Machine-learning-based models for the optimization of post-cervical spinal laminoplasty outpatient follow-up schedules.

Background: Patients undergo regular clinical follow-up after laminoplasty for cervical myelopathy. However, those whose symptoms significantly improve and remain stable do not need to conform to a regular follow-up schedule. Based on the 1-year postoperative outcomes, we aimed to use a machine-learning (ML) algorithm to predict 2-year postoperative outcomes.

Methods: We enrolled 80 patients who underwent cervical laminoplasty for cervical myelopathy. The patients' Japanese Orthopedic Association (JOA) scores (range: 0-17) were analyzed at the 1-, 3-, 6-, and 12-month postoperative timepoints to evaluate their ability to predict the 2-year postoperative outcomes. The patient acceptable symptom state (PASS) was defined as a JOA score ≥ 14.25 at 24 months postoperatively and, based on clinical outcomes recorded up to the 1-year postoperative timepoint, eight ML algorithms were developed to predict PASS status at the 24-month postoperative timepoint. The performance of each of these algorithms was evaluated, and its generalizability was assessed using a prospective internal test set.

Results: The long short-term memory (LSTM)-based algorithm demonstrated the best performance (area under the receiver operating characteristic curve, 0.90 ± 0.13).

Conclusions: The LSTM-based algorithm accurately predicted which group was likely to achieve PASS at the 24-month postoperative timepoint. Although this study included a small number of patients with limited available clinical data, the concept of using past outcomes to predict further outcomes presented herein may provide insights for optimizing clinical schedules and efficient medical resource utilization.

Trial registration: This study was registered as a clinical trial (Clinical Trial No. NCT02487901), and the study protocol was approved by the Seoul National University Hospital Institutional Review Board (IRB No. 1505-037-670).