CD3+ T-cell count prediction for anti-thymocyte globulin treatment monitorization in kidney transplant recipients: a machine learning model - Scorecard - 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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Clinical Scorecard: Predicting CD3+ T-cell Levels for Monitoring Antithymocyte Globulin Therapy in Kidney Transplant Patients: A Machine Learning Approach

At a Glance

CategoryDetail
Condition
Key MechanismsAntithymocyte globulin (ATG) therapy induces lymphocyte depletion to prevent acute rejection of transplanted grafts.
Target Population
Care Setting

Key Highlights

    Guideline-Based Recommendations

    Diagnosis

      Management

        Monitoring & Follow-up

          Risks

            Patient & Prescribing Data

            Adult kidney transplant patients receiving ATG induction therapy.

            Machine learning models can guide ATG treatment without the need for CD3+ T-cell quantification.

            Clinical Best Practices

              Related Resources & Content

              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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