Article

This study used the random forest algorithm to predict outcomes of intramuscular
psoas lengthening as part of a single event multi-level surgery in patients with
cerebral palsy. Data related to preoperative medical history, physical exam, and
instrumented three-dimensional gait analysis were extracted from a historic
database in a motion analysis center. Data from 800 limbs of patients with
diplegic cerebral palsy were analyzed. An index quantifying the overall deviation
in pelvic tilt and hip flexion was used to define outcome categories. The random
forest algorithm was used to derive criteria that predicted the outcome of a
limb. The criteria were applied to limbs that underwent psoas lengthening with
outstanding results (accuracy=.78, sensitivity=.82, specificity=.73). The
criteria were then validated using an extended retrospective case-control design.
Case limbs met the criteria and underwent psoas lengthening. Control limbs met
the criteria, but did not undergo psoas lengthening. Over-treated limbs failed
the criteria and underwent psoas lengthening. Other-treated limbs failed the
criteria and did not undergo psoas lengthening. The rate of good outcomes among
Cases exceeded that observed among controls (82% vs. 60%, relative risk=1.37),
and far exceeded that observed in Over-treated limbs (27%). Other-treated limbs
had good outcomes 52% of the time. Application of the criteria in the future is
estimated to increase the overall rate of good pelvis-hip outcomes from 58% to
72% among children with diplegia who undergo single-event multi-level surgery
(SEMLS).
CI - Copyright (c) 2012 Elsevier B.V. All rights reserved.

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