The clinical demand and supply of blood in India: A National level estimation study.

BACKGROUND: Estimating the clinical demand for blood and components arising in a health facility is crucial to ensure timely availability of blood. This study aims to estimate disease-specific clinical demand, supply and utilization of whole blood and components in India. METHODS: We conducted a national level cross-sectional study in five randomly selected states from five regions of the country. We included 251 public and private facilities representing primary, secondary and tertiary care facilities. We collected annual disease-specific demand, supply and utilization of blood and components using a structured tool. We estimated the national demand by extrapolating the study data (demand and beds) to the total number of estimated beds in the country. FINDINGS: According to the study, the total clinical demand of 251 health facilities with 51,562 beds was 474,627 whole blood units. Based on this, the clinical demand for India was estimated at 14·6 million whole blood units (95 CI: 14·59-14·62), an equivalent of 36·3 donations per 1,000 eligible populations, which will address whole blood and component requirement. The medicine specialty accounted for 6·0 million units (41·2%), followed by surgery 4·1 million (27·9%), obstetrics and gynecology 3·3 million (22·4%) and pediatrics 1·2 million (8·5%) units. The supply was 93% which is equivalent to 33·8 donations against the demand. CONCLUSION: The study indicated a demand and supply gap of 2.5 donations per 1,000 eligible persons which is around one million units. The gap emphasises the need for sustained and concerted efforts from all stakeholders and for increasing the awareness about repeat voluntary non-remunerated blood donation (VNRBD); optimizing the availability of blood components through efficient blood component separation units; promoting modern principles of patient blood management and strengthening capacities of human resources in the blood transfusion system in India.

Knee meniscal retears after repair: A systematic review comparing diagnostic imaging modalities.

The meniscus is a relatively avascular fibrocartilaginous structure that provides a key role in shock absorption and load transmission. However, accurate diagnosis of meniscal retear can present a clinical challenge. The purpose of this study was to conduct a systematic review on the available literature, which compare the sensitivities, specificities, and accuracies of different diagnostic modalities of diagnosing knee meniscal retears in patients who have undergone surgical meniscal repair, such as Magnetic Resonance Imaging (MRI), Magnetic Resonance Arthrography with intraarticular contrast (direct MRA), and a combination of MRI and direct MRA. Two authors independently searched two databases (PubMed and Scopus) for literature related to knee meniscus retear according to the PRISMA guidelines. Four studies were found, which resulted in 291 patients with 293 menisci. All studies were published in 2008 and 2014. In our analysis, we calculated sensitivity to be 78.79% (95% CI, 64.07-93.51), specificity to be 56.58% (95% CI, 20.21-92.94), and overall accuracy to be 66.25% (95% CI, 54.29-78.22) for MRI and sensitivity to be 87.84% (95% CI, 83.93-91.74), specificity to be 88.68% (95% CI, 81.93-95.43), and overall accuracy to be 87.22% (95% CI, 82.22-91.62) for direct MRA. We recommend the use of direct MRA for the diagnosis of meniscal retears due to its higher sensitivity, specificity, and accuracy as compared to MRI and its reduced cost and invasive nature as compared to second-look arthroscopy. However, our review is limited by the number of studies available on this topic. More studies using study designs such as randomized controlled trials, involving MRI, direct MRA, and combinations of such techniques, should be performed to accurately assess the different techniques and aid in designing guidelines to guide the diagnosis of meniscal retears following meniscal repair.

A national level estimation of population need for blood in India.

BACKGROUND: The population need for blood is the total volume required to transfuse all the individuals who need transfusion in a defined population over a defined period. The clinical demand will arise when people with a disease or condition who require transfusion, access healthcare services, and subsequently the clinicians request blood. Essentially, the conversion of need to demand must be maximum to avoid preventable mortality and morbidity. The study estimated the population need for blood in India. METHODS: The methodology included a comprehensive literature review to determine the diseases and conditions requiring transfusion, the population at risk, and prevalence or incidence; and Delphi method to estimate the percentage of people requiring transfusion, and the quantum. RESULTS: The estimated annual population need was 26.2 million units (95% CI; 17.9-38.0) of whole blood to address the need for red cells and other components after the separation process. The need for medical conditions was 11.0 million units (95% CI:8.7-14.7), followed by surgery 6.6 million (95% CI:3.8-10.0), pediatrics 5.0 million (95% CI:3.5-7.0), and obstetrics and gynecology 3.6 million units (95% CI:1.9-6.2). The gap between need and demand which depends upon the access and efficiency of healthcare service provision was estimated at 13 million units. CONCLUSION: The study brings evidence to highlight the gap between need and demand and the importance of addressing it. It cannot be just the responsibility of blood transfusion or health systems, it requires a multi-sectoral approach to address the barriers affecting the conversion of need to clinical demand for blood.

Management of relapse in acute promyelocytic leukaemia treated with up-front arsenic trioxide-based regimens.

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 (4·5%) died during salvage therapy. An autologous stem cell transplant (auto-SCT) was offered to all patients who achieved MR, 35/63 (55·6%) 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 90·3 (5·3)% versus 58·6 (10·4)% (P = 0·004), and 87·1 (6·0)% versus 47·7 (10·3)% (P = 0·001) respectively. On multivariate analysis, failure to consolidate MR with an auto-SCT was associated with a significantly increased risk of relapse [hazard ratio (HR) 4·91, 95% confidence interval (CI) 1·56-15·41; P = 0·006]. 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 long-term survival.

Demystifying the varying case fatality rates (CFR) of COVID-19 in India: Lessons learned and future directions.

INTRODUCTION: At the end of the second week of June 2020, the SARS-CoV-2 responsible for COVID-19 infected above 7.5 million people and killed over 400,000 worldwide. Estimation of case fatality rate (CFR) and determining the associated factors are critical for developing targeted interventions. METHODOLOGY: The state-level adjusted case fatality rate (aCFR) was estimated by dividing the cumulative number of deaths on a given day by the cumulative number confirmed cases 8 days before, which is the average time-lag between diagnosis and death. We conducted fractional regression analysis to determine the predictors of aCFR. RESULTS: As of 13 June 2020, India reported 225 COVID-19 cases per million population (95% CI:224-226); 6.48 deaths per million population (95% CI:6.34-6.61) and an aCFR of 3.88% (95% CI:3.81-3.97) with wide variation between states. High proportion of urban population and population above 60 years were significantly associated with increased aCFR (p=0.08, p=0.05), whereas, high literacy rate and high proportion of women were associated with reduced aCFR (p<0.001, p=0.03). The higher number of cases per million population (p=0.001), prevalence of diabetes and hypertension (p=0.012), cardiovascular diseases (p=0.05), and any cancer (p<0.001) were significantly associated with increased aCFR. The performance of state health systems and proportion of public health expenditure were not associated with aCFR. CONCLUSIONS: Socio-demographic factors and burden of non-communicable diseases (NCDs) were found to be the predictors of aCFR. Focused strategies that would ensure early identification, testing and effective targeting of non-literate, elderly, urban population and people with comorbidities are critical to control the pandemic and fatalities.