A mathematical model of SARS-CoV-2 immunity predicts paxlovid rebound.

Nirmatrelvir/ritonavir (Paxlovid), an oral antiviral medication targeting SARS-CoV-2, remains an important treatment for COVID-19. Initial studies of nirmatrelvir/ritonavir were performed in SARS-CoV-2 unvaccinated patients without prior confirmed SARS-CoV-2 infection; however, most individuals have now either been vaccinated and/or have experienced SARS-CoV-2 infection. After nirmatrelvir/ritonavir became widely available, reports surfaced of "Paxlovid rebound," a phenomenon in which symptoms (and SARS-CoV-2 test positivity) would initially resolve, but after finishing treatment, symptoms and test positivity would return. We used a previously described parsimonious mathematical model of immunity to SARS-CoV-2 infection to model the effect of nirmatrelvir/ritonavir treatment in unvaccinated and vaccinated patients. Model simulations show that viral rebound after treatment occurs only in vaccinated patients, while unvaccinated (SARS-COV-2 naïve) patients treated with nirmatrelvir/ritonavir do not experience any rebound in viral load. This work suggests that an approach combining parsimonious models of the immune system could be used to gain important insights in the context of emerging pathogens.

No Quick Fixes: Integrating Population Health Education and Quality Improvement in a Large Residency Program.

ABSTRACT: The intersection of population health (PH), quality improvement (QI), and health disparities is increasingly a focus in graduate medical education. However, it remains unclear how trainees may best improve disparities within clinical training environments. We integrated PH education for residents participating in a practical QI experience in a continuity clinic serving an underserved population. We analyzed whether PH education increased confidence in creating care plans and implementing team-based care strategies after selection of one of three QI metrics with known health disparities led to improvement. Posteducational session, attendees had odds of confidence in creating care plans 10.0 (95% confidence interval [CI] 4.6-21.7) times the presession period, whereas nonattendees' confidence was unchanged (OR 1.63, 95% CI 0.78-3.4). Residents participating in the QI project did not have higher confidence in creating a care plan at baseline (20% vs. 9.6%, p = .09) nor any additional shift in confidence versus other residents (p = .57). There were no differences in QI metric performance rate trends for residents choosing a specific QI metric versus those that did not (p > .33 for all comparisons). PH didactics can increase resident confidence around PH topics. However, translating such learning into outcomes and improved health equity may require dedicated efforts across residency training.

Heart rate variability and adrenal size provide clues to sudden cardiac death in hospitalized COVID-19 patients.

PURPOSE: To examine the association between a measure of heart rate variability and sudden cardiac death (SCD) in COVID-19 patients. METHODS: Patients with SARS-COV-2 infection admitted to Columbia University Irving Medical Center who died between 4/25/2020 and 7/14/2020 and had an autopsy were examined for root mean square of successive differences (RMSSD), organ weights, and evidence of SCD. RESULTS: Thirty COVID-19 patients were included and 12 had SCD. The RMSSD over 7 days without vs with SCD was median 0.0129 (IQR 0.0074-0.026) versus 0.0098 (IQR 0.0056-0.0197), p < 0.0001. The total adjusted adrenal weight of the non-SCD group was 0.40 g/kg (IQR 0.35-0.55) versus 0.25 g/kg (IQR 0.21-0.31) in the SCD group, p = 0.0007. CONCLUSIONS: Hospitalized patients with COVID-19 who experienced SCD had lower parasympathetic activity (RMSSD) and smaller sized adrenal glands. Further research is required to replicate these findings.

Identification of Endotypes of Hospitalized COVID-19 Patients.

Background: Characterization of coronavirus disease 2019 (COVID-19) endotypes may help explain variable clinical presentations and response to treatments. While risk factors for COVID-19 have been described, COVID-19 endotypes have not been elucidated. Objectives: We sought to identify and describe COVID-19 endotypes of hospitalized patients. Methods: Consensus clustering (using the ensemble method) of patient age and laboratory values during admission identified endotypes. We analyzed data from 528 patients with COVID-19 who were admitted to telemetry capable beds at Columbia University Irving Medical Center and discharged between March 12 to July 15, 2020. Results: Four unique endotypes were identified and described by laboratory values, demographics, outcomes, and treatments. Endotypes 1 and 2 were comprised of low numbers of intubated patients (1 and 6%) and exhibited low mortality (1 and 6%), whereas endotypes 3 and 4 included high numbers of intubated patients (72 and 85%) with elevated mortality (21 and 43%). Endotypes 2 and 4 had the most comorbidities. Endotype 1 patients had low levels of inflammatory markers (ferritin, IL-6, CRP, LDH), low infectious markers (WBC, procalcitonin), and low degree of coagulopathy (PTT, PT), while endotype 4 had higher levels of those markers. Conclusions: Four unique endotypes of hospitalized patients with COVID-19 were identified, which segregated patients based on inflammatory markers, infectious markers, evidence of end-organ dysfunction, comorbidities, and outcomes. High comorbidities did not associate with poor outcome endotypes. Further work is needed to validate these endotypes in other cohorts and to study endotype differences to treatment responses.

Divergent COVID-19 Disease Trajectories Predicted by a DAMP-Centered Immune Network Model.

COVID-19 presentations range from mild to moderate through severe disease but also manifest with persistent illness or viral recrudescence. We hypothesized that the spectrum of COVID-19 disease manifestations was a consequence of SARS-CoV-2-mediated delay in the pathogen-associated molecular pattern (PAMP) response, including dampened type I interferon signaling, thereby shifting the balance of the immune response to be dominated by damage-associated molecular pattern (DAMP) signaling. To test the hypothesis, we constructed a parsimonious mechanistic mathematical model. After calibration of the model for initial viral load and then by varying a few key parameters, we show that the core model generates four distinct viral load, immune response and associated disease trajectories termed "patient archetypes", whose temporal dynamics are reflected in clinical data from hospitalized COVID-19 patients. The model also accounts for responses to corticosteroid therapy and predicts that vaccine-induced neutralizing antibodies and cellular memory will be protective, including from severe COVID-19 disease. This generalizable modeling framework could be used to analyze protective and pathogenic immune responses to diverse viral infections.

Yelp Reviews Of Hospital Care Can Supplement And Inform Traditional Surveys Of The Patient Experience Of Care.

Little is known about how real-time online rating platforms such as Yelp may complement the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey, which is the US standard for evaluating patients' experiences after hospitalization. We compared the content of Yelp narrative reviews of hospitals to the topics in the HCAHPS survey, called domains in HCAHPS terminology. While the domains included in Yelp reviews covered the majority of HCAHPS domains, Yelp reviews covered an additional twelve domains not found in HCAHPS. The majority of Yelp topics that most strongly correlate with positive or negative reviews are not measured or reported by HCAHPS. The large collection of patient- and caregiver-centered experiences found on Yelp can be analyzed with natural language processing methods, identifying for policy makers the measures of hospital quality that matter most to patients and caregivers. The Yelp measures and analysis can also provide actionable feedback for hospitals.

What do patients say about emergency departments in online reviews? A qualitative study.

BACKGROUND: Patients have adopted web-based tools to report on the quality of their healthcare experiences. We seek to examine online reviews for US emergency departments (EDs) posted on Yelp, a popular consumer ratings website. METHODS: We conducted a qualitative analysis of unstructured, publicly accessible reviews for hospitals available on http://www.yelp.com. We collected all reviews describing experiences of ED care for a stratified random sample of 100 US hospitals. We analysed the content of the reviews using themes derived from the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) inpatient care survey. We also used modified grounded theory to iteratively code the text of the reviews, identifying additional themes specific to emergency care. The data were double-coded, and discrepancies were evaluated to ensure consensus. RESULTS: Of the 1736 total reviews, 573 (33%) described patient experiences involving the ED. The reviews contained several themes assessed by the HCAHPS survey, including communication with nurses, communication with doctors, and pain control. The reviews also contained key themes specific to emergency care: waiting and efficiency; decisions to seek care in the ED; and events following discharge, including administrative difficulties. CONCLUSIONS: These exploratory findings suggest that online reviews for EDs contain similar themes to survey-based assessments of inpatient hospital care as well as themes specific to emergency care. Consumer rating websites allow patients to provide rapid and public feedback on their experience of medical care. Web-based platforms may offer a novel strategy for assessing patient-centred quality in emergency care.

Defibrillation in the movies: a missed opportunity for public health education.

AIM: To characterize defibrillation and cardiac arrest survival outcomes in movies. METHODS: Movies from 2003 to 2012 with defibrillation scenes were reviewed for patient and rescuer characteristics, scene characteristics, defibrillation characteristics, additional interventions, and cardiac arrest survival outcomes. Resuscitation actions were compared with chain of survival actions and the American Heart Association (AHA) Emergency Cardiovascular Care (ECC) 2020 Impact Goals. Cardiac arrest survival outcomes were compared with survival rates reported in the literature and targeted by the AHA ECC 2020 Impact Goals. RESULTS: Thirty-five scenes were identified in 32 movies. Twenty-five (71%) patients were male, and 29 (83%) rescuers were male. Intent of defibrillation was resuscitation in 29 (83%) scenes and harm in 6 (17%) scenes. Cardiac arrest was the indication for use in 23 (66%) scenes, and the heart rhythm was made known in 18 scenes (51%). When the heart rhythm was known, defibrillation was appropriately used for ventricular tachycardia or ventricular fibrillation in 5 (28%) scenes and inappropriately used for asystole in 7 (39%) scenes. In 8 scenes with in-hospital cardiac arrest, 7 (88%) patients survived, compared to survival rates of 23.9% reported in the literature and 38% targeted by an AHA ECC 2020 Impact Goal. In 12 movie scenes with out-of-hospital cardiac arrest, 8 (67%) patients survived, compared to survival rates of 7.9-9.5% reported in peer-reviewed literature and 15.8% targeted by an AHA ECC 2020 Impact Goal. CONCLUSION: In movies, defibrillation and cardiac arrest survival outcomes are often portrayed inaccurately, representing missed opportunities for public health education.

Crowdsourcing--harnessing the masses to advance health and medicine, a systematic review.

OBJECTIVE: Crowdsourcing research allows investigators to engage thousands of people to provide either data or data analysis. However, prior work has not documented the use of crowdsourcing in health and medical research. We sought to systematically review the literature to describe the scope of crowdsourcing in health research and to create a taxonomy to characterize past uses of this methodology for health and medical research. DATA SOURCES: PubMed, Embase, and CINAHL through March 2013. STUDY ELIGIBILITY CRITERIA: Primary peer-reviewed literature that used crowdsourcing for health research. STUDY APPRAISAL AND SYNTHESIS METHODS: Two authors independently screened studies and abstracted data, including demographics of the crowd engaged and approaches to crowdsourcing. RESULTS: Twenty-one health-related studies utilizing crowdsourcing met eligibility criteria. Four distinct types of crowdsourcing tasks were identified: problem solving, data processing, surveillance/monitoring, and surveying. These studies collectively engaged a crowd of >136,395 people, yet few studies reported demographics of the crowd. Only one (5 %) reported age, sex, and race statistics, and seven (33 %) reported at least one of these descriptors. Most reports included data on crowdsourcing logistics such as the length of crowdsourcing (n = 18, 86 %) and time to complete crowdsourcing task (n = 15, 71 %). All articles (n = 21, 100 %) reported employing some method for validating or improving the quality of data reported from the crowd. LIMITATIONS: Gray literature not searched and only a sample of online survey articles included. CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS: Utilizing crowdsourcing can improve the quality, cost, and speed of a research project while engaging large segments of the public and creating novel science. Standardized guidelines are needed on crowdsourcing metrics that should be collected and reported to provide clarity and comparability in methods.