Researchers who had been using Fitbit data to help predict surgical outcomes have a new method to more accurately gauge how patients may recover from spine surgery.
Using machine-learning techniques developed at the AI for Health Institute at Washington University in St. Louis, Chenyang Lu, PhD, the Fullgraf Professor at the university’s McKelvey School of Engineering, collaborated with Jacob Greenberg, MD, an assistant professor of neurosurgery at the School of Medicine, to develop a way to more accurately predict recovery from lumbar spine surgery.
The results, published this month in the journal Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, show that their model outperforms previous models to predict spine surgery outcomes. This is important because in lower back surgery and many other types of orthopedic operations, outcomes vary widely depending on the patient’s structural disease but also on varying physical and mental health characteristics across patients.
Surgical recovery is influenced by both physical and mental health before the operation. Some people may have excessive worry in the face of pain that can make pain and recovery worse. Others may suffer from physiological problems that worsen pain. If physicians can get a heads-up on the various pitfalls a patient faces, they can better tailor treatment plans.
“By predicting the outcomes before the surgery, we can help establish some expectations and help with early interventions and identify high risk factors,” said Ziqi Xu, a PhD student in Lu’s lab and first author on the paper.
Previous work in predicting surgery outcomes typically used patient questionnaires given once or twice in clinics, capturing a static slice of time.
“It failed to capture the long-term dynamics of physical and psychological patterns of the patients,” Xu said. Prior work training machine-learning algorithms focused on just one aspect of surgery outcome “but ignored the inherent multidimensional nature of surgery recovery,” she added.