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Introduction: The most efficient risk stratification algorithms are expected to deliver robust and indefectible identification of high-risk children with hypertrophic cardiomyopathy (HCM). Here we compare algorithms for risk stratification in primary prevention in HCM children and investigate whether novel indices of biatrial performance improve these algorithms.
Methods And Results: The endpoints were defined as sudden cardiac death, resuscitated cardiac arrest, or appropriate implantable cardioverter-defibrillator discharge. We examined the prognostic utility of classic American College of Cardiology/American Heart Association (ACC/AHA) risk factors, the novel HCM Risk-Kids score and the combination of these with indices of biatrial dynamics. The study consisted of 55 HCM children (mean age 12.5 ± 4.6 years, 69.1% males); seven had endpoints (four deaths, three appropriate ICD discharges). A strong trend (DeLong = 0.08) was observed towards better endpoint identification performance of the HCM Risk-Kids Model compared to the ACC/AHA strategy. Adding the atrial conduit function component significantly improved the prediction capabilities of the AHA/ACC Model (DeLong = 0.01) and HCM Risk-Kids algorithm (DeLong = 0.04).
Conclusions: The new HCM Risk-Kids individualised algorithm and score was capable of identifying high-risk children with very good accuracy. The inclusion of one of the atrial dynamic indices improved both risk stratification strategies.
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http://dx.doi.org/10.3390/jcm10040650 | DOI Listing |
J Gerontol A Biol Sci Med Sci
September 2025
Institute of Epidemiology and Medical Biometry, Ulm University, Ulm, Germany.
Background: Ambulatory older residents in long-term care(LTC) have the highest risk of falling. However, the relationship between ambulatory activity (steps per day) and fall risk in LTC is unclear. This study examined whether baseline daily step count, functional capacity and cognitive function predicted falls in LTC residents, and whether functional capacity modified the relationship between step count and fall risk.
View Article and Find Full Text PDFJ Intensive Care Med
September 2025
Medical Intensive Care Unit, 108 Military Central Hospital, Hanoi, Vietnam.
Background: Bedside ultrasound is increasingly utilized to assess muscle mass in critically ill patients, providing a noninvasive and real-time tool for early risk stratification. Muscle wasting is known to be associated with adverse outcomes in septic shock, but its prognostic value using ultrasound in this population remains underexplored. This study aimed to investigate the association between changes in rectus femoris cross-sectional area (CSA), assessed by bedside ultrasound, and 28-day mortality in patients with septic shock.
View Article and Find Full Text PDFJ Intensive Care Med
September 2025
Independent Researcher, Outcomes Research, Atlanta, GA, USA.
Purpose: Obesity is a risk factor for sepsis complications in older adults. We assessed the impact of metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUHO) on outcomes in septic shock.
Methods: We conducted a retrospective analysis using the National Inpatient Sample (2016-2020) to identify a cohort of 1,737,075 patients aged 65 years and older who were hospitalized with septic shock, as defined by ICD-10 diagnosis codes.
Neuroradiology
September 2025
Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Purpose: To develop and validate an integrated model based on MR high-resolution vessel wall imaging (HR-VWI) radiomics and clinical features to preoperatively assess periprocedural complications (PC) risk in patients with intracranial atherosclerotic disease (ICAD) undergoing percutaneous transluminal angioplasty and stenting (PTAS).
Methods: This multicenter retrospective study enrolled 601 PTAS patients (PC+, n = 84; PC -, n = 517) from three centers. Patients were divided into training (n = 336), validation (n = 144), and test (n = 121) cohorts.
J Ultrasound Med
September 2025
Department of Ultrasound, Donghai Hospital Affiliated to Kangda College of Nanjing Medical University, Lianyungang, China.
Objective: The aim of this study is to evaluate the prognostic performance of a nomogram integrating clinical parameters with deep learning radiomics (DLRN) features derived from ultrasound and multi-sequence magnetic resonance imaging (MRI) for predicting survival, recurrence, and metastasis in patients diagnosed with triple-negative breast cancer (TNBC) undergoing neoadjuvant chemotherapy (NAC).
Methods: This retrospective, multicenter study included 103 patients with histopathologically confirmed TNBC across four institutions. The training group comprised 72 cases from the First People's Hospital of Lianyungang, while the validation group included 31 cases from three external centers.