Long-term effects of local radiotherapy on growth and vertebral features in children with high-risk neuroblastoma.

BACKGROUND: To evaluate the effects of local radiotherapy (RT) on growth, we evaluated the chronological growth profiles and vertebral features of children with high-risk neuroblastoma. METHODS: Thirty-eight children who received local photon or proton beam therapy to the abdomen or retroperitoneum between January 2014 and September 2019 were included. Simple radiography of the thoracolumbar spine was performed before and every year after RT. The height and vertical length of the irradiated vertebral bodies (VBs) compared with the unirradiated VBs (vertebral body ratio, VBR) were analyzed using the linear mixed model. Shape feature analysis was performed to compare the irradiated and unirradiated vertebrae. RESULTS: The follow-up was a median of 53.5 months (range, 21-81 months) after RT. A decline in height z-scores was mainly found in the early phase after treatment. In the linear mixed model with height, the initial height (fixed, p < 0.001), sex (time interaction, p = 0.008), endocrine dysfunction (time interaction, 0.019), and age at diagnosis (fixed and time interaction, both p = 0.002) were significant. Unlike the trend in height, the change in VBR (ΔVBR) decreased gradually (p < 0.001). The ΔVBR in the group that received more than 30 Gy decreased more than in the group that received smaller doses. In the shape feature analysis, the irradiated VBs changed to a more irregular surface that were neither round nor rectangular. CONCLUSION: The irradiated VBs in children were gradually restricted compared to the unirradiated VBs in long-term follow-up, and higher RT doses were significantly affected. Radiation-induced irregular features of VBs were observed.

Outcomes of Concomitant Maze Procedure in Tricuspid Repair for Severe Tricuspid Regurgitation.

BACKGROUND: We aimed to analyze the impact of concomitant Maze procedure on the clinical and rhythm outcomes, and echocardiographic parameters in tricuspid repair for patients with severe tricuspid regurgitation (TR) and persistent atrial fibrillation (AF). METHODS: Patients who had severe TR and persistent AF and underwent tricuspid valve (TV) repair were included in the study. Both primary TR and secondary TR were included in the current study. The study population was stratified according to Maze procedure. The primary outcome was major adverse cardiovascular and cerebrovascular event (MACCE) at 15 years post-surgery. Propensity-score matching analyses was performed to adjust baseline differences. RESULTS: Three hundred seventy-one patients who underwent tricuspid repair for severe TR and persistent AF from 1994 to 2021 were included, and 198 patients (53.4%) underwent concomitant Maze procedure. The maze group showed 10-year sinus rhythm (SR) restoration rate of 55%. In the matched cohort, the maze group showed a lower cumulative incidence of cardiac death (4.6% vs. 14.4%, P = 0.131), readmission for heart failure (8.1% vs. 22.2%, P = 0.073), and MACCE (21.1% vs. 42.1%, P = 0.029) at 15 years compared to the non-maze group. Left atrial (LA) diameter significantly decreased in the maze group at 5 years (53.3 vs. 59.6 mm, P < 0.001) after surgery compared to preoperative level, and there was a significant difference in the change of LA diameter over time between the two groups (P = 0.013). CONCLUSION: The Maze procedure during TV repair in patients with severe TR and persistent AF showed acceptable SR rates and lower MACCE rates compared to those without the procedure, while also promoting LA reverse remodeling.

Machine learning methods for developing a predictive model of the incidence of delirium in cardiac intensive care units.

INTRODUCTION AND OBJECTIVES: Delirium, recognized as a crucial prognostic factor in the cardiac intensive care unit (CICU), has evolved in response to the changing demographics among critically ill cardiac patients. This study aimed to create a predictive model for delirium for patients in the CICU. METHODS: This study included consecutive patients admitted to the CICU of the Samsung Medical Center. To assess the candidate variables for the model: we applied the following machine learning methods: random forest, extreme gradient boosting, partial least squares, and Plmnet-elastic.net. After selecting relevant variables, we performed a logistic regression analysis to derive the model formula. Internal validation was conducted using 100-repeated hold-out validation. RESULTS: We analyzed 2774 patients, 677 (24.4%) of whom developed delirium in the CICU. Machine learning-based models showed good predictive performance. Clinically significant and frequently important predictors were selected to construct a delirium prediction scoring model for CICU patients. The model included albumin level, international normalized ratio, blood urea nitrogen, white blood cell count, C-reactive protein level, age, heart rate, and mechanical ventilation. The model had an area under the receiver operating characteristics curve (AUROC) of 0.861 (95%CI, 0.843-0.879). Similar results were obtained in internal validation with 100-repeated cross-validation (AUROC, 0.854; 95%CI, 0.826-0.883). CONCLUSIONS: Using variables frequently ranked as highly important in four machine learning methods, we created a novel delirium prediction model. This model could serve as a useful and simple tool for risk stratification for the occurrence of delirium at the patient's bedside in the CICU.

Enhancing Identification of High-Risk cN0 Lung Adenocarcinoma Patients Using MRI-Based Radiomic Features.

Purpose: To develop an MRI-based radiomics model to predict high-risk pathologic features for lung adenocarcinoma: micropapillary and solid pattern (MPsol), spread through air space (STAS), and poorly differentiated patterns. Materials and Methods: As a prospective study, we screened clinical N0 lung cancer patients who were surgical candidates and had undergone both 18F-fluorodeoxyglucose (FDG) positron emission tomography-CT (PET/CT) and chest CT from August 2018 to January 2020. We recruited patients meeting our proposed imaging criteria indicating high-risk, that is, poorer prognosis of lung adenocarcinoma, using CT and FDG PET/CT. If possible, these patients underwent an MRI examination from which we extracted 77 radiomics features from T1-contrast-enhanced and T2-weighted images. Additionally, patient demographics, SUVmax (maximum standardized uptake value) on FDG PET/CT, and the mean ADC value on DWI, were considered together to build prediction models for high-risk pathologic features. Results: Among 616 patients, 72 patients met the imaging criteria for high-risk lung cancer and underwent lung MRI. The MR-eligible group showed a higher prevalence of nodal upstaging (29.2% vs. 4.2%, p<0.001), vascular invasion (6.5% vs. 2.1%, p=0.011), high-grade pathologic features (p<0.001), worse 4-year disease free survival (p<0.001) compared with non-MR-eligible group. The prediction power for MR-based radiomics model predicting high-risk pathologic features was good, with mean area under the receiver operating curve (AUC) value measuring 0.751-0.886 in test sets. Adding clinical variables increased the predictive performance for MPsol and the poorly differentiated pattern using the 2021 grading system (AUC 0.860 and 0.907, respectively). Conclusion: Our imaging criteria can effectively screen high-risk lung cancer patients and predict high-risk pathologic features by our MR-based prediction model using radiomics.

Characteristics of subclinical tuberculosis compared to active symptomatic tuberculosis using nationwide registry cohort in Korea: prospective cohort study.

Objective: The clinical manifestations of tuberculosis (TB) range from asymptomatic to disseminated depending on the microbiological and immunological status, making the diagnosis challenging. To improve our understanding of the disease progression mechanism, we aimed to identify the characteristics of subclinical TB and important predictors of symptom development. Methods: From July 2018 to June 2019, we systemically collected data from the National Surveillance System of South Korea on patients with pulmonary TB, and compared the characteristics of subclinical and active symptomatic TB patients. Results: A total of 4,636 patients with pulmonary TB were included, and the prevalence of subclinical TB was 37.1% (1,720/4,636). In subclinical TB patients, the positivity rates of acid-fast bacilli (AFB) smear and culture were 16.2 and 50.2%, respectively. Subclinical TB patients were younger (55.6 ± 19.2 vs. 60.7 ± 19.5, P < 0.001), had a higher body mass index (21.7 ± 3.1 vs. 21.0 ± 3.5, P < 0.001), less under Medicaid support, and had lower rates of chronic lung disease, AFB smear and culture positivity, and bilateral disease. Regarding the characteristic differences of individual TB-related symptoms, age was positively associated with dyspnoea and general weakness but negatively associated with chest pain, haemoptysis, and weight loss. Male patients were more prone to weight loss. Chronic lung disease was related to symptoms including cough/phlegm, dyspnoea, and haemoptysis, while autoimmune diseases were associated with fever and weight loss. Conclusions: The development of TB-related symptoms was associated with microbiological burden and clinical characteristics including underlying comorbidities, which should be evaluated carefully.