ImportanceSARS-CoV-2 infection is associated with persistent, relapsing, or new symptoms or other health effects occurring after acute infection, termed postacute sequelae of SARS-CoV-2 infection (PASC), also known as long COVID. Characterizing PASC requires analysis of prospectively and uniformly collected data from diverse uninfected and infected individuals.ObjectiveTo develop a definition of PASC using self-reported symptoms and describe PASC frequencies across cohorts, vaccination status, and number of infections.Design, Setting, and ParticipantsProspective observational cohort study of adults with and without SARS-CoV-2 infection at 85 enrolling sites (hospitals, health centers, community organizations) located in 33 states plus Washington, DC, and Puerto Rico. Participants who were enrolled in the RECOVER adult cohort before April 10, 2023, completed a symptom survey 6 months or more after acute symptom onset or test date. Selection included population-based, volunteer, and convenience sampling.ExposureSARS-CoV-2 infection.Main Outcomes and MeasuresPASC and 44 participant-reported symptoms (with severity thresholds).ResultsA total of 9764 participants (89% SARS-CoV-2 infected; 71% female; 16% Hispanic/Latino; 15% non-Hispanic Black; median age, 47 years [IQR, 35-60]) met selection criteria. Adjusted odds ratios were 1.5 or greater (infected vs uninfected participants) for 37 symptoms. Symptoms contributing to PASC score included postexertional malaise, fatigue, brain fog, dizziness, gastrointestinal symptoms, palpitations, changes in sexual desire or capacity, loss of or change in smell or taste, thirst, chronic cough, chest pain, and abnormal movements. Among 2231 participants first infected on or after December 1, 2021, and enrolled within 30 days of infection, 224 (10% [95% CI, 8.8%-11%]) were PASC positive at 6 months.Conclusions and RelevanceA definition of PASC was developed based on symptoms in a prospective cohort study. As a first step to providing a framework for other investigations, iterative refinement that further incorporates other clinical features is needed to support actionable definitions of PASC.

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BACKGROUND: Basic life support education for schoolchildren has become a key initiative to increase bystander cardiopulmonary resuscitation rates. Our objective was to review the existing literature on teaching schoolchildren basic life support to identify the best practices to provide basic life support training in schoolchildren. METHODS: After topics and subgroups were defined, a comprehensive literature search was conducted. Systematic reviews and controlled and uncontrolled prospective and retrospective studies containing data on students <20 years of age were included. RESULTS: Schoolchildren are highly motivated to learn basic life support. The CHECK-CALL-COMPRESS algorithm is recommended for all schoolchildren. Regular training in basic life support regardless of age consolidates long-term skills. Young children from 4 years of age are able to assess the first links in the chain of survival. By 10 to 12 years of age, effective chest compression depths and ventilation volumes can be achieved on training manikins. A combination of theoretical and practical training is recommended. Schoolteachers serve as effective basic life support instructors. Schoolchildren also serve as multipliers by passing on basic life support skills to others. The use of age-appropriate social media tools for teaching is a promising approach for schoolchildren of all ages. CONCLUSIONS: Schoolchildren basic life support training has the potential to educate whole generations to respond to cardiac arrest and to increase survival after out-of-hospital cardiac arrest. Comprehensive legislation, curricula, and scientific assessment are crucial to further develop the education of schoolchildren in basic life support.

Purpose of review Out-of-hospital cardiac arrest (OHCA) is a time-critical emergency in which a rapid response following the chain of survival is crucial to save life. Disparities in care can occur at each link in this pathway and hence produce health inequities. This review summarises the health inequities that exist for OHCA patients and suggests how they may be addressed. Recent findings There is international evidence that the incidence of OHCA is increased with increasing deprivation and in ethnic minorities. These groups have lower rates of bystander CPR and bystander-initiated defibrillation, which may be due to barriers in accessing cardiopulmonary resuscitation training, provision of public access defibrillators, and language barriers with emergency call handlers. There are also disparities in the ambulance response and in-hospital care following resuscitation. These disadvantaged communities have poorer survival following OHCA. Summary OHCA disproportionately affects deprived communities and ethnic minorities. These groups experience disparities in care throughout the chain of survival and this appears to translate into poorer outcomes. Addressing these inequities will require coordinated action that engages with disadvantaged communities.

Background Intensive Care Unit (ICU) readmissions in patients with heart failure (HF) result in a significant risk of death and financial burden for patients and healthcare systems. Prediction of at-risk patients for readmission allows for targeted interventions that reduce morbidity and mortality. Methods and results We presented a process mining/deep learning approach for the prediction of unplanned 30-day readmission of ICU patients with HF. A patient’s health records can be understood as a sequence of observations called event logs; used to discover a process model. Time information was extracted using the DREAM (Decay Replay Mining) algorithm. Demographic information and severity scores upon admission were then combined with the time information and fed to a neural network (NN) model to further enhance the prediction efficiency. Additionally, several machine learning (ML) algorithms were developed to be used as the baseline models for the comparison of the results. Results By using the Medical Information Mart for Intensive Care III (MIMIC-III) dataset of 3411 ICU patients with HF, our proposed model yielded an area under the receiver operating characteristics (AUROC) of 0.930, 95% confidence interval of [0.898–0.960], the precision of 0.886, sensitivity of 0.805, accuracy of 0.841, and F-score of 0.800 which were far better than the results of the best baseline model and the existing literature. Conclusions The proposed approach was capable of modeling the time-related variables and incorporating the medical history of patients from prior hospital visits for prediction. Thus, our approach significantly improved the outcome prediction compared to that of other ML-based models and health calculators.