Syndromic surveillance of population-level COVID-19 burden with cough monitoring in a hospital emergency waiting room.

Syndromic surveillance is an effective tool for enabling the timely detection of infectious disease outbreaks and facilitating the implementation of effective mitigation strategies by public health authorities. While various information sources are currently utilized to collect syndromic signal data for analysis, the aggregated measurement of cough, an important symptom for many illnesses, is not widely employed as a syndromic signal. With recent advancements in ubiquitous sensing technologies, it becomes feasible to continuously measure population-level cough incidence in a contactless, unobtrusive, and automated manner. In this work, we demonstrate the utility of monitoring aggregated cough count as a syndromic indicator to estimate COVID-19 cases. In our study, we deployed a sensor-based platform (Syndromic Logger) in the emergency room of a large hospital. The platform captured syndromic signals from audio, thermal imaging, and radar, while the ground truth data were collected from the hospital's electronic health record. Our analysis revealed a significant correlation between the aggregated cough count and positive COVID-19 cases in the hospital (Pearson correlation of 0.40, p-value < 0.001). Notably, this correlation was higher than that observed with the number of individuals presenting with fever (ρ = 0.22, p = 0.04), a widely used syndromic signal and screening tool for such diseases. Furthermore, we demonstrate how the data obtained from our Syndromic Logger platform could be leveraged to estimate various COVID-19-related statistics using multiple modeling approaches. Aggregated cough counts and other data, such as people density collected from our platform, can be utilized to predict COVID-19 patient visits related metrics in a hospital waiting room, and SHAP and Gini feature importance-based metrics showed cough count as the important feature for these prediction models. Furthermore, we have shown that predictions based on cough counting outperform models based on fever detection (e.g., temperatures over 39°C), which require more intrusive engagement with the population. Our findings highlight that incorporating cough-counting based signals into syndromic surveillance systems can significantly enhance overall resilience against future public health challenges, such as emerging disease outbreaks or pandemics.

Passive Monitoring of Crowd-Level Cough Counts in Waiting Areas produces Reliable Syndromic Indicator for Total COVID-19 Burden in a Hospital Emergency Clinic.

Syndromic surveillance is an effective tool for enabling the timely detection of infectious disease outbreaks and facilitating the implementation of effective mitigation strategies by public health authorities. While various information sources are currently utilized to collect syndromic signal data for analysis, the aggregated measurement of cough, an important symptom for many illnesses, is not widely employed as a syndromic signal. With recent advancements in ubiquitous sensing technologies, it becomes feasible to continuously measure population-level cough incidence in a contactless, unobtrusive, and automated manner. In this work, we demonstrate the utility of monitoring aggregated cough count as a syndromic indicator to estimate COVID-19 cases. In our study, we deployed a sensor-based platform (Syndromic Logger) in the emergency room of a large hospital. The platform captured syndromic signals from audio, thermal imaging, and radar, while the ground truth data were collected from the hospital's electronic health record. Our analysis revealed a significant correlation between the aggregated cough count and positive COVID-19 cases in the hospital (Pearson correlation of 0.40, p-value < 0.001). Notably, this correlation was higher than that observed with the number of individuals presenting with fever (ρ = 0.22, p = 0.04), a widely used syndromic signal and screening tool for such diseases. Furthermore, we demonstrate how the data obtained from our Syndromic Logger platform could be leveraged to estimate various COVID-19-related statistics using multiple modeling approaches. Our findings highlight the efficacy of aggregated cough count as a valuable syndromic indicator associated with the occurrence of COVID-19 cases. Incorporating this signal into syndromic surveillance systems for such diseases can significantly enhance overall resilience against future public health challenges, such as emerging disease outbreaks or pandemics.

Characterization and management of type II and complex endoleaks after fenestrated/branched endovascular aneurysm repair.

INTRODUCTION: Endoleaks are more common after fenestrated/branched endovascular aneurysm repair (F/B-EVAR) than infrarenal EVAR secondary to the length of aortic coverage and number of component junctions. Although reports have focused on type I and III endoleaks, less is known regarding type II endoleaks after F/B-EVAR. We hypothesized that type II endoleaks would be common and often complex (associated with additional endoleak types), given the potential for multiple inflow and outflow sources. We sought to describe the incidence and complexity of type II endoleaks after F/B-EVAR. METHODS: F/B-EVAR data prospectively collected at a single institution in an investigational device exemption clinical trial (G130210) were retrospectively analyzed (2014-2021). Endoleaks were characterized by type, time to detection, and management. Primary endoleaks were defined as those present on completion imaging or at first postoperative imaging, and secondary were those on subsequent imaging. Recurrent endoleaks were those that developed after a successfully resolved endoleak. Reinterventions were considered for type I or III endoleaks or any endoleak associated with sac growth >5 mm. Technical success defined as the absence of flow in the aneurysm sac at procedure conclusion and methods of intervention were captured. RESULTS: Among 335 consecutive F/B-EVARs (mean ± standard deviation follow-up: 2.5 ± 1.5 years), 125 patients (37%) experienced 166 endoleaks (81 primary, 72 secondary, and 13 recurrent). Of these 125 patients, 50 (40% of patients) underwent 71 interventions for 60 endoleaks. Type II endoleaks were the most frequent (n = 100, 60%), with 20 identified during the index procedure, 12 (60%) of which resolved before 30-day follow-up. Of the 100 type II endoleaks, 20 (20%; 12 primary, 5 secondary, and 3 recurrent) were associated with sac growth; 15 (75%) of those with associated sac growth underwent intervention. At intervention, 6 (40%) were reclassified as complex, with a concomitant type I or type III endoleak. Initial technical success for endoleak treatment was 96% (68 of 71). There were 13 recurrences, all of which were associated with complex endoleaks. CONCLUSIONS: Nearly half of the patients who underwent F/B-EVAR experienced an endoleak. The majority were classified as type II, with nearly a fifth associated with sac expansion. Interventions for a type II endoleak frequently led to reclassification as complex, with a concomitant type I or III endoleak not appreciated on computed tomography angiography and/or duplex. Further study is needed to determine if the primary treatment goal for complex aneurysm repair is sac stability or sac regression, as this would inform both the importance of properly classifying endoleaks noninvasively and the intervention threshold for managing type II endoleaks.

The Novel Therapeutic Landscape for Relapsed/Refractory Diffuse Large B Cell Lymphoma.

Diffuse large B cell lymphoma (DLBCL) is an aggressive malignancy that has been traditionally treated with anthracycline-based chemotherapy, but approximately one-third of patients relapse after first-line therapy or have primary refractoriness. In this focused review, we discuss the 7 novel Food & Drug Administration (FDA)-approved medications for relapsed/refractory (R/R) DLBCL. We describe 5 CD19-targeted therapies, 3 of which are chimeric antigen receptor (CAR)-T cell therapies. We also highlight novel non-cell-based targeted therapies and discuss optimal sequencing considerations based on the goal of treatment, with an emphasis on CAR-T cell therapy as curative intent. We consider the limited tolerability of certain novel agents, prospects for elderly patients, and financial aspects of these approaches. We discuss advantages and limitations of these targeted therapies based on seminal clinical trials. Finally, we summarize ongoing trials involving promising agents making their way into the pharmacologic pipeline. These therapies include allogeneic CAR-T treatments and multi-antigen targeting therapies such as the CD19/CD22 CAR-T and the CD3/CD20 bispecific antibodies mosunetuzumab and odronextamab. We summarize our approach based on the best available evidence as we enter 2022.

NExUS-Heart: Novel examinations using smart technologies for heart health-Data sharing from commercial wearable devices and telehealth engagement in participants with or at risk of atrial fibrillation.

Background: Telemedicine and commercial wearable devices capable of detecting atrial fibrillation (AF) have revolutionized arrhythmia care during coronavirus disease 2019. However, not much is known about virtual patient-provider interactions or device sharing behaviors. Objective: The purpose of this study was to characterize how participants with or at risk of AF are engaging with their providers in the context of telemedicine and using commercially wearable devices to manage their health. Methods: We developed a survey to describe participant behaviors around telemedicine encounters and commercial wearable device use. The survey was distributed to participants diagnosed with AF or those at risk of AF (as determined by being at least 65 years old and having a CHA2DS2-VASc stroke risk score of >2) in the University of Massachusetts Memorial Health Care system. Results: The survey was distributed to 23,530 patients, and there were 1222 (5.19%) participant responses. Among the participants, 327 (26.8%) had AF and 895 (73.2%) were at risk of AF. Neither device ownership nor device type use differed by AF status. After adjusting for covariates that may influence surveyed participant communication patterns, we found that participants with AF were more likely to share their wearable device-derived data with providers (adjusted odds ratio 1.87; 95% confidence interval 1.02-3.41). Rates of sharing physical activity or sleep data were low for both groups and did not differ by AF status. Conclusion: Compared with participants at risk of developing AF, those with AF were more likely to share heart rate and rhythm data from their commercial wearable devices with providers. However, both groups had similar rates of sharing physical activity and sleep data, telemedicine engagement, and technology use and ownership.