Title:	A PARSIMONIOUS MACHINE LEARNING APPROACH TO DETECT INAPPROPRIATE TREATMENTS IN SPINE SURGERY ON THE BASIS OF PATIENT-REPORTED OUTCOMES
Author(s):	Lorenzo Famiglini, Frida Milella, Pedro Berjano and Federico Cabitza
ISBN:	978-989-8704-40-5
Editors:	Piet Kommers and Mário Macedo
Year:	2022
Edition:	Single
Keywords:	Patient-Reported Outcome Measures, Machine Learning, Prediction, Appropriateness, Spine Surgery
Type:	Full Paper
First Page:	220
Last Page:	227
Language:	English
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Full Contents:	click to dowload
Paper Abstract:	Patient-reported outcome Measures (PROMs) are validated questionnaires or self-report instruments of the perception of patients about their own health status in response to a medical intervention. In the era of patient-centred care, consideration about the effectiveness of a medical treatment should be grounded not only on the physicians' assessment but also on the increase in PROM scores that is perceived as relevant by the patients, namely the minimum clinically important differences (MCID). In this study, we collected data from the IRCCS Galeazzi Orthopaedic Institute (IOG) of Milan, Italy, to develop a preoperative machine learning model predicting the non-achievement of the MCID threshold in specific PROM scores 6 months after spine surgery, namely the Oswestry Disability Index (ODI) and the Physical Score of the Short Form 36 (SF-36). At IOG, nearly 39% of spinal surgeries do not achieve a minimum clinically important improvement and, of these, 22% are associated with negative outcomes. This is mainly due to the fact that IOG is a tertiary healthcare facility that receives the most critical cases from a vast territory (practically from all over the country) and it is known that spinal deformities and other related problems are difficult to solve, especially in an aging population. In this view, it is important to early identify those patients who will likely not benefit from treatment and, therefore, will not reach the MCID. This would help avoid overdiagnosis, reduce overuse and the related costs for unnecessary treatment, as well as optimise the allocation of resources and support more appropriate choices in shared decision-making.
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