Rapid-cycle designs to adapt interventions for COVID-19 in safety-net healthcare systems.

Racial/ethnic minority, low socioeconomic status, and rural populations are disproportionately affected by COVID-19. Developing and evaluating interventions to address COVID-19 testing and vaccination among these populations are crucial to improving health inequities. The purpose of this paper is to describe the application of a rapid-cycle design and adaptation process from an ongoing trial to address COVID-19 among safety-net healthcare system patients. The rapid-cycle design and adaptation process included: (a) assessing context and determining relevant models/frameworks; (b) determining core and modifiable components of interventions; and (c) conducting iterative adaptations using Plan-Do-Study-Act (PDSA) cycles. PDSA cycles included: Plan. Gather information from potential adopters/implementers (e.g., Community Health Center [CHC] staff/patients) and design initial interventions; Do. Implement interventions in single CHC or patient cohort; Study. Examine process, outcome, and context data (e.g., infection rates); and, Act. If necessary, refine interventions based on process and outcome data, then disseminate interventions to other CHCs and patient cohorts. Seven CHC systems with 26 clinics participated in the trial. Rapid-cycle, PDSA-based adaptations were made to adapt to evolving COVID-19-related needs. Near real-time data used for adaptation included data on infection hot spots, CHC capacity, stakeholder priorities, local/national policies, and testing/vaccine availability. Adaptations included those to study design, intervention content, and intervention cohorts. Decision-making included multiple stakeholders (e.g., State Department of Health, Primary Care Association, CHCs, patients, researchers). Rapid-cycle designs may improve the relevance and timeliness of interventions for CHCs and other settings that provide care to populations experiencing health inequities, and for rapidly evolving healthcare challenges such as COVID-19.

Racial/ethnic minority, low socioeconomic status, and rural populations experience a disproportionate burden of COVID-19. Finding ways to address COVID-19 among these populations is crucial to improving health inequities. The purpose of this paper is to describe the rapid-cycle design process for a research project to address COVID-19 testing and vaccination among safety-net healthcare system patients. The project used real-time information on changes in COVID-19 policy (e.g., vaccination authorization), local case rates, and the capacity of safety-net healthcare systems to iteratively change interventions to ensure interventions were relevant and timely for patients. Key changes that were made to interventions included a change to the study design to include vaccination as a focus of the interventions after the vaccine was authorized; change in intervention content according to the capacity of local Community Health Centers to provide testing to patients; and changes to intervention cohorts such that priority groups of patients were selected for intervention based on characteristics including age, residency in an infection "hot spot," or race/ethnicity. Iteratively improving interventions based on real-time data collection may increase intervention relevance and timeliness, and rapid-cycle adaptions can be successfully implemented in resource constrained settings like safety-net healthcare systems.

Towards a multi-scale computer modeling workflow for simulation of pulmonary ventilation in advanced COVID-19.

Physics-based multi-scale in silico models offer an excellent opportunity to study the effects of heterogeneous tissue damage on airflow and pressure distributions in COVID-19-afflicted lungs. The main objective of this study is to develop a computational modeling workflow, coupling airflow and tissue mechanics as the first step towards a virtual hypothesis-testing platform for studying injury mechanics of COVID-19-afflicted lungs. We developed a CT-based modeling approach to simulate the regional changes in lung dynamics associated with heterogeneous subject-specific COVID-19-induced damage patterns in the parenchyma. Furthermore, we investigated the effect of various levels of inflammation in a meso-scale acinar mechanics model on global lung dynamics. Our simulation results showed that as the severity of damage in the patient's right lower, left lower, and to some extent in the right upper lobe increased, ventilation was redistributed to the least injured right middle and left upper lobes. Furthermore, our multi-scale model reasonably simulated a decrease in overall tidal volume as the level of tissue injury and surfactant loss in the meso-scale acinar mechanics model was increased. This study presents a major step towards multi-scale computational modeling workflows capable of simulating the effect of subject-specific heterogenous COVID-19-induced lung damage on ventilation dynamics.

Implications of microscale lung damage for COVID-19 pulmonary ventilation dynamics: A narrative review.

The COVID-19 pandemic surges on as vast research is produced to study the novel SARS-CoV-2 virus and the disease state it induces. Still, little is known about the impact of COVID-19-induced microscale damage in the lung on global lung dynamics. This review summarizes the key histological features of SARS-CoV-2 infected alveoli and links the findings to structural tissue changes and surfactant dysfunction affecting tissue mechanical behavior similar to changes seen in other lung injury. Along with typical findings of diffuse alveolar damage affecting the interstitium of the alveolar walls and blood-gas barrier in the alveolar airspace, COVID-19 can cause extensive microangiopathy in alveolar capillaries that further contribute to mechanical changes in the tissues and may differentiate it from previously studied infectious lung injury. Understanding microlevel damage impact on tissue mechanics allows for better understanding of macroscale respiratory dynamics. Knowledge gained from studies into the relationship between microscale and macroscale lung mechanics can allow for optimized treatments to improve patient outcomes in case of COVID-19 and future respiratory-spread pandemics.