We report a case of 48 years-old male bipolar disorder patient with acute kidney injury due to severe lithium intoxication. He was taking lithium 300 mg per day but suddenly ingested all 30 tablets of lithium 300 mg (total 9,000 mg) at one sitting to commit suicide. On his arrival at the emergency room, the serum lithium concentration was 12.47 mEq/L. Thereafter he was treated with hemodialysis four times for 4-4.5 hour per session and recovered without any prominent sequela. The serum lithium level checked 9 days after hemodialysis was 0.1 mEq/L. Therefore we suggest that clinicians should consider such a strategy as mandatory hemodialysis in a patient with severe kidney intoxication when serum lithium concentrations are very high or rapidly increasing.
Acute Kidney Injury ; Bipolar Disorder ; Emergencies ; Humans ; Kidney ; Lithium ; Male ; Renal Dialysis ; Suicide ; Tablets

OBJECTIVE: A randomized double-blind cross-over trial was conducted in patients with persistent auditory hallucinations (AHs) to investigate whether bilateral repetitive transcranial magnetic stimulation (rTMS) at the temporoparietal area or Broca's area is more effective at high- or low-frequencies compared to a sham condition. METHODS: Twenty three patients with persistent AHs who remained stable on the same medication for 2 months were enrolled. They were randomized to one of four conditions: low-frequency (1 Hz)-rTMS to the temporoparietal area (L-TP), high-frequency (20 Hz)-rTMS to the temporoparietal area (H-TP), high-frequency (20 Hz)-rTMS to Broca's area (H-B), or sham. RESULTS: All the four rTMS conditions resulted in significant decrease in the scores under the auditory hallucination rating scale and hallucination change scale over time. However, there were no significant treatment effects or interaction between time and treatment, suggesting no superior effects of the new paradigms over the sham condition. CONCLUSION: Our findings suggest that bilateral rTMS at the temporoparietal area or Broca's area with high- or low-frequency does not produce superior effects in reducing AHs compared to sham stimulation.
Cross-Over Studies* ; Frontal Lobe ; Hallucinations* ; Humans ; Schizophrenia* ; Transcranial Magnetic Stimulation*

Objectives: In the Fourth Industrial Revolution, there is a focus on managing diverse medical data to improve healthcare and prevent disease. The challenges include tracking detailed medical records across multiple institutions and the necessity of linking domestic public medical entities for efficient data sharing. This study explores MyHealthWay, a Korean healthcare platform designed to facilitate the integration and transfer of medical data from various sources, examining its development, importance, and legal implications. Methods: To evaluate the management status and utilization of MyHealthWay, we analyzed data types, security, legal issues, domestic versus international issues, and infrastructure. Additionally, we discussed challenges such as resource and infrastructure constraints, regulatory hurdles, and future considerations for data management. Results: The secure sharing of medical information via MyHealthWay can reduce the distance between patients and healthcare facilities, fostering personalized care and self-management of health. However, this approach faces legal challenges, particularly relating to data standardization and access to personal health information. Legal challenges in data standardization and access, particularly for secondary uses such as research, necessitate improved regulations. There is a crucial need for detailed governmental guidelines and clear data ownership standards at institutional levels. Conclusions This report highlights the role of Korea's MyHealthWay, which was launched in 2023, in transforming healthcare through systematic data integration. Challenges include data privacy and legal complexities, and there is a need for data standardization and individual empowerment in health data management within a systematic medical big data framework.

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.