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Predicting the outcome of intramuscular psoas lengthening in children with cerebral palsy using preoperative gait
Michael H Schwartz1, Adam Rozumalski, Walter Truong
1Gillette Children's Specialty Healthcare, St. Paul, MN 55101, United States. schwa021@umn.edu
Insights
This study developed criteria using a random forest algorithm to predict outcomes for psoas lengthening surgery in children with cerebral palsy. Applying these criteria can improve surgical success rates for better pelvis-hip function.
Area of Science:
- Orthopedics
- Biomechanical Engineering
- Pediatric Surgery
Background:
- Cerebral palsy often requires surgical intervention, including psoas lengthening, to improve mobility.
- Predicting surgical outcomes is crucial for optimizing treatment in pediatric patients.
- Single event multi-level surgery (SEMLS) aims to address multiple musculoskeletal issues simultaneously.
Purpose of the Study:
- To develop and validate predictive criteria for intramuscular psoas lengthening outcomes in cerebral palsy.
- To utilize machine learning to identify key factors influencing surgical success.
- To enhance the rate of good pelvis-hip outcomes in children undergoing SEMLS.
Main Methods:
- Retrospective analysis of 800 limbs from patients with diplegic cerebral palsy.
- Application of the random forest algorithm to historical gait analysis and clinical data.
- Validation through a case-control study comparing limbs with and without psoas lengthening based on derived criteria.
Main Results:
- The random forest algorithm achieved high accuracy (.78), sensitivity (.82), and specificity (.73) in predicting outcomes.
- Psoas lengthening in limbs meeting the criteria yielded significantly better outcomes (82%) compared to controls (60%) and over-treated limbs (27%).
- Implementing the criteria is projected to increase good pelvis-hip outcomes from 58% to 72% in SEMLS.
Conclusions:
- The developed random forest-based criteria effectively predict outcomes of psoas lengthening in cerebral palsy.
- These criteria can guide surgical decisions to improve functional results and reduce suboptimal outcomes.
- Future application of these predictive criteria holds significant potential for optimizing SEMLS in pediatric cerebral palsy.
Abstract:
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).
