Using artificial intelligence‐based technologies to detect clinically relevant changes of gross motor function in children with cerebral palsy

Abstract

AbstractAimTo compare the 66‐item Gross Motor Function Measure (GMFM‐66) with the reduced version of the GMFM‐66 (rGMFM‐66) with respect to the detection of clinically relevant changes in gross motor function in children with cerebral palsy (CP).MethodThe study was a retrospective single centre analysis of children with CP who participated in a rehabilitation programme. Overall, 1352 pairs of GMFM‐66 and rGMFM66 measurements with a time interval of 5 to 7 months were available. To measure clinically relevant changes in gross motor function, the individual effect size (iES) was calculated.ResultsThe study population consisted of 1352 children (539 females), mean age 6 years 4 months (SD 2 years 4 months). The iES based on the GMFM‐66 and the rGMFM‐66 showed a significant correlation (r = 0.84, p < 0.001). The analysis of the area under the receiver operating characteristic curve showed an excellent agreement for clinically relevant gross motor improvement (Cohen’s d ≥ 0.5; area under the curve = 0.90 [95% confidence interval 0.88–0.92]) or deterioration (Cohen’s d ≤ −0.5; area under the curve = 0.95 [95% confidence interval 0.92–0.97]).InterpretationPerforming the rGMFM‐66 saves time compared to the full GMFM‐66. The rGMFM‐66 showed good agreement with the GMFM‐66 with respect to the detection of clinically relevant changes in gross motor function in children with CP, so its use in everyday clinical practice seems justifiable.What this paper adds The reduced version of the 66‐item Gross Motor Function Measure (rGMFM‐66) detects clinically relevant changes in gross motor function in children with cerebral palsy. The rGMFM‐66 correlates highly with the full GMFM‐66. The rGMFM‐66 can be used in clinical practice when the time schedule is limited.