Association between the COVID-19 pandemic and first cancer treatment modality: a population-based cohort study.

BACKGROUND: Physicians were directed to prioritize using nonsurgical cancer treatment at the beginning of the COVID-19 pandemic. We sought to quantify the impact of this policy on the modality of first cancer treatment (surgery, chemotherapy, radiotherapy or no treatment). METHODS: In this population-based study using Ontario data from linked administrative databases, we identified adults diagnosed with cancer from January 2016 to November 2020 and their first cancer treatment received within 1 year postdiagnosis. Segmented Poisson regressions were applied to each modality to estimate the change in mean 1-year recipient volume per thousand patients (rate) at the start of the pandemic (the week of Mar. 15, 2020) and change in the weekly trend in rate during the pandemic (Mar. 15, 2020, to Nov. 7, 2020) relative to before the pandemic (Jan. 3, 2016, to Mar. 14, 2020). RESULTS: We included 321 535 people diagnosed with cancer. During the first week of the COVID-19 pandemic, the mean rate of receiving upfront surgery over the next year declined by 9% (rate ratio 0.91, 95% confidence interval [CI] 0.88-0.95), and chemotherapy and radiotherapy rates rose by 30% (rate ratio 1.30, 95% CI 1.23-1.36) and 13% (rate ratio 1.13, 95% CI 1.07-1.19), respectively. Subsequently, the 1-year rate of upfront surgery increased at 0.4% for each week (rate ratio 1.004, 95% CI 1.002-1.006), and chemotherapy and radiotherapy rates decreased by 0.9% (rate ratio 0.991, 95% CI 0.989-0.994) and 0.4% (rate ratio 0.996, 95% CI 0.994-0.998), respectively, per week. Rates of each modality resumed to prepandemic levels at 24-31 weeks into the pandemic. INTERPRETATION: An immediate and sustained increase in use of nonsurgical therapy as the first cancer treatment occurred during the first 8 months of the COVID-19 pandemic in Ontario. Further research is needed to understand the consequences.

Early survival for patients newly diagnosed with cancer during COVID-19 in Ontario, Canada: A population-based cohort study.

BACKGROUND: Little is known about the association between the COVID-19 pandemic and early survival among newly diagnosed cancer patients. METHODS: This retrospective population-based cohort study used linked administrative datasets from Ontario, Canada. Adults (≥18 years) who received a cancer diagnosis between March 15 and December 31, 2020, were included in a pandemic cohort, while those diagnosed during the same dates in 2018/2019 were included in a pre-pandemic cohort. All patients were followed for one full year after the date of diagnosis. Cox proportional hazards regression models were used to assess survival in relation to the pandemic, patient characteristics at diagnosis, and the modality of first cancer treatment as a time-varying covariate. Interaction terms were explored to measure the pandemic association with survival for each cancer type. RESULTS: Among 179,746 patients, 53,387 (29.7%) were in the pandemic cohort and 37,741 (21.0%) died over the first post-diagnosis year. No association between the pandemic and survival was found when adjusting for patient characteristics at diagnosis (HR 0.99 [95% CI 0.96-1.01]), while marginally better survival was found for the pandemic cohort when the modality of treatment was additionally considered (HR 0.97 [95% CI 0.95-0.99]). When examining each cancer type, only a new melanoma diagnosis was associated with a worse survival in the pandemic cohort (HR 1.25 [95% CI 1.05-1.49]). CONCLUSIONS: Among patients able to receive a cancer diagnosis during the pandemic, one-year overall survival was not different than those diagnosed in the previous 2 years. This study highlights the complex nature of the COVID-19 pandemic impact on cancer care.

Incident Cancer Detection During the COVID-19 Pandemic.

BACKGROUND: Resource restrictions were established in many jurisdictions to maintain health system capacity during the COVID-19 pandemic. Disrupted healthcare access likely impacted early cancer detection. The objective of this study was to assess the impact of the pandemic on weekly reported cancer incidence. PATIENTS AND METHODS: This was a population-based study involving individuals diagnosed with cancer from September 25, 2016, to September 26, 2020, in Ontario, Canada. Weekly cancer incidence counts were examined using segmented negative binomial regression models. The weekly estimated backlog during the pandemic was calculated by subtracting the observed volume from the projected/expected volume in that week. RESULTS: The cohort consisted of 358,487 adult patients with cancer. At the start of the pandemic, there was an immediate 34.3% decline in the estimated mean cancer incidence volume (relative rate, 0.66; 95% CI, 0.57-0.75), followed by a 1% increase in cancer incidence volume in each subsequent week (relative rate, 1.009; 95% CI, 1.001-1.017). Similar trends were found for both screening and nonscreening cancers. The largest immediate declines were seen for melanoma and cervical, endocrinologic, and prostate cancers. For hepatobiliary and lung cancers, there continued to be a weekly decline in incidence during the COVID-19 period. Between March 15 and September 26, 2020, 12,601 fewer individuals were diagnosed with cancer, with an estimated weekly backlog of 450. CONCLUSIONS: We estimate that there is a large volume of undetected cancer cases related to the COVID-19 pandemic. Incidence rates have not yet returned to prepandemic levels.

The Collateral Impact of the COVID-19 Pandemic on HPV-positive Oropharyngeal Cancer Diagnosis

Purpose We aim to assess the potential impact of the COVID-19 pandemic on diagnostic delays in HPV-positive oropharyngeal cancer (OPC), and to describe their underlying reasons. Methods All HPV+ OPC referred to a tertiary cancer centre and diagnosed between June-December 2019 (Pre-Pandemic cohort) vs June-December 2020 (Pandemic cohort) were reviewed. TNM classification, gross-tumor-volumes (GTV) and intervals between sign/symptom onset and treatment initiation were compared between the cohorts. Reasons for delay (>6 months from onset of signs/symptoms to a positive biopsy of the primary tumor, or a delay specifically mentioned in the patient chart) in establishing the diagnosis were recorded per clinician's documentation, and categorized as COVID-related or non-COVID-related. Results A total of 157 consecutive HPV+ OPC patients were identified (Pre-Pandemic: 92;Pandemic: 65). Compared to the Pre-Pandemic cohort, Pandemic cohort patients had a higher proportion of N2-N3 (32% vs 15%, p=0.019) and stage III (38% vs 23%, p=0.034) disease at presentation. The differences in proportions with >6 months delay from symptom onset to establishing the diagnosis (29% vs 20%, p=0.16) or to first treatment (49% vs 38%, p=0.22) were not statistically different. 47% of diagnostic delays in the Pandemic cohort were potentially attributable to COVID-19. Conclusion We observed a collateral impact of the COVID-19 pandemic on HPV+ OPC care through more advanced stage at presentation and a non-significant but numerically longer interval to diagnosis. This could adversely impact patient outcomes and future resource allocation. Both COVID-19-related or unrelated factors contribute to diagnostic delay. Tailored interventions to reduce delays are warranted.

The collateral impact of the COVID-19 pandemic on HPV-positive oropharyngeal cancer diagnosis.

PURPOSE: We aim to assess the potential impact of the COVID-19 pandemic on diagnostic delays in HPV-positive oropharyngeal cancer (OPC), and to describe their underlying reasons. METHODS: All HPV + OPC referred to a tertiary cancer centre and diagnosed between June-December 2019 (Pre-Pandemic cohort) vs June-December 2020 (Pandemic cohort) were reviewed. TNM classification, gross-tumor-volumes (GTV) and intervals between sign/symptom onset and treatment initiation were compared between the cohorts. Reasons for delay (>6 months from onset of signs/symptoms to a positive biopsy of the primary tumor, or a delay specifically mentioned in the patient chart) in establishing the diagnosis were recorded per clinician's documentation, and categorized as COVID-related or non-COVID-related. RESULTS: A total of 157 consecutive HPV + OPC patients were identified (Pre-Pandemic: 92; Pandemic: 65). Compared to the Pre-Pandemic cohort, Pandemic cohort patients had a higher proportion of N2-N3 (32 % vs 15 %, p = 0.019) and stage III (38 % vs 23 %, p = 0.034) disease at presentation. The differences in proportions with > 6 months delay from symptom onset to establishing the diagnosis (29 % vs 20 %, p = 0.16) or to first treatment (49 % vs 38 %, p = 0.22) were not statistically different. 47 % of diagnostic delays in the Pandemic cohort were potentially attributable to COVID-19. CONCLUSION: We observed a collateral impact of the COVID-19 pandemic on HPV + OPC care through more advanced stage at presentation and a non-significant but numerically longer interval to diagnosis. This could adversely impact patient outcomes and future resource allocation. Both COVID-19-related and unrelated factors contribute to diagnostic delays. Tailored interventions to reduce delays are warranted.

Timeliness and Modality of Treatment for New Cancer Diagnoses During the COVID-19 Pandemic in Canada.

Importance: The impact of COVID-19 on the modality and timeliness of first-line cancer treatment is unclear yet critical to the planning of subsequent care. Objective: To explore the association of the COVID-19 pandemic with modalities of and wait times for first cancer treatment. Design, Setting, and Participants: This retrospective population-based cohort study using administrative data was conducted in Ontario, Canada, among adults newly diagnosed with cancer between January 3, 2016, and November 7, 2020. Participants were followed up from date of diagnosis for 1 year, until death, or until June 26, 2021, whichever occurred first, to ensure a minimum of 6-month follow-up time. Exposures: Receiving a cancer diagnosis in the pandemic vs prepandemic period, using March 15, 2020, the date when elective hospital procedures were halted. Main Outcomes and Measures: The main outcome was a time-to-event variable describing number of days from date of diagnosis to date of receiving first cancer treatment (surgery, chemotherapy, or radiation) or to being censored. For each treatment modality, a multivariable competing-risk regression model was used to assess the association between time to treatment and COVID-19 period. A secondary continuous outcome was defined for patients who were treated 6 months after diagnosis as the waiting time from date of diagnosis to date of treatment. Results: Among 313 499 patients, the mean (SD) age was 66.4 (14.1) years and 153 679 (49.0%) were male patients. Those who were diagnosed during the pandemic were less likely to receive surgery first (subdistribution hazard ratio [sHR], 0.97; 95% CI, 0.95-0.99) but were more likely to receive chemotherapy (sHR, 1.26; 95% CI, 1.23-1.30) or radiotherapy (sHR, 1.16; 95% CI, 1.13-1.20) first. Among patients who received treatment within 6 months from diagnosis (228 755 [73.0%]), their mean (SD) waiting time decreased from 35.1 (37.2) days to 29.5 (33.6) days for surgery, from 43.7 (34.1) days to 38.4 (30.6) days for chemotherapy, and from 55.8 (41.8) days to 49.0 (40.1) days for radiotherapy. Conclusions and Relevance: In this cohort study, the pandemic was significantly associated with greater use of nonsurgical therapy as initial cancer treatment. Wait times were shorter in the pandemic period for those treated within 6 months of diagnosis. Future work needs to examine how these changes may have affected patient outcomes to inform future pandemic guideline development.

Characterizing the Impact of Procedure Funding on the Covid-19 Generated Procedure Gap in Ontario: A Population-Based Analysis.

BACKGROUND: Surgical procedures in Canada were historically funded through global hospital budgets. Activity-based funding models were developed to improve access, equity, timeliness and value of care for priority areas. COVID-19 upended health priorities and resulted in unprecedented disruptions to surgical care which created a significant procedure gap. We hypothesized that activity-based funding models influenced the magnitude and trajectory of this procedure gap. METHODS: Population-based analysis of procedure rates comparing pandemic (March 1, 2020 to December 31, 2021) to a pre-pandemic baseline (January 1, 2017 to February 29, 2020) in Ontario, Canada. Poisson generalized estimating equation models were used to predict expected rates in the pandemic based on the pre-pandemic baseline. Analyses were stratified by procedure type (out-patient, in-patient), body region, and funding category (activity-based funding programs vs. global budget). RESULTS: 281,328 fewer scheduled procedures were performed during the COVID-19 period compared to the pre-pandemic baseline (Rate Ratio 0.78; 95%CI 0.77-0.80). In-patient procedures saw a larger reduction (24.8%) in volume compared to out-patient procedures (20.5%). An increase in the proportion of procedures funded through activity-based programs was seen during the pandemic (52%) relative to the pre-pandemic baseline (50%). Body systems funded predominantly through global hospital budgets (e.g. gynecology, otologic surgery) saw the least months at or above baseline volumes whereas those with multiple activity-based funding options (e.g. musculoskeletal, abdominal) saw the most months at or above baseline volumes. CONCLUSIONS: Those needing procedures funded though global hospital budgets may have been disproportionately disadvantaged by pandemic-related health care disruptions.

Incident Cancer Detection During Multiple Waves of COVID-19: The Tsunami After the Earthquake.

No population-based study exists to demonstrate the full-spectrum impact of COVID-19 on hindering incident cancer detection in a large cancer system. Building upon our previous publication in JNCCN, we conducted an updated analysis using 12 months of new data accrued in the pandemic era (extending the study period from September 26, 2020, to October 2, 2021) to demonstrate how multiple COVID-19 waves affected the weekly cancer incidence volume in Ontario, Canada, and if we have fully cleared the backlog at the end of each wave.

Cancer Patients First Treated with Chemotherapy: Are They More Likely to Receive Surgery in the Pandemic?

Due to the ramping down of cancer surgery in early pandemic, many newly diagnosed patients received other treatments first. We aimed to quantify the pandemic-related shift in rate of surgery following chemotherapy. This is a retrospective population-based cohort study involving adults diagnosed with cancer between 3 January 2016 and 7 November 2020 in Ontario, Canada who received chemotherapy as first treatment within 6-months of diagnosis. Competing-risks regression models with interaction effects were used to quantify the association between COVID-19 period (receiving a cancer diagnosis before or on/after 15 March 2020) and receipt of surgical reSection 9-months after first chemotherapy. Among 51,653 patients, 8.5% (n = 19,558) of them ultimately underwent surgery 9-months after chemotherapy initiation. Receipt of surgery was higher during the pandemic than before (sHR 1.07, 95% CI 1.02-1.13). Material deprivation was independently associated with lower receipt of surgery (least vs. most deprived quintile: sHR 1.11, 95% CI 1.04-1.17), but did not change with the pandemic. The surgical rate increase was most pronounced for breast cancer (sHR 1.13, 95% CI 1.06-1.20). These pandemic-related shifts in cancer treatment requires further evaluations to understand the long-term consequences. Persistent material deprivation-related inequity in cancer surgical access needs to be addressed.

Comparison of Use of Neoadjuvant Systemic Treatment for Breast Cancer and Short-term Outcomes Before vs During the COVID-19 Era in Ontario, Canada.

Importance: In response to an increase in COVID-19 infection rates in Ontario, several systemic treatment (ST) regimens delivered in the adjuvant setting for breast cancer were temporarily permitted for neoadjuvant-intent to defer nonurgent breast cancer surgical procedures. Objective: To examine the use and compare short-term outcomes of neoadjuvant-intent vs adjuvant ST in the COVID-19 era compared with the pre-COVID-19 era. Design, Setting, and Participants: This was a retrospective population-based cohort study in Ontario, Canada. Patients with cancer starting selected ST regimens in the COVID-19 era (March 11, 2020, to September 30, 2020) were compared to those in the pre-COVID-19 era (March 11, 2019, to March 10, 2020). Patients were diagnosed with breast cancer within 6 months of starting systemic therapy. Main Outcomes and Measures: Estimates were calculated for the use of neoadjuvant vs adjuvant ST, the likelihood of receiving a surgical procedure, the rate of emergency department visits, hospital admissions, COVID-19 infections, and all-cause mortality between treatment groups over time. Results: Among a total of 10 920 patients included, 7990 (73.2%) started treatment in the pre-COVID-19 era and 7344 (67.3%) received adjuvant ST; the mean (SD) age was 61.6 (13.1) years. Neoadjuvant-intent ST was more common in the COVID-19 era (1404 of 2930 patients [47.9%]) than the pre-COVID-19 era (2172 of 7990 patients [27.2%]), with an odds ratio of 2.46 (95% CI, 2.26-2.69; P < .001). This trend was consistent across a range of ST regimens, but differed according to patient age and geography. The likelihood of receiving surgery following neoadjuvant-intent chemotherapy was similar in the COVID-19 era compared with the pre-COVID-19 era (log-rank P = .06). However, patients with breast cancer receiving neoadjuvant-intent hormonal therapy were significantly more likely to receive surgery in the COVID-19 era (log-rank P < .001). After adjustment, there were no significant changes in the rate of emergency department visits over time between patients receiving neoadjuvant ST, adjuvant ST, or ST only during the ST treatment period or postoperative period. Hospital admissions decreased in the COVID-19 era for patients who received neoadjuvant ST compared with adjuvant ST or ST alone (P for interaction = .01 for both) in either setting. Conclusions and Relevance: In this cohort study, patients were more likely to start neoadjuvant ST in the COVID-19 era, which varied across the province and by indication. There was limited evidence to suggest any substantial impact on short-term outcomes.

Single-cell profiling of the antigen-specific response to BNT162b2 SARS-CoV-2 RNA vaccine.

RNA-based vaccines against SARS-CoV-2 have proven critical to limiting COVID-19 disease severity and spread. Cellular mechanisms driving antigen-specific responses to these vaccines, however, remain uncertain. Here we identify and characterize antigen-specific cells and antibody responses to the RNA vaccine BNT162b2 using multiple single-cell technologies for in depth analysis of longitudinal samples from a cohort of healthy participants. Mass cytometry and unbiased machine learning pinpoint an expanding, population of antigen-specific memory CD4+ and CD8+ T cells with characteristics of follicular or peripheral helper cells. B cell receptor sequencing suggest progression from IgM, with apparent cross-reactivity to endemic coronaviruses, to SARS-CoV-2-specific IgA and IgG memory B cells and plasmablasts. Responding lymphocyte populations correlate with eventual SARS-CoV-2 IgG, and a participant lacking these cell populations failed to sustain SARS-CoV-2-specific antibodies and experienced breakthrough infection. These integrated proteomic and genomic platforms identify an antigen-specific cellular basis of RNA vaccine-based immunity.

Delivery of Cancer Care in Ontario, Canada, During the First Year of the COVID-19 Pandemic.

Importance: The COVID-19 pandemic has impacted cancer systems worldwide. Quantifying the changes is critical to informing the delivery of care while the pandemic continues, as well as for system recovery and future pandemic planning. Objective: To quantify change in the delivery of cancer services across the continuum of care during the COVID-19 pandemic. Design, Setting, and Participants: This population-based cohort study assessed cancer screening, imaging, diagnostic, treatment, and psychosocial oncological care services delivered in pediatric and adult populations in Ontario, Canada (population 14.7 million), from April 1, 2019, to March 1, 2021. Data were analyzed from May 1 to July 31, 2021. Exposures: COVID-19 pandemic. Main Outcomes and Measures: Cancer service volumes from the first year of the COVID-19 pandemic, defined as April 1, 2020, to March 31, 2021, were compared with volumes during a prepandemic period of April 1, 2019, to March 31, 2020. Results: During the first year of the pandemic, there were a total of 4 476 693 cancer care services, compared with 5 644 105 services in the year prior, a difference of 20.7% fewer services of cancer care, representing a potential backlog of 1 167 412 cancer services. While there were less pronounced changes in systemic treatments, emergency and urgent imaging examinations (eg, 1.9% more parenteral systemic treatments) and surgical procedures (eg, 65% more urgent surgical procedures), major reductions were observed for most services beginning in March 2020. Compared with the year prior, during the first pandemic year, cancer screenings were reduced by 42.4% (-1 016 181 screening tests), cancer treatment surgical procedures by 14.1% (-8020 procedures), and radiation treatment visits by 21.0% (-141 629 visits). Biopsies to confirm cancer decreased by up to 41.2% and surgical cancer resections by up to 27.8% during the first pandemic wave. New consultation volumes also decreased, such as for systemic treatment (-8.2%) and radiation treatment (-9.3%). The use of virtual cancer care increased for systemic treatment and radiation treatment and psychosocial oncological care visits, increasing from 0% to 20% of total new or follow-up visits prior to the pandemic up to 78% of total visits in the first pandemic year. Conclusions and Relevance: In this population-based cohort study in Ontario, Canada, large reductions in cancer service volumes were observed. While most services recovered to prepandemic levels at the end of the first pandemic year, a substantial care deficit likely accrued. The anticipated downstream morbidity and mortality associated with this deficit underscore the urgent need to address the backlog and recover cancer care and warrant further study.

Impact of cancer surgery slowdowns on patient survival during the COVID-19 pandemic: a microsimulation modelling study.

BACKGROUND: With the declaration of the global pandemic, surgical slowdowns were instituted to conserve health care resources for anticipated surges in patients with COVID-19. The long-term implications on survival of these slowdowns for patients with cancer in Canada is unknown. METHODS: We constructed a microsimulation model based on real-world population data on cancer care from Ontario, Canada, from 2019 and 2020. Our model estimated wait times for cancer surgery over a 6-month period during the pandemic by simulating a slowdown in operating room capacity (60% operating room resources in month 1, 70% in month 2, 85% in months 3-6), as compared with simulated prepandemic conditions with 100% resources. We used incremental differences in simulated wait times to model survival using per-day hazard ratios for risk of death. Primary outcomes included life-years lost per patient and per cancer population. We conducted scenario analyses to evaluate alternative, hypothetical scenarios of different levels of surgical slowdowns on risk of death. RESULTS: The simulated model population comprised 22 799 patients waiting for cancer surgery before the pandemic and 20 177 patients during the pandemic. Mean wait time to surgery prepandemic was 25 days and during the pandemic was 32 days. Excess wait time led to 0.01-0.07 life-years lost per patient across cancer sites, translating to 843 (95% credible interval 646-950) life-years lost among patients with cancer in Ontario. INTERPRETATION: Pandemic-related slowdowns of cancer surgeries were projected to result in decreased long-term survival for many patients with cancer. Measures to preserve surgical resources and health care capacity for affected patients are critical to mitigate unintended consequences.

Assessing the Impact of the COVID-19 Pandemic on Emergency Department Use for Patients Undergoing Cancer-Directed Surgeries.

Emergency department (ED) use is a concern for surgery patients, physicians and health administrators particularly during a pandemic. The objective of this study was to assess the impact of the pandemic on ED use following cancer-directed surgeries. This is a retrospective cohort study of patients undergoing cancer-directed surgeries comparing ED use from 7 January 2018 to 14 March 2020 (pre-pandemic) and 15 March 2020 to 27 June 2020 (pandemic) in Ontario, Canada. Logistic regression models were used to (1) determine the association between pandemic vs. pre-pandemic periods and the odds of an ED visit within 30 days after discharge from hospital for surgery and (2) to assess the odds of an ED visit being of high acuity (level 1 and 2 as per the Canadian Triage and Acuity Scale). Of our cohort of 499,008 cancer-directed surgeries, 468,879 occurred during the pre-pandemic period and 30,129 occurred during the pandemic period. Even though there was a substantial decrease in the general population ED rates, after covariate adjustment, there was no significant decrease in ED use among surgical patients (OR 1.002, 95% CI 0.957-1.048). However, the adjusted odds of an ED visit being of high acuity was 23% higher among surgeries occurring during the pandemic (OR 1.23, 95% CI 1.14-1.33). Although ED visits in the general population decreased substantially during the pandemic, the rate of ED visits did not decrease among those receiving cancer-directed surgery. Moreover, those presenting in the ED post-operatively during the pandemic had significantly higher levels of acuity.

Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy.

For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrait of the cells reacting to a novel virus before disease-specific knowledge and tools are established. However, single cell analysis tools can struggle to reveal rare cells that are under 0.1% of the population. Here, the machine learning workflow Tracking Responders EXpanding (T-REX) was created to identify changes in both rare and common cells across human immune monitoring settings. T-REX identified cells with highly similar phenotypes that localized to hotspots of significant change during rhinovirus and SARS-CoV-2 infections. Specialized MHCII tetramer reagents that mark rhinovirus-specific CD4+ cells were left out during analysis and then used to test whether T-REX identified biologically significant cells. T-REX identified rhinovirus-specific CD4+ T cells based on phenotypically homogeneous cells expanding by ≥95% following infection. T-REX successfully identified hotspots of virus-specific T cells by comparing infection (day 7) to either pre-infection (day 0) or post-infection (day 28) samples. Plotting the direction and degree of change for each individual donor provided a useful summary view and revealed patterns of immune system behavior across immune monitoring settings. For example, the magnitude and direction of change in some COVID-19 patients was comparable to blast crisis acute myeloid leukemia patients undergoing a complete response to chemotherapy. Other COVID-19 patients instead displayed an immune trajectory like that seen in rhinovirus infection or checkpoint inhibitor therapy for melanoma. The T-REX algorithm thus rapidly identifies and characterizes mechanistically significant cells and places emerging diseases into a systems immunology context for comparison to well-studied immune changes.

The psychological burden of waiting for procedures and patient-centred strategies that could support the mental health of wait-listed patients and caregivers during the COVID-19 pandemic: A scoping review.

BACKGROUND: Waiting for procedures delayed by COVID-19 may cause anxiety and related adverse consequences. OBJECTIVE: To synthesize research on the mental health impact of waiting and patient-centred mitigation strategies that could be applied in the COVID-19 context. METHODS: Using a scoping review approach, we searched 9 databases for studies on waiting lists and mental health and reported study characteristics, impacts and intervention attributes and outcomes. RESULTS: We included 51 studies that focussed on organ transplant (60.8%), surgery (21.6%) or cancer management (13.7%). Most patients and caregivers reported anxiety, depression and poor quality of life, which deteriorated with increasing wait time. The impact of waiting on mental health was greater among women and new immigrants, and those of younger age, lower socio-economic status, or with less-positive coping ability. Six studies evaluated educational strategies to develop coping skills: 2 reduced depression (2 did not), 1 reduced anxiety (2 did not) and 2 improved quality of life (2 did not). In contrast, patients desired acknowledgement of concerns, peer support, and periodic communication about wait-list position, prioritization criteria and anticipated procedure date. CONCLUSIONS: Findings revealed patient-centred strategies to alleviate the mental health impact of waiting for procedures. Ongoing research should explore how to optimize the impact of those strategies for diverse patients and caregivers, particularly in the COVID-19 context. PATIENT OR PUBLIC CONTRIBUTION: Six patients and four caregivers waiting for COVID-19-delayed procedures helped to establish eligibility criteria, plan data extraction and review a draft and final report.

Reprise du retard en chirurgie causé par la COVID-19 en Ontario : étude de modélisation de séries chronologiques.

CONTEXTE: Pour limiter la propagation de la maladie à coronavirus 2019 (COVID-19), de nombreux pays ont décidé de réduire le nombre d'interventions chirurgicales non urgentes, ce qui a créé des retards en chirurgie partout dans le monde. Notre objectif était d'évaluer l'ampleur du retard pour ce type d'interventions en Ontario, au Canada, ainsi que le temps et les ressources nécessaires pour y remédier. MÉTHODES: Nous avons consulté 6 bases de données administratives décrivant la population ontarienne et canadienne pour dégager la distribution du volume chirurgical et de la cadence des salles d'opération pour chaque type d'interventions et chaque région, et connaître la durée d'occupation d'un lit d'hôpital et d'un lit de soins intensifs. Les données utilisées concernent l'ensemble ou une partie de la période du 1er janvier 2017 au 13 juin 2020. Nous avons estimé l'ampleur du retard accumulé et prédit le temps nécessaire pour le reprendre dans un scénario avec capacité d'appoint de + 10 % (ajout d'un jour à 50 % de la capacité par semaine) à l'aide de modèles de séries chronologiques, de modèles de files d'attente et d'une analyse de sensibilité probabiliste. RÉSULTATS: Entre le 15 mars et le 13 juin 2020, le retard en chirurgie à l'échelle de l'Ontario s'est accru de 148 364 opérations (intervalle de prévision à 95 % 124 508­174 589) au total, et en moyenne de 11 413 opérations par semaine. Pour reprendre le retard accumulé, il faudra environ 84 semaines (intervalle de confiance [IC] à 95 % 46­145) et une cadence hebdomadaire de 717 patients (IC à 95 % 326­1367), qui elle demande 719 heures passées au bloc opératoire (IC à 95 % 431­1038), 265 lits d'hôpital (IC à 95 % 87­678) et 9 lits de soins intensifs (IC à 95 % 4­20) par semaine. INTERPRÉTATION: L'ampleur du retard en chirurgie dû à la COVID-19 laisse entrevoir de graves conséquences pour la phase de reprise en Ontario. Le cadre qui nous a servi à modéliser la reprise du retard peut être adapté ailleurs, avec des données locales, pour faciliter la planification.

A Cytometrist's Guide to Coordinating and Performing Effective COVID-19 Research.

Cytometry is playing a crucial role in addressing the COVID-19 pandemic. In this commentary-written by a variety of stakeholders in the cytometry, immunology, and infectious disease communities-we review cytometry's role in the COVID-19 response and discuss workflow issues critical to planning and executing effective research in this emerging field. We discuss sample procurement and processing, biosafety, technology options, data sharing, and the translation of research findings into clinical environments. © 2020 International Society for Advancement of Cytometry.

Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy.

For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrait of the cells reacting to a novel virus before disease specific knowledge and tools are established. However, single cell analysis tools can struggle to reveal rare cells that are under 0.1% of the population. Here, the machine learning workflow Tracking Responders Expanding (T-REX) was created to identify changes in both very rare and common cells in diverse human immune monitoring settings. T-REX identified cells that were highly similar in phenotype and localized to hotspots of significant change during rhinovirus and SARS-CoV-2 infections. Specialized reagents used to detect the rhinovirus-specific CD4+ cells, MHCII tetramers, were not used during unsupervised analysis and instead 'left out' to serve as a test of whether T-REX identified biologically significant cells. In the rhinovirus challenge study, T-REX identified virus-specific CD4+ T cells based on these cells being a distinct phenotype that expanded by ≥95% following infection. T-REX successfully identified hotspots containing virus-specific T cells using pairs of samples comparing Day 7 of infection to samples taken either prior to infection (Day 0) or after clearing the infection (Day 28). Mapping pairwise comparisons in samples according to both the direction and degree of change provided a framework to compare systems level immune changes during infectious disease or therapy response. This revealed that the magnitude and direction of systemic immune change in some COVID-19 patients was comparable to that of blast crisis acute myeloid leukemia patients undergoing induction chemotherapy and characterized the identity of the immune cells that changed the most. Other COVID-19 patients instead matched an immune trajectory like that of individuals with rhinovirus infection or melanoma patients receiving checkpoint inhibitor therapy. T-REX analysis of paired blood samples provides an approach to rapidly identify and characterize mechanistically significant cells and to place emerging diseases into a systems immunology context.