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Correction Equation for Hemoglobin Values Obtained Using Point of Care Tests-A Step towards Realistic Anemia Burden Estimates.

Gomathi RamaswamyAbhishek JaiswalKashish VohraRavneet KaurMohan BairwaArchana SinghVani SethiKapil Yadav
Published in: Diagnostics (Basel, Switzerland) (2022)
Digital hemoglobinometers have been used as point-of-care tests (POCT) to estimate the burden of anemia in community-based studies and national-level surveys in India. As the accuracy of hemoglobin estimated in POCT varies, there is a need for adjustments to the POCT-hemoglobin to ensure they are closer to reality and are comparable. We used data (collected between 2016 and 2020) (N = 1145) from four studies from India: three among pregnant women and 6-59-month-old children from Haryana and the fourth from a national nutritional survey among 1-19-year-old children. We compared the same individuals' POCT-hemoglobin (capillary blood) and automated hematology analyzers (AHA) hemoglobin (venous blood) and developed a predictive linear regression model to obtain the correction equation for POCT-hemoglobin. We analyzed paired data from 1145 participants. The correction equation for obtaining the true hemoglobin value = 3.35 + 0.71 × POCT-hemoglobin using capillary blood (adjusted R2-64.4% and mean squared error -0.841 g/dL). In comparison with the AHA-hemoglobin, the mean difference of POCT-hemoglobin was 0.2 g/dL, while with the predicted Hb obtained from the correction equation it was 0.01 g/dL. The correction equation was the first attempt at deriving the true hemoglobin values from the POCTs. There is a need for multi-country collaborative studies to improve the correction equation by adjusting for factors affecting hemoglobin estimation.
Keyphrases
  • red blood cell
  • quality improvement
  • machine learning
  • chronic kidney disease
  • young adults
  • big data
  • cross sectional
  • artificial intelligence