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Turner syndrome phalangeal screening based on a two‐stage linear regression concept
Author(s) -
Tiu ChuiMei,
Liu TzuChiang,
Hsieh ChiWen,
Niu DauMing,
Chen JenDar,
Jong TaiLang
Publication year - 2009
Publication title -
pediatrics international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.49
H-Index - 63
eISSN - 1442-200X
pISSN - 1328-8067
DOI - 10.1111/j.1442-200x.2008.02797.x
Subject(s) - medicine , phalanx , abnormality , skeleton (computer programming) , bone age , short stature , stage (stratigraphy) , sign (mathematics) , linear regression , orthodontics , anatomy , mathematics , statistics , biology , paleontology , mathematical analysis , psychiatry
Background: Turner syndrome (TS) is a congenital chromosomal abnormality, resulting in short stature, short fourth metacarpal, and retarded skeletal maturation in children. The existing methods of diagnosis, which include carpal angle, metacarpal sign, and body mass index (BMI), cannot accurately diagnose TS. The authors propose a novel procedure for examining the hand skeleton to distinguish between normal individuals and patients with TS. Methods: This investigation was divided into two parts. In the first part, existing methods (evaluation of the metacarpal sign, measurement of the carpal angle, and determination of BMI) were used. Examination in the second part was based on the two‐stage screening method (TSSM). In the first stage in TSSM, the ratio of the lengths of the distal–middle phalanges of the fifth digit was determined in normal subjects with average range of satisfactory body height and TS patients. A suitable cut‐off was found on linear regression and used to divide the plot into TS patients and normal subjects. In the second stage, the normal section was transferred to another group based on bone and chronological ages. A greater number of patients were diagnosed with TS using this method. Finally, four cut‐off parameters were determined on linear regression analysis. Results with optimal sensitivity and specificity were automatically obtained. Results: The combination of TSSM with optimal programming (sensitivity = 0.81 and specificity = 0.91) was satisfactory for diagnosing TS patients. Conclusion: TSSM can suitably evaluate growth of the hand skeleton to distinguish between normal individuals and patients with TS.