Screening of nutritional and genetic anemias using elastic light scattering†,Lab on a Chip

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Screening of nutritional and genetic anemias using elastic light scattering†
Lab on a Chip ( IF 6.1 ) Pub Date : 2018-09-28 00:00:00 , DOI: 10.1039/c8lc00377g
Lieshu Tong _{1,

2,

3,

4} , Josef Kauer _{5,

6,

7,

8,

9} , Xi Chen _{10,

11,

12,

13,

14} , Kaiqin Chu _{1,

2,

3,

4} , Hu Dou _{10,

11,

12,

13,

14} , Zachary J. Smith _{1,

2,

3,

4}

Affiliation

Department of Precision Machinery and Precision Instrumentation
University of Science and Technology of China
Hefei
China
Beuth Hochschule für Technik Berlin
Berlin
Germany
NeuroCure Clinical Research Center
Charité - Universitätsmedizin Berlin
Department of Clinical laboratory
Ministry of Education Key Laboratory of Child Development and Disorders
Key Laboratory of Pediatrics in Chongqing
Chongqing International Science and Technology Cooperation Center for Child Development and Disorders
Children's Hospital of Chongqing Medical University

Anemia affects more than ¼ of the world's population, mostly concentrated in low-resource areas, and carries serious health risks. Yet current screening methods are inadequate due to their inability to separate iron deficiency anemia (IDA) from genetic anemias such as thalassemia trait (TT), thus preventing targeted supplementation of oral iron. Here we present an accurate approach to diagnose anemia and anemia type using measures of pediatric red cell morphology determined through machine learning applied to optical light scattering measurements. A partial least squares model shows that our system can accurately extract mean cell volume, red cell size heterogeneity, and mean cell hemoglobin concentration with high accuracy. These clinical parameters (or the raw data itself) can be submitted to machine learning algorithms such as quadratic discriminants or support vector machines to classify a patient into healthy, IDA, or TT. A clinical trial conducted on 268 Chinese children, of which 49 had IDA and 24 had TT, shows >98% sensitivity and specificity for diagnosing anemia, with 81% sensitivity and 86% specificity for discriminating IDA and TT. The majority of the misdiagnoses are IDA patients with particularly severe anemia, possibly requiring hospital care. Therefore, in a screening paradigm where anyone testing positive for TT is sent to the hospital for gold-standard diagnosis and care, we maximize patient benefit while minimizing use of scarce resources.

更新日期：2018-09-28

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