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 Breakthrough chemotherapy–induced vomiting (CIV) is defined as CIV occurring after adequate antiemetic prophylaxis. Olanzapine and metoclopramide are two drugs recommended for the treatment of breakthrough CIV in children, without adequate evidence. We conducted an open‐label, single‐center, phase 3 randomized controlled trial comparing the safety and efficacy of olanzapine and metoclopramide for treating breakthrough CIV. Procedure Children aged 5‐18 years who developed breakthrough CIV after receiving highly emetogenic chemotherapy or moderately emetogenic chemotherapy were randomly assigned to the metoclopramide or olanzapine arm. The primary objective of the study was to compare the complete response (CR) rates between patients receiving olanzapine or metoclopramide for treating breakthrough CIV during 72 hours after the administration of the study drug. Secondary objectives were to compare CR rates for nausea and toxicities between the two arms. Results Eighty patients were analyzed (39 in the olanzapine arm and 41 in the metoclopramide arm). CR rates were significantly higher in the olanzapine arm compared with the metoclopramide arm for vomiting (72% vs 39%, P = 0.003) and nausea (59% vs 34%, P = 0.026). Seven patients in the metoclopramide arm crossed over to the olanzapine arm and none crossed over in the olanzapine arm (P < 0.001). The mean nausea score in the olanzapine arm was significantly lower than the metoclopramide arm after the initiation of the rescue antiemetic (P = 0.01). Hyperglycemia and drowsiness were more commonly seen in the olanzapine arm. Conclusion Olanzapine is superior to metoclopramide for the treatment of breakthrough CIV in children. Drowsiness and hyperglycemia need to be monitored closely in children receiving olanzapine for breakthrough CIV.

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.

IntroductionThe rising economic burden of cancer on healthcare system and patients in India has led to the increased demand for evidence in order to inform policy decisions such as drug price regulation, setting reimbursement package rates under publicly financed health insurance schemes and prioritising available resources to maximise value of investments in health. Economic evaluations are an integral component of this important evidence. Lack of existing evidence on healthcare costs and health-related quality of life (HRQOL) makes conducting economic evaluations a very challenging task. Therefore, it is imperative to develop a national database for health expenditure and HRQOL for cancer.Methods and analysisThe present study proposes to develop a National Cancer Database for Cost and Quality of Life (CaDCQoL) in India. The healthcare costs will be estimated using a patient perspective. A cross-sectional study will be conducted to assess the direct out-of-pocket expenditure (OOPE), indirect cost and HRQOL among cancer patients who will be recruited at seven leading cancer centres from six states in India. Mean OOPE and HRQOL scores will be estimated by cancer site, stage of disease and type of treatment. Economic impact of cancer care on household financial risk protection will be assessed by estimating prevalence of catastrophic health expenditures and impoverishment. The national database would serve as a unique open access data repository to derive estimates of cancer-related OOPE and HRQOL. These estimates would be useful in conducting future cost-effectiveness analyses of management strategies for value-based cancer care.Ethics and disseminationApproval was granted by Institutional Ethics Committee vide letter no. PGI/IEC-03/2020-1565 of Post Graduate Institute of Medical Education and Research, Chandigarh, India. The study results will be published in peer-reviewed journals and presented to the policymakers at national level.

Recent reports suggest that in the TKI era, the survival of chronic myeloid leukemia approaches that of general population. The real-world situation may be different. We analyzed patients (C 18 years) with chronic phase (CP) CML enrolled over a 7-year period (2002-2008) in an imatinib access program. Event was defined as non-achievement/loss of complete hematological response (CHR), loss of cytogenetic response or progression to accelerated (AP)/blast phase (BC). Progression was defined as development of AP/BC. Any delay of C 1 week in reporting for drug refills was categorized as non-adherence. Of the 443 patients with CP-CML who started imatinib [median age: 36 years (18-70); High risk: 32% (Sokal) and 14% (Hasford/EUTOS)], 162 (37%) had received prior therapy [mostly hydroxyurea (N = 153]. CHR was achieved by 430 (97%). After a median follow up of 109.5 months (3.4-184.3), the EFS, PFS and OS at 10 years was 43%, 75% and 76% respectively. Superior EFS was predicted by low-risk Hasford score and adherence to therapy. Adherence to therapy was the only factor which predicted EFS on multivariate analysis (HR 0.64, 95% CI 0.50-0.83, P = 0.001). Long-term follow up of patients with CP-CML reflects poorer survival than those reported from clinical trials and reflects multiple issues that affect ''real-world'' patients. The continued drop in EFS, noted during long-term follow up, might take time to impact the PFS and OS due to the chronic nature of the disease. Sustained adherence to therapy is important for optimum long-term outcomes.