Artificial neural networks outperform linear regression in estimating 9-month patient-reported outcomes after upper extremity fractures with increasing number of variables

Niels Brinkman; Romil Shah; Job Doornberg; David Ring; Stephen Gwilym; Prakash Jayakumar

doi:10.1097/OI9.0000000000000284

Artificial neural networks outperform linear regression in estimating 9-month patient-reported outcomes after upper extremity fractures with increasing number of variables

OTA Int. 2023 Dec 22;6(5 Suppl):e284. doi: 10.1097/OI9.0000000000000284. eCollection 2023 Dec.

Authors

Niels Brinkman¹, Romil Shah¹, Job Doornberg^{2

3}, David Ring¹, Stephen Gwilym⁴, Prakash Jayakumar¹

Affiliations

¹ Department of Surgery and Perioperative Care, The University of Texas at Austin, Dell Medical School, Austin, TX.
² Department of Orthopaedic & Trauma Surgery, University Medical Center, Groningen, the Netherlands.
³ Department of Orthopaedic Trauma, Flinders University, Adelaide, Australia.
⁴ The Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, The University of Oxford, Oxford, United Kingdom.

Abstract

Objective: To compare performance between linear regression (LR) and artificial neural network (ANN) models in estimating 9-month patient-reported outcomes (PROs) after upper extremity fractures using various subsets of early mental, social, and physical health variables.

Methods: We studied 734 patients with isolated shoulder, elbow, or wrist fracture who completed demographics, mental and social health measures, and PROs at baseline, 2-4 weeks, and 6-9 months postinjury. PROs included 3 measures of capability (QuickDASH, PROMIS-UE-PF, PROMIS-PI) and one of pain intensity. We developed ANN and LR models with various selections of variables (20, 23, 29, 34, and 54) to estimate 9-month PROs using a training subset (70%) and internally validated them using another subset (15%). We assessed the accuracy of the estimated value being within one MCID of the actual 9-month PRO value in a test subset (15%).

Results: ANNs outperformed LR in estimating 9-month outcomes in all models except the 20-variable model for capability measures and 20-variable and 23-variable models for pain intensity. The accuracy of ANN versus LR in the primary model (29-variable) was 83% versus 73% (Quick-DASH), 68% versus 65% (PROMIS-UE-PF), 66% versus 62% (PROMIS-PI), and 78% versus 65% (pain intensity). Mental and social health factors contributed most to the estimations.

Conclusion: ANNs outperform LR in estimating 9-month PROs, particularly with a larger number of variables. Given the otherwise relatively comparable performance, aspects such as practicality of collecting greater sets of variables, nonparametric distribution, and presence of nonlinear correlations should be considered when deciding between these statistical methods.

Keywords: artificial neural network; capability; comparison; estimating; increasing number of variables; linear regression; machine learning; model performance; pain intensity; pain interference; patient-reported outcomes; prediction modeling; psychosocial.