Evaluation of Low-cost Techniques for Detecting Sickle Cell Disease and β-thalassemia in Nepal and Canada
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 145
- 试验地点
- 5
- 主要终点
- Sensitivity, Specificity, Positive Predictive Value and Negative Predictive Value
研究概览
简要总结
Sickle cell disease (SCD) is an inherited blood disorder associated with acute illness and organ damage. In high resource settings, early screening and treatment greatly improve quality of life. In low resource settings, however, mortality rate for children is high (50-90%). Low-cost and accurate screening techniques are critical to reducing the burden of the disease, especially in remote/rural settings. The most common and severe form of SCD is sickle cell anemia (SCA), caused by the inheritance of genes causing abnormal forms of hemoglobin (called sickle hemoglobin or hemoglobin S) from both parents. The asymptomatic or carrier form of the disease, known as sickle cell trait (SCT), is caused by the inheritance of only one variant gene from one of the parents. In areas such as Nepal, β-thalassemia (another inherited blood disorder) and SCD are both prevalent, and some combinations of these diseases lead to severe symptoms.
The purpose of this study is to determine the accuracy of low-cost point-of-care techniques for screening and detecting sickle cell disease, sickle cell trait, and β-thalassaemia, which will subsequently inform on feasible solutions for detecting the disease in rural, remote, or low-resource settings. One of the goals of the study is to evaluate the feasibility of techniques, such as the sickling test with low-cost microscopy and machine learning, HbS solubility test, commercial lateral-flow assays (HemoTypeSC and Sickle SCAN), and the Gazelle Hb variant test, to supplement or replace gold standard tests (HPLC or electrophoresis), which are expensive, require highly trained personnel, and are not easily accessible in remote/rural settings.
The investigators hypothesize that:
- an automated sickling test (standard sickling test enhanced using low-cost microscopy and machine learning) has a higher overall accuracy than conventional screening techniques (solubility and sickling tests) to detect hemoglobin S in blood samples
- the automated sickling test can additionally classify SCD, SCT and healthy individuals with a sensitivity greater than 90%, based on morphology changes of red blood cells, unlike conventional sickling or solubility tests that do not distinguish between SCD and SCT cases
- Gazelle diagnostic device can detect β-thalassaemia and SCD/SCT with an overall accuracy greater than 90%, compared with HPLC as the reference test
详细描述
Overall, the hypothesis is that an assessment of the performance and accuracies of low-cost point-of-care techniques (automated sickling test, solubility test, lateral-flow assays, Gazelle Hb variant test) against HPLC tests will provide researchers and health workers with feasible alternative options for screening and detecting SCD, SCT and β-thalassaemia in a variety of situations based on the needs of the communities and the resources available.
Objectives
Objectives specific to the current study are to:
- Determine accuracy (sensitivity and specificity) of automated sickling test to detect HbS, compared to gold standard HPLC, and to conventional solubility test
- Determine whether SCD, SCT and healthy individuals can be classified using the automated sickling test that leverages machine learning on images of blood films under hypoxia
- Validate accuracy (>95% sensitivity and specificity) of lateral- flow assays (HemoTypeSC and Sickle SCAN) to detect SCD/SCT, and of Gazelle variant test to detect SCD, SCT, and β-thalassaemia; and determine if low-cost techniques can potentially replace HPLC/electrophoresis tests in rural and remote settings
Long-term objectives of the overall project are to:
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Diagnostic
- 盲法
- None
盲法说明
All the participants and study team members will be informed of the tests and devices used in the study.
入排标准
- 年龄范围
- 1 Year 至 —（Child, Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Since the techniques evaluated in the study aims at detecting sickle cell disease (SCD), sickle cell trait (SCT), and β- thalassemia, the following number of participants will be included in Nepal:
- •20 individuals with SCD (HbSS)
- •20 individuals with SCT (HbAS)
- •20 individuals with sickle cell/β-thalassemia compound heterozygous form (HbS/β-thalassemia)
- •20 individuals with β-thalassemia (Hbβ/β-thalassemia)
- •20 individuals with β-thalassemia trait or carrier form (HbA/β- thalassemia)
- •20 healthy individual participants or normal participants (HbAA, participants without any known hemoglobin disorders, such as SCD, SCT or β-thalassemia)
- •The following number of participants will be included in Canada:
- •30 individuals with SCD (HbSS)
- •30 individuals with SCT (HbAS)
- •30 healthy individual participants or normal participants (HbAA, participants without any known hemoglobin disorders, such as SCD, SCT or β-thalassemia)
- •Participants older than 1 year of age at the time of drawing blood will be eligible. Signed and dated consent or assent forms will be required by the participants or their parents/guardians.
排除标准
- •The exclusion criteria for the study:
- •Transfusion within the last 3 months
- •Pregnancy Participants who wish to withdraw from the study will also be excluded.
结局指标
主要结局
Sensitivity, Specificity, Positive Predictive Value and Negative Predictive Value
时间窗: baseline
The following metrics will be determined for the low-cost tests to be evaluated as indicated below (where TP = true positive, TN = true negative, FP = false positive, FN = false negative): 1. Sensitivity = TP/(TP + FN) 2. Specificity = TN/(FP + TN) 3. Positive predictive value = TP/(TP + FP) 4. Negative predictive value = TN/(TN + FN) These metrics will be calculated for the low-cost technologies against the reference test, HPLC, for detecting the presence of sickle hemoglobin and β- thalassemia. The low-cost technologies include automated sickling test (standard sickling test enhanced using low-cost microscopy and machine learning), solubility test, HemoTypeSC, Sickle SCAN, and Gazelle Hb Variant test. The test results of the low-cost technologies will be compared with those of the reference test to get the values of TP, TN, FP and FN, which will then be used to calculate the metrics listed above.
次要结局
未报告次要终点
研究者
Boris Stoeber
Professor in the Department of Electrical and Computer Engineering and in the Department of Mechanical Engineering
University of British Columbia