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While the early effects of the COVID-19 pandemic on the United States labor market are well-established, less is known about the long-term impact of SARS-CoV-2 infection and Long COVID on employment. To address this gap, we analyzed self-reported data from a prospective, national cohort study to estimate the effects of SARS-CoV-2 symptoms at three months post-infection on missed workdays and return to work. The analysis included 2,939 adults in the Innovative Support for Patients with SARS-CoV-2 Infections Registry (INSPIRE) study who tested positive for their initial SARS-CoV-2 infection at the time of enrollment, were employed before the pandemic, and completed a baseline and three-month electronic survey. At three months post-infection, 40.8% of participants reported at least one SARS-CoV-2 symptom and 9.6% of participants reported five or more SARS-CoV-2 symptoms. When asked about missed work due to their SARS-CoV-2 infection at three months, 7.2% of participants reported missing ≥10 workdays and 13.9% of participants reported not returning to work since their infection. At three months, participants with ≥5 symptoms had a higher adjusted odds ratio of missing ≥10 workdays (2.96, 95% CI 1.81–4.83) and not returning to work (2.44, 95% CI 1.58–3.76) compared to those with no symptoms. Prolonged SARS-CoV-2 symptoms were common, affecting 4-in-10 participants at three-months post-infection, and were associated with increased odds of work loss, most pronounced among adults with ≥5 symptoms at three months. Despite the end of the federal Public Health Emergency for COVID-19 and efforts to “return to normal”, policymakers must consider the clinical and economic implications of the COVID-19 pandemic on people’s employment status and work absenteeism, particularly as data characterizing the numerous health and well-being impacts of Long COVID continue to emerge. Improved understanding of risk factors for lost work time may guide efforts to support people in returning to work.

The mandatory service of essential workers during the COVID-19 pandemic was associated with high job stress, increased SARS-CoV-2 exposure, and limited time for recovery following infection. Understanding outcomes for frontline workers can inform planning for future pandemics. To compare patient-reported outcomes by employment type and SARS-CoV-2 status. Data from the INSPIRE registry, which enrolled COVID-positive and COVID-negative adults between 12/7/2020-8/29/2022 was analyzed. Patient-reported outcomes were collected quarterly over 18 months. Participants were recruited across eight US sites. Employed INSPIRE participants who completed a short (3-month) and long-term (12-18 month) survey. SARS-CoV-2 index status and employment type (essential healthcare worker [HCW], essential non-HCW, and non-essential worker [\"general worker\"]). PROMIS-29 (mental and physical health summary) and PROMIS Cognitive SF-CF 8a (cognitive function) scores were assessed at baseline, short-term (3-months), and long-term (12-18 months) timepoints using GEE modeling. Of the 1,463 participants: 53.5% were essential workers (51.4% HCWs, 48.6% non-HCWs) and 46.5% were general workers. Most associations between outcomes and employment type became non-significant after adjusting for sociodemographics, comorbidities, COVID-19 vaccination, and SARS-CoV-2 variant period. However, among COVID-negative participants, essential HCWs had higher cognitive scores at baseline (β: 3.91, 95% CI [1.32, 6.50]), short term: (β: 3.49, 95% CI: [0.80, 6.18]) and long-term: (β: 3.72, 95% CI: [0.98, 6.46]) compared to general workers. Among COVID-positive participants, essential non-HCWs had significantly worse long-term physical health summary scores (β:-1.22, 95% CI: [-2.35, -0.09]) compared to general workers. Differences in outcomes by worker status were largely explained by baseline characteristics. However, compared to general workers, essential HCW status had higher cognitive function in the absence of SARS-CoV-2 infection at all timepoints, while essential non-HCWs were most vulnerable to poor recovery in long-term physical health following SARS-CoV-2 infection. Preparation efforts for future pandemics may consider enhanced protection and post-infection resources for frontline workers.

Reports on medium and long-term sequelae of SARS-CoV-2 infections largely lack quantification of incidence and relative risk. We describe the rationale and methods of the Innovative Support for Patients with SARS-CoV-2 Registry (INSPIRE) that combines patient-reported outcomes with data from digital health records to understand predictors and impacts of SARS-CoV-2 infection. INSPIRE is a prospective, multicenter, longitudinal study of individuals with symptoms of SARS-CoV-2 infection in eight regions across the US. Adults are eligible for enrollment if they are fluent in English or Spanish, reported symptoms suggestive of acute SARS-CoV-2 infection, and if they are within 42 days of having a SARS-CoV-2 viral test (i.e., nucleic acid amplification test or antigen test), regardless of test results. Recruitment occurs in-person, by phone or email, and through online advertisement. A secure online platform is used to facilitate the collation of consent-related materials, digital health records, and responses to self-administered surveys. Participants are followed for up to 18 months, with patient-reported outcomes collected every three months via survey and linked to concurrent digital health data; follow-up includes no in-person involvement. Our planned enrollment is 4,800 participants, including 2,400 SARS-CoV-2 positive and 2,400 SARS-CoV-2 negative participants (as a concurrent comparison group). These data will allow assessment of longitudinal outcomes from SARS-CoV-2 infection and comparison of the relative risk of outcomes in individuals with and without infection. Patient-reported outcomes include self-reported health function and status, as well as clinical outcomes including health system encounters and new diagnoses. Participating sites obtained institutional review board approval. Enrollment and follow-up are ongoing. This study will characterize medium and long-term sequelae of SARS-CoV-2 infection among a diverse population, predictors of sequelae, and their relative risk compared to persons with similar symptomatology but without SARS-CoV-2 infection. These data may inform clinical interventions for individuals with sequelae of SARS-CoV-2 infection.

The automatic detection of pulse during out-of-hospital cardiac arrest (OHCA) is necessary for the early recognition of the arrest and the detection of return of spontaneous circulation (end of the arrest). The only signal available in every single defibrillator and valid for the detection of pulse is the electrocardiogram (ECG). In this study we propose two deep neural network (DNN) architectures to detect pulse using short ECG segments (5 s), i.e., to classify the rhythm into pulseless electrical activity (PEA) or pulse-generating rhythm (PR). A total of 3914 5-s ECG segments, 2372 PR and 1542 PEA, were extracted from 279 OHCA episodes. Data were partitioned patient-wise into training (80%) and test (20%) sets. The first DNN architecture was a fully convolutional neural network, and the second architecture added a recurrent layer to learn temporal dependencies. Both DNN architectures were tuned using Bayesian optimization, and the results for the test set were compared to state-of-the art PR/PEA discrimination algorithms based on machine learning and hand crafted features. The PR/PEA classifiers were evaluated in terms of sensitivity (Se) for PR, specificity (Sp) for PEA, and the balanced accuracy (BAC), the average of Se and Sp. The Se/Sp/BAC of the DNN architectures were 94.1%/92.9%/93.5% for the first one, and 95.5%/91.6%/93.5% for the second one. Both architectures improved the performance of state of the art methods by more than 1.5 points in BAC.

With recent negative studies of amiodarone and lidocaine for cardiac arrest, research into other antiarrhythmics is warranted. Literature on procainamide in cardiac arrest is limited. We evaluated procainamide for out-of-hospital cardiac arrests (OHCA) from the Resuscitation Outcomes Consortium (ROC). We included all ROC Epistry 3 OHCAs with an initial shockable rhythm that received an antiarrhythmic. We stratified cases by antiarrhythmic: procainamide, amiodarone, or lidocaine. The outcomes were prehospital return of spontaneous circulation (ROSC), ROSC in the ED, and survival to hospital discharge. We defined propensity scores based on possible confounders utilizing 1:1 propensity score matching to compare procainamide to amiodarone and lidocaine. We analyzed the matched data using logistic regression. We also used multivariable logistic regression to evaluate the association between antiarrhythmic and outcomes. 3087 subjects met inclusion criteria; 51 patients received only procainamide, 1776 received amiodarone, and 1418 received lidocaine. On propensity score analysis and compared to procainamide, amiodarone had similar prehospital ROSC (OR 0.7, 95% CI 0.3–1.8), ED ROSC (OR 0.6, 95% CI 0.3–1.3), and survival (OR 1.0, 95% CI 0.3–3.1). Lidocaine also had a similar prehospital ROSC (OR 0.9, 95% CI 0.4–2.2), ED ROSC (OR 1.2, 95% CI 0.5–2.7), and survival (OR 1.4, 95% CI 0.5–4.0). However, using multivariable regression, amiodarone had lower prehospital ROSC than procainamide (aOR 0.3, 95% CI 0.1–0.6). While associated with increased prehospital ROSC when compared with amiodarone using multivariable regression, procainamide otherwise had similar prehospital ROSC, ED ROSC, and survival. The role of procainamide in OHCA remains unclear.

In this trial, patients with out-of-hospital cardiac arrest received amiodarone, lidocaine, or placebo for shock-refractory ventricular fibrillation or pulseless ventricular tachycardia. There were no significant between-group differences in survival to hospital discharge. Out-of-hospital cardiac arrest is responsible for more than 300,000 deaths each year in North America. 1 Many out-of-hospital cardiac arrests are attributable to ventricular fibrillation or pulseless ventricular tachycardia. Although ventricular fibrillation or pulseless ventricular tachycardia is regarded as the most treatable presentation of out-of-hospital cardiac arrest because of its responsiveness to shock, 2 most defibrillation attempts do not result in sustained return of spontaneous circulation. 3 Ventricular fibrillation or pulseless ventricular tachycardia commonly persists or recurs after shock, and there is a significant inverse relationship between the duration of ventricular fibrillation or pulseless ventricular tachycardia, or the frequency of acute recurrences, and . . .

Pulseless electrical activity (PEA) is characterized by the disassociation of the mechanical and electrical activity of the heart and appears as the initial rhythm in 20–30% of out-of-hospital cardiac arrest (OHCA) cases. Predicting whether a patient in PEA will convert to return of spontaneous circulation (ROSC) is important because different therapeutic strategies are needed depending on the type of PEA. The aim of this study was to develop a machine learning model to differentiate PEA with unfavorable (unPEA) and favorable (faPEA) evolution to ROSC. An OHCA dataset of 1921 5s PEA signal segments from defibrillator files was used, 703 faPEA segments from 107 patients with ROSC and 1218 unPEA segments from 153 patients with no ROSC. The solution consisted of a signal-processing stage of the ECG and the thoracic impedance (TI) and the extraction of the TI circulation component (ICC), which is associated with ventricular wall movement. Then, a set of 17 features was obtained from the ECG and ICC signals, and a random forest classifier was used to differentiate faPEA from unPEA. All models were trained and tested using patientwise and stratified 10-fold cross-validation partitions. The best model showed a median (interquartile range) area under the curve (AUC) of 85.7(9.8)% and a balance accuracy of 78.8(9.8)%, improving the previously available solutions at more than four points in the AUC and three points in balanced accuracy. It was demonstrated that the evolution of PEA can be predicted using the ECG and TI signals, opening the possibility of targeted PEA treatment in OHCA.

Patients with cardiac arrest were assigned to either early analysis of cardiac rhythm (after 30 to 60 seconds of cardiopulmonary resuscitation) or later analysis (after 180 seconds). There was no significant difference between the groups in survival to hospital discharge. Out-of-hospital cardiac arrest is a common and lethal problem, leading to an estimated 330,000 deaths each year in the United States and Canada. 1 Overall, the rate of survival to hospital discharge among patients with an out-of-hospital cardiac arrest who are treated by emergency medical services (EMS) personnel is low but varies greatly, with rates ranging from 3.0% to 16.3%. 1 This variation in the rate of survival can be attributed partly to local variations in the five key links in the chain of survival: rapid EMS access, early cardiopulmonary resuscitation (CPR), early defibrillation, early advanced cardiac life support, and effective care . . .

A secondary arrest is frequent in patients that recover spontaneous circulation after an out-of-hospital cardiac arrest (OHCA). Rearrest events are associated to worse patient outcomes, but little is known on the heart dynamics that lead to rearrest. The prediction of rearrest could help improve OHCA patient outcomes. The aim of this study was to develop a machine learning model to predict rearrest. A random forest classifier based on 21 heart rate variability (HRV) and electrocardiogram (ECG) features was designed. An analysis interval of 2 min after recovery of spontaneous circulation was used to compute the features. The model was trained and tested using a repeated cross-validation procedure, on a cohort of 162 OHCA patients (55 with rearrest). The median (interquartile range) sensitivity (rearrest) and specificity (no-rearrest) of the model were 67.3% (9.1%) and 67.3% (10.3%), respectively, with median areas under the receiver operating characteristics and the precision–recall curves of 0.69 and 0.53, respectively. This is the first machine learning model to predict rearrest, and would provide clinically valuable information to the clinician in an automated way.

In this trial, over 23,000 patients with out-of-hospital cardiac arrest were assigned to standard CPR with a chest compression-to-ventilation ratio of 30:2 or to continuous chest compressions. There was no significant between-group difference in survival to hospital discharge. Standard cardiopulmonary resuscitation (CPR) consists of manual chest compressions to maintain blood flow and positive-pressure ventilation to maintain oxygenation until spontaneous circulation is restored. 1 Chest compressions are interrupted frequently by ventilations given as rescue breathing during the treatment of out-of-hospital cardiac arrest. 2 – 4 These interruptions reduce blood flow and potentially reduce the effectiveness of CPR. 5 One strategy to reduce the interruption of compressions is to provide asynchronous positive-pressure ventilation while not pausing for ventilations. The interruption of chest compressions has been associated with decreased survival in animals with cardiac arrest. 6 In nonasphyxial arrest, continuous compressions were as effective as compressions . . .