Background As the number of COVID-19 cases continues to rise, public health efforts must focus on preventing avoidable fatalities. Understanding the demographic and clinical characteristics of deceased COVID-19 patients; and estimation of time-interval between symptom onset, hospital admission and death could inform public health interventions focusing on preventing mortality due to COVID-19. Methods We obtained COVID-19 death summaries from the official dashboard of the Government of Tamil Nadu, between 10th May and July 10, 2020. Of the 1783 deaths, we included 1761 cases for analysis. Results The mean age of the deceased was 62.5 years (SD: 13.7). The crude death rate was 2.44 per 100,000 population; the age-specific death rate was 22.72 among above 75 years and 0.02 among less than 14 years, and it was higher among men (3.5 vs 1.4 per 100,000 population). Around 85% reported having any one or more comorbidities; Diabetes (62%), hypertension (49.2%) and CAD (17.5%) were the commonly reported comorbidities. The median time interval between symptom onset and hospital admission was 4 days (IQR: 2, 7); admission and death was 4 days (IQR: 2, 7) with a significant difference between the type of admitting hospital. One-fourth of (24.2%) deaths occurred within a day of hospital admission. Conclusion Elderly, male, people living in densely populated areas and people with underlying comorbidities die disproportionately due to COVID-19. While shorter time-interval between symptom onset and admission is essential, the relatively short time interval between admission and death is a concern and the possible reasons must be evaluated and addressed to reduce avoidable mortality.

Interactive group psychoeducation is effective for changing the attitude of parents with intellectually disabled children, and is a viable option to be developed in situations where resources are limited.

The standard of care for patients with acute promyelocytic leukaemia (APL) relapsing after front-line treatment with arsenic trioxide (ATO)based regimens remains to be defined. A total of 67 patients who relapsed after receiving ATO-based up-front therapy and were also salvaged using an ATO-based regimen were evaluated. The median (range) age of patients was 28 (4-54) years. While 63/67 (94%) achieved a second molecular remission (MR) after salvage therapy, three (4Á5%) died during salvage therapy. An autologous stem cell transplant (auto-SCT) was offered to all patients who achieved MR, 35/63 (55Á6%) opted for auto-SCT the rest were administered an ATO + all-trans retinoic acid maintenance regimen. The mean (SD) 5-year Kaplan-Meier estimate of overall survival and event-free survival of those who received auto-SCT versus those who did not was 90Á3 (5Á3)% versus 58Á6 (10Á4)% (P = 0Á004), and 87Á1 (6Á0)% versus 47Á7 (10Á3)% (P = 0Á001) respectively. On multivariate analysis, failure to consolidate MR with an auto-SCT was associated with a significantly increased risk of relapse [hazard ratio (HR) 4Á91, 95% confidence interval (CI) 1Á56-15Á41; P = 0Á006]. MR induction with ATO-based regimens followed by an auto-SCT in children and young adults with relapsed APL who were treated with front-line ATO-based regimens was associated with excellent longterm survival.

Background The prevalence of dry eye disease is increasing globally and requires the attention of healthcare professionals as it worsens patients’ quality of life. No published studies on the epidemiology of dry eyes have been found in Dubai. Purpose To describe the epidemiology, prevalence, severity, and associated factors of dry eyes in Dubai, United Arab Emirates, in 2019. Methods This was an analytical, cross-sectional, survey-based study. An online survey was distributed by email to Mohammed Bin Rashid University students, staff, and faculty and to the staff at Mediclinic City and Parkview Hospitals in Dubai, United Arab Emirates, from April–June 2019. The survey included demographic questions and the Ocular Surface Disease Index (OSDI). Results The survey was completed by 452 participants; the majority were females (288/452; 63.7 %). The prevalence of dry eyes in Dubai was estimated to be 62.6 % (283/452), with severely dry eyes being the most prevalent (119/283; 42 %). Females, high daily screen time (> 6 h), and the use of contact lenses were found to be associated with dry eyes (P-value < 0.05, 95 % confidence interval). Age was found to be negatively correlated with prevalence of dry eyes. Exposure to smoking/shisha, history of eye injury/surgery, and nationality were not associated with dry eyes. Conclusions This is the first cross-sectional study to investigate the prevalence of dry eyes in Dubai (62.6 %). The majority of participants had severe dry eyes symptoms. Severely dry eyes were more common among females and users of contact lenses.

A trial was designed to evaluate the role of enhanced parental attitude towards management of intellectual disability in the acquisition of adaptive behaviour. Fifty-seven children with intellectual disability and their parents were randomly assigned to 12 weeks of either multimodal adaptive behaviour training plus interactive group psycho-education (intervention group); or multimodal adaptive behaviour training plus didactic lectures (control group). Blinded raters were involved. Completers’ and intention-to-treat analyses were conducted. In the intention-to-treat sample, 22 of 29 children in the intervention group compared with four of 28 children in the control group showed a significant improvement in the acquisition of adaptive behaviour. The minimum additive efficacy provided by the enhanced parental attitude was 80 percent. Meaningful clinical benefits on various measures were found for the intervention group after training. Parental attitude intervention should be included in adaptive behaviour training for children with intellectual disability, as enhanced parental attitude has short-term positive effects.

No abstract

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.