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ImportanceThe prevalence of urinary tract infection (UTI), bacteremia, and bacterial meningitis in febrile infants with SARS-CoV-2 is largely unknown. Knowledge of the prevalence of these bacterial infections among febrile infants with SARS-CoV-2 can inform clinical decision-making.ObjectiveTo describe the prevalence of UTI, bacteremia, and bacterial meningitis among febrile infants aged 8 to 60 days with SARS-CoV-2 vs without SARS-CoV-2.Design, Setting, and ParticipantsThis multicenter cross-sectional study was conducted as part of a quality improvement initiative at 106 hospitals in the US and Canada. Participants included full-term, previously healthy, well-appearing infants aged 8 to 60 days without bronchiolitis and with a temperature of at least 38 °C who underwent SARS-CoV-2 testing in the emergency department or hospital between November 1, 2020, and October 31, 2022. Statistical analysis was performed from September 2022 to March 2023.ExposuresSARS-CoV-2 positivity and, for SARS-CoV-2–positive infants, the presence of normal vs abnormal inflammatory marker (IM) levels.Main Outcomes and MeasuresOutcomes were ascertained by medical record review and included the prevalence of UTI, bacteremia without meningitis, and bacterial meningitis. The proportion of infants who were SARS-CoV-2 positive vs negative was calculated for each infection type, and stratified by age group and normal vs abnormal IMs.ResultsAmong 14 402 febrile infants with SARS-CoV-2 testing, 8413 (58.4%) were aged 29 to 60 days; 8143 (56.5%) were male; and 3753 (26.1%) tested positive. Compared with infants who tested negative, a lower proportion of infants who tested positive for SARS-CoV-2 had UTI (0.8% [95% CI, 0.5%-1.1%]) vs 7.6% [95% CI, 7.1%-8.1%]), bacteremia without meningitis (0.2% [95% CI, 0.1%-0.3%] vs 2.1% [95% CI, 1.8%-2.4%]), and bacterial meningitis (<0.1% [95% CI, 0%-0.2%] vs 0.5% [95% CI, 0.4%-0.6%]). Among infants aged 29 to 60 days who tested positive for SARS-CoV-2, 0.4% (95% CI, 0.2%-0.7%) had UTI, less than 0.1% (95% CI, 0%-0.2%) had bacteremia, and less than 0.1% (95% CI, 0%-0.1%) had meningitis. Among SARS-CoV-2–positive infants, a lower proportion of those with normal IMs had bacteremia and/or bacterial meningitis compared with those with abnormal IMs (<0.1% [0%-0.2%] vs 1.8% [0.6%-3.1%]).Conclusions and RelevanceThe prevalence of UTI, bacteremia, and bacterial meningitis was lower for febrile infants who tested positive for SARS-CoV-2, particularly infants aged 29 to 60 days and those with normal IMs. These findings may help inform management of certain febrile infants who test positive for SARS-CoV-2.

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Background The importance of culprit lesion identification is critical for risk stratification of a patient with an ST-Elevation Myocardial Infarction (STEMI). The aforementioned provide patients with a more elaborated strategy of management and treatment either they are treated with PCI or less invasive techniques such as thrombolysis. We report a novel approach that employs AI-guided electrocardiogram (EKG) algorithms for rapid and accurate identification of the culprit STEMI vessel. Purpose To create an innovative, machine learning tool for a more effective risk stratification of STEMI patients. Methods An observational, retrospective, case-control study. Sample: 2,542 exclusively STEMI diagnosis EKG records that included post discharge feedback from healthcare centers, confirming diagnosis and culprit vessel (Left Main Coronary Artery [LMCA]; Left Anterior Descending [LAD]; Right Coronary Artery [RCA]; Left Circumflex Artery [LCX]; Saphenous Vein Graft [SVG]). Records excluded other patient and medical information. The sample was derived from the private International Telemedical Systems (ITMS) database. LUMENGT-AI Algorithm was employed. Preprocessing: detection of QRS complexes using a wavelet system, segmentation of each EKG into individual heartbeats (27,125 total beats) with fixed window of 0.4s to the left and 0.9s to the right of main QRS; Classification: A 1-D convolutional neural network was implemented; “LCMA”, “LAD”, “LCX”, “RCA”, “SVG”, and “No Information” classes were considered for each heartbeat; individual probabilities were aggregated to generate the final label for each record. Training & Testing: 90% and 10% of the sample was used, respectively. Experiments: Intel PC i7 8750H processor at 2.21GHz, 16GB RAM, Windows 10 OS with NVidia GTX 1070 GPU, 8GB RAM. Results Global Accuracy: 79.4%; LAD: Sensitivity 86.2%; Specificity 84.8%. RCA: Sensitivity 85.7%; Specificity 83.7%. LCX: Sensitivity 43.5%; Specificity 96.9%. Conclusions Coupling an AI-augmented algorithm and 12-lead EKG provides encouraging results for STEMI culprit vessel localization. Overall, risk stratification is possible for individual lesions located in the LAD and RCA. However, our approach yielded uncertain results in the LCX territory. We plan to continue to exploring variables for improvement of our results.