Machine Learning analysis of human pulmonary extracellular matrix architecture identifies disease-specific remodeling patterns

Background: Normal organ function is critically dependent on an intact three-dimensional architecture. Structural abnormalities induced by pathological situations instruct cells to behave abnormally and promoting disease progression oftentimes leading to organ failure. Current approaches do not allow for high-resolution (HR) threedimensional (3D) visualisation and analysis of human organ structure. Method(s): Here, we develop a method to perfuse human tissue segments to remove cells and study the 3D structural scaffold, which could be applied to any organ. Our approach enables HR-3D imaging of organ architecture, which we apply to study healthy and diseased human lung, specifically emphysema, usual interstitial pneumonia, pulmonary sarcoidosis, and COVID-19. Result(s): Our imaging reveals major structural abnormalities previously unseen by existing methodologies. Furthermore, we identify disease-specific patterns of structural remodelling using machine learning, including the altered spatial relationship between extracellular matrix (ECM) proteins collagen type IV, elastin and fibrillar collagen present across all diseases. Conclusion(s): Given the importance of organ structure on function, our approach opens the possibility to understand human physiology in a new way, which may assist in future disease diagnosis and explain the detrimental pulmonary effects of the diseases studied here.

Digital transformation of professional healthcare practices: fitness seeking across a rugged value landscape

Digital transformation (DT) is typically described as a strategic, top-down initiative where new digital technologies fundamentally disrupt an organisation's structure, procedures, and processes to enhance its value proposition. We propose a middle-range theory which highlights that DT of professional practices in healthcare follows a different path. To build this theory, we transpose the metaphor of a "fitness landscape” from evolutionary biology to a professional healthcare context to build an intermediate conceptualisation, which is then refined through an empirical study. Our theory highlights that external events, such as the COVID-19 pandemic, changing patient behaviours or the availability of new digital resources, transform the "value landscape” upon which healthcare professionals create and deliver healthcare services to patients. Empowered by their professional autonomy and driven by their service orientation, healthcare professionals search for new paths and peaks for value creation and delivery across a rugged landscape. As digital resources are leveraged, new value propositions in practice emerge, and professional healthcare practices are digitally transformed. © The Operational Research Society 2023.

Post-Pandemic HVAC Systems Strategies For High-Rise Office Buildings

The impact of the COVID-19 pandemic on building design and strategy will be as revolutionary as the rise of the first skyscraper. COVID-19 and potential future pandemics have forever changed the design approach and methodology for high-rise office buildings. Heating, ventilation and air-conditioning (HVAC) operational changes since the onset of the pandemic have been important to improve wellness and increase occupant comfort. These include moves recommended by ASHRAE (such as more outside air, better filters), along with air-cleaning technologies that can readily be added to existing systems, such as Ultraviolet Germicidal Irradiation (UVGI) and Bipolar Ionization (BPI). But what if future tall buildings were designed to better respond to a pandemic from the start? Moving forward, high-performing buildings should be configured with mechanical systems that minimize or eliminate air mixing between floors. They should optimize ventilation effectiveness within the space. In the increasingly connected world, intelligent sensors can provide air quality data that is useful for both operators and occupants. With forward-thinking transparency, the data can be compiled into meaningful metrics and shared with occupants to give them insight into building operations and performance. © 2020, Council on Tall Buildings and Urban Habitat. All rights reserved.

Risk factors for fatigue and impaired function eight months after hospital admission with COVID-19

INTRODUCTION: We aimed to evaluate post-COVID-19 fatigue, change in functional capacity and health-related quality of life (HRQoL) eight months after discharge from hospital due to COVID-19. METHODS: A total of 83 patients (35 women) admitted to the Copenhagen University Hospital - North Zealand Hospital, Denmark, for COVID-19 during the period from March to June 2020 were evaluated eight months after discharge using validated questionnaires quantifying fatigue, HRQoL and post-COVID-19 functional status. Follow-up data were correlated with measures of pre-COVID-19 status (anthropometrics, comorbidities) and measures of severity of the acute infection. RESULTS: A total of 22 (65%) women and 12 (26%) men reported excessive fatigue. In all, 20 women (67%) and 17 men (37%) reported decreased physical function. Female sex was associated with fatigue. Loss of physical function was associated with pre-COVID-19 presence of heart disease and absence of lung disease. Severity of the acute COVID-19 infection was not associated with fatigue or change in functional status. Fatigue and functional status were correlated with both generic HRQoL and lung disease-specific HRQoL. CONCLUSIONS: Female sex was associated with a higher risk of fatigue eight months after hospitalisation with COVID-19 infection. Regarding loss of functional capacity after COVID-19, we found an apparently protective effect of pre-COVID-19 lung disease. Our findings underscore the urgent need for further research and the importance of evaluating those recovering from COVID-19 for symptoms of excessive fatigue and change in functional capacity irrespective of the severity of the initial infection. FUNDING: none. TRIAL REGISTRATION: not relevant.

Cardiac arrhythmias 3 months after hospitalization for COVID-19

Background: The long-term frequencies of cardiac arrhythmias in hospitalized coronavirus disease 2019 (COVID-19) patients have not been thoroughly investigated. Purpose: To describe the prevalence of cardiac arrhythmias, 3-4 months after hospitalization for COVID-19. Methods and results: Participants with COVID-19 discharged from five large Norwegian hospitals were invited to participate in a prospective cohort study. We examined 201 participants (44% females, mean age 58.5 years) with 24-hour electrocardiogram 3-4 months after discharge. Body mass index (BMI) was 28.3±4.5 kg/m2 (mean ± SD), and obesity (BMI >30) was found in 70 participants (34%). Clinically significant arrhythmias were defined as;ventricular tachycardia (non-sustained or sustained), premature ventricular contractions (PVC) exceeding 200/24 h, or coupled PVC, atrial fibrillation/flutter, second-degree atrioventricular block (AV-block) type 2, complete AV-block, sinoatrial (SA) block exceeding 3 s, premature AVnodal beats in bigeminy, supraventricular tachycardia (SVT) exceeding 30 s, and sinus bradycardia with less than 30 beats/min. High-sensitive cardiac troponin T (hs-cTnT) was measured at the 3-month follow-up. Results: Cardiac arrhythmias were found in 27% (n=54) of the participants. Ventricular premature contractions and non-sustained ventricular tachycardia were the most common arrhythmias, found in 22% (n=44) of the participants. Premature ventricular contractions were the most frequent cardiac arrhythmia. More than 200 PVCs per day were observed in 37 participants (18%) with a mean of 1300 PVC/day, and in 35 (95%) of these participants, the PVCs were polymorphic. Among 10 patients experiencing NSVT, 5 participants had previous CVD, including coronary heart disease (n=1), 1 atrial fibrillation, 2 venous thromboembolism, 4 heart failure. Atrial fibrillation was found in seven patients (3%), none of them of new-onset. SA block >3 seconds was only observed in one patient, and no incidence of high degree AV block was discovered. Pre-existing cardiovascular disease or hypertension (CVDH) were reported in 40% (n=81) of the participants. The CVDH group had an increased amount of arrhythmia compared to the group free of CVDH (p=0.04). High PVCs showed a fair correlation with hs-cTnT levels at 3 months (ρ=0.21 p=0.048). Conclusions: Three months following hospital discharge with COVID-19, cardiac arrhythmia was found in every fourth participant and was associated with a higher concentration of hs-cTnT at 3 months. The clinical implications of persistent ventricular arrhythmia following COVID-19 is not clear, but ventricular ectopy has been linked to increased risk of cardiac disease, including cardiomyopathy and sudden cardiac death. (Figure Presented).

Improvement in incident resolution time with implementation of an electronic patient management solution at a community oncology practice

Background: Value-based care models such as the Oncology Care Model incentivize practices to reduce hospitalizations and emergency department (ED) visits. Texas Oncology found that most ED visits occurred during regular business hours. Prolonged patient call back times were consistently rated poorly on satisfaction surveys and often led to ED visits for symptoms that could be managed in our offices. We partnered with Navigating Cancer (NC) to implement an electronic patient management technology solution. Methods: For each of our 200 locations, call volume was estimated based on clinic volume. We then reallocated or hired dedicated triage nurses and operators. Incoming calls were entered into the NC dashboard by operators as incidents which were routed based on symptom priority following system generated prompts. Incident volumes and resolution times were tracked. We instituted PDSA cycles at all locations with a goal of less than 90-minute resolution of symptom-related incidents Utilizing the electronic dashboard allowed us to continue this initiative during the COVID-19 public health emergency as our staff could work remotely. Nurses were able to document if a potential ED visit was avoided. These data points allowed our practice to establish comprehensive and strategic actions plans for quality improvement. Results: We finalized implementation of the system in February of 2020. Total incidents for 2020 were over 1 million, averaging over 5000 per location. Resolution time for all incidents started at 3.2 hours pre-implementation and improved to 2.2 hours in December of 2020. Resolution times for symptom-related incidents started at 2.3 hours pre-implementation and ended at 1.5 hours in December of 2020 with over 60% resolved under one hour. 8% of symptom-related incidents resulted in definite or probable ED avoidances by nursing assessment. Shortness of breath, vomiting, chills, and weakness were the top symptom types addressed for ED avoidances. Conclusions: An electronic patient management solution with PDSA cycles of quality improvement can markedly reduce call back times, especially for symptom related calls. We believe managing symptoms in a timely fashion will lower ED visits and hospitalizations as well as improve patient satisfaction. We will report on these outcomes once available.