TY - JOUR
T1 - Narrative Review of Predictive Analytics of Patient-Reported Outcomes in Adult Spinal Deformity Surgery
AU - Lehner, Kurt
AU - Ehresman, Jeff
AU - Pennington, Zach
AU - Ahmed, A. Karim
AU - Lubelski, Daniel
AU - Sciubba, Daniel M.
N1 - Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This supplement was supported by a grant from AO Spine North America.
Funding Information:
The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Daniel M. Sciubba: Consultant for Baxter, DePuy-Synthes, Globus Medical, K2M, Medtronic, NuVasive, Stryker. Unrelated grant support from Baxter Medical, North American Spine Society, and Stryker. The other authors have no conflicts of interest to disclose.
Publisher Copyright:
© The Author(s) 2020.
PY - 2021/4
Y1 - 2021/4
N2 - Study Design: Narrative review Objective: Decision making in surgery for adult spinal deformity (ASD) is complex due to the multifactorial etiology, numerous surgical options, and influence of multiple medical and psychosocial factors on patient outcomes. Predictive analytics provide computational tools to analyze large data sets and generate hypotheses regarding new data. In this review, we examine the use of predictive analytics to predict patient-reported outcomes (PROs) in ASD surgery. Methods: A search of PubMed, Web of Science, and Embase databases was performed to identify all potentially relevant studies up to February 1, 2020. Studies were included based on the use of predictive analytics to predict PROs in ASD. Results: Of 57 studies identified and reviewed, 7 studies were included. Multiple algorithms including supervised and unsupervised methods were used. Significant heterogeneity was observed with choice of PROs modeled including ODI, SRS22, and SF36, assessment of model accuracy, and with the model accuracy and area under the receiver operating curve values (ranging from 30% to 86% and 0.57 to 0.96, respectively). Models were built with data sets of patients ranging from 89 to 570 patients with a range of 22 to 267 variables. Conclusions: Predictive analytics makes accurate predictions regarding PROs regarding pain, disability, and work and social function; PROs regarding satisfaction, self-image, and psychologic aspects of ASD were predicted with the lowest accuracy. Our review demonstrates a relative paucity of studies on ASD with limited databases. Future studies should include larger and more diverse databases and provide external validation of preexisting models.
AB - Study Design: Narrative review Objective: Decision making in surgery for adult spinal deformity (ASD) is complex due to the multifactorial etiology, numerous surgical options, and influence of multiple medical and psychosocial factors on patient outcomes. Predictive analytics provide computational tools to analyze large data sets and generate hypotheses regarding new data. In this review, we examine the use of predictive analytics to predict patient-reported outcomes (PROs) in ASD surgery. Methods: A search of PubMed, Web of Science, and Embase databases was performed to identify all potentially relevant studies up to February 1, 2020. Studies were included based on the use of predictive analytics to predict PROs in ASD. Results: Of 57 studies identified and reviewed, 7 studies were included. Multiple algorithms including supervised and unsupervised methods were used. Significant heterogeneity was observed with choice of PROs modeled including ODI, SRS22, and SF36, assessment of model accuracy, and with the model accuracy and area under the receiver operating curve values (ranging from 30% to 86% and 0.57 to 0.96, respectively). Models were built with data sets of patients ranging from 89 to 570 patients with a range of 22 to 267 variables. Conclusions: Predictive analytics makes accurate predictions regarding PROs regarding pain, disability, and work and social function; PROs regarding satisfaction, self-image, and psychologic aspects of ASD were predicted with the lowest accuracy. Our review demonstrates a relative paucity of studies on ASD with limited databases. Future studies should include larger and more diverse databases and provide external validation of preexisting models.
KW - adult spinal deformity
KW - patient-reported outcomes
KW - predictive analytics
KW - review
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U2 - 10.1177/2192568220963060
DO - 10.1177/2192568220963060
M3 - Article
C2 - 33034220
AN - SCOPUS:85092311404
SN - 2192-5682
VL - 11
SP - 89S-95S
JO - Global Spine Journal
JF - Global Spine Journal
IS - 1_suppl
ER -