CD3+ T-cell count prediction for anti-thymocyte globulin treatment monitorization in kidney transplant recipients: a machine learning model - Takeaways - MDSpire

CD3+ T-cell count prediction for anti-thymocyte globulin treatment monitorization in kidney transplant recipients: a machine learning model

  • By

  • Ahmet B. Ak

  • Hayri K. Goren

  • Nuri B. Hasbal

  • Nur I. Genc

  • Sidar Copur

  • Lasin Ozbek

  • Burak Kocak

  • Adrian Covic

  • Mehmet Kanbay

  • June 18, 2026

  • 0 min

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  • 1

    The study developed a machine learning model to predict CD3+ T-cell depletion in kidney transplant patients undergoing ATG therapy.

  • 2

    In a cohort of 397 patients, 57.2% achieved the day-1 CD3+ T-cell < 50 cell/μl threshold after ATG induction.

  • 3

    The machine learning model outperformed logistic regression, achieving ROC-AUC values of 0.75 and 0.80 for Day 1 predictions.

  • 4

    The model provides a cost-effective alternative for monitoring ATG therapy without requiring flow cytometry for CD3+ T-cell counts.

  • 5

    This research highlights the potential of machine learning in enhancing clinical decision-making for kidney transplant immunosuppression.

Original Source(s)

CD3+ T-cell count prediction for anti-thymocyte globulin treatment monitorization in kidney transplant recipients: a machine learning model

  • by Ahmet B. Ak, Hayri K. Goren, Nuri B. Hasbal, Nur I. Genc, Sidar Copur, Lasin Ozbek, Burak Kocak, Adrian Covic, Mehmet Kanbay

  • June 18, 2026

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