Science Bulletin

Background: Pediatric dilated cardiomyopathy (DCM) is a leading cause of heart failure and transplantation, with variable prognosis and high early mortality. This study developed and validated a nomogram predicting short-term mortality risk to guide clinical decisions. Methods: The data were sourced from the Pediatric Cardiomyopathy Database at Shandong Provincial Hospital. Cox regression analysis was conducted to determine outcome-associated factors, and a nomogram was developed to estimate 1, 3, and 5year mortality risks for children with DCM. Model effectiveness was assessed through the concordance index (C-index) and area under the receiver operating characteristic curve (AUC). Additionally, calibration curves and decision curve analysis (DCA) were employed to evaluate the model's predictive accuracy and clinical relevance. Results: A cohort of 106 children diagnosed with primary DCM and who underwent genetic analysis was studied, with a median diagnostic age of 10 months (ranging from 5 to 84 months), comprising 50 girls (47.2%). The rate of detecting genetic mutations was 28.3%, uncovering 14 gene variants linked to DCM, with TTN mutations being the most common. Both univariate and multivariate Cox regression analyses indicated that both sex and NT-proBNP levels had a significant impact on survival rates among pediatric DCM patients.The model exhibited strong discriminative performance, calibration, and clinical net benefit, as assessed by the C-index, calibration plots, and decision curve analysis (DCA). Conclusions: The prediction model created in this research shows strong accuracy in forecasting survival rates at 1, 3, and 5 years for children with DCM, highlighting its significant relevance in clinical settings.

We assembled a multimodal clinical dataset describing demographics, placement history, prenatal substance exposure (PSE), birth characteristics, adverse childhood experiences (ACEs), International Classification of Diseases (ICD) diagnoses, and laboratory results for 3,685+ pediatric patients evaluated between 2014 and 2024 at the University of Minnesotas Adoption Medicine Clinic (AMC). Data were curated from electronic medical records through a combined manual and automated extraction protocol using a standardized operating procedure. The resulting dataset integrates structured EMR fields including neuropsychological, laboratory, and diagnostic information with manually pulled fields of ACE scores, PSE history, and placement history. We provide an overview of the population represented and describe the datasets structure, variable definitions, and validation procedures. This resource enables investigations into how early adversity impacts medical and developmental outcomes, and provides one of the largest standardized clinical placement history, PSE, and ACE datasets in an adoption and foster care pediatric population.

Congenital heart disease contributes substantially to chronic morbidity, growth impairment, and repeated healthcare utilization among children. Evidence regarding nutritional burden and outpatient healthcare patterns among pediatric patients with congenital heart disease in Indonesia remains limited. This study aimed to evaluate clinical characteristics, nutritional status, healthcare utilization, and factors associated with malnutrition among pediatric outpatients with congenital heart disease at Adam Malik General Hospital, Indonesia. A retrospective observational study was conducted using medical records of pediatric outpatients treated between January and December 2024. Demographic characteristics, cardiac diagnoses, nutritional status, complications, and outpatient visit history were analyzed. Logistic regression analysis was performed to identify factors associated with malnutrition. A total of 606 pediatric outpatients were included. Non cyanotic congenital heart disease predominated the cohort, with ventricular septal defect representing the most common diagnosis followed by patent ductus arteriosus and atrial septal defect. Nearly half of all patients demonstrated underweight or severe underweight nutritional status, while pulmonary hypertension emerged as the most frequent complication. Younger pediatric age groups and higher cumulative clinical burden independently increased the odds of malnutrition. Children with congenital heart disease at this tertiary referral center carried a substantial nutritional and clinical burden. Early nutritional surveillance and integrated long term outpatient management may improve growth outcomes and reduce chronic disease burden in resource limited settings.

Cardiovascular disease is the leading cause of death worldwide. Sudden cardiac death (SCD) accounts for roughly 50% of all cardiac deaths. The electrocardiogram (ECG) is widely used for early diagnosis of cardiac disease. However, the complexity of accurate interpretation limits the ECG's efficacy. Modern deep learning methods have been applied to assist clinicians in diagnosis. We applied Neural Architecture Search (NAS), an automated machine learning technique, to identify optimal deep learning architectures for classifying cardiac arrhythmias from ECGs. We applied the Differentiable Architecture Search strategy to an AutoFormer search space to identify optimal self-attention architectures for arrhythmia classification. We trained, validated, and tested the resulting model on the PhysioNet Challenge 2021 dataset (n = 88,253), comprising ECGs across three continents. We performed a hyperparameter optimisation on the NAS output, exploring input patch size, class weighting, and loss function. We evaluated performance using the PhysioNet Challenge metric and the area under the receiver operating characteristic curve (AUROC). The NAS converged towards minimal architectural configurations (embedding dimension: 384, depth: 4, self-attention heads: 4, MLP ratio: 1) with a validation challenge metric of 0.66 (PhysioNet Challenge 21 Winner: 0.63). The NAS-created network achieved an AUROC of 0.97 and a challenge metric of 0.71 during testing. Normal Sinus Rhythm and Sinus Tachycardia achieved AUROCs of 0.99. Low-QRS Voltage and T-wave abnormality were the worst-performing arrhythmias, with AUROCs of 0.89 and 0.90, respectively. We interpret that architectural simplicity drives performance in arrhythmia classification. Because SCD is unexpected, prevention strategies in free-living environments require lightweight computational resources suitable for wearable devices. Class imbalance fundamentally limits classification performance for rare arrhythmias such as Low-QRS Voltage and T-wave inversion, irrespective of hyperparameter choices. However, the self-attention mechanism can autonomously abstract clinical representations, simplifying clinical deployment by eliminating the need for an explicit feature-extraction pipeline.

Advances in single-cell and spatial assays have revolutionized the scale and resolution of molecular tissue profiling. Here we present MetaPlaq, a multimodal atlas of human atherosclerotic arterial beds comprising over a million cells across single-cell transcriptomics, epigenomics and high-resolution spatial expression assays. We map granular cell states and disease-relevant transcriptional programs within the native tissue context of coronary arteries. Furthermore, we map cardiovascular GWAS signals to smooth muscle cells (SMCs) and endothelial cells (ECs) and uncover the cis-regulatory architecture governing their phenotypic transitions. Our comprehensive epigenomic reference allowed us to build cell-specific enhancer-gene link maps and multimodal gene regulatory networks (GRNs) underlying disease-relevant states such as osteogenic SMCs and ECs undergoing mesenchymal transition. We also integrate SMC and EC disease-associated gene sets with GRNs to nominate key transcription factors such as PRRX1, BNC2 and ELK3 regulating atherosclerosis-relevant transcriptional programs. Finally, we layer single-cell and spatial modalities to fine-map GWAS variants with improved cell and anatomical context. We highlight candidate cell-specific regulatory mechanisms at less characterized CAD loci, including FGD5 and MCF2L in ECs. Together, this atlas represents an important step towards fully interpreting genetic risk loci and informing new therapeutic strategies for cardiovascular disease.

Background: Quantitative lipid assessment is central to identifying rupture-prone coronary plaques and represents a therapeutic target for lipid-lowering therapy. Near-infrared spectroscopy (NIRS)-derived lipid core burden index (LCBI) is well validated and widely used for detecting lipid-rich lesions. Optical frequency domain imaging (OFDI) is increasingly adopted for guiding percutaneous coronary intervention (PCI) due to its high-resolution structural imaging capabilities. Depolarization-sensitive OFDI (depOFDI) provides intrinsic lipid contrast and may enable combined structural and compositional plaque characterization within a single OFDI-based platform. Objective: To define an OFDI-derived lipid metric and evaluate its agreement with NIRS-derived LCBI. Methods: Thirty-three patients underwent both polarization-sensitive OFDI and NIRS-intravascular ultrasound imaging during PCI. After exclusion of 4 datasets, 29 co-registered pullbacks were analyzed. A signal-to-noise-corrected depolarization metric was used to identify lipid-rich regions and generate depOFDI chemograms. maxLCBI4mm value and location, as well as total LCBI, were computed and compared with NIRS. Results: depOFDI demonstrated strong agreement with NIRS, showing high correlation for maxLCBI4mm (r^2 = 0.862) and total LCBI (r^2 = 0.867), along with strong spatial concordance for the location of the maxLCBI4mm (r^2 = 0.900). Bland-Altman analysis of LCBI4mm showed minimal bias (10.7) with 95% limits of agreement of [81.4 to 102.8]. Conclusions: depOFDI enables accurate quantification of lipid burden alongside the high-resolution structural information inherently provided by OFDI. Because depolarization metrics can be derived from polarization-diverse detection available in many commercial OFDI systems, this approach provides a practical pathway toward comprehensive plaque characterization within existing PCI workflows, without the need for additional imaging modalities.

Objective: Hispanic/Latinx populations in the U.S. experience higher rates of chronic disease linked to physical inactivity, yet digital health interventions remain largely inaccessible to more than 16 million Hispanic/Latinx adults with limited English proficiency. While large language models (LLMs) offer scalable personalization, their use in non-English behavioral coaching is unexplored. This study introduces MHC-Coach-ES, a Spanish-language LLM fine-tuned on the Transtheoretical Model (TTM) of behavior change. Materials and Methods: We fine-tuned Llama 3-70B-Instruct using a two-stage pipeline. First, the model was adapted to Spanish health and motivational language using a 2.21-million-token corpus. Second, it was instruction-tuned on 3,268 translated human written messages to align the model with the Transtheoretical Model (TTM) of Behavioral Change. We compared MHC-Coach-ES with Llama 3-70B-Instruct and translated human-expert messages using a forced-choice preference survey (N = 77) and blinded expert review (N = 2). Results: Spanish-speaking participants significantly preferred MHC-Coach-ES messages over translated human-expert messages (81% preference, P<0.001). Linguistic analysis showed that MHC-Coach-ES produced more temporally anchored messages than the base model (65% vs. 20%), while maintaining readability. In blinded evaluation, clinical experts rated MHC-Coach-ES higher for alignment with Transtheoretical Model stages than human-expert messages (4.83 vs. 4.38 out of 5). The base model also outperformed translated expert messages across preference and expert ratings. Conclusions: Generative AI can operationalize behavioral science frameworks in Spanish, offering a scalable approach to reducing health disparities. The strong performance of both MHC-Coach-ES and the base model highlights the promise of generative and personalized approaches over translation-based localization for theory-driven behavioral interventions.

Background: Adverse childhood experiences (ACEs) are traumatic or adverse events in early life that can have lasting effects on behavioral, emotional, and psychological functioning. Prior research suggests ACEs relate to later psychiatric outcomes through threshold, cumulative, and individual-specific risk patterns. Few studies, however, have operationalized all three models to test ACE-specific associations with diagnosed psychiatric disorders in individuals who are adopted or with foster care histories. Methods: We conducted a cross-sectional retrospective study using electronic health record data from foster care and adopted patients aged 0-21 years old seen at the University of Minnesota Adoption Medicine Clinic (UMN-AMC) between 2014-2024. Extracted measures included ACE history, demographics, and psychiatric diagnoses. We used latent class analysis and logistic regression to identify clusters of adversity and estimate associations with psychiatric diagnosis domains, adjusting for Sex and Age at Initial Visit. Results: ACEs showed a threshold pattern across psychiatric domains, with higher ACE counts associated with greater odds of psychiatric diagnoses. Individual risk modeling indicated that exposure to abuse or violence was associated with higher odds of psychiatric diagnoses. Across cumulative and individual risk approaches, Anxiety Disorders, Mood Disorders, and Behavioral or Emotional Disorders showed the greatest sensitivity to adversity. Conclusion: Current ACE models may not fully capture neurodevelopmental impacts reflected in diagnosed psychiatric disorders among adolescents, particularly in high-risk groups such as foster and adopted individuals. In a large clinic sample our findings support a nuanced association between ACEs and later psychiatric diagnoses and highlight the need for ACE-focused assessment, prevention, and treatment strategies tailored to foster care and adopted populations.

Diastolic heart failure (HF) in primary cardiomyopathy is under-recognized and often diagnosed late, particularly in children. While recent studies have advanced understanding of HF with preserved ejection fraction in older adults, the prevalence, outcomes and molecular drivers of diastolic HF in pediatric and young adult cardiomyopathy remain poorly defined, where disease is typically driven by primary myocardial disease rather than acquired co-morbidities. The Canadian Cardiomyopathy Collaborative (C3) was assembled to leverage three of Canadas leading pediatric and adult cardiomyopathy biobank registries. Its flagship initiative, Artificial Intelligence to Model Diastolic Heart Failure (AID-HF), aims to integrate deep phenotyping - including comprehensive diastolic function assessment - with genomics, lipidomics and proteomics and apply machine learning to identify biological and clinical signatures that drive cardiac function and outcomes in cardiomyopathy. Harmonized phenotyping and multiomics protocols across registries will create a uniquely integrated national data resource and enable the goals of AID-HF i.e., earlier diagnosis and new therapeutic targets for diastolic HF in cardiomyopathy.

Resuscitation in the oldest old at the end of life is associated with potential harm, raising concerns about misalignment with patients goals of care. This study aimed to elucidate changes in the use of resuscitation among the oldest old in Japan following the revision of the national guideline on end-of-life care which explicitly incorporates the concept of advance care planning. We conducted a repeated cross-sectional study using the National Database of Health Insurance Claims Open Data, including adults aged [&ge;]85 years, from April 2014 to March 2024. The annual number of resuscitation procedures per 100,000 individuals aged [&ge;]85 years was used as the measure of frequency. Resuscitation included closed-chest cardiopulmonary resuscitation (CPR) and endotracheal intubation. Interrupted time series analysis was used to examine changes following the 2018 revision of the national end-of-life care guideline. The frequencies of CPR and endotracheal intubation declined before 2018 (CPR: age 85-89, -68.4 [-87.9 to -48.8]; age [&ge;]90, -106.7 [-131.5 to -82.0]; intubation: age 85-89, -57.5 [-71.8 to -43.2]; age [&ge;]90, -69.5 [-80.7 to -58.3]), but the decline attenuated thereafter (CPR: age 85-89, +56.2 [28.0 to 84.5]; age [&ge;]90, +84.1 [50.7 to 117.6]; intubation: age 85-89, +36.6 [8.5 to 64.7]; age [&ge;]90, +38.3 [23.8 to 52.8]). These findings provide insight into the changes in resuscitation trends following policy interventions supporting end-of-life decision-making. Further studies are needed to better understand the mechanisms underlying this change.

Abstract Introduction The ARRIVE trial first demonstrated that elective induction of labour (IOL) at 39 weeks in low-risk pregnancies reduced the likelihood of caesarean section (CS) without compromising perinatal safety; however, the generalizability of these findings remains debated, leading to uncertainty in clinical practice. The LIRIC trial aims to evaluate whether 39-week elective IOL reduces CS rates compared with expectant management, while exploring its impact on infant neurodevelopment and multi-omics profiles. Methods and analysis This is a single-centre, open-label, randomized controlled trial in China. A total of 1,074 low-risk pregnant women (nulliparous or multiparous) will be randomly assigned (1:1 ratio) to either 39-week IOL or expectant management. The primary outcome is the caesarean section (CS) rate. Secondary outcomes include a composite of severe neonatal morbidity and perinatal mortality and infant neurodevelopmental scores (Bayley-4 and ASQ-3), among others. Data analysis will follow the Intention-to-Treat (ITT) principle. Biospecimen will be collected for metagenomic and metabolomic analyses, with results to be reported separately. Ethics and dissemination The protocol has been approved by the Ethics Committee of Women's Hospital, School of Medicine, Zhejiang University. Informed consent will be obtained from all participants. Results will be disseminated via peer-reviewed journals, and standardized infant developmental reports will be provided to participants to enhance study benefit. Trial registration number NCT07082530.

Lead is a toxic metal ubiquitous in our environment. While dramatic reductions in lead sources have paralleled equivalent decreases in lead-poisoning rates, chronic lead exposure remains a critical public health concern. Childhood lead exposure (at its lowest levels) is liked to changes in cognitive development but less is known about lead's effects on children's brain structure, especially as a result of in utero exposure. We measured prenatal and early-postnatal lead exposure in shed deciduous teeth of 448 9- and 10-year-old children (from 20 United States cities) and linked those lead levels to childhood brain structure, cognition/behavior, and neighborhood- and family-level socioeconomic characteristics. Here we show negative associations between tooth-lead levels and the thickness of the brain's cortex, particularly in regions linked to language processing. With increasing tooth-lead levels, children of lower-income (versus higher-income) families showed steeper declines in receptive vocabulary. Caregiver-reported behavioral problems exhibited similar associations. With in utero exposure linked to adverse neurodevelopmental outcomes (well before lead exposure and its risks are evaluated by healthcare professionals), prenatal screening of maternal lead levels/exposure, coupled with recommended strategies to reduce its placental transmission, may help reduce lead's effects on future generations.

Catheter ablation is an established rhythm-control strategy for atrial fibrillation, but outcomes in persistent atrial fibrillation (PsAF) remain heterogeneous across evolving strategies and energy modalities. An updated synthesis is needed to define current effectiveness and adverse-event profiles in the modern ablation era. We conducted a systematic review and meta-analysis of prospective clinical trials of catheter ablation for PsAF published from 2010 through December 2025. We included randomized and nonrandomized prospective interventional studies reporting effectiveness and adverse events, and pooled outcomes using random-effects models. Prespecified subgroup analyses evaluated ablation strategy (pulmonary vein isolation [PVI] vs PVI with adjunctive lesion sets [PVI+]), ablation modality (radiofrequency [RF], cryoballoon [CRYO], and pulsed field [PF]), and endpoint definition (recurrence-only vs composite measures). Thirty-two studies (9,194 patients) met inclusion criteria; 28 (7,948 patients) contributed to effectiveness analyses. The pooled 12-month arrhythmia-free proportion was 0.65 (95% CI, 0.61-0.68), with substantial heterogeneity. Effectiveness was numerically higher with PVI+ than PVI-only (0.66 [0.60-0.72] vs 0.63 [0.59-0.67]), similar for PF (0.65 [0.57-0.72]) and RF (0.65 [0.61-0.69]), and slightly lower for CRYO (0.64 [0.54-0.74]). Recurrence-only endpoints yielded higher effectiveness than composite endpoints (0.67 [0.63-0.71] vs 0.60 [0.55-0.64]). Safety analyses included 32 studies (9,002 patients). Adverse events were low but heterogeneous (0%-14.56%); pooled vascular access and pericardial complication incidences were each 1%, while thromboembolic events, accessory organ injury, and mortality were rare (pooled 0%). PF ablation showed numerically lower overall complication incidences than RF and CRYO. In contemporary trials, catheter ablation for PsAF shows moderate effectiveness and low overall adverse-event risk. Adjunctive strategies and PF ablation are promising, but no approach is consistently superior. These findings support tailored, patient-specific ablation selection in PsAF.

Background: Prenatal substance exposure (PSE) occurs when an individual is exposed to substances in utero. PSEs may have lasting effects on mental health. We tested whether PSEs show threshold, cumulative, or individual substance associations with childhood psychiatric diagnoses. Methods: Clinical variables (demographics, ICD-9/10 diagnoses, PSE history) were extracted from electronic health records from the University of Minnesota Adoption Medicine Clinic. PSEs were identified from caregiver and child-protective-services narratives and/or toxicology (cord tissue/blood, meconium). For each ICD-9/10 diagnostic category, we fit logistic regression models comparing (1) exposure thresholds (0, 1, 2, 3, 4+ exposures), (2) a cumulative exposure count, and (3) individual substances to estimate marginal odds ratios (ORs) with 95% Confidence Intervals (CIs). Results: Psychiatric diagnoses increased with the number of PSEs. Relative to no exposure, odds of an Anxiety Disorder rose from OR 1.47 (95% CI 1.16-1.87) with one exposure to OR 2.03 (1.64-2.52) with >=4 exposures. Higher cumulative exposure scores were associated with Anxiety Disorders (OR 1.28, 1.18-1.38), Behavioral and Emotional Disorders (OR 1.42, 1.31-1.54), Substance Use Disorders (OR 1.52, 1.29-1.79), and Mood Disorders (OR 1.16, 1.04-1.30). Alcohol, tobacco, and marijuana exposures were associated with increased odds of at least one psychiatric diagnosis, and each substance showed at least one significant diagnostic cluster when modeled independently. Conclusion: Increasing numbers of PSEs were associated with higher odds of psychiatric diagnoses, with patterns varying by substance and outcome. These findings motivate research on exposure timing and combinations to support earlier identification and intervention for at-risk children.

Intro: Characteristics of the pulse wave transmitted through the carotid arteries are predictive of cognitive decline and cerebrovascular health in humans. This study aimed to identify risk factor trajectories in childhood, adolescence and early adulthood that are associated with forward compression wave intensity (FCWI) in the common carotid artery in adults aged 28 years. Methods: Systolic blood pressure (SBP), body mass index (BMI) and fasting blood glucose (FBG) measured at multiple time-points when participants were aged between 8-20 years were included in a trajectory analysis. At age 28 years, FCWI was measured in 402 (M=206, F=196) participants who underwent a Duplex ultrasound assessment of the common carotid artery. Statistical analysis assessed differences in FCWI between each trajectory group for males and females separately. Results: In males, four trajectory groups were identified for BMI, three for SBP, and two for FBG. In females, three trajectory groups were identified for BMI, SBP, and FG. In males, having higher BMI (P=0.006), SBP (P=0.021) and FBG (P=0.002) from ages 8-20 years was associated with greater FCWI at age 28 years. In females, no associations were found between FCWI at age 28-years and trajectory groups for BMI (P=0.185), SBP (P=0.289) or FBG (P=0.070). Conclusion: Having high BMI, SBP and FBG throughout childhood, adolescence and early adulthood was associated with higher FCWI in the carotid artery at age 28 years in males, but not females. This may have a direct impact on the etiology of cognitive decline and cerebrovascular disease in later life.

Abstract Background Prediction of long-term neurodevelopmental outcomes remains challenging after perinatal asphyxia. Here, we studied whether computational metrics of brain function derived from neonatal EEG are associated with long-term neurodevelopment in infants with perinatal asphyxia. Methods Total of 36 term-born infants with perinatal asphyxia with or without hypoxic-ischemic encephalopathy were studied with neonatal multichannel electroencephalography (EEG). We computed local EEG amplitudes and phase-amplitude coupling (PAC), as well as large-scale functional cortical networks estimated using amplitude-amplitude correlations (AAC) and phase-phase correlations (PPC). These EEG-derived markers were tested for associations with neurodevelopmental outcomes at two years, assessed using the Griffiths Scales of Child Development, 3rd edition (GMDS-III). Results EEG amplitudes showed positive associations with GMDS-III Foundations of Learning and General Development scores across most electrodes during quiet sleep, with the strongest effects observed at frontal and central regions (r = 0.44-0.66). PAC showed negative associations with the same scores mainly over parietal and temporal regions (r = -0.45 to -0.55). Cortical AAC networks demonstrated the most robust and widespread negative associations in all frequency bands during quiet sleep (r = -0.47 to -0.54), with 70-72% of connections significant in high delta frequency. In turn, PPC networks showed frequency-selective and more spatially constrained negative associations during quiet sleep (r = -0.48 to -0.53), involving 5-12% of the network. Conclusions Both local and network-based metrics in the newborn brain show significant association with neurodevelopmental outcome at 2 years after perinatal asphyxia.

Background: Cardiovascular magnetic resonance (CMR) quantification of the left ventricular (LV) volumes and ejection fraction (EF) typically involves manual segmentation of many short axis (SAx) and long axis (LAx) slices of the left ventricle. The scan time and the number of breath holds is proportional to the number of slices. We aimed to evaluate a geometric model of the left ventricle that could enable planimetry from a reduced number of slices. We sought to determine whether acceptable accuracy was retained for evaluating the End Diastolic Volume (EDV), End Systolic Volume (ESV), Stroke Volume (SV), and EF to provide a rapid and reliable clinical alternative. Methods: A cohort of 342 patients, median age: 54 (40 - 65) years, with full-stack CMR examinations was used. Nine geometrical combinations were evaluated: 3, 4 or 5 short axis slices and one of three LAx orientations (2-chamber, 3-chamber or 4-chamber) by retrospectively decimating the full-stack acquisition. LV volumes were calculated as a sum of trapezoidal approximations for apical and mid-cavity slices and a generalized prismoidal model at the base. The accuracy of the volume calculations was quantified against the full-stack reference for the EDV, ESV, SV, and EF using concordance correlation coefficient (CCC), two-way repeated measures ANOVA, pairwise tests, and Bayes factor log10(BF10) analysis. Results: The choice of the long axis (LAx) view was the most influential driver of accuracy (g2 = 0.104, for EDV), approximately 50 times more impactful than the number of SAx slices (g2 = 0.002, for EDV). Volumes calculated using the combination of 2-chamber LAx view and 5 SAx slices had the highest concordance with the full stack (CCC>0.90). While the estimated absolute volumes displayed a systematic negative bias, EF and SV remained highly robust due to bias cancellation. For a 2ch + 5 SAx protocol, EF bias was just 0.83% (LoA: -6.18 to 7.84%), with a minimum detectable change (MDC) of 7.01%, compared to 8.7% reported for expert human readers, suggesting strong concordance. Bayesian paired-samples t-tests yielded log10(BF10) = 6.42 in favor of 5 SAx over 3 SAx, constituting decisive evidence on the Jeffreys scale. The bias and limits of agreement (LoA) for stroke volume and ejection fraction were found to be lower than scan-rescan reproducibility in literature. Conclusion: This reduced-slice geometric model allows for reduced number of breath holds compared to a conventional full-stack CMR acquisition and provides an acceptable accuracy with bias less than scan-rescan variability.

Background: Comprehensive assessment of hypertension-mediated organ damage (HMOD) across multiple organ systems in sub-Saharan Africa is limited. We assessed the prevalence and correlates of multidomain HMOD in a geographically diverse population in Ghanaian adult. Methods: This cross-sectional secondary analysis of the Ghana Heart Study, which included 1,106 adults aged [&ge;]18 years from four Ghanaian regions between September 2016 and March 2017. Multidomain HMOD was determined using a pre-specified 9-domain composite score [&ge;]2, using an ESH/ESC 2018 guideline-informed selection of HMOD domain with baPWV instead of carotid-femoral PWV (cfPWV), due to device unavailability, and a threshold of [&ge;]14 m/s which was derived from analysis within the cohort. LODO sensitivity analyses were used to address issues of predictor-outcome circularity. We used logistic regression models to examine association between each predictor and multidomain HMOD, adjusted for age, systolic blood pressure, body mass index, presence of dyslipidaemia and smoking status. We also performed receiver operating characteristic (ROC) analysis to determine correlates of multidomain HMOD and compare the discriminative ability of each predictor against the others. Results: The mean age of participants was 46.9{+/-}17.2 years of which 58% were females. Multidomain HMOD was observed in 21.3% (235/1,106; zero-imputation lower bound 21.2%) of participants studied. There was a marked increase in the prevalence of multidomain HMOD with advancing age. Thus, while 8.6% (44/ 511) of adults<45years had multidomain HMOD, 20.6% (63/306) of 45- to 59-yr-olds and 44.4% (128/ 288) of individuals [&ge;]60 years had multidomain HMOD. HMOD-positive adults were older (59.1{+/-}8.4 vs 43.6{+/-}13.4y, p<0.001), had higher systolic BP (147{+/-}22 vs 123{+/-}21 mmHg, p<0.001), and had higher prevalence of hypertension (73% vs 28%, p<0.001) than their HMOD-negative counterparts. Using the primary (circular) specification, the strongest co-occurrence among all domains of HMOD was observed between peripheral artery disease and other HMOD (OR 41.2, 95% CI 20.7-81.6; p<0.001) followed by valvular burden and other HMOD (OR 14.4, 95% CI 4.8-43.8; p<0.001) and between ECG-LVH and other HMOD (OR 9.0, 95% CI 5.9-13.8; p<0.001) (S2 Table). After LODO correction to remove the self-inclusive co-occurrence between each predictor domain and the outcome (all p-values calculated in S2 Table), there was no significant association between the remaining 8 HMOD domains and the prevalence of multidomain HMOD (all p-values>0.05; S2 Table). This was not the case for baPWV, however. Thus, whereas the AUC of the best performing non-self-inclusive HMOD domain (ECG-CMD) only reached 0.688{+/-}0.016 (vs 0.827{+/-}0.008 for self-inclusive AUC calculated for the sake of interest only and provided as supplementary material), baPWV demonstrated good discriminative capacity (LODO-adjusted AUC = 0.702, 95% CI 0.654-0.751; S3 Fig). However, this AUC did not significantly exceed that for age alone (AUC = 0.752; {Delta}AUC = -0.050, 95% CI ?0.103 to 0.03; p=0.106; S3 Fig). Most importantly, after adjustment for SBP (a direct mediator in this pathway), the LODO AUC for baPWV did not exceed that for the single variable age (S3 Fig), indicating that baPWV does not possess independent discriminative power for multidomain HMOD above and beyond the information provided by SBP and age. Importantly, however, the adjusted OR for baPWV did not reach statistical significance (OR 1.094, 95% CI 0.986-1.213; p=0.091), suggesting that while circularity prevented validation of biological association, it did not prove the absence of association altogether. Sensitivity analysis (estimating total as opposed to direct effect) in which SBP was excluded from the regression model to estimate the total effect of baPWV on the prevalence of HMOD showed that, indeed, the OR for baPWV was significantly elevated (OR 1.261; 95% CI 1.150-1.382; p<0.001) in this specification. The effect of SBP, a direct mediator in this pathway, therefore apparently accounted for the non-significance in the original model entirely. Formal mediation analysis using the aforementioned specification yielded that SBP indeed mediated 69.9% (95% CI 41.3-128.8%) of the effect of baPWV on the prevalence of HMOD. Conclusions: One in five Ghanaian adults has hypertension-mediated organ damage in multiple HMOD domains. baPWV has good discriminative power for HMOD risk prediction in a Ghanaian adult population under the non-circular LODO estimand (LODO- adjusted AUC = 0.702; 95% CI: 0.654, 0.751) than the PCE (AUC = 0.496; 95% CI: 0.438, 0.555; {Delta}AUC = +0.206; p < 0.001). However, baPWV LODO AUC (0.702) was not statistically significantly greater than age alone (AUC = 0.752; 95% CI: 0.730, 0.774; {Delta}AUC = -0.050, p = 0.106). AUC for self- inclusive model was provided in supplementary materials for the reader's perusal, and that AUC (0.827; 95% CI: 0.794, 0.860) is circular. The prevalence of ECG-LVH was substantially higher (42%) than that of echocardiographic- LVH (5.9%) in this Black African population. These findings support further research on the role of baPWV for HMOD risk prediction in a Ghanaian adult population. Prospective validation of baPWV would be needed before clinical use.

Introduction: Actigraphy sleep-wake classification methods increasingly seek to leverage raw acceleration data and machine-learning-based classification, but performance evaluation in pediatrics is limited. We trained machine-learning models using pediatric data and compared their sleep-wake classification performance with existing algorithms for children. Methods: Sixty-five children (46% female, ages 5.3 to 17.7 years) completed in-lab overnight polysomnography and wore a GENEActiv device on their non-dominant wrist. The acceleration data were converted into 30-second epochs and aligned with physician-scored sleep-wake data from electroencephalography. Seven machine-learning models were trained using leave-one-subject-out cross-validation. Epoch-by-epoch analyses generated performance metrics (e.g., balanced accuracy [BA]) and discrepancy analyses provided overall sleep duration bias estimates. The combination of highest performance and least bias was used to rank using Euclidean distance scores - where a lower score represents closer to perfect performance and zero bias. For benchmarking, we included GGIR sleep scoring algorithms and an adult trained random forest classifier. Results: Overall, 560.1 hours of polysomnography and actigraphy data were collected (74.4% of epochs were scored as sleep). The pediatric-trained local-global long-short term memory (LSTM) classifier had the most optimal epoch-by-epoch performance (e.g., BA=0.85, sensitivity=0.88, specificity=0.83, ROC-AUC=0.95, and Cohen kappa=0.67). These metrics exceeded that of an adult-trained random forest classifier and GGIR-based algorithms. Discrepancy analyses revealed that overall sleep duration was underestimated by an average of 25 minutes using the LSTM classifier with no proportional bias. Conclusion: We trained seven pediatric sleep-wake classifiers that had strong ability to detect sleep and wake, with the LSTM classifier being most optimal.

Objective: Cross-sectional studies indicate associations between self-reported social media use and adolescent wellbeing outcomes. We aimed to evaluate longitudinal associations of objectively measured smartphone and social media use with psychosocial wellbeing. Design: Observational study with one year of follow-up Setting: High schools in Finland from 2022 to 2023 Population: 259 adolescent girls (mean age 16.3 years at baseline) Main outcome measures: screenshots depicting smartphone and social media use, Bergen Social Media Addiction Scale (BSMAS), Generalized Anxiety Disorder-7 questionnaire, Body Appreciation Scale 2 (BAS-2) and visual analogue scales (VAS) of mood, tiredness, and loneliness Results: Across one year of follow-up, anxiety, body appreciation, and mood improved, but possible social media addiction increased from 15% to 17%. Social media addiction at baseline was associated with increased anxiety (r=0.29, p<0.001), lower body appreciation (r=-0.15, p=0.022), and more loneliness (r=0.20, p=0.001) at follow-up. Anxiety at baseline was associated with social media addiction at follow-up (r=0.19, p=0.005). The highest quartile of TikTok users reported more social media addiction (BSMAS 19 [IQR 16-21] vs. 17 [IQR 14-20]; p=0.009) and lower body appreciation (BAS-2 32 [IQR 28-38] vs. 35 [IQR 29-40]; p=0.003) than did others. The highest quartile of Snapchat users reported more social media addiction (BSMAS 19 [IQR 15-21] vs. 17 [IQR 14-20]; p=0.007) and tiredness (VAS 21 [IQR 13-32] vs. 26 [IQR 15-35]; p=0.049) than did others. Conclusions: Consistent with cross-sectional studies, social media addiction was associated with poorer psychosocial outcomes across follow-up. Policies to protect adolescents from social media addiction are urgently needed.