, gender, potassium level, and time interval from intubation to ROSC were significant variables in the multivariable analysis. In a multivariable logistic regression model that included these variables, the area under the receiver operating characteristic curve was 0.838. CONCLUSION: OHCA patients with ICH achieve ROSC after intubation in a shorter amount of time than those without ICH.
Adult ; Advanced Cardiac Life Support ; Airway Management ; Blood Gas Analysis ; Brain ; Cardiopulmonary Resuscitation ; Female ; Humans ; Intracranial Hemorrhages ; Intubation ; Logistic Models ; Out-of-Hospital Cardiac Arrest ; Potassium ; Prognosis ; Prospective Studies ; Retrospective Studies ; ROC Curve

Background: Hyperkalemia is a potentially fatal condition that mandates rapid identification in emergency departments (EDs). Although a 12-lead electrocardiogram (ECG) can indicate hyperkalemia, subtle changes in the ECG often pose detection challenges. An artificial intelligence application that accurately assesses hyperkalemia risk from ECGs could revolutionize patient screening and treatment. We aimed to evaluate the efficacy and reliability of a smartphone application, which utilizes camera-captured ECG images, in quantifying hyperkalemia risk compared to human experts. Methods: We performed a retrospective analysis of ED hyperkalemic patients (serum potassium ≥ 6 mmol/L) and their age- and sex-matched non-hyperkalemic controls. The application was tested by five users and its performance was compared to five board-certified emergency physicians (EPs). Results: Our study included 125 patients. The area under the curve (AUC)-receiver operating characteristic of the application’s output was nearly identical among the users, ranging from 0.898 to 0.904 (median: 0.902), indicating almost perfect interrater agreement (Fleiss’ kappa 0.948). The application demonstrated high sensitivity (0.797), specificity (0.934), negative predictive value (NPV) (0.815), and positive predictive value (PPV) (0.927). In contrast, the EPs showed moderate interrater agreement (Fleiss’ kappa 0.551), and their consensus score had a significantly lower AUC of 0.662. The physicians’ consensus demonstrated a sensitivity of 0.203, specificity of 0.934, NPV of 0.527, and PPV of 0.765. Notably, this performance difference remained significant regardless of patients’ sex and age (P < 0.001 for both). Conclusion Our findings suggest that a smartphone application can accurately and reliably quantify hyperkalemia risk using initial ECGs in the ED.

Background: Rapid revascularization is the key to better patient outcomes in ST-elevation myocardial infarction (STEMI). Direct activation of cardiac catheterization laboratory (CCL) using artificial intelligence (AI) interpretation of initial electrocardiography (ECG) might help reduce door-to-balloon (D2B) time. To prove that this approach is feasible and beneficial, we assessed the non-inferiority of such a process over conventional evaluation and estimated its clinical benefits, including a reduction in D2B time, medical cost, and 1-year mortality. Methods: This is a single-center retrospective study of emergency department (ED) patients suspected of having STEMI from January 2021 to June 2021. Quantitative ECG (QCG™), a comprehensive cardiovascular evaluation system, was used for screening. The non-inferiority of the AI-driven CCL activation over joint clinical evaluation by emergency physicians and cardiologists was tested using a 5% non-inferiority margin. Results: Eighty patients (STEMI, 54 patients [67.5%]) were analyzed. The area under the curve of QCG score was 0.947. Binned at 50 (binary QCG), the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 98.1% (95% confidence interval [CI], 94.6%, 100.0%), 76.9% (95% CI, 60.7%, 93.1%), 89.8% (95% CI, 82.1%, 97.5%) and 95.2% (95% CI, 86.1%, 100.0%), respectively. The difference in sensitivity and specificity between binary QCG and the joint clinical decision was 3.7% (95% CI, −3.5%, 10.9%) and 19.2% (95% CI, −4.7%, 43.1%), respectively, confirming the non-inferiority. The estimated median reduction in D2B time, evaluation cost, and the relative risk of 1-year mortality were 11.0 minutes (interquartile range [IQR], 7.3–20.0 minutes), 26,902.2 KRW (22.78 USD) per STEMI patient, and 12.39% (IQR, 7.51–22.54%), respectively. Conclusion AI-assisted CCL activation using initial ECG is feasible. If such a policy is implemented, it would be reasonable to expect some reduction in D2B time, medical cost, and 1-year mortality.

Background/Aims: Metabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by fat accumulation in the liver. MASLD encompasses both steatosis and MASH. Since MASH can lead to cirrhosis and liver cancer, steatosis and MASH must be distinguished during patient treatment. Here, we investigate the genomes, epigenomes, and transcriptomes of MASLD patients to identify signature gene set for more accurate tracking of MASLD progression. Methods: Biopsy-tissue and blood samples from patients with 134 MASLD, comprising 60 steatosis and 74 MASH patients were performed omics analysis. SVM learning algorithm were used to calculate most predictive features. Linear regression was applied to find signature gene set that distinguish the stage of MASLD and to validate their application into independent cohort of MASLD. Results: After performing WGS, WES, WGBS, and total RNA-seq on 134 biopsy samples from confirmed MASLD patients, we provided 1,955 MASLD-associated features, out of 3,176 somatic variant callings, 58 DMRs, and 1,393 DEGs that track MASLD progression. Then, we used a SVM learning algorithm to analyze the data and select the most predictive features. Using linear regression, we identified a signature gene set capable of differentiating the various stages of MASLD and verified it in different independent cohorts of MASLD and a liver cancer cohort. Conclusions We identified a signature gene set (i.e., CAPG, HYAL3, WIPI1, TREM2, SPP1, and RNASE6) with strong potential as a panel of diagnostic genes of MASLD-associated disease.