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Background: Older patients with cancer are at a higher risk of invasive infections. Vaccination is an effective approach to decrease the mortality and morbidity associated with infections. Objective(s): Our primary objective was to evaluate the proportion of older patients with cancer who had received routine vaccinations against pneumococcal, influenza, and coronavirus disease 2019 (COVID-19). Our secondary objective was to identify the factors associated with vaccine uptake such as age, sex, education, marital status, comorbidities, and place of residence. Material(s) and Method(s): This cross-sectional observational study was conducted in the geriatric oncology outpatient clinic of the Department of Medical Oncology at the Tata Memorial Hospital, a tertiary care cancer hospital in Mumbai, India, from February 2020 to January 2023. We included all patients aged >=60 years who were evaluated in the geriatric oncology clinic during the study period and for whom the immunization details were available. The uptake of COVID-19 vaccine was calculated from March 2021 onwards, which was when the COVID-19 vaccine became available to patients aged >=60 years in India. Result(s): We enrolled 1762 patients;1342 (76.2%) were male. The mean age was 68.4 (SD, 5.8) years;795 (45%) patients were from the west zone of India. Only 12 (0.68%) patients had received the pneumococcal vaccine, and 13 (0.7%) had received the influenza vaccine. At least one dose of the COVID-19 vaccine had been taken by 1302 of 1562 patients (83.3%). On univariate logistic regression, education, marital status, geographic zone of residence, and primary tumor site were correlated with the uptake of COVID-19 vaccine. Factors associated with a greater COVID-19 vaccine uptake included education (up to Std 10 and higher vs. less than Std 10: Odds Ratio [OR], 1.46;95% confidence interval [CI], 1.07-1.99;P = 0.018, and illiterate vs. less than Std 10: OR, 0.70;95% CI, 0.50-0.99;P = 0.041), marital status (unmarried vs. married: OR, 0.27;95% CI, 0.08-1.08;P = 0.046, and widow/widower vs. married: OR, 0.67;95% CI, 0.48-0.94;P = 0.017), lung and gastrointestinal vs. head-and-neck primary tumors (lung cancer vs. head-and-neck cancer: OR, 1.60;95% CI, 1.02-2.47;P = 0.038, and gastrointestinal vs.head-and-neck cancer: OR, 2.18;95% CI, 1.37-3.42;P < 0.001), and place of residence (west zone vs. central India: OR, 0.34;95% CI, 0.13-0.75;P = 0.015). Conclusion(s): Fewer than 1 in 100 older Indian patients with cancer receive routine immunization with influenza and pneumococcal vaccines. Hearteningly, the uptake of COVID-19 vaccination in older Indian patients with cancer is over 80%, possibly due to the global recognition of its importance during the pandemic. Similar measures as those used to increase the uptake of COVID-19 vaccines during the pandemic may be beneficial to increase the uptake of routine vaccinations.Copyright © 2023 Cancer Research, Statistics, and Treatment.
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BACKGROUND AND AIM: National surveys soliciting family experiences present challenges and opportunities. We performed a pan-Canadian, multi-centered, online survey of family experiences with restricted family presence in PICU during COVID-19. Sites chose from 6 respondent approach methods. This sub-study explores relationships between invitation methods and response rates. METHOD(S): Information was collected from the 11 participating sites via e-mail-based survey to determine: eligible participant numbers;invitation method;time from PICU admission to survey invitation;contact methods for bereaved and non-bereaved families;participation barriers and facilitators. Responses were quantified using descriptive statistics and Spearman's rank order correlation. Free texts were inductively coded. RESULT(S): Sites invited families of PICU patients admitted during 4-month periods, beginning March 2020 (n=9) and/or November 2020 (n=3). Invitations were sent a mean (SD) of 7.7 (2.8) months post-admission. The overall survey response rate was 270/1005 invited families (27%). The mean institution response rate was 27% (SD=13%, range 4-50%) and was highest for sites using postal invite with telephone follow-up (43%, n=2), followed by telephone approach (26%, n=7), text message paired with social media posts and posters (22%, n=1), and postal invite alone (n=1, 8%). Bereaved families received a personalized telephone call. Time from admission to invitation was inversely correlated with response rate (rs = -0.70, p=0.02). Most common recruitment barriers were lack of funded research personnel (n=4) and REB requiring initial contact by care team rather than researchers (n=3). CONCLUSION(S): Multi-centre surveys with unfunded site participation face challenges. Postal invitation with telephone follow-up may improve response rates.
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Disaster may be natural or man-made, for example, terrorist attack, earthquakes, landslides, cyclones and storms/wave surges, floods or disease epidemics, and insect/animal plagues like COVID-19. Due to disaster, normal patterns of life get disturbed affecting the physical and psychological health. It is challenging to predict the likelihood of occurrence of disaster but people should have the aim to handle this acute and long term. Any type of disaster affecting the health stresses for healthcare. Due to this pandemic situation, the health of the person is affected. In the current scenario, so much has been impacted due to COVID-19. People are affected because they didn’t get proper help, timely and admissible solutions for the same. When no one is prepared for this type of situation like disasters, people face issues like availability of hospitals and medicine, loss of their family, etc. To handle this problem, the Internet of things (IoT) is playing an important role in healthcare. There are so many android apps and IoT devices for health monitoring. To minimize the impact of this disaster or to predict it early, technical and medical innovations are necessary. One such example is Aarogya Setu app that is making use of GPS and Bluetooth to track coronavirus-infected people. IoT devices generate a huge amount of data that needs to be analyzed. This chapter will discuss different IoT devices, data analytics, and machine learning (ML) algorithms that are used to predict disasters, thus, affecting the health. © 2023 by Apple Academic Press, Inc.
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Background: Patients with relapsed or refractory (R/R) LBCL after first-line treatment who are unable to undergo high-dose chemotherapy (HDCT) and hematopoietic stem cell transplantation (HSCT) hae poor outcomes and limited treatment options. Aims: PILOT (NCT03483103) ealuated liso-cel, an autologous, CD19-directed chimeric antigen receptor (CAR) T cell product, as second-line treatment in patients with R/R LBCL not intended for HSCT. Methods: Eligible patients were adults with R/R LBCL after first-line treatment who were not deemed candidates for HDCT and HSCT by their physician and met ≥ 1 frailty criteria as follows: age ≥ 70 years, Eastern Cooperatie Oncology Group performance status (ECOG PS) of 2, diffusing capacity for carbon monoxide ≤ 60%, left entricular ejection fraction < 50%, creatinine clearance < 60 mL/min, or alanine aminotransferase/aspartate aminotransferase > 2 × the upper limit of normal. Bridging therapy was allowed. Patients receied lymphodepletion with cyclophosphamide and fludarabine, followed 2-7 days later by liso-cel infusion at a target dose of 100 × 106 CAR+ T cells. Cytokine release syndrome (CRS) was graded per Lee 2014 criteria. Neurological eents (NE) were defined as inestigator-identified neurological aderse eents related to liso-cel and graded using the National Cancer Institute Common Terminology Criteria for Aderse Eents, ersion 4.03. The primary endpoint was objectie response rate (ORR) per independent reiew committee;all patients had ≥ 6 months of follow-up from first response. Results: Of 74 patients who underwent leukapheresis, 61 receied liso-cel and 1 receied nonconforming product (ie, product wherein one of the CD8 or CD4 cell components did not meet one of the requirements to be considered lisocel). Common reasons for preinfusion dropout included death and loss of eligibility (5 each). For liso-cel-treated patients, median age was 74 years (range, 53-84;79% ≥ 70 years) and 69%, 26%, and 5% met 1, 2, and 3 frailty criteria, respectiely;26% had ECOG PS of 2 and 44% had Hematopoietic Cell Transplantation-specific Comorbidity Index score ≥ 3. After first-line treatment, 54% were chemotherapy refractory, 21% relapsed within 12 months, and 25% relapsed after 12 months;51% of patients receied bridging chemotherapy. Median (range) onstudy follow-up was 12.3 months (1.2-26.5). ORR and complete response rate were 80% and 54%, respectiely (Table). Median duration of response and progression-free surial were 12.1 months and 9.0 months, respectiely. Median oerall surial has not been reached. The most frequent treatment-emergent aderse eents (TEAE) were neutropenia (51%), fatigue (39%), and CRS (38%), with grade 3 CRS in 1 patient (2%) and no grade 4/5 CRS eents. Any-grade NEs were seen in 31% (n = 19) of patients;grade 3 NEs occurred in 5% (n = 3) of patients and no grade 4/5 NEs were reported. Seen percent (n = 4) receied tocilizumab only, 3% (n = 2) receied corticosteroids only, and 20% (n = 12) receied both tocilizumab and corticosteroids for treatment of CRS and/or NEs. Oerall, grade ≥ 3 TEAEs occurred in 79% (n = 48) of patients, with grade 5 TEAEs in 2 patients (both due to COVID-19). Two patients (3%) had grade 3/4 infections and 15 (25%) had grade ≥3 neutropenia at Day 29. Summary/Conclusion: In the PILOT study, liso-cel as second-line treatment in patients with LBCL who met ≥ 1 frailty criteria and for whom HSCT was not intended demonstrated substantial and durable oerall and complete responses, with no new safety concerns. (Table Presented).
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Background: Pts with R/R LBCL after first-line (1L) treatment (tx) who are unable to undergo high-dose chemotherapy (HDCT) and HSCT have poor outcomes and limited tx options. PILOT (NCT03483103) evaluated liso-cel, an autologous, CD19-directed chimeric antigen receptor (CAR) T cell product, as 2L tx in pts with R/R LBCL not intended for HSCT. Methods: Eligible pts were adults with R/R LBCL after 1L tx who were not deemed candidates for HDCT and HSCT by their physician and met ≥ 1 frailty criteria: age ≥ 70 yr, ECOG PS = 2, DLCO ≤ 60%, LVEF < 50%, CrCl < 60 mL/min, or ALT/AST > 2 × ULN. Bridging tx was allowed. Pts received lymphodepletion with cyclophosphamide and fludarabine, followed 2-7 days later by liso-cel at a target dose of 100 × 106 CAR+ T cells. Cytokine release syndrome (CRS) was graded per Lee 2014 criteria and neurological events (NE) per NCI CTCAE, version 4.03. Primary endpoint was ORR per independent review committee (IRC);all pts had ≥ 6 mo followup (f/u) from first response. Results: Of 74 pts leukapheresed, 61 received liso-cel and 1 received nonconforming product. Common reasons for pre-infusion dropout included death and loss of eligibility (5 each). For liso-cel-treated pts, median age was 74 yr (range, 53-84;79% ≥ 70 yr) and 69%, 26%, and 5% met 1, 2, and 3 frailty criteria, respectively;26% had ECOG PS = 2 and 44% had HCT-CI score ≥ 3. After 1L tx, 54% were chemotherapy refractory, 21% relapsed ≤ 12 mo, and 25% relapsed > 12 mo;51% of pts received bridging chemotherapy. Median (range) on-study f/u was 12.3 mo (1.2-26.5). ORR and CR rate was 80% and 54%, respectively. Median DOR and PFS was 12.1 mo and 9.0 mo, respectively. Median OS has not been reached (Table). Most frequent tx-emergent AEs (TEAE) were neutropenia (51%), fatigue (39%), and CRS (38%), with grade (gr) 3 CRS in 1 pt (2%) and no gr 4/5 CRS. Any-grade NEs were seen in 31%, gr 3 in 5% (n = 3), and no gr 4/5 NEs;7% received tocilizumab, 3% corticosteroids, and 20% both for tx of CRS/NEs. Overall, gr ≥ 3 TEAEs occurred in 79%, with gr 5 in 2 pts (both due to COVID-19). Two pts (3%) had gr 3/4 infections and 15 (25%) had gr ≥3 neutropenia at Day 29. Conclusions: In the PILOT study, liso-cel as 2L tx in pts with LBCL who met ≥ 1 frailty criteria and for whom HSCT was not intended demonstrated substantial and durable overall and complete responses, with no new safety concerns.
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Introduction: Axicabtagene ciloleucel (axi-cel) is an autologous anti-CD19 Chimeric Antigen Receptor (CAR) T-cell therapy that induces durable responses in patients with relapsed or refractory large B-cell lymphoma. At a median of 27.1 months follow-up on the ZUMA-1 trial, median overall survival (OS) was 25.8 months with 39% progression free survival (PFS) at 2 years post-infusion (Locke, Lancet Onc 2019). We previously reported outcomes of axi-cel patients treated with standard of care therapy at a median follow up of 12.9 months, including 42% who did not meet eligibility criteria for ZUMA-1 based on co-morbidities (Nastoupil, JCO 2020). Here we report results from this cohort at a median follow up of 32.4 months, as well as late outcomes of interest including cytopenias, infections and secondary malignancies. Methods and Results: The US Lymphoma CAR-T Consortium comprised of 17 US academic centers who contributed data independent of the manufacturer. Two hundred and ninety-eight patients underwent leukapheresis with intent to manufacture standard of care axi-cel as of September 30, 2018. In infused patients (n=275), OS and PFS were calculated from date of infusion. After median follow-up of 32.4 months (95% CI 31.1 - 34.3), median OS was not reached (95% CI 25.6 - not evaluable) (Figure 1A) with 1-, 2- and 3-year OS of 68.5% (95% CI 62.6-73.7), 56.4% (95% CI 50.1-62.2) and 52.2% (95% CI 45.7-58.2%), respectively. Median PFS was 9 months (95% CI 5.9-19.6) (Figure 1B);1-, 2- and 3-year PFS was 47.4% (95% CI 41.4-53.2), 41.6% (95% CI 35.6-47.5) and 37.3% (95% CI 31.3-43.2), respectively. Twenty-seven PFS events occurred at or after 1 year post infusion;19 events were progressive lymphoma, with the latest relapse observed 28 months after axi-cel infusion. Eight patients died while in remission from their lymphoma: 4 from secondary malignancy, 3 from infection, and 1 from unknown causes. Results of multivariable modeling were similar to our prior analysis: factors associated with both a shorter PFS and shorter OS included male sex, elevated pre-lymphodepletion LDH, and poor ECOG status. Complete blood count and B- and T-cell recovery data were collected at 1 and 2-years post-infusion, excluding patients who had relapsed or been treated for secondary malignancy at time of collection (Table 1). Rates of neutropenia (absolute neutrophil count ≤1000) at 1- and 2- years were 9.2% (10/109) and 11.2% (9/80) and rates of CD4 count ≤200/ul were 62% (23/37) and 27% (7/26). Recovery of B cells was seen in 54% (15/28) and 57% (13/23) at 1-and 2-years post infusion. Infections were reported in 31.2% (34/109) patients between 6- and 12-months post infusion, and 17% (18/109) were severe, requiring either hospitalization and/or IV antibiotics. Twenty-one patients (24%, 21/89) had an infection between 1- and 2- years, 11% of which were severe. Twenty percent (10/49) of patients between 2- and 3-years had an infection and 4 (8%) were severe. Neutropenia, low CD4 counts, and IgG levels were not associated with infection, though patients with infection between 6-12 months were more likely to have received IVIG (p<0.001). No patient in this cohort died of COVID-19. Twenty-two of 275 (8%) patients were diagnosed with subsequent malignancy after axi-cel treatment: 14/275 (5%) patients were diagnosed with myeloid malignancies (MDS (n=12), AML (n=1), CMML (n=1));other malignancies included squamous cell carcinoma of skin (n=3);sarcoma (n=1);endometrial (n=1);lung (n=1);mesothelioma (n=1) and AITL (n=1). Patients with myeloid malignancy had a median age of 62 at axi-cel apheresis (IQR 56-67), 64% were male and median lines of prior therapy was 4 (IQR 3-6), including 36% with a prior autologous stem cell transplant. Eleven patients were in remission from lymphoma at myeloid malignancy diagnosis, while 3 were diagnosed after progression and interval therapy. Conclusion: This multi-center retrospective study showed similar long-term results to the ZUMA-1 trial, despite including patients who did not meet ZUMA-1 eligibility criteria ba ed on comorbidities. Sixteen percent of PFS events were seen after 1 year, largely due to disease progression. Late infection was common but was not explained by persistent neutropenia or low CD4 counts. Subsequent malignancy, including MDS, occurred in 8% of patients and require further study to better identify patients at risk. (Figure Presented).
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Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) on children with underlying liver disease (LD) is unknown. We aim to report outcomes for pediatric patients with LD from the joint North American Society for Pediatric Gastroenterology, Hepatology and Nutrition (NASPGHAN) and the Society of Pediatric Liver Transplantation (SPLIT) SARS-CoV2 registry Methods: We collected data from patients younger than 21 years with LD from 6 countries and laboratory-confirmed SARS-CoV2 infection reported to a multicenter observational cohort study between April 2020 and May 2021. Results: Seventy-three (59% male,55% white, 23% Hispanic) children with a median age of 9 years were reported in the registry. The most common causes of LD were biliary atresia (22%) followed by autoimmune hepatitis (16%) and non-alcoholic fatty liver disease (16%). Five patients (7%) presented in acute liver failure (ALF);all recovered without the need for a liver transplant. Four patients presented with multisystem inflammatory syndrome in children (2 with ALF, 2 without ALF) with one death reported. The most common presenting symptoms were constitutional (49%) including fever and fatigue followed by respiratory symptoms (47%). Twenty two percent (n=16) of patients were asymptomatic at the time of diagnosis. Twentythree percent had radiologic evidence of pneumonia and 14% reported co-infections. Median peak INR was 1.4, peak total bilirubin 2.9 (mg/dl), peak ALT 129 (IU/l) and nadir albumin 3.1 (g/dl). Sixty-four percent of patients required hospitalization;40% (n=19) in the ICU and 60% (n=28) non-ICU for a median of 6 and 7 days, respectively. Twenty-two percent of patients required respiratory support including mechanical ventilation (n=6), high-frequency oscillatory ventilation (n=3), highflow nasal cannula (n=5) and regular nasal cannula (n=2) for a median of 6 days. Nine patients required vasoactive agents, 3 required renal replacement therapy and 2 patients required ECMO. Sixty-six percent did not receive any SARSCoV2 directed treatment. Twelve (16%) patients developed new liver-related complications including ascites (n=9), GI bleeding (n=2), encephalopathy (n=3), progression of endstage liver disease (n=2) and infection (n=1). There were a total of 3 (4.1%) deaths (20yr, 17yr and 6month of age at time of death) reported secondary to acute on chronic liver failure with respiratory failure and multiorgan failure Conclusion: Contrary to healthy children, almost 2/3rd pediatric patients with LD testing positive for SARS-CoV2 required hospitalization with death reported in 4% of cases. Acute liver failure is rare with SARS-CoV2 infection with recovery reported without the need for liver transplantation. Close monitoring is needed due to an increased risk of underlying liver disease complications and death, particularly in children with end-stage liver disease awaiting transplantation.
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Lungs can be affected by various bacterial and viral infections other than novel coronavirus, popular as COVID-19. Any machine learning technique should be capable to differentiate among these infections and classify an image to generate inference matching with actual cause of disease. In this paper, we have analyzed the performance of VGG19 for diagnosis of COVID-19 using X-ray images of lungs infected by bacterial and viral pneumonia. The visual clarity of X-ray images is very low compared to CT scan. However, the accuracy obtained by supports our claim of using VGG19 as a low coast and easily accessible automated alternate to CT scan based diagnosis. Datasets having images of 3-class (including normal, viral pneumonia and COVID-19) and 4-class (including normal, bacterial, viral pneumonia and COVID-19) categories were used to analyze the performance of VGG19 Deep Transfer Learning Model for accurate diagnosis of COVID-19. Sensitivity and accuracy of VGG19 were compared with AlexNet and ResNet19 models. VGG19 produced an accuracy of 98.2% with 3-class dataset and 94.4% with 4-class dataset. © 2021, Springer Nature Singapore Pte Ltd.