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Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
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A new approach to assessing calcium status via a machine learning algorithm.

Candice Bancal1, Florian Salipante2, Nassim Hannas3

  • 1Laboratoire de biochimie et biologie moléculaire, CHU Nîmes, France.

Clinica Chimica Acta; International Journal of Clinical Chemistry
|December 22, 2022
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Summary

Assessing calcium status using corrected calcium can be unreliable. A machine learning algorithm offers improved accuracy compared to total calcium, with ionized calcium recommended for definitive results.

Keywords:
AlgorithmArtificial intelligenceCalciumCorrected calciumIonized calcium

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Area of Science:

  • Biochemistry
  • Clinical Chemistry
  • Medical Diagnostics

Background:

  • Calcium is vital for biological functions, with ionized calcium (Ca2+) being the active form.
  • Current clinical practice often relies on total or corrected calcium assays for calcium status assessment.
  • These conventional methods have limitations in accurately reflecting biologically active calcium levels.

Purpose of the Study:

  • To compare the performance of total and corrected calcium assays against ionized calcium for assessing calcium status.
  • To develop and evaluate a machine learning (ML) algorithm for more accurate calcium status prediction.
  • To identify factors influencing the accuracy of corrected calcium measurements.

Main Methods:

  • Retrospective analysis comparing total calcium, corrected calcium, and ionized calcium measurements.
  • Development of a machine learning algorithm to predict calcium status.
  • Evaluation of assay agreement and classification accuracy.

Main Results:

  • Total calcium showed 74% agreement with ionized calcium, outperforming corrected calcium (58% agreement).
  • Total calcium was highly accurate for hypocalcemic samples (93% agreement).
  • The ML algorithm achieved 81% correct classifications, unaffected by patient variables or calcium status.

Conclusions:

  • Corrected calcium assays are frequently inaccurate and should be used cautiously.
  • The developed ML algorithm provides a more reliable assessment of calcium status than total calcium.
  • Ionized calcium measurement remains the gold standard when uncertainty exists regarding calcium status.