Evolution of single-lead ECG for STEMI detection using a deep learning approach.

BACKGROUND: While ST-Elevation Myocardial Infarction (STEMI) door-to-balloon times are often below 90 min, symptom to door times remain long at 2.5-h, due at least in part to a delay in diagnosis. OBJECTIVES: To develop and validate a machine learning-guided algorithm which uses a single­lead electrocardiogram (ECG) for STEMI detection to speed diagnosis. METHODS: Data was extracted from the Latin America Telemedicine Infarct Network (LATIN), a population-based Acute Myocardial Infarction (AMI) program that provides care to patients in Brazil, Colombia, Mexico, and Argentina through telemedicine. SAMPLE: the first dataset was comprised of 8511 ECGs that were used for various machine learning experiments to test our Deep Learning approach for STEMI diagnosis. The second dataset of 2542 confirmed STEMI diagnosis EKG records, including specific ischemic heart wall information (anterior, inferior, and lateral), was derived from the previous dataset to test the STEMI localization model. Preprocessing: Detection of QRS complexes by wavelet system, segmentation of each EKG record into individual heartbeats with fixed window of 0.4 s to the left and 0.9 s to the right of main. Training & Testing: 90% and 10% of the total dataset, respectively, were used for both models. CLASSIFICATION: two 1-D convolutional neural networks were implemented, two classes were considered for first models (STEMI/Not-STEMI) and three classes for the second model (Anterior/Inferior/Lateral) each corresponding to the heart wall affected. These individual probabilities were aggregated to generate the final label for each model. RESULTS: The single­lead ECG strategy was able to provide an accuracy of 90.5% for STEMI detection with Lead V2, which also yielded the best results overall among individual leads. STEMI Localization model provided promising results for anterior and inferior wall STEMIs but remained suboptimal for Lateral STEMI. CONCLUSIONS: An Artificial Intelligence-enhanced single­lead ECG is a promising screening tool. This technology provides an autonomous and accurate STEMI diagnostic alternative that can be incorporated into wearable devices, potentially providing patients reliable means to seek treatment early and offers the potential to thereby improve STEMI outcomes in the long run.

The impact of incorporating toilet-training status in the pediatric Rome IV criteria for functional constipation in infant and toddlers.

BACKGROUND: The diagnosis of functional constipation (FC) is based on the Rome criteria. The last edition of the criteria (Rome IV) for infants and toddlers modified the criteria to differentiate toilet-trained (TT) and non-toilet-trained (NTT) children. These changes have not been validated. We aimed to understand the impact of adding toilet training to the diagnostic criteria and to assess the prevalence of FC. METHODS: Parents of infants and toddlers from six outpatient clinics (four public, two private) located in three geographically dispersed cities in Colombia completed validated questionnaires to diagnose functional gastrointestinal disorders according to Spanish version of Rome IV criteria (QPGS-IV). RESULTS: A total of 1334 children (24.4 months ±15.0) participated: 482 (36%) TT and 852 (64%) NTT. The prevalence of FC was 21.1%. The prevalence increased with age, 0-1 years 7.7%; 2 years 18.2%; 3 years 23.7%; and 4 years 37.2%. TT vs NTT for FC 41.9% vs 9.3%, respectively (OR 7.06, 95% CI 5.26-9.47, P < .0001). TT more likely to report ≥ 3 criteria (OR = 2.43, 95% CI 1.41-4.21, P = .0015). 18.3% of TT had episodes of fecal incontinence that met the frequency required by Rome for FC (≤1 episode/week). However, 87.1% had fecal incontinence less often. 7.4% of them characterized as large quantity. CONCLUSION: We found no changes in the prevalence of FC using the Rome IV criteria vs Rome III. TT children are more likely to have FC. Study suggests that changes in Rome IV criteria were potentially clinically relevant and to have adequate face validity. Future studies should confirm our findings.