Antibody Response after One and/or Two Doses of BNT162b2 Anti- Sars-Cov-2 mRNA Vaccine in Patients Treated By CAR T-Cells Therapy

Introduction: Data regarding the efficacy of anti-severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) messenger RNA vaccines in immunocompromised hosts are scarce and no data yet appear to be available for patients with hematological malignancies who also received chimeric antigen receptor-T (CAR-T) cells therapy. Methods: The efficacy and safety of one and/or two injections of the BNT162b2 (Pfizer-BioNTech) vaccine was evaluated retrospectively in 23 CAR-T recipients in our Hematology Department, compared to a cohort of 25 healthy caregivers, vaccinated concomitantly between January 28 and May 31, 2021. None of these individuals had a previous clinical history of COVID-19. Results: Overall, the patients (14 males and 9 females) had a median age of 62 years old (range: 21-79) and had received CAR-T for high-grade lymphoma (n= 20) or acute lymphoblastic leukemia (n= 3). Eight and 3 had been respectively previously autografted or allografted and two were allografted after CAR-T. All patients were pretreated for lymphodepletion by fludarabine + cyclophosphamide before CAR-T infusion. The CAR-T provided were axicabtagene ciloleucel (Yescarta, Kite/Gilead, n=16, tisagenlecleucel (Kymriah, Novartis Pharma, n=5 and brexucabtagene autoleucel (KTE-X19, Tecartus, Kite/Gilead, n=1). One additional patient received allogeneic UCART19 (Servier). The median delay between CAR-T administration and the first vaccine (V1) was 401 days (d; range: 113-819). All patients but 2 were in complete remission at V1 and 3 were still on therapy (revlimid n=1, tafasitamab n=1, chemotherapy n=1). After V1, antibody response to the SARS-CoV-2 spike protein receptor-binding domain was tested by the Roche Elecsys® assay at a median time of 29 d (range: 16-32) in 19 patients and 23 d (range:18-32) in controls. At that time, only 4/23 patients (21%) but all controls (100%) had a positive anti-spike antibody response (p<0.001). Among seropositive cases, median IgG titers were higher in controls (35.1 U/mL, range 2.2->250) than in patients (5.9 U/mL range 4.1-41.6, p=0.06). The highest IgG titer (>250) was obtained in 2 controls. The median delay between V1 and the second vaccine (V2) was 28 d (range: 14-46) for patients and 23 d (range: 18-32) for controls. Among the 20 patients tested after V2, 17 had also been tested after V1 while 3 were tested only after V2. All controls were tested after V2. The second serology assay was performed at a median interval from V2 of 52 d (range: 21-99) for patients and 58 d (range: 32-71) for controls. This serology assay was positive in 6 patients (30%), while all controls (100%, p<0.001) had again a positive response. Three out of these 6 patients (15% of all patients) achieved the highest IgG titer according to the serology assay used. Among the 4 patients with positive antibody titers after V1, 3 remained positive including one reaching the highest IgG titer. The fourth patient has not yet received V2. Median IgG titers could not be compared with controls because various methods of detection were used after V2. However, all controls tested again by Roche Elecsys® displayed the highest IgG titer (>250) after V2. The two patients in relapse and treated by chemotherapy or tafasitamab did not develop antibodies after V2 conversely to the patient under maintenance by revlimid. The delay between CAR-T infusion and vaccine did not influence the antibody response nor did the rate of lymphopenia as almost all patients remained under a lymphocyte threshold of 1x10 9/L. Finally, with a median follow up from V1 of 77 d (range: 49-127) in patients and 81 d (range: 62-95) in controls, no COVID-19 infection has been documented in any of these participants. Conclusion: This study shows that the administration of two doses of BNT162b2 anti-SARS-CoV-2 messenger RNA vaccine provides a low rate of seroconversion (30%) in recipients of CAR-T therapy. This is likely related to the profound B-cell depletion induced by this treatment precisely targeting CD19+ cells. Investigation of the development of specific T-cell responses in these individuals could provide more information about the efficacy of vaccination in this context. Disclosures Moreau: Celgene BMS: Honoraria; Sanofi: Honoraria; Janssen: Honoraria; Abbvie: Honoraria; Amgen: Honoraria; Oncopeptides: Honoraria.

Assessing Vaccine Coverage and Timeliness in Bamako, Mali after the Introduction of Rotavirus Vaccine: A Modified Immunization Cluster Survey

Vaccine coverage and timeliness are critical metrics for evaluating the performance of immunization programs. Following the introduction of rotavirus vaccine in Bamako, Mali, we conducted two cluster surveys spaced approximately 1 year apart to evaluate these metrics among children 9 to 20 months of age. Using the child’s immunization card or the medical record at the center of administration, each selected child’s immunization status was determined at 9 and 12 months of age. Deviations from the WHO-recommended immunization schedule were described by the median delay and fraction of children receiving doses outside of recommended age ranges. Overall, 1,002 children were enrolled in the two surveys combined; 80.1% of children born 7 to 12 months after introduction (survey 1) received three doses of pentavalent rotavirus vaccine (ROTA3) by 9 months of age, which increased to 86.1% among children born 17 to 26 months after introduction (survey 2). Concomitantly, coverage with the third dose of diphtheria-pertussis-tetanus-containing vaccine (DPT3) by age 9 months was 86.5% (survey 1) and 88.9% (survey 2); by age 12 months, 61.3% and 72.4% of children, respectively, had received all scheduled immunizations. The median delay in ROTA3 and DPT3 administration were similar at about 3.4 weeks. Within 3 years of introduction, coverage of rotavirus vaccine among Bamako infants achieved coverage similar to DPT3 and is approaching the Global Vaccine Action Plan goal of 90% coverage by 2020. However, timeliness of coverage remains a concern.

Patient Delay and Stage of Diagnosis among Breast Cancer in Bangladesh

Early diagnosis and prompt treatment is the principal goal of physicians dealing with breast cancer. In breast cancer care there are several causes of delay. Delay in diagnosis may be either patient or health provider originated. Besides there are no effective surveillance system, no nationwide active campaign for early diagnosis and screening. Hence delay in diagnosis is not unlikely. The study was done in a Cross sectional setting during January to December 2009 at the Department of Oncology of Bangabandhu Sheikh Mujib Medical University, Dhaka Medical College Hospital, National Institute of Cancer Research & Hospital. A total 106 breast cancer patients were recruited for the study from the mentioned study places. Our data suggests on average 40 days delay from development of first symptom to attending traditional or unqualified provider. Time taken in transition from unqualified provider to qualified medical personnel was around another month. The time taken by medical personnel to establish the diagnosis through histopathology was around another 12.5 ±7.0 days, thereafter to perform surgery after diagnosis it took 17.8 ± 18.1 days. After surgery another 31.05±33.8 days were needed to start adjuvant chemotherapy or radiotherapy. Mean total time lapsed from appearance of symptom to perform surgery among breast cancer patients was 114.3±53.2 days with a median delay of 117.5 days. Mean total time lapsed from appearance of symptom to have chemotherapy or radiotherapy among breast cancer patients was 151.2±62.8 days with a median delay of 124 days. Regarding the cause of delay, in 53.8% cases, delay was resulted due to lack of awareness, in 38.9% cases delay was due to lack of money for treatment, in 7.5% case delay was due to the attempt to rely on alternative medicine, in 14.2% cases patients were reluctant, in 18% cases delay was due to problem in diagnosis. In 13.2% cases physician was treating the patient for other reason, in 36% cases patient herself made the delay for fear of consequences of disease or treatment and in 22% cases delay occurred due to long waiting period for surgery. In most cases delay resulted from multiple reasons. Patients with stages III & IV were found to have significantly more total delay than stage I & II diseases (p<.05). Patients with higher educated level made less delay than lower educated level. In conclusion, most important delay is caused by patient's own criteria and negligence. Awareness raising campaign may play an important role in reduction of the delay. Faridpur Med. Coll. J. 2021;16(1):12-16

Descriptive and retrospective analysis of diffuse glioma patients with symptomatic SARS-CoV2 infection during the first wave of the pandemic

Background Little is known about diffuse glioma patients infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV2). Methods We performed a descriptive and retrospective analysis of 41 diffuse glioma patients with symptomatic SARS-CoV2 infection during the first wave of the COVID-19 pandemic. Results Confusion with or without fever was the most common neurological symptom (32%) supporting SARS-CoV2 testing in glioma patients with acute and unexplained confusion. Sixteen patients (39%) died after a median delay of 13 days. While multiple clinical, biological, and pathological features, COVID-19- or diffuse glioma-related, at hospital admission appeared to have a pejorative prognostic impact, none was significantly associated with death. Oncological treatments were interrupted at COVID-19 diagnosis and re-initiated with a median delay of 30 days after the end of COVID-19 symptoms. Conclusions Interestingly, our retrospective study describes for the first time the characteristics of a cohort of diffuse glioma patients with symptomatic COVID-19. Diffuse glioma patients with poorly symptomatic COVID-19 did not come to the attention of physicians and were not enrolled in the study skewing the denominator for prognostic analysis. Further studies are warranted to specify prognosis of overall population of diffuse glioma patients with COVID-19, including asymptomatic patients, and interactions of prognostic factors of both COVID-19 and diffuse gliomas.

Time intervals between U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMA) new cancer therapy approvals.

1575 Background: Novel therapies are transforming cancer care. Regulatory review and approval are essential to deliver safe and efficacious innovations to patients. Studies prior to 2010 describe quicker approval decisions for new oncology drug registrations with the FDA compared to the EMA (median delay 238 days). Both regulatory agencies have subsequently improved procedures to expedite approval times. We compared regulatory market authorisation dates at the FDA and EMA for new oncology therapies from 2010-2020. Methods: New oncology therapeutic approvals between 2010-2020 were identified from the FDA and EMA regulatory databases. We analysed only initial approvals (not supplementary licenses) for active anti-cancer therapies (excluding biosimilars and supportive drugs). The delay in regulatory approval between the FDA and EMA was calculated in calendar days. We further analysed therapies by therapeutic class, evaluating for significant differences. Results: We identified 108 new therapy registrations during the study period. 104 (96.3%) therapies were approved by the FDA and 90 (83.3%) had EMA market authorisation. 4 (3.7%) drugs were not FDA registered, including 3 unsuccessful applications and 1 which sought licensing in a different indication. 18 (16.5%) drugs were not EMA registered, including 9 (8.8%) which did not pursue EMA licensing, 3 (2.9%) withdrawn licensing applications, 3 (2.9%) sought licensing in different tumour group/indication, 1 (0.9%) rejected application and 2 (1.9%) with applications under review at submission date. Of the 86 drugs approved by both agencies, 80 were approved first by the FDA and 6 by the EMA. The median delay in approval between the FDA and EMA was 227 days (IQR:124-354 days). Table shows approvals by therapeutic class. The shortest median time difference for approval was for monoclonal antibodies (171 days) with the longest for kinase inhibitors (281 days). Conclusions: This study shows more new oncology therapies are approved by the FDA than the EMA. Patients in the US typically have access to approved therapies earlier than in Europe. From 2010 to 2020 the median delay between FDA and EMA approval was 227 days, falling by 11 days compared to 2003-10, [non-statistically significant]. Such lengthy delays could exceed the life expectancy of many patients with advanced cancer. Innovations for accelerated approval at both the FDA (e.g. Project Orbis) and EMA (e.g., PRIME) have potential to lead to faster approval.[Table: see text]

Primary Delay in Diagnosis in Head and Neck Cancer: Non Covid Causes in Covid-19 Pandemic Era

INTRODUCTION Corona virus disease (COVID-19) has been one of the cause for delay in diagnosing and management for all non COVID diseases since the pandemic started especially for head neck cancer where exposure of sites are considered high risk for disease spread. Head neck cancer need to be addressed early as per reporting various oncological guidelines in view of better prognosis if addressed early. We evaluated the non- COVID cause for primary delay by patients so that it can be addressed for better oncological care. MATERIAL AND METHODS We evaluated 35 cases of head neck cancer reported in outpatient of department of Otorhinolaryngology and head neck surgery. The inclusion criteria were the subsites( oral cavity, oropharynx, larynx and hypopharynx ) that need to be addressed promptly irrespective of COVID and the patients had given consent. Statistical Package for the Social Sciences (SPSS) version 16 used for statistical analysis and Mann Whitney U test applied for 2 independent variables. RESULTS Patients characteristic, tumor characteristics and symptoms were evaluated. Advanced age (>60 years) and male predominance were seen in 51.4% and 54.3% respectively. Median delay was noticed for 4 months . When variables were evaluated for delay, male sex and village resident showed significant primary delay in comparison to female sex and city residents with p value of 0.003 and 0.03 respectively. CONCLUSION Non COVID cause for primary delay was prevalent. The median delay noticed 4 months and need to be addressed for proper good oncological outcome and proper health care delivery because of when the pandemic will last is not clear.

Early detection of cholera epidemics to support control in fragile states: estimation of delays and potential epidemic sizes

Background Cholera epidemics continue to challenge disease control, particularly in fragile and conflict-affected states. Rapid detection and response to small cholera clusters is key for efficient control before an epidemic propagates. To understand the capacity for early response in fragile states, we investigated delays in outbreak detection, investigation, response, and laboratory confirmation, and we estimated epidemic sizes. We assessed predictors of delays, and annual changes in response time. Methods We compiled a list of cholera outbreaks in fragile and conflict-affected states from 2008 to 2019. We searched for peer-reviewed articles and epidemiological reports. We evaluated delays from the dates of symptom onset of the primary case, and the earliest dates of outbreak detection, investigation, response, and confirmation. Information on how the outbreak was alerted was summarized. A branching process model was used to estimate epidemic size at each delay. Regression models were used to investigate the association between predictors and delays to response. Results Seventy-six outbreaks from 34 countries were included. Median delays spanned 1–2 weeks: from symptom onset of the primary case to presentation at the health facility (5 days, IQR 5–5), detection (5 days, IQR 5–6), investigation (7 days, IQR 5.8–13.3), response (10 days, IQR 7–18), and confirmation (11 days, IQR 7–16). In the model simulation, the median delay to response (10 days) with 3 seed cases led to a median epidemic size of 12 cases (upper range, 47) and 8% of outbreaks ≥ 20 cases (increasing to 32% with a 30-day delay to response). Increased outbreak size at detection (10 seed cases) and a 10-day median delay to response resulted in an epidemic size of 34 cases (upper range 67 cases) and < 1% of outbreaks < 20 cases. We estimated an annual global decrease in delay to response of 5.2% (95% CI 0.5–9.6, p = 0.03). Outbreaks signaled by immediate alerts were associated with a reduction in delay to response of 39.3% (95% CI 5.7–61.0, p = 0.03). Conclusions From 2008 to 2019, median delays from symptom onset of the primary case to case presentation and to response were 5 days and 10 days, respectively. Our model simulations suggest that depending on the outbreak size (3 versus 10 seed cases), in 8 to 99% of scenarios, a 10-day delay to response would result in large clusters that would be difficult to contain. Improving the delay to response involves rethinking the integration at local levels of event-based detection, rapid diagnostic testing for cluster validation, and integrated alert, investigation, and response.

Factors related to tuberculosis delays: Evidence from nationwide retrospective study in Portugal

Background Globally, tuberculosis (TB) remains one of the top 10 causes of deaths and the leading cause from a single infectious agent. Delayed TB diagnosis and/or treatment may result in the transmission of bacilli, increasing infectivity, the risk of severe disease states, morbidity and mortality. It is essential to identify the factors that prolong delays in TB services so that health planners can initiate necessary measures to control TB infections. Methods A nationwide retrospective study was conducted from 2010 until 2013 to analyze tuberculosis delays under the setting of the Portuguese National Tuberculosis Control Programme. There were 16824 participants who were from 25 administrative districts under 7 regions and were originated from 70 countries in the world. The log-rank test, Cox's regression, and the Kaplan-Meier method have employed to analyze TB delay data. Results The median of patients` delay was 34 days with interquartile ranges (IQR) 50 days. Alcohol addicted people with TB infection were delayed by 40 days with 95% CI 37.73-42.28 whereas the non-addicted people took 33 days with 95% CI 32.35-33.65. The median diagnostic delay was 12 days with an IQR of 38 days. The female participants were delayed more than that of male (median delay for female 17 days with 95% CI 15.80-18.19) in TB diagnosis. Further, comorbidities e.g. lung cancer affected TB candidates were delayed more than their counterparts (median delay 37 days with 95% CI 23.29-50.70). The median of public health delays was 63 days with IQR 72 days. The females were delayed more than that of males (median delay 68 days with 95% CI 66.06-69.94). The adjusted Cox's regression identifies the features - older age, female, drug addiction, and community residence as potential factors that might affect TB delays. Conclusions It is essential to emphasize on the influencing dynamics - older age, female patients, HIV patients, alcohol addiction, and comorbidities to minimize TB delays. Key messages To minimize spreading risk of TB infections the dynamics of TB delays e.g. older age, female patients, drug, and alcohol addiction, comorbidities should be prioritized in the TB control programs. Special attention should be given to other lung diseases while diagnosing TB infections.

“Patiently waiting”: a multivariate logistic regression analysis to understand waiting times

Background Outpatient visits waiting time poses a significant challenge in countries facing rising health demands, particularly those that have universal access to public health care. In Italy, despite major improvements, many patients still experience extensive delays accessing specialist care. Our study describes waiting times for the 14 most critical specialist “first” visits provided by the national health care system in the Milan Health Protection Agency territory (3,48 million inhabitants) and investigates whether specific patient, prescription and hospital variables are associated to an increased risk of delay in waiting time. Methods A multivariate logistic regression analysis of the relationship between specialty, age, sex, priority class, exemption from prescription charges, hospital organization, patient region, and Hospital district was performed to investigate whether specific variables are associated to the odds ratios (OR) for having to wait more than the maximum time limit. Results Out of the 1,174,283 visits performed in 2019, 90% were provided within the maximum waiting time. Visits were provided beyond maximum time in 20% (median delay=2 days) of priority class 1 visits, 24% (median delay=7 days) of class 2, 22% (median delay=37 days) of class 3, and 4% (median delay=65 days) of class 4. All analysed variables were significantly correlated (p &lt; 0.001) to the OR for having to wait beyond the priority class specific limit. In particular: female sex (OR = 1.074), residing outside Lombardy (OR = 0.696), class 1 priority (OR = 4.939), exemption from prescription charges (OR = 1.107), research Hospital (private: OR = 5.937, public: OR = 5.156) and ophthalmology (OR = 8.822). Conclusions Our results show that most visits were provided within the time limits. However, waiting times seem to be a major issue when assessing certain specialties, hospitals, and priority classes. This data should guide health policy makers interested in tackling the waiting time issue. Key messages This study highlights the importance of monitoring outpatient waiting times. Strategies and policies to tackle the problem of waiting times should be made upon reliable data and transparent criteria in order to meet patients’ needs.

Has the Non-Resection Rate Decreased During the Last Two Decades among Patients Undergoing Surgical Exploration for Pancreatic Adenocarcinoma?

Purpose To determine if improvement in imaging reduces the non-resection rate (NRR) among patients with pancreatic ductal adenocarcinoma (PDAC). Methods From 2000 to 2019, 751 consecutive patients with PDAC were considered eligible for a intention-to-treat pancreatectomy and entered the operating room. In April 2011, our institution acquired a dual energy spectral computed tomography (CT) scanner and liver diffusion weighted magnetic resonance imaging (DW-MRI) was included in the imaging workup. We consequently considered 2 periods of inclusion: period #1 (February 2000–March 2011) and period #2 (April 2011–August 2019). Results All patients underwent a preoperative CT scan with a median delay to surgery of 18 days. Liver DW-MRI was performed among 407 patients (54%). Median delay between CT and surgery decreased (21 days to 16 days, P <.01), and liver DW-MRI was significantly most prescribed during period #2 (14% vs 75%, P <.01). According to the intraoperative findings, the overall NRR was 24.5%, and remained stable over the two periods (24% vs 25%, respectively). While vascular invasion, liver metastasis, and carcinomatosis rates remained stable, para-aortic lymph nodes invasion rate (0.4% vs 4.6%; P <0.001) significantly increased over the 2 periods. The mean size of the bigger extra pancreatic tumor significantly decrease (7.9mm vs 6.4mm ( P <.01), respectively) when the resection was not done. In multivariate analysis, CA 19-9<500U/mL ( P <.01), and liver DW-MRI prescription ( P <.01) favoured the resection. Conclusions Due to changes in our therapeutic strategies, the NRR did not decrease during two decades despite imaging improvement.