The COVID-19 Pandemic and In-Person Visit Rate Disruptions Among Patients With Hematologic Neoplasms in the US in 2020 to 2021.

Importance: The COVID-19 pandemic has led to a reduction in routine in-person medical care; however, it is unknown whether there have been any changes in visit rates among patients with hematologic neoplasms. Objective: To examine associations between the COVID-19 pandemic and in-person visits and telemedicine use among patients undergoing active treatment for hematologic neoplasms. Design, Setting, and Participants: Data for this retrospective observational cohort study were obtained from a nationwide electronic health record-derived, deidentified database. Data for patients with hematologic neoplasms who had received at least 1 systemic line of therapy between March 1, 2016, and February 28, 2021, were included. Treatments were categorized into 3 types: oral therapy, outpatient infusions, and inpatient infusions. The data cutoff date was April 30, 2021, when study analyses were conducted. Main Outcomes and Measures: Monthly visit rates were calculated as the number of documented visits (telemedicine or in-person) per active patient per 30-day period. We used time-series forecasting methods on prepandemic data (March 2016 to February 2020) to estimate expected rates between March 1, 2020, and February 28, 2021 (if the pandemic had not occurred). Results: This study included data for 24 261 patients, with a median age of 68 years (IQR, 60-75 years). A total of 6737 patients received oral therapy, 15 314 received outpatient infusions, and 8316 received inpatient infusions. More than half of patients were men (14 370 [58%]) and non-Hispanic White (16 309 [66%]). Early pandemic months (March to May 2020) demonstrated a significant 21% reduction (95% prediction interval [PI], 12%-27%) in in-person visit rates averaged across oral therapy and outpatient infusions. Reductions in in-person visit rates were also significant for all treatment types for multiple myeloma (oral therapy: 29% reduction; 95% PI, 21%-36%; P = .001; outpatient infusions: 11% reduction; 95% PI, 4%-17%; P = .002; inpatient infusions: 55% reduction; 95% PI, 27%-67%; P = .005), for oral therapy for chronic lymphocytic leukemia (28% reduction; 95% PI, 12%-39%; P = .003), and for outpatient infusions for mantle cell lymphoma (38% reduction; 95% PI, 6%-54%; P = .003) and chronic lymphocytic leukemia (20% reduction; 95% PI, 6%-31%; P = .002). Telemedicine visit rates were highest for patients receiving oral therapy, with greater use in the early pandemic months and a subsequent decrease in later months. Conclusions and Relevance: In this cohort study of patients with hematologic neoplasms, documented in-person visit rates for those receiving oral therapy and outpatient infusions significantly decreased during the early pandemic months but returned to close to projected rates in the later half of 2020. There were no statistically significant reductions in the overall in-person visit rate for patients receiving inpatient infusions. There was higher telemedicine use in the early pandemic months, followed by a decline, but use was persistent in the later half of 2020. Further studies are needed to ascertain associations between the COVID-19 pandemic and subsequent cancer outcomes and the evolution of telemedicine use for care delivery.

Smart Device for CO2 Measuring and Supplementing with O2 Using IOT

Globally, atmospheric carbon dioxide (CO2) concentration is rising due to rising carbon-based fuel consumption and ongoing deforestation. As carbon dioxide levels grow due to the warming trend, the atmosphere's temperature is predicted to climb. Increased fatigue, headaches, and tinnitus are just a few health issues that high CO2 concentrations in the atmosphere can cause. The electrical activities of the brain, the heart, and the lungs have all been demonstrated to change significantly after a brief exposure to 0.1 percent CO2. Continuous measurements of the atmospheric CO2 content have recently been shown to help evaluate the ventilation conditions in buildings or rooms. Additionally, it prevents the development of the severe acute respiratory syndrome coronavirus 2 (Severe acute respiratory). The coronavirus, known as a powerful acute respiratory, can make people ill. This has grown to be a significant concern in emergency medicine. © 2022 IEEE.

Performance drift in a mortality prediction algorithm among patients with cancer during the SARS-CoV-2 pandemic.

Sudden changes in health care utilization during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic may have impacted the performance of clinical predictive models that were trained prior to the pandemic. In this study, we evaluated the performance over time of a machine learning, electronic health record-based mortality prediction algorithm currently used in clinical practice to identify patients with cancer who may benefit from early advance care planning conversations. We show that during the pandemic period, algorithm identification of high-risk patients had a substantial and sustained decline. Decreases in laboratory utilization during the peak of the pandemic may have contributed to drift. Calibration and overall discrimination did not markedly decline during the pandemic. This argues for careful attention to the performance and retraining of predictive algorithms that use inputs from the pandemic period.

MASI: A Novel Combination of Mask and Shield with near-N95 Efficiency

Mask wearing has become critical for preventing the aerosolization and inhalation of virus-laden particles during the ongoing COVID-19 global pandemic. However, facial masks with effective filtration are either not readily accessible (e.g., N95) or have reduced filtration efficiency due to air gaps between the mask and wearer (e.g., cloth masks). We have developed a novel combination of a mask and shield named Mask And Shield Integrated (MASI) that provides nearly the same levels of protection as an N95 mask by addressing these issues. Magnetic latches reduce gaps between the mask and wearer, while a novel fin structure on the shield provides protection against floating particles. A series of experiments was performed to study MASI’s efficacy in both eliminating mask gaps and also providing N95-like filtration efficiency. MASI was found to solve both problems, thus providing a low-cost mask solution that can be applied to a broad range of environments to prevent inhalation of small air-borne particles. IEEE

Trends in Enrollment in Employer-Sponsored Health Insurance in the US Before and During the COVID-19 Pandemic, January 2019 to June 2021.

This cross-sectional study compares trends in employer-sponsored health insurance coverage in the US before and during the COVID-19 pandemic.

Trends in Low-Value Cancer Care During the COVID-19 Pandemic (preprint)

Background Low-value services are common in cancer care. The onset of the COVID-19 pandemic caused a dramatic decrease in health care utilization, leading many to suspect that low-value cancer services may decrease. Methods In this retrospective cohort study, we used administrative claims from the HealthCore Integrated Research Environment, a repository of medical and pharmacy data from US health plans representing over 80 million members, to identify 204,581 patients diagnosed with breast, colorectal, and/or lung cancer between January 1, 2015, and March 31, 2021. We used linear probability models to investigate the relation between the onset of COVID-19 pandemic and 5 guideline-based metrics of low-value cancer care: 1) Positron Emission Tomography/Computed Tomography (PET/CT) instead of conventional CT imaging for initial staging; 2) conventional fractionation instead of hypofractionation for early-stage breast cancer; 3) non-guideline-based antiemetic use for minimal-, low-, or moderate-to-high-risk chemotherapies; 4) off-pathway systemic therapy; and 5) aggressive end-of-life care. Results Among 204,581 patients, the mean [SD] age was 63.1 [13.2], 68.1% were female, 83,593 (40.8%) had breast cancer, 56,373 (27.5%) had colon cancer, and 64,615 (31.5%) had lung cancer. Rates of low-value cancer services did not exhibit meaningful declines during the pandemic: PET/CT imaging, adjusted percentage point difference 1.87 (95% CI -0.13 to 3.87); conventional radiotherapy, adjusted percentage point difference 3.93 (95% CI 1.50 to 6.36); off-pathway systemic therapy, adjusted percentage point difference 0.82 (95% CI -0.62 to 2.25); non-guideline-based antiemetics, adjusted percentage point difference -3.62 (95% CI -4.97 to -2.27); aggressive end-of-life care, adjusted percentage point difference 2.71 (95% CI -0.59 to 6.02). Discussion Low-value cancer care remained prevalent through the pandemic. Policymakers should consider changes to payment and incentive design to turn the tide toward higher-value cancer care.

Digital Health Applications in Oncology: An Opportunity to Seize.

Digital health advances have transformed many clinical areas including psychiatric and cardiovascular care. However, digital health innovation is relatively nascent in cancer care, which represents the fastest growing area of health-care spending. Opportunities for digital health innovation in oncology include patient-facing technologies that improve patient experience, safety, and patient-clinician interactions; clinician-facing technologies that improve their ability to diagnose pathology and predict adverse events; and quality of care and research infrastructure to improve clinical workflows, documentation, decision support, and clinical trial monitoring. The COVID-19 pandemic and associated shifts of care to the home and community dramatically accelerated the integration of digital health technologies into virtually every aspect of oncology care. However, the pandemic has also exposed potential flaws in the digital health ecosystem, namely in clinical integration strategies; data access, quality, and security; and regulatory oversight and reimbursement for digital health technologies. Stemming from the proceedings of a 2020 workshop convened by the National Cancer Policy Forum of the National Academies of Sciences, Engineering, and Medicine, this article summarizes the current state of digital health technologies in medical practice and strategies to improve clinical utility and integration. These recommendations, with calls to action for clinicians, health systems, technology innovators, and policy makers, will facilitate efficient yet safe integration of digital health technologies into cancer care.

Patient and clinician nudges to improve symptom management in advanced cancer using patient-generated health data: study protocol for the PROStep randomised controlled trial.

INTRODUCTION: Patients with advanced cancers often face significant symptoms from their cancer and adverse effects from cancer-associated therapy. Patient-generated health data (PGHD) are routinely collected information about symptoms and activity levels that patients either directly report or passively record using devices such as wearable accelerometers. The objective of this study was to test the impact of an intervention integrating remote collection of PGHD with clinician and patient nudges to inform communication between patients with advanced cancer and their oncology team regarding symptom burden and functional status. METHODS AND ANALYSIS: This single-centre prospective randomised controlled trial randomises patients with metastatic gastrointestinal or lung cancers into one of three arms: (A) usual care, (B) an intervention that integrates PGHD (including weekly text-based symptom surveys and passively recorded step counts) into a dashboard delivered to oncology clinicians at each visit and (C) the same intervention as arm B but with an additional text-based active choice intervention to patients to encourage discussing their symptoms with their oncology team. The study will enrol approximately 125 participants. The coprimary outcomes are patient perceptions of their oncology team's understanding of their symptoms and their functional status. Secondary outcomes are intervention utility and adherence. ETHICS AND DISSEMINATION: This study has been approved by the institutional review board at the University of Pennsylvania. Study results will be disseminated using methods that describe the results in ways that key stakeholders can best understand and implement. TRIAL REGISTRATION NUMBERS: NCT04616768 and 843 616.

Performance Drift in a Clinical Prognostic Algorithm during the SARS-CoV-2 Pandemic (preprint)

Research Objective: Health systems use clinical predictive algorithms to allocate resources to high-risk patients. Such algorithms are trained using historical data and are later implemented in clinical settings. During this implementation period, predictive algorithms are prone to performance changes (drift) due to exogenous shocks in utilization or shifts in patient characteristics. Our objective was to examine the impact of sudden utilization shifts during the SARS-CoV-2 pandemic on the performance of an electronic health record (EHR)-based prognostic algorithm. Study Design: We studied changes in the performance of Conversation Connect, a validated machine learning algorithm that predicts 180-day mortality among outpatients with cancer receiving care at medical oncology practices within a large academic cancer center. Conversation Connect generates mortality risk predictions before each encounter using data from 159 EHR variables collected in the six months before the encounter. Since January 2019, Conversation Connect has been used as part of a behavioral intervention to prompt clinicians to consider early advance care planning conversations among patients with [≥]10% mortality risk. First, we descriptively compared encounter-level characteristics in the following periods: January 2019-February 2020 (pre-pandemic), March-May 2020 (early-pandemic), and June-December 2020 (later-pandemic). Second, we quantified changes in high-risk patient encounters using interrupted time series analyses that controlled for pre-pandemic trends and demographic, clinical, and practice covariates. Our primary metric of performance drift was false negative rate (FNR). Third, we assessed contributors to performance drift by comparing distributions of key EHR inputs across periods and predicting later pandemic utilization using pre-pandemic inputs. Population Studied: 237,336 in-person and telemedicine medical oncology encounters. Principal Findings: Age, race, average patient encounters per month, insurance type, comorbidity counts, laboratory values, and overall mortality were similar among encounters in the pre-, early-, and later-pandemic periods. Relative to the pre-pandemic period, the later-pandemic period was characterized by a 6.5-percentage-point decrease (28.2% vs. 34.7%) in high-risk encounters (p<0.001). FNR increased from 41.0% (95% CI 38.0-44.1%) in the pre-pandemic period to 57.5% (95% CI 51.9-63.0%) in the later pandemic period. Compared to the pre-pandemic period, the early and later pandemic periods had higher proportions of telemedicine encounters (0.01% pre-pandemic vs. 20.0% early-pandemic vs. 26.4% later-pandemic) and encounters with no preceding laboratory draws (17.7% pre-pandemic vs. 19.8% early-pandemic vs. 24.1% later-pandemic). In the later pandemic period, observed laboratory utilization was lower than predicted (76.0% vs 81.2%, p<0.001). In the later-pandemic period, mean 180-day mortality risk scores were lower for telemedicine encounters vs. in-person encounters (10.3% vs 11.2%, p<0.001) and encounters with no vs. any preceding laboratory draws (1.5% vs. 14.0%, p<0.001). Conclusions: During the SARS-CoV-2 pandemic period, the performance of a machine learning prognostic algorithm used to prompt advance care planning declined substantially. Increases in telemedicine and declines in laboratory utilization contributed to lower performance. Implications for Policy or Practice: This is the first study to show algorithm performance drift due to SARS-CoV-2 pandemic-related shifts in telemedicine and laboratory utilization. These mechanisms of performance drift could apply to other EHR clinical predictive algorithms. Pandemic-related decreases in care utilization may negatively impact the performance of clinical predictive algorithms and warrant assessment and possible retraining of such algorithms.

Impact of the COVID-19 Pandemic on Treatment Patterns for Patients With Metastatic Solid Cancer in the United States.

BACKGROUND: The COVID-19 pandemic has led to delays in patients seeking care for life-threatening conditions; however, its impact on treatment patterns for patients with metastatic cancer is unknown. We assessed the COVID-19 pandemic's impact on time to treatment initiation (TTI) and treatment selection for patients newly diagnosed with metastatic solid cancer. METHODS: We used an electronic health record-derived longitudinal database curated via technology-enabled abstraction to identify 14 136 US patients newly diagnosed with de novo or recurrent metastatic solid cancer between January 1 and July 31 in 2019 or 2020. Patients received care at approximately 280 predominantly community-based oncology practices. Controlled interrupted time series analyses assessed the impact of the COVID-19 pandemic period (April-July 2020) on TTI, defined as the number of days from metastatic diagnosis to receipt of first-line systemic therapy, and use of myelosuppressive therapy. RESULTS: The adjusted probability of treatment within 30 days of diagnosis was similar across periods (January-March 2019 = 41.7%, 95% confidence interval [CI] = 32.2% to 51.1%; April-July 2019 = 42.6%, 95% CI = 32.4% to 52.7%; January-March 2020 = 44.5%, 95% CI = 30.4% to 58.6%; April-July 2020 = 46.8%, 95% CI= 34.6% to 59.0%; adjusted percentage-point difference-in-differences = 1.4%, 95% CI = -2.7% to 5.5%). Among 5962 patients who received first-line systemic therapy, there was no association between the pandemic period and use of myelosuppressive therapy (adjusted percentage-point difference-in-differences = 1.6%, 95% CI = -2.6% to 5.8%). There was no meaningful effect modification by cancer type, race, or age. CONCLUSIONS: Despite known pandemic-related delays in surveillance and diagnosis, the COVID-19 pandemic did not affect TTI or treatment selection for patients with metastatic solid cancers.

Automated Text-Based Symptom Monitoring With Rapid Clinician Triage for Patients With Cancer and Suspected or Confirmed COVID-19.

PURPOSE: Patients with cancer are at greater risk of developing severe symptoms from COVID-19 than the general population. We developed and tested an automated text-based remote symptom-monitoring program to facilitate early detection of worsening symptoms and rapid assessment for patients with cancer and suspected or confirmed COVID-19. METHODS: We conducted a feasibility study of Cancer COVID Watch, an automated COVID-19 symptom-monitoring program with oncology nurse practitioner (NP)-led triage among patients with cancer between April 23 and June 30, 2020. Twenty-six patients with cancer and suspected or confirmed COVID-19 were enrolled. Enrolled patients received twice daily automated text messages over 14 days that asked "How are you feeling compared to 12 hours ago? Better, worse, or the same?" and, if worse, "Is it harder than usual for you to breathe?" Patients who responded worse and yes were contacted within 1 hour by an oncology NP. RESULTS: Mean age of patients was 62.5 years. Seventeen (65%) were female, 10 (38%) Black, and 15 (58%) White. Twenty-five (96%) patients responded to ≥ 1 symptom check-in, and overall response rate was 78%. Four (15%) patients were escalated to the triage line: one was advised to present to the emergency department (ED), and three were managed in the outpatient setting. Median time from escalation to triage call was 11.5 minutes. Four (15%) patients presented to the ED without first escalating their care via our program. Participant satisfaction was high (Net Promoter Score: 100, n = 4). CONCLUSION: Implementation of an intensive remote symptom monitoring and rapid NP triage program for outpatients with cancer and suspected or confirmed COVID-19 infection is possible. Similar tools may facilitate more rapid triage for patients with cancer in future pandemics.

Impact of the COVID-19 pandemic on treatment patterns for US patients with metastatic solid cancer (preprint)

BackgroundThe COVID-19 pandemic has led to delays in patients seeking care for life-threatening conditions; however, its impact on treatment patterns for patients with metastatic cancer is unknown. We assessed the COVID-19 pandemics impact on time to treatment initiation (TTI) and treatment selection for patients newly diagnosed with metastatic solid cancer. MethodsWe used an electronic health record-derived longitudinal database curated via technology-enabled abstraction to identify 14,136 US patients newly diagnosed with de novo or recurrent metastatic solid cancer between January 1 and July 31 in 2019 or 2020. Patients received care at [~]280 predominantly community-based oncology practices. Controlled interrupted time series analyses assessed the impact of the COVID-19 pandemic period (April-July 2020) on TTI, defined as the number of days from metastatic diagnosis to receipt of first-line systemic therapy, and use of myelosuppressive therapy. ResultsThe adjusted probability of treatment within 30 days of diagnosis [95% confidence interval] was similar across periods: January-March 2019 41.7% [32.2%, 51.1%]; April-July 2019 42.6% [32.4%, 52.7%]; January-March 2020 44.5% [30.4%, 58.6%]; April-July 2020 46.8% [34.6%, 59.0%]; adjusted percentage-point difference-in-differences 1.4% [-2.7%, 5.5%]. Among 5,962 patients who received first-line systemic therapy, there was no association between the pandemic period and use of myelosuppressive therapy (adjusted percentage-point difference-in-differences 1.6% [-2.6%, 5.8%]). There was no meaningful effect modification by cancer type, race, or age. ConclusionsDespite known pandemic-related delays in surveillance and diagnosis, the COVID-19 pandemic did not impact time to treatment initiation or treatment selection for patients with metastatic solid cancers.

Optimizing Flow Cytometric Analysis of Immune Cells in Samples Requiring Cryopreservation from Tumor-Bearing Mice.

Most shared resource flow cytometry facilities do not permit analysis of radioactive samples. We are investigating low-dose molecular targeted radionuclide therapy (MTRT) as an immunomodulator in combination with in situ tumor vaccines and need to analyze radioactive samples from MTRT-treated mice using flow cytometry. Further, the sudden shutdown of core facilities in response to the COVID-19 pandemic has created an unprecedented work stoppage. In these and other research settings, a robust and reliable means of cryopreservation of immune samples is required. We evaluated different fixation and cryopreservation protocols of disaggregated tumor cells with the aim of identifying a protocol for subsequent flow cytometry of the thawed sample, which most accurately reflects the flow cytometric analysis of the tumor immune microenvironment of a freshly disaggregated and analyzed sample. Cohorts of C57BL/6 mice bearing B78 melanoma tumors were evaluated using dual lymphoid and myeloid immunophenotyping panels involving fixation and cryopreservation at three distinct points during the workflow. Results demonstrate that freezing samples after all staining and fixation are completed most accurately matches the results from noncryopreserved equivalent samples. We observed that cryopreservation of living, unfixed cells introduces a nonuniform alteration to PD1 expression. We confirm the utility of our cryopreservation protocol by comparing tumors treated with in situ tumor vaccines, analyzing both fresh and cryopreserved tumor samples with similar results. Last, we use this cryopreservation protocol with radioactive specimens to demonstrate potentially beneficial effector cell changes to the tumor immune microenvironment following administration of a novel MTRT in a dose- and time-dependent manner.

An antiviral trap made of protein nanofibrils and iron oxyhydroxide nanoparticles.

Minimizing the spread of viruses in the environment is the first defence line when fighting outbreaks and pandemics, but the current COVID-19 pandemic demonstrates how difficult this is on a global scale, particularly in a sustainable and environmentally friendly way. Here we introduce and develop a sustainable and biodegradable antiviral filtration membrane composed of amyloid nanofibrils made from food-grade milk proteins and iron oxyhydroxide nanoparticles synthesized in situ from iron salts by simple pH tuning. Thus, all the membrane components are made of environmentally friendly, non-toxic and widely available materials. The membrane has outstanding efficacy against a broad range of viruses, which include enveloped, non-enveloped, airborne and waterborne viruses, such as SARS-CoV-2, H1N1 (the influenza A virus strain responsible for the swine flu pandemic in 2009) and enterovirus 71 (a non-enveloped virus resistant to harsh conditions, such as highly acidic pH), which highlights a possible role in fighting the current and future viral outbreaks and pandemics.