Alcoholic celiac plexus blocks have been used to relieve intractable upper abdominal cancer pain. Various techniques have been proposed including the use of the X-ray and CT scanner to improve results and to avoid complications. We used the CT scanner and the X-ray fluroscope for 36 cases of alcoholic celiac plexus blocks to determine the depth and position of the needle without previous diagnostic blocks. Good to excellent pain relief occurred in more than 72% of the patients and the advantages of the use of the CT scanner showed that 1) placement of the needle tip is easily established according to the surrounding anatomical structures. 2) the operator can introduce the needle without anxiety. 3) the time required for block are saved.
Alcoholics ; Anxiety ; Celiac Plexus* ; Humans ; Needles

Anterograde amnesia in Behcet's disease is a rare occurrence. A 50-year-old man presented with anterograde amnesia. He had been suffering multiple oral aphthous ulcers and genital ulcers with erythema nodosum. A neurological examination revealed prominent anterograde memory disturbance. Brain MRI revealed high signal intensity lesions involving the anterior thalamus, posterior part of the basal ganglia and the mesial temporal lobe. We report a rare case of Behcet's disease manifesting severe anterograde amnesia resulting from thalamic and mesial temporal lesions.
Amnesia, Anterograde* ; Basal Ganglia ; Brain ; Erythema Nodosum ; Humans ; Magnetic Resonance Imaging ; Memory ; Middle Aged ; Neurologic Examination ; Stomatitis, Aphthous ; Temporal Lobe ; Thalamus ; Ulcer

BACKGROUND AND OBJECTIVES: Ten to twenty percent of children with Kawasaki disease (KD) do not respond to initial intravenous immunoglobulin (IVIG) treatment. If untreated, approximately 15% to 25% of KD patients have complications. The aim of this study was to find useful predictors of responsiveness to initial IVIG treatment in KD. SUBJECTS AND METHODS: We retrospectively reviewed medical records of 91 children diagnosed with KD at Myong Ji Hospital from March 2012 to April 2014. Before and after (24 hours to 36 hours) IVIG treatment, the following laboratory data were obtained: hemoglobin (Hb) level, white blood cell count, proportion of neutrophil, lymphocyte and eosinophil, platelet count, erythrocyte sedimentation rate (ERS), C-reactive protein (CRP), creatine kinase (CK), creatine kinase MB (CK-MB), and N-terminal pro-brain natriuretic peptide (NT-proBNP). Subjects were then divided into two groups: IVIG-responsive or IVIG-resistant. RESULTS: Of 91 patients, 11 (12%) required retreatment. By univariate analysis, before-IVIG laboratory parameters of white blood cell count, % neutrophil, ERS, CRP, sodium, CK, CK-MB, and NT-proBNP were significantly different between IVIG-responsive and IVIG-resistant patient groups. In the after-IVIG laboratory parameters, Hb level, white blood cell count, % neutrophil, % lymphocyte, CRP, CK, CK-MB, and NT-pro-BNP were significantly different between the two groups. While the mean-differences were not statistically significant, fractional change (FC)-CRP and FC-% neutrophil showed significant difference. By multivariate analysis, FC-CRP was confirmed to be an independent predictor for initial IVIG resistance. CONCLUSION: Fractional change-C-reactive protein might be a useful and important value for predicting initial IVIG resistance in KD patients.
Blood Sedimentation ; C-Reactive Protein ; Child ; Creatine Kinase ; Eosinophils ; Humans ; Immunization, Passive* ; Immunoglobulins ; Immunoglobulins, Intravenous ; Leukocyte Count ; Lymphocytes ; Medical Records ; Mucocutaneous Lymph Node Syndrome* ; Multivariate Analysis ; Neutrophils ; Platelet Count ; Retreatment ; Retrospective Studies ; Risk Factors ; Sodium

BACKGROUND: Interest in pharmacist participation in the multidisciplinary intensive care team is increasing. However, studies examining pharmacist interventions in the medical intensive care unit (MICU) are limited in Korea. The aim of this study was to describe the current status of pharmacist interventions and to identify common pharmacologic problems requiring pharmacist intervention in the MICU. METHODS: Between September 2013 and August 2014, a retrospective, observational study was conducted in the 22-bed MICU at a university hospital. Data were obtained from two trained pharmacists who participated in MICU rounds three times a week. In addition to patient characteristics, data on the cause, type, related drug, and acceptance rate of interventions were collected. RESULTS: In 340 patients, a total of 1211 pharmacologic interventions were performed. The majority of pharmacologic interventions were suggested by pharmacists at multidisciplinary rounds in the MICU. The most common pharmacologic interventions were adjustment of dosage and administration (n = 328, 26.0%), followed by parenteral/enteral nutritional support (n = 228, 18.1%), the provision of drug information (n = 228, 18.1%), and advice regarding pharmacokinetics (n = 118, 9.3%). Antimicrobial agents (n = 516, 42.6%) were the most frequent type of drug associated with pharmacist interventions. The acceptance rate of interventions was 84.1% with most accepted by physicians within 24 hours (n = 602, 92.8%). CONCLUSIONS: Medication and nutritional problems are frequently encountered pharmacotherapeutic problems in the MICU. Pharmacist interventions play an important role in the management of these problems.
Anti-Infective Agents ; Humans ; Critical Care ; Intensive Care Units* ; Korea ; Nutritional Support ; Observational Study ; Pharmacists* ; Pharmacokinetics ; Retrospective Studies

Maternal and child health is an important issue throughout the world. Given their impact on maternal and child health, nutritional issues need to be carefully addressed. Accordingly, the effect of maternal, child, and nutritional disorders on disability-adjusted life years (DALYs) should be calculated. The present study used DALYs to estimate the burden of disease of maternal, neonatal, and nutritional disorders in the Korean population in 2012. For this purpose, we used claim data of the Korean National Health Insurance Service, DisMod II, and death data of the Statistics Korea and adhered to incidence-based DALY estimation methodology. The total DALYs per 100,000 population were 376 in maternal disorders, 64 in neonatal disorders, and 58 in nutritional deficiencies. The leading causes of DALYs were abortion in maternal disorders, preterm birth complications in neonatal disorders, and iron-deficiency anemia in nutritional deficiencies. Our findings shed light on the considerable burden of maternal, neonatal, and nutritional conditions, emphasizing the need for health care policies that can reduce morbidity and mortality.
Anemia, Iron-Deficiency ; Child ; Child Health ; Delivery of Health Care ; Humans ; Korea* ; Malnutrition ; Mortality ; National Health Programs ; Nutrition Disorders* ; Premature Birth

Objectives: ：The high lifetime prevalence of depression in Korea is related to problems such as suicide and decreased productivity, as well as the cost of disease due to increased use of medical services, which can cause great socioeconomic loss. Therefore, in this study, the burden of disease of depression and the importance of managing mental health diseases, which are increasing day by day, are suggested to be helpful in determining priorities in health policy establishment. Methods: ：In this study, the socio-economic cost of depression was calculated by dividing it into direct cost and indirect cost. For statistical data, data from the National Health Insurance Service of the public and statistics on diseases of national interest were mainly used. Results: ：As a result, the socio-economic cost of depression in 2019 estimated in this study was calculated to be a total of KRW 4.83 trillion, with direct costs 692.9 billion won and indirect costs 4.13 trillion won. Among them, the cost due to decrease in work performance accounted for the largest portion, accounting for 65.5%. Conclusions ：As the socio-economic burden due to depression is expected to increase in the future, it is necessary to establish a systematic funding plan for the treatment and management of depressed patients in daily life.

Background: Increasing numbers of studies and research about artificial intelligence (AI) and machine learning (ML) have led to their application in clinical trials. The purpose of this study is to analyze computer-based new technologies (AI/ML) applied on clini-cal trials registered on ClinicalTrials.gov to elucidate current usage of these technologies. Methods: As of March 1st, 2023, protocols listed on ClinicalTrials.gov that claimed to use AI/ML and included at least one of the following interventions—Drug, Biological, Dietary Supplement, or Combination Product—were selected. The selected protocols were classified according to their context of use: 1) drug discovery; 2) toxicity prediction; 3) enrichment; 4) risk stratification/management; 5) dose selection/optimization; 6) adherence; 7) synthetic control; 8) endpoint assessment; 9) postmarketing surveillance; and 10) drug selection. Results The applications of AI/ML were explored in 131 clinical trial protocols. The areas where AI/ML was most frequently utilized in clinical trials included endpoint assessment (n=80), followed by dose selection/optimization (n=15), risk stratification/management (n=13), drug discovery (n=4), adherence (n=4), drug selection (n=1) and enrichment (n=1). Conclusion: The most frequent application of AI/ML in clinical trials is in the fields of endpoint assessment, where the utilization is primarily focuses on the diagnosis of disease by imag-ing or video analyses. The number of clinical trials using artificial intelligence will increase as the technology continues to developrapidly, making it necessary for regulatory associates to establish proper regulations for these clinical trials.

Background: Increasing numbers of studies and research about artificial intelligence (AI) and machine learning (ML) have led to their application in clinical trials. The purpose of this study is to analyze computer-based new technologies (AI/ML) applied on clini-cal trials registered on ClinicalTrials.gov to elucidate current usage of these technologies. Methods: As of March 1st, 2023, protocols listed on ClinicalTrials.gov that claimed to use AI/ML and included at least one of the following interventions—Drug, Biological, Dietary Supplement, or Combination Product—were selected. The selected protocols were classified according to their context of use: 1) drug discovery; 2) toxicity prediction; 3) enrichment; 4) risk stratification/management; 5) dose selection/optimization; 6) adherence; 7) synthetic control; 8) endpoint assessment; 9) postmarketing surveillance; and 10) drug selection. Results The applications of AI/ML were explored in 131 clinical trial protocols. The areas where AI/ML was most frequently utilized in clinical trials included endpoint assessment (n=80), followed by dose selection/optimization (n=15), risk stratification/management (n=13), drug discovery (n=4), adherence (n=4), drug selection (n=1) and enrichment (n=1). Conclusion: The most frequent application of AI/ML in clinical trials is in the fields of endpoint assessment, where the utilization is primarily focuses on the diagnosis of disease by imag-ing or video analyses. The number of clinical trials using artificial intelligence will increase as the technology continues to developrapidly, making it necessary for regulatory associates to establish proper regulations for these clinical trials.

Background: Increasing numbers of studies and research about artificial intelligence (AI) and machine learning (ML) have led to their application in clinical trials. The purpose of this study is to analyze computer-based new technologies (AI/ML) applied on clini-cal trials registered on ClinicalTrials.gov to elucidate current usage of these technologies. Methods: As of March 1st, 2023, protocols listed on ClinicalTrials.gov that claimed to use AI/ML and included at least one of the following interventions—Drug, Biological, Dietary Supplement, or Combination Product—were selected. The selected protocols were classified according to their context of use: 1) drug discovery; 2) toxicity prediction; 3) enrichment; 4) risk stratification/management; 5) dose selection/optimization; 6) adherence; 7) synthetic control; 8) endpoint assessment; 9) postmarketing surveillance; and 10) drug selection. Results The applications of AI/ML were explored in 131 clinical trial protocols. The areas where AI/ML was most frequently utilized in clinical trials included endpoint assessment (n=80), followed by dose selection/optimization (n=15), risk stratification/management (n=13), drug discovery (n=4), adherence (n=4), drug selection (n=1) and enrichment (n=1). Conclusion: The most frequent application of AI/ML in clinical trials is in the fields of endpoint assessment, where the utilization is primarily focuses on the diagnosis of disease by imag-ing or video analyses. The number of clinical trials using artificial intelligence will increase as the technology continues to developrapidly, making it necessary for regulatory associates to establish proper regulations for these clinical trials.

Background: Increasing numbers of studies and research about artificial intelligence (AI) and machine learning (ML) have led to their application in clinical trials. The purpose of this study is to analyze computer-based new technologies (AI/ML) applied on clini-cal trials registered on ClinicalTrials.gov to elucidate current usage of these technologies. Methods: As of March 1st, 2023, protocols listed on ClinicalTrials.gov that claimed to use AI/ML and included at least one of the following interventions—Drug, Biological, Dietary Supplement, or Combination Product—were selected. The selected protocols were classified according to their context of use: 1) drug discovery; 2) toxicity prediction; 3) enrichment; 4) risk stratification/management; 5) dose selection/optimization; 6) adherence; 7) synthetic control; 8) endpoint assessment; 9) postmarketing surveillance; and 10) drug selection. Results The applications of AI/ML were explored in 131 clinical trial protocols. The areas where AI/ML was most frequently utilized in clinical trials included endpoint assessment (n=80), followed by dose selection/optimization (n=15), risk stratification/management (n=13), drug discovery (n=4), adherence (n=4), drug selection (n=1) and enrichment (n=1). Conclusion: The most frequent application of AI/ML in clinical trials is in the fields of endpoint assessment, where the utilization is primarily focuses on the diagnosis of disease by imag-ing or video analyses. The number of clinical trials using artificial intelligence will increase as the technology continues to developrapidly, making it necessary for regulatory associates to establish proper regulations for these clinical trials.