We conducted a 2-year multicentre prospective observational study to determine the epidemiology of and mortality associated with invasive fungal diseases (IFDs) among patients with haematological disorders in Asia. Eleven institutions from 8 countries/regions participated, with 412 subjects (28.2% possible, 38.3% probable and 33.5% proven IFDs) recruited. The epidemiology of IFDs in participating institutions was similar to Western centres, with Aspergillus spp. (65.9%) or Candida spp. (26.7%) causing the majority of probable and proven IFDs. The overall 30-day mortality was 22.1%. Progressive haematological disorder (odds ratio [OR] 5.192), invasive candidiasis (OR 3.679), and chronic renal disease (OR 6.677) were independently associated with mortality.

, caused by the severe acute respiratory syndrome corona virus 2 (SARS-CoV-2), was declared a pandemic by the World Health Organization on March 9, 2020. Hematopoietic stem-cell transplantation (HSCT) recipients may be highly susceptible to infection and related pulmonary complications due to nascent immune systems or organ damage from treatment-related toxicities.Poor outcomes in such group of patients were linked to older age, steroid therapy at the time of COVID-19 infection, and COVID-19 infection within a year of HSCT. We studied a cohort of 28 hematopoietic stem cell transplant recipients (male 17, M:F ratio of 1.5) with COVID-19 infection from 1st June 2020, through 31st December 2020 for outcome. Fever was the most common symptom at the time of presentation in 22 (78.5%) patients. Mortality rate at Day 28 and Day 42 was found to be 4/28 (14.3%) and 7/28 (25%) respectively. Patients within one year of HSCT and severe infection had higher day 28 mortality (with p values = 0.038)''. There was no relation of mortality with type of transplant.

Context:The distribution of various subtypes of lymphomas in India is different from other parts of the world. There is scarce multicentric data on the pattern and outcomes of lymphomas in India.Aims:The aim of this study is to evaluate the histopathological and the clinical pattern and treatment outcomes of lymphomas in India based on the retrospective data collected from a multicenter registry.Materials and Methods:Retrospective data was collected at 13 public and private hospitals in India for patients diagnosed with lymphoma between January 2005 and December 2009. The data collection was performed in the setting of a multicenter lymphoma registry Survival analyses were performed using the Kaplan-Meier method and compared using the log-rank test.Results:Non-Hodgkin's lymphoma (NHL) constituted 83.17% and Hodgkin's lymphoma (HL) for 16.83% of the 1733 registered and analyzed cases. Diffuse large B cell lymphoma (DLBCL) was the most common NHL (55%) followed by follicular lymphoma (11%). CHOP was the most common chemotherapy regimen administered (84%) while rituximab was used in 42.7% of those with DLBCL. Survival analysis of treatment naïve DLBCL patients (n = 791) was performed. Of these, 29% were lost to follow-up, 20% with active disease. The median follow-up in surviving patients is 31 (range: 1-88) months. Median progression-free survival (PFS) and overall survival (OS) in DLBCL patients has not reached. There was no significant difference in median PFS (69 months vs. 61 months, P = 0.1341), but OS was significant not reached (NR) vs. NR, P = 0.0012) within international prognostic index high or intermediate subgroups. Rituximab use was associated with significantly prolonged PFS (NR vs. 82 months, P = 0.0123), but not OS (NR vs. NR, P = 0.2214). Cox regression analysis in treatment naïve DLBCL patients showed a performatnce status, stage and receipt of six or more cycles of chemotherapy to be significantly associated with OS and all of the preceding plus rituximab use significantly associated with PFS.Conclusions:Our analysis confirms previous reports of distribution of lymphoma subtypes in India and suggests that patients who are able to receive the full course of chemotherapy achieve a better outcome. This indicates the importance of ensuring compliance to treatment utilizing various measures including patient and family counseling. Prospective studies are required to confirm these findings.

Adolescent and young adult (AYA) patients with acute lymphoblastic leukaemia (ALL) have inferior survival when compared to children. The causes are multiple and include bad biology, differences in treatment approaches, and other complex social, economic and psychological factors that affect therapy adherence. 1 Intensive 'paediatric' regimens improve outcomes, but these come with the cost of higher toxicity, which may even negate these benefit of reduced relapse. 2-5 To understand the real-world data from India, we analysed the outcomes of AYA ALL (aged 15-29 years, treated between 2012 and 2017) from a retrospective database maintained by the Hematology Cancer Consortium (HCC). Baseline data of all patients (including those who were not treated) diagnosed within the period stipulated by a particular centre were captured, including reasons for not availing treatment. Survival outcomes were estimated for treated patients (censored on 31 July 2019). For this analysis, 'high risk' was defined based on white blood cell count (WBC) at diagnosis (B cell >30 9 10 9 /l, T cell >100 9 10 9 /l). Protocols such as Multicentre protocol 841 (MCP-841), Berlin-Frankfurt-M€ unster 95 (BFM-95), BFM-90, and Children's Oncology Group (COG) were considered 'paediatric type', whereas German Multicentre ALL (GMALL), hyperfractionated cyclophosphamide, vincristine, doxorubicin, and dexamethasone (Hyper-CVAD), and UKALL were considered 'adult type'. Minimal residual disease (MRD) >0Á01% (when assessed by flow cytometry) was considered positive. Of the 1383 patients registered, 1141(82Á5%) underwent treatment (Supplementary Table S1 and S2, baseline characteristics), and 242 did not start treatment (Fig 1). The inability to afford treatment was the commonest cause for not initiating treatment (105/1383, 7Á6%). There were no Fig 1. Flowchart depicting the outcomes of patients who were included in the registry. Of the 1383 patients, only 1141 started therapy (induction) and 863 (76%) achieved complete remission (CR). At last follow-up, 574 were in CR and on follow-up. A total of 336/1383 (24%) patients either did not start therapy (N = 242), or abandoned therapy after starting induction (N = 94) (A). (B) Comparison of induction outcomes between those treated with 'paediatric' and 'adult' protocols. There were no differences in terms of achievement of CR (76% vs. 73%, P = 0Á509), induction mortality (4Á7% vs. 3Á2%, P = 0Á842), or minimal residual disease (MRD) positivity rate (36% vs. 42%, P = 0Á382). (C) The commonest cause of induction mortality was infection (56%) followed by progressive disease (23%).

Background: Remission induction is the most intensive phase of acute myeloid leukemia (AML) treatment, associated with significant morbidity and mortality. Collaborative research and advances in supportive care have steadily improved outcomes in developed countries with induction mortality less than 5%. Challenges for treatment in resource limited settings are varied including delayed presentation, higher disease burden, baseline infections and poor general condition precluding standard intensive therapy, higher rates of resistant infections, and several social and financial constraints. Consequently, a significant proportion of patients do not receive definitive therapy and for those who are treated there is a considerably high risk of induction mortality. In an attempt to identify the subset of patients with highest risk of death during induction, we have developed a multivariate model of induction mortality score using baseline features relevant to our clinical setting by utilizing Indian acute leukemia research database [INwARD] established in 2018 by Hematology Cancer Consortium (HCC). Method: Retrospective data from January 2018 to May 2019 for adult AML was collected from 11 member institutions in a central online data management system. Selection of potential variables that would predict mortality was based on clinical and statistical significance. Thus, 10 variables defining baseline patient and disease characteristics (age, ECOG performance status, duration of symptoms in days, albumin, creatinine, bilirubin, white cell count, platelet, peripheral blood blast percentage, and presence of infection requiring intravenous antibiotic within one week prior of starting induction), were considered for the predictive model using machine learning algorithms: Logistic regression (LR) and Support Vector Machine (SVM). SVM was chosen as the best algorithm based on the AUCs. In order to get robust threshold, sensitivity, specificity and predictive values bootstrapping was done 10,000 times. The final statistics were based on the mean (SD) of bootstrapped sample. R software was used to bootstrap and analyze the data. Result: Of the 611 adult AML cases available during study period, 392 treated with the intensive '3+7' or its abbreviated regimen were considered for analysis. Median age of this cohort was 36 years (range 18 - 67), male to female ratio 1.34. European Leukemia Net (ELN) risk group distribution is shown in Fig 1a. Complete remission was attained in 52.8%. Induction mortality was 16.9 % ranging from 6.1% to 43% across different centers. Most common cause of death was infection (66.7%). Multi-drug resistant blood stream infection was documented in 25.4% cases. For the SVM model for predicting induction mortality using 10 covariates, the AUC based on the bootstrap validation was 91.3% with the best threshold probability being 0.262 (Fig 1b). Thus, a cut off score of 0.262 in the SVM model predicted induction death with sensitivity of 93.6% and specificity of 87.7%. Performance of each variable in the SVM model is shown in Fig 1c and comparison of the LR and SVM model in Fig 1d. Conclusion: Score predicting induction death with high accuracy will be a valuable tool in guiding clinicians against the use of intensive induction therapy, in tailoring of treatment as per individual patients' risk and proper resource allocation. Despite the limitations of retrospective data, wide disparity in resources, patient profile and treatment costs across centers accounting for variability in mortality rates, this study represents one major attempt to find answer to a locally relevant clinical problem of high induction mortality in a cohort of young adult AML, utilizing contemporary pooled data through multi-center collaboration. Optimal cut off point for the score needs to be validated in independent patient cohorts and have to be re-calibrated periodically. Further, an online calculator is being designed on the HCC online system to work as ready reckoner for clinicians. Figure 1 a) ELN risk group distribution b) descriptive statistics of bootstrapped SVM Model c) performance of each covariate in SVM model d) ROC Curve: Comparison of LR and SVM method Figure 1 Disclosures No relevant conflicts of interest to declare.