Clustering and predicting VCA patient populations - a machine learning approach into vascularized composite allotransplantation - Takeaways - MDSpire
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Analyzing and Forecasting Patient Groups in Vascularized Composite Allotransplantation Using Machine Learning Techniques

  • By

  • Leonard Knoedler

  • Tobias Niederegger

  • Thomas Schaschinger

  • Carsten Stahl

  • Jule Brandt

  • Curtis L. Cetrulo

  • Max Heiland

  • Gabriel Hundeshagen

  • Jan O. Voss

  • Alexandre G. Lellouch

  • August 19, 2026

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  • 1

    Vascularized composite allotransplantation (VCA) aids recovery after tissue loss but lacks comprehensive recipient profiling data.

  • 2

    Clustering analyses of 107 VCA recipients identified gender and VCA type as key distinguishing factors among patients.

  • 3

    Upper-limb and face transplants were predominantly performed on males, while uterus transplants were concentrated in specific UNOS regions.

  • 4

    42% of recipients were categorized as noise in clustering, indicating significant heterogeneity within the VCA population.

  • 5

    ARIMA models forecast stable distributions of age and BMI among VCA recipients until 2027, despite observed temporal trends.

Original Source(s)

Clustering and predicting VCA patient populations - a machine learning approach into vascularized composite allotransplantation

  • by Leonard Knoedler, Tobias Niederegger, Thomas Schaschinger, Carsten Stahl, Jule Brandt, Curtis L. Cetrulo, Max Heiland, Gabriel Hundeshagen, Jan O. Voss, Alexandre G. Lellouch

  • August 19, 2026

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