Background and Purpose: Patient-Reported Measured Outcomes (PROMs) are essential to gain a full understanding of a patient’s condition, and in spine surgery, these questionnaires are of help when tailoring a surgical strategy. Electronic registries allow for a systematic collection and storage of PROMs, making them readily available for clinical and research purposes. This study aimed to investigate the reliability between the electronic and paper form of ODI (Oswestry Disability Index), SF-36 (Short Form Health Survey 36) and COMI-back (Core Outcome Measures Index for the back) questionnaires. Methods: A prospective analysis was performed of ODI, SF-36 and COMI-back questionnaires collected in paper and electronic format in two patients’ groups: Pre-Operatively (PO) or at follow-up (FU). All patients, in both groups, completed the three questionnaires in paper and electronic form. The correlation between both methods was assessed with the Intraclass Correlation Coefficients (ICC). Results: The data from 100 non-consecutive, volunteer patients with a mean age of 55.6 ± 15.0 years were analysed. For all of the three PROMs, the reliability between paper and electronic questionnaires results was excellent (ICC: ODI = 0.96; COMI = 0.98; SF36-MCS = 0.98; SF36-PCS = 0.98. For all p < 0.001). Conclusions: This study proved an excellent reliability between the electronic and paper versions of ODI, SF-36 and COMI-back questionnaires collected using a spine registry. This validation paves the way for stronger widespread use of electronic PROMs. They offer numerous advantages in terms of accessibility, storage, and data analysis compared to paper questionnaires.

The study aims to create a preoperative model from baseline demographic and health-related quality of life scores (HRQOL) to predict a good to excellent early clinical outcome using a machine learning (ML) approach. A single spine surgery center retrospective review of prospectively collected data from January 2016 to December 2020 from the institutional registry (SpineREG) was performed. The inclusion criteria were age ≥ 18 years, both sexes, lumbar arthrodesis procedure, a complete follow up assessment (Oswestry Disability Index—ODI, SF-36 and COMI back) and the capability to read and understand the Italian language. A delta of improvement of the ODI higher than 12.7/100 was considered a “good early outcome”. A combined target model of ODI (Δ ≥ 12.7/100), SF-36 PCS (Δ ≥ 6/100) and COMI back (Δ ≥ 2.2/10) was considered an “excellent early outcome”. The performance of the ML models was evaluated in terms of sensitivity, i.e., True Positive Rate (TPR), specificity, i.e., True Negative Rate (TNR), accuracy and area under the receiver operating characteristic curve (AUC ROC). A total of 1243 patients were included in this study. The model for predicting ODI at 6 months’ follow up showed a good balance between sensitivity (74.3%) and specificity (79.4%), while providing a good accuracy (75.8%) with ROC AUC = 0.842. The combined target model showed a sensitivity of 74.2% and specificity of 71.8%, with an accuracy of 72.8%, and an ROC AUC = 0.808. The results of our study suggest that a machine learning approach showed high performance in predicting early good to excellent clinical results.