Zaffa_Reg
✕
  • Home
  • Formazione
  • Insegnamenti
  • Società scientifiche
  • News
    • Blog
    • Media
    • Attività scientifiche
    • Congressi
    • Pubblicazioni
  • Contatti
  • Home
  • Formazione
  • Insegnamenti
  • Società scientifiche
  • News
    • Blog
    • Media
    • Attività scientifiche
    • Congressi
    • Pubblicazioni
  • Contatti

Abstract

This study aimed to develop and validate a clinical decision-making algorithm, the ‘Best ACL-treatment Based on the Years of the Knee’ (BABY-Knee) Algorithm, for treating acute anterior cruciate ligament (ACL) injuries in skeletally immature patients. The algorithm integrates magnetic resonance imaging (MRI) findings and patient-specific characteristics to differentiate cases suitable for conservative management from those requiring surgical intervention.

A prospective cohort of 75 skeletally immature patients (mean age: 13.9 ± 2.2 years) diagnosed with ACL rupture at a single institution between February 2022 and October 2024 was evaluated. Patients were categorized as surgical or non-surgical candidates based on the BABY-Knee Algorithm, which incorporates six weighted criteria: MRI-detected meniscal tears, lateral tibiofemoral bone bruises, skeletal age, injury mechanism and rotatory laxity. Outcomes of initial management were retrospectively analyzed for algorithm validation.

Of the 75 patients, 55 (73.3%) underwent surgical reconstruction, while 20 (26.7%) were managed conservatively. Conservative treatment failed in 12 cases (60%), necessitating surgical intervention. Retrospective application of the algorithm yielded a positive predictive value of 91.7% for identifying surgical candidates and a negative predictive value of 87.5% for successful conservative treatment.

The BABY-Knee Algorithm demonstrated high reliability in guiding treatment decisions for skeletally immature patients with acute ACL injuries, predicting outcomes of conservative treatment in nearly 90% of cases. Further studies are required to confirm its applicability in additional prospective case series.

Leggi l’articolo
Zaffa_Reg
 

Contatto partnership cliniche ed universitarie

+39 0516366075

stefano.zaffagnini@unibo.it

Realizzazione a cura di Sphaera

Privacy Policy Cookie Policy