Shaikh Muhammad Shariq, Ahmed Sibtain, Farrukh Saba, Bayunus Shahnawaz
Department of Pathology and Laboratory Medicine, The Aga Khan University Hospital, Stadium Road, Karachi, 74800, Pakistan.
Clinical Research Fellow, Leeds Teaching Hospitals NHS Trust, Leeds, LS97TF, UK.
BMC Med Inform Decis Mak. 2025 Jan 9;25(1):15. doi: 10.1186/s12911-025-02857-4.
Reference intervals (RIs) are crucial for distinguishing healthy from sick individuals and vary across age groups. Hemoglobinopathies are common in Pakistan, making the quantification of hemoglobin variants essential for screening. Direct RIs are established by measuring values from a healthy reference population, whereas indirect RIs, use statistical analysis of routine lab data to estimate values, making it feasible in settings where direct data is unavailable. Since Pakistan lacks locally established Hemoglobin A2 RIs for infants, this study aims to fill that gap using an indirect data mining method to improve diagnostic accuracy for hemoglobinopathies.
It was a retrospective observational study. Hemoglobin A2 measurements from all patients aged birth to 1 year between January 2015 and December 2022 were retrieved from the laboratory management system at Aga Khan University Hospital. The study population represented the entire geographical distribution of the country. Hemoglobin A2 was measured using the Bio-Rad Variant™ II analyzer. RIs were computed using an indirect KOSMIC algorithm, which assumes non-pathologic samples follow a Gaussian distribution after Box-Cox transformation.
A total of 88,690 specimens were analyzed for HbA2. After excluding patients with multiple specimens, RIs were calculated for 22,713 infants, stratified into five age sub-groups. The 2.5th and 97.5th percentile results showed good agreement with RIs from Mayo Clinic Laboratories.
This study supports data mining as an alternative method for establishing HbA2 RIs, especially in resource-limited settings. The results are specific to the studied population, instrument, and reagent, and they elucidate the fluctuations in HbA2 synthesis with age. These intervals will enhance clinical decision-making based on HbA2 results.
参考区间（RIs）对于区分健康个体和患病个体至关重要，且会因年龄组的不同而有所差异。血红蛋白病在巴基斯坦很常见，因此对血红蛋白变异体进行定量分析对于筛查至关重要。直接参考区间是通过测量健康参考人群的值来确定的，而间接参考区间则利用常规实验室数据的统计分析来估计值，这使得在无法获取直接数据的情况下也可行。由于巴基斯坦缺乏针对婴儿的本地建立的血红蛋白A2参考区间，本研究旨在使用间接数据挖掘方法填补这一空白，以提高血红蛋白病的诊断准确性。
这是一项回顾性观察研究。从阿迦汗大学医院的实验室管理系统中检索了2015年1月至2022年12月期间所有出生至1岁患者的血红蛋白A2测量值。研究人群代表了该国的整个地理分布。使用伯乐Variant™ II分析仪测量血红蛋白A2。参考区间使用间接KOSMIC算法计算，该算法假设非病理样本在Box-Cox变换后遵循高斯分布。
共分析了88,690份血红蛋白A2样本。在排除有多个样本的患者后，为22,713名婴儿计算了参考区间，并将其分为五个年龄亚组。第2.5百分位数和第97.5百分位数的结果与梅奥诊所实验室的参考区间显示出良好的一致性。
本研究支持将数据挖掘作为建立血红蛋白A2参考区间的替代方法，特别是在资源有限的环境中。结果特定于所研究的人群、仪器和试剂，并且阐明了血红蛋白A2合成随年龄的波动情况。这些区间将增强基于血红蛋白A2结果的临床决策。
