To develop and evaluate an automated clinical decision support system (CDSS) capable of computing both categorical and exact-percentage cardiovascular risk (CVR) in routine clinical practice. We devel Show more
To develop and evaluate an automated clinical decision support system (CDSS) capable of computing both categorical and exact-percentage cardiovascular risk (CVR) in routine clinical practice. We developed and implemented an automated CDSS (AlinIQ®) for CVR assessment within the Clinical Laboratory Department of Hospital Universitari i Politècnic La Fe (València, Spain), applied to patients referred from Primary Care. The analytical profile-requested either via automatic trigger or proactive clinician order-included total cholesterol, HDL-C, LDL-C, non-HDL-C, triglycerides, lipoprotein(a) [Lp(a)], serum creatinine, and estimated glomerular filtration rate. Additional required inputs were smoking status, systolic blood pressure, country of origin, and age at diabetes onset. The CDSS automatically computed SCORE2, SCORE2-OP, SCORE2-Diabetes, and SCORE2 Asia-Pacific, generating both categorical strata and exact-percentage CVR. Lp(a)-adjusted CVR was derived using coefficients from the Spanish Atherosclerosis Society. Following the ESC 2025 Focused Update, patients meeting predefined clinical criteria for moderate, high, or very high CVR were directly assigned to the corresponding category without SCORE calculation. The system also incorporated modifying risk factors, generating standardized interpretive comments and personalized lipid targets. Over one month, 2289 screenings were requested; 189 (8.3%) were excluded. A total of 171 patients (7.5%) were classified as high risk based on score-based calculation (164 [95.9%] triggered automatically), and 29 (1.6%) as very high risk (23 [79.3%] triggered automatically). 119 individuals (6.8%) were reclassified to a higher risk category after Lp(a)-based adjustment. This CDSS provides a scalable and reproducible framework for laboratory-driven cardiovascular prevention. Show less
Psychological problems are a common concern among children and adolescents. Identifying distinct profiles of internalizing and externalizing problems offers a comprehensive understanding of the psycho Show more
Psychological problems are a common concern among children and adolescents. Identifying distinct profiles of internalizing and externalizing problems offers a comprehensive understanding of the psychological challenges faced by this population. This study aimed to explore mental health profiles in a sample of 5652 Spanish children and adolescents aged 8-16 years, examining differences between these profiles and reference groups. Using the Child and Adolescent Assessment System (SENA) and latent profile analysis (LPA), six profiles were identified among children and seven among adolescents, based on internalizing, externalizing and contextual problems. Among children, the most frequent profiles were 'moderate problems, low conflict' and 'low problems', while adolescents most commonly exhibited 'moderate internalizing, low externalizing' and 'low problems' profiles. Significant differences across profiles were linked to emotional regulation, self-esteem and social competence. These findings provide a nuanced perspective on the mental health landscape of Spanish youth, emphasizing the critical role of early detection of emotional difficulties in educational settings. Furthermore, the study offers valuable insights for developing tailored interventions to meet the specific emotional needs of children and adolescents. Show less