Validity and accuracy of a machine learning predictive model in the exploitation of patient-related outcomes in spine surgery

TL;DR

A machine learning model accurately predicts patient outcomes after lumbar spine surgery, helping personalize care.

Contribution

A deep learning algorithm was prospectively validated for predicting postoperative outcomes in lumbar surgery patients.

Findings

The algorithm predicted outcomes with 81.6% accuracy using preoperative data.

Postoperative outcomes varied significantly between MCID and no-MCID groups.

Remote patient follow-up enhanced the sensitivity of outcome definitions.

Abstract

Lumbar spine disorders are among the most prevalent and disabling conditions worldwide. Patient selection for surgery remains highly complex, and the benefits of surgical interventions remain uncertain, potentially depending on patients’ baseline health characteristics. Patient-related outcome measurements represent a standard method for assessing treatment success in lumbar surgery. The aim of this study is to prospectively validate the accuracy of a deep learning algorithm in predicting the clinical outcomes of patients undergoing lumbar surgery [minimal clinically important difference (MCID)/no-MCID].

This study is multicentric, longitudinal, and prospective study was conducted over a 16-month period (September 2021 to December 2022). Patients with a surgical indication for lumbar decompression were included preoperatively and enrolled in the Surgery Medical Outcomes (SuMO©) mobile application to fill in the preoperative and postoperative data. Patients were classified into two categories according to their postoperative outcomes. The MCID was defined using the Oswestry Disability Index (ODI), combined with the intake of opioids and the presence of motor loss in patients. These results were then compared to the prediction of the algorithm based on preoperative data to determine the accuracy of the algorithm.

A total of 119 patients were enrolled preoperatively, and postoperative follow-up data were obtained for 103 patients. The mean preoperative ODI was 0.43 (SD 0.17). The postoperative mean ODI was 0.28 (SD 0.18) at 1 month and 0.14 (SD 0.16) at 3 months. At 8 months, the mean postoperative ODI in the MCID group was 0.12, while it was 0.26 in the no-MCID group. The algorithm predicted the outcome with an accuracy of 81.6% (receiver operating characteristic score).

This study confirms the validity and accuracy of the algorithm in prospectively predicting postoperative outcomes, as well as the sensitivity of the MCID definition, especially when coupled with remote, patient-centered follow-up. Artificial intelligence-based algorithms may help physicians in their future daily practice by addressing personalized patient care.

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