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Background Spontaneous preterm birth (sPTB) remains a major contributor to neonatal morbidity and mortality, with limited reliable early prediction tools. Existing biomarkers, such as the insulin-like growth factor-binding protein 4 (IBP4) to sex hormone-binding globulin (SHBG) ratio, offer modest predictive performance and are restricted to mid-gestation use (18–20 weeks), limiting their utility for timely intervention. We aimed to develop and validate a novel serological test based on early-gestational sampling to predict the risk of sPTB. Methods We conducted a meta-analysis of 18 placental transcriptomic datasets to identify candidate genes associated with sPTB, resulting in 21 protein candidates tested by targeted proteomics. We developed a three-protein panel (glutathione peroxidase 3, GPX3; nidogen-1, NID1; and pappalysin-2, PAPPA2) and validated it in four independent cohorts (456 subjects and 1048 serum specimens) from the USA and Asia. Longitudinal serum samples were collected from 5 weeks and were analyzed using mass spectrometry and ELISA platforms. Predictor performance was compared to the IBP4/SHBG ratio. Results The three-protein predictor (GPX3, NID1, and PAPPA2) demonstrated reproducible and superior performance across cohorts: AUC 0.74 (95% CI 0.59–0.88) in Alabama, 0.93 (95% CI 0.88–0.99) in California, 0.80 (95% CI 0.75–0.85) in Asia 1, and 0.83 (95% CI 0.70–0.95) in Asia 2. This outperformed the IBP4/SHBG ratio, which achieved AUCs of 0.68 (95% CI 0.50–0.89), 0.77 (95% CI 0.67–0.88), 0.59 (95% CI 0.52–0.65), and 0.61 (95% CI 0.50–0.75), respectively. Across obstetric trimesters, the three-protein panel maintained high predictive accuracy in the first and second trimesters ( AUROC 0.82–0.97), the window when preventive interventions such as progesterone, cerclage, and low-dose aspirin are most effective. Kaplan–Meier analyses confirmed significantly earlier delivery among high-risk pregnancies identified by the three-protein panel. Conclusions This maternal serum test provides a reliable approach for early risk assessment of sPTB. The three-protein panel demonstrated reproducible performance across cohorts and across PPROM-positive and PPROM-negative phenotypes, with the strongest discrimination in the first and second trimesters, when preventive therapies such as progesterone or cerclage are most effective. These findings support its potential as an early, clinically actionable screening tool for improving pregnancy outcomes.

In previous pilot work we demonstrated that a novel automated signal analysis tool could accurately identify successful ablation sites during Wolff-Parkinson-White (WPW) ablation at a single center. We sought to validate and refine this signal analysis tool in a larger multi-center cohort of children with WPW. A retrospective review was performed of signal data from children with WPW who underwent ablation at two pediatric arrhythmia centers from 2008-2015. All patients with WPW ≤ 21 years who underwent invasive electrophysiology study and ablation with ablation signals available for review were included. Signals were excluded if temperature or power delivery was inadequate or lesion time was < 5 seconds. Ablation lesions were reviewed for each patient. Signals were classified as successful if there was loss of antegrade and retrograde accessory pathway (AP) conduction or unsuccessful if ablation did not eliminate AP conduction. Custom signal analysis software analyzed intracardiac electrograms for amplitudes, high and low frequency components, integrated area, and signal timing components to create a signal score. We validated the previously published signal score threshold 3.1 in this larger, more diverse cohort and explored additional scoring options. Logistic regression with lasso regularization using Youden's index criterion and a cost-benefit criterion to identify thresholds was considered as a refinement to this score. 347 signals (141 successful, 206 unsuccessful) in 144 pts were analyzed [mean age 13.2 ± 3.9 years, 96 (67%) male, 66 (45%) left sided APs]. The software correctly identified the signals as successful or unsuccessful in 276/347 (80%) at a threshold of 3.1. The performance of other thresholds did not significantly improve the predictive ability. A signal score threshold of 3.1 provided the following diagnostic accuracy for distinguishing a successful from unsuccessful signal: sensitivity 83%, specificity 77%, PPV 71%, NPV 87%. An automated signal analysis software tool reliably distinguished successful versus unsuccessful ablation electrograms in children with WPW when validated in a large, diverse cohort. Refining the tools using an alternative threshold and statistical method did not improve the original signal score at a threshold of 3.1. This software was effective across two centers and multiple operators and may be an effective tool for ablation of WPW.

The increasing adoption of digital health technologies has amplified the need for robust, interoperable solutions to manage complex healthcare data. We present the Spezi Data Pipeline, an open-source Python toolkit designed to streamline the analysis of digital health data, from secure access and retrieval through processing, visualization, and export. The Pipeline is integrated into the larger Stanford Spezi open-source ecosystem for developing research and translational digital health software systems. Leveraging Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR)-based data representations, the Pipeline enables standardized handling of diverse data types, including sensor-derived observations, electrocardiogram (ECG) recordings, and clinical questionnaires-across research and clinical environments. We detail the modular system architecture and demonstrate its application using real-world data from the Pediatric Apple Watch Study (PAWS) at Stanford University, in which the Pipeline facilitated efficient extraction, transformation, and clinician-driven review of Apple Watch ECG data, supporting annotation and comparative analysis alongside traditional monitors. By reducing the need for bespoke data engineering and enabling prospective, clinician-in-the-loop analysis within standardized workflows, the Spezi Data Pipeline supports reproducible and interoperable clinical research using routinely collected digital health data.

We previously demonstrated that a pediatric cardiology boot camp can improve knowledge acquisition and decrease anxiety for trainees. We sought to determine if boot camp participants entered fellowship with a knowledge advantage over fellows who did not attend and if there was moderate-term retention of that knowledge. A 2-day training program was provided for incoming pediatric cardiology fellows from eight fellowship programs in April 2016. Hands-on, immersive experiences and simulations were provided in all major areas of pediatric cardiology. Knowledge-based examinations were completed by each participant prior to boot camp (PRE), immediately post-training (POST), and prior to the start of fellowship in June 2016 (F/U). A control group of fellows who did not attend boot camp also completed an examination prior to fellowship (CTRL). Comparisons of scores were made for individual participants and between participants and controls. A total of 16 participants and 16 control subjects were included. Baseline exam scores were similar between participants and controls (PRE 47 ± 11% vs. CTRL 52 ± 10%; p  = 0.22). Participants’ knowledge improved with boot camp training (PRE 47 ± 11% vs. POST 70 ± 8%; p  < 0.001) and there was excellent moderate-term retention of the information taught at boot camp (PRE 47 ± 11% vs. F/U 71 ± 8%; p  < 0.001). Testing done at the beginning of fellowship demonstrated significantly better scores in participants versus controls (F/U 71 ± 8% vs. CTRL 52 ± 10%; p  < 0.001). Boot camp participants demonstrated a significant improvement in basic cardiology knowledge after the training program and had excellent moderate-term retention of that knowledge. Participants began fellowship with a larger fund of knowledge than those fellows who did not attend.

Background Arrhythmia symptoms are frequent complaints in children and often require a pediatric cardiology evaluation. Data regarding the clinical utility of wearable technologies are limited in children. We hypothesize that an Apple Watch can capture arrhythmias in children. Methods We present an analysis of patients ≤18 years-of-age who had signs of an arrhythmia documented by an Apple Watch. We include patients evaluated at our center over a 4-year-period and highlight those receiving a formal arrhythmia diagnosis. We evaluate the role of the Apple Watch in arrhythmia diagnosis, the results of other ambulatory cardiac monitoring studies, and findings of any EP studies. Results We identify 145 electronic-medical-record identifications of Apple Watch , and find arrhythmias confirmed in 41 patients (28%) [mean age 13.8 ± 3.2 years]. The arrythmias include: 36 SVT (88%), 3 VT (7%), 1 heart block (2.5%) and wide 1 complex tachycardia (2.5%). We show that invasive EP study confirmed diagnosis in 34 of the 36 patients (94%) with SVT (2 non-inducible). We find that the Apple Watch helped prompt a workup resulting in a new arrhythmia diagnosis for 29 patients (71%). We note traditional ambulatory cardiac monitors were worn by 35 patients (85%), which did not detect arrhythmias in 10 patients (29%). In 73 patients who used an Apple Watch for recreational or self-directed heart rate monitoring, 18 (25%) sought care due to device findings without any arrhythmias identified. Conclusion We demonstrate that the Apple Watch can record arrhythmia events in children, including events not identified on traditionally used ambulatory monitors. Plain language summary Wearable devices, such as smart watches, have become popular for the monitoring of health, particularly for people with heart conditions. Wearable devices have been well-studied in adults, however there is less information available on their effectiveness in monitoring children’s health. We reviewed the heart electrical recordings of a group of children who submitted recordings obtained from their Apple Watches during moments when they felt as though their heart’s rhythm was abnormal. The Apple Watches captured rhythm abnormalities that matched the diagnoses obtained using heart monitors used clinically. This study shows that use of Apple Watches can enable clinicians to identify abnormalities that many traditional at-home monitoring devices do not detect. Thus, wearable devices, such as the Apple Watch, could be used to help identify heart rhythm disorders in children. Zahedivash et al. undertake a single center retrospective analysis of patients less than 18 years of age with history of an arrhythmia to determine whether a wearable device can capture arrhythmias. Arrhythmias are identified in 28% of patients, mainly the difficult to identify supraventricular tachycardias.

BackgroundKawasaki disease (KD) is the leading cause of acquired heart disease in children. The major challenge in KD diagnosis is that it shares clinical signs with other childhood febrile control (FC) subjects. We sought to determine if our algorithmic approach applied to a Taiwan cohort.MethodsA single center (Chang Gung Memorial Hospital in Taiwan) cohort of patients suspected with acute KD were prospectively enrolled by local KD specialists for KD analysis. Our previously single-center developed computer-based two-step algorithm was further tested by a five-center validation in US. This first blinded multi-center trial validated our approach, with sufficient sensitivity and positive predictive value, to identify most patients with KD diagnosed at centers across the US. This study involved 418 KDs and 259 FCs from the Chang Gung Memorial Hospital in Taiwan.FindingsOur diagnostic algorithm retained sensitivity (379 of 418; 90.7%), specificity (223 of 259; 86.1%), PPV (379 of 409; 92.7%), and NPV (223 of 247; 90.3%) comparable to previous US 2016 single center and US 2020 fiver center results. Only 4.7% (15 of 418) of KD and 2.3% (6 of 259) of FC patients were identified as indeterminate. The algorithm identified 18 of 50 (36%) KD patients who presented 2 or 3 principal criteria. Of 418 KD patients, 157 were infants younger than one year and 89.2% (140 of 157) were classified correctly. Of the 44 patients with KD who had coronary artery abnormalities, our diagnostic algorithm correctly identified 43 (97.7%) including all patients with dilated coronary artery but one who found to resolve in 8 weeks.InterpretationThis work demonstrates the applicability of our algorithmic approach and diagnostic portability in Taiwan.

Recurrence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive detection in infected but recovered individuals has been reported. Patients who have recovered from coronavirus disease 2019 (COVID-19) could profoundly impact the health care system. We sought to define the kinetics and relevance of PCR-positive recurrence during recovery from acute COVID-19 to better understand risks for prolonged infectivity and reinfection. A series of 414 patients with confirmed SARS-Cov-2 infection, at The Second Affiliated Hospital of Southern University of Science and Technology in Shenzhen, China from January 11 to April 23, 2020. Statistical analyses were performed of the clinical, laboratory, radiologic image, medical treatment, and clinical course of admission/quarantine/readmission data, and a recurrence predictive algorithm was developed. 16.7% recovered patients with PCR positive recurring one to three times, despite being in strict quarantine. Younger patients with mild pulmonary respiratory syndrome had higher risk of PCR positivity recurrence. The recurrence prediction model had an area under the ROC curve of 0.786. This case series provides characteristics of patients with recurrent SARS-CoV-2 positivity. Use of a prediction algorithm may identify patients at high risk of recurrent SARS-CoV-2 positivity and help to establish protocols for health policy.

Markers of aging hold promise in the context of colorectal cancer (CRC) care. Utilizing high-resolution metabolomic profiling, we can unveil distinctive age-related patterns that have the potential to predict early CRC development. Our study aims to unearth a panel of aging markers and delve into the metabolomic alterations associated with aging and CRC. We assembled a serum cohort comprising 5,649 individuals, consisting of 3,002 healthy volunteers, 715 patients diagnosed with colorectal advanced precancerous lesions (APL), and 1,932 CRC patients, to perform a comprehensive metabolomic analysis. We successfully identified unique age-associated patterns across 42 metabolic pathways. Moreover, we established a metabolic aging clock, comprising 9 key metabolites, using an elastic net regularized regression model that accurately estimates chronological age. Notably, we observed significant chronological disparities among the healthy population, APL patients, and CRC patients. By combining the analysis of circulative carcinoembryonic antigen levels with the categorization of individuals into the \"hypo\" metabolic aging subgroup, our blood test demonstrates the ability to detect APL and CRC with positive predictive values of 68.4% (64.3%, 72.2%) and 21.4% (17.8%, 25.9%), respectively. This innovative approach utilizing our metabolic aging clock holds significant promise for accurately assessing biological age and enhancing our capacity to detect APL and CRC.

Background:The transition from residency to paediatric cardiology fellowship is challenging due to the new knowledge and technical skills required. Online learning can be an effective didactic modality that can be widely accessed by trainees. We sought to evaluate the effectiveness of a paediatric cardiology Fellowship Online Preparatory Course prior to the start of fellowship.Methods:The Online Preparatory Course contained 18 online learning modules covering basic concepts in anatomy, auscultation, echocardiography, catheterisation, cardiovascular intensive care, electrophysiology, pulmonary hypertension, heart failure, and cardiac surgery. Each online learning module included an instructional video with pre-and post-video tests. Participants completed pre- and post-Online Preparatory Course knowledge-based exams and surveys. Pre- and post-Online Preparatory Course survey and knowledge-based examination results were compared via Wilcoxon sign and paired t-tests.Results:151 incoming paediatric cardiology fellows from programmes across the USA participated in the 3 months prior to starting fellowship training between 2017 and 2019. There was significant improvement between pre- and post-video test scores for all 18 online learning modules. There was also significant improvement between pre- and post-Online Preparatory Course exam scores (PRE 43.6 ± 11% versus POST 60.3 ± 10%, p < 0.001). Comparing pre- and post-Online Preparatory Course surveys, there was a statistically significant improvement in the participants’ comfort level in 35 of 36 (97%) assessment areas. Nearly all participants (98%) agreed or strongly agreed that the Online Preparatory Course was a valuable learning experience and helped alleviate some anxieties (77% agreed or strongly agreed) related to starting fellowship.Conclusion:An Online Preparatory Course prior to starting fellowship can provide a foundation of knowledge, decrease anxiety, and serve as an effective educational springboard for paediatric cardiology fellows.

The transition from residency to subspecialty fellowship in a procedurally driven field such as pediatric cardiology is challenging for trainees. We describe and assess the educational value of a pediatric cardiology “boot camp” educational tool designed to help prepare trainees for cardiology fellowship. A two-day intensive training program was provided for pediatric cardiology fellows in July 2015 at a large fellowship training program. Hands-on experiences and simulations were provided in: anatomy, auscultation, echocardiography, catheterization, cardiovascular intensive care (CVICU), electrophysiology (EP), heart failure, and cardiac surgery. Knowledge-based exams as well as surveys were completed by each participant pre-training and post-training. Pre- and post-exam results were compared via paired t tests, and survey results were compared via Wilcoxon rank sum. A total of eight participants were included. After boot camp, there was a significant improvement between pre- and post-exam scores (PRE 54 ± 9 % vs. POST 85 ± 8 %; p  ≤ 0.001). On pre-training survey, the most common concerns about starting fellowship included: CVICU emergencies, technical aspects of the catheterization/EP labs, using temporary and permanent pacemakers/implantable cardiac defibrillators (ICDs), and ECG interpretation. Comparing pre- and post-surveys, there was a statistically significant improvement in the participants comfort level in 33 of 36 (92 %) areas of assessment. All participants (8/8, 100 %) strongly agreed that the boot camp was a valuable learning experience and helped to alleviate anxieties about the start of fellowship. A pediatric cardiology boot camp experience at the start of cardiology fellowship can provide a strong foundation and serve as an educational springboard for pediatric cardiology fellows.