Hereditary hemorrhagic telagiectasia (HHT), which is characterized by the classic triad of mucocutaneous telangiectases, arteriovenous malformations (AVMs) and inheritance, is an autosomal dominant disorder. The characteristic manifestations of HHT are all due to abnormalities of the vascular structure. This report deals with the case of a 14-year-old girl with typical features of HHT that include recurrent epistaxis, mucocutanous telangiectases, pulmonary and cerebral AVMs and a familial occurrence.
Adolescent ; Arteriovenous Malformations ; Epistaxis ; Female ; Humans ; Inheritance Patterns ; Recurrence ; Telangiectasia, Hereditary Hemorrhagic* ; Telangiectasis ; Wills

PURPOSE: To evaluate the potential prognostic value of the antithrombin-III (AT-III) level in the children with acute lung injury (ALI), we analyzed several early predictive factors of death including AT-III level at the onset of ALI and compared the relative risk of them for mortality. METHODS: Over a 18-month period, a total of 198 children were admitted to our pediatric intensive care unit and 21 mechanically ventilated patients met ALI criteria, as defined by American-European consensus conference, i.e., bilateral pulmonary infiltrates and PaO2/FiO2 lower than 300 without left atrial hypertension. Demographic variables, hemodynamic and respiratory parameters, underlying diseases, as well as Pediatric Risk of Mortality-III (PRISM-III) scores and Lung Injury Score (LIS) at admission were collected. AT-III levels were measured within 3 hours after admission. These variables were compared between survivors and non-survivors and entered into a multiple logistic regression analysis to evaluate their independent prognostic roles. RESULTS: The overall mortality rate was 38.1% (8/21). Non-survivors showed lower age, lower lung compliance, higher PEEP, higher oxygenation index (OI), lower arterial pH, lower PaO2/FiO2, higher PRISM-III score and LIS, and lower AT-III level. PRISM-III score, LIS, OI and decreased AT-III level (less than 70%) were independently associated with a risk of death and the odds ratio of decreased AT-III level for mortality is 2.75 (95% confidence interval; 1.28-4.12) CONCLUSION: These results suggest that the decreased level of AT-III is an important prognostic factor in children with ALI and the replacement of AT-III may be considered as an early therapeutic trial.
Acute Lung Injury* ; Child* ; Consensus ; Hemodynamics ; Humans ; Hydrogen-Ion Concentration ; Hypertension ; Intensive Care Units ; Logistic Models ; Lung Compliance ; Lung Injury ; Mortality ; Odds Ratio ; Oxygen ; Survivors

No abstract available.
Catheterization, Central Venous* ; Central Venous Catheters* ; Embolism, Air* ; Humans ; Idiopathic Pulmonary Fibrosis*

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.

PURPOSE: Acute interstitial pneumonia (AIP) is a rare disease, but its prognosis is fatal because of lack of efficient treatment modality. Recently, it has been reported that there was epidemic AIP in Korea. This study aims to investigate the past and current status of AIP in Korea. METHODS: We performed a nationwide survey and a prospective study. From August 6 to 15, 2008, a questionnaire survey was conducted to identify the prevalence, local distribution, and response to current treatments. The questionnaire was answered by pediatrician working in 23 referral centers in Korea. In addition, 5 referral centers in Seoul performed a preliminary prospective observational study by obtaining clinical data and specimens from appropriate patients. The Korea Centers for Disease Control and Prevention analyzed the samples for possible pathogens. RESULTS: The survey showed 78 AIP cases had occurred and 36 patients had died. Lung biopsy was performed only on 20 patients. In 2008, 9 AIP cases developed. In a prospective study, 9 (M:F=5:4) patients developed AIP in spring and 7 (78%) died, with the mean rate of death occurring 46 days after diagnosis. Human corona virus 229 E, cytomegalovirus, influenza A virus, influenza B virus, and parainfluenza virus were isolated from the respiratory specimens. CONCLUSION: This study showed nationwide prevalence of AIP in Korea. In addition, because of the high mortality rate and rapid progress, pediatricians need to be aware of the disease. Further studies and a nationwide network are required for reducing the morbidity and mortality rates related to AIP.
Biopsy ; Centers for Disease Control and Prevention (U.S.) ; Child ; Cytomegalovirus ; Humans ; Influenza A virus ; Influenza B virus ; Korea ; Lung ; Lung Diseases, Interstitial ; Paramyxoviridae Infections ; Prevalence ; Prognosis ; Prospective Studies ; Surveys and Questionnaires ; Rare Diseases ; Referral and Consultation ; Viruses

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.