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, researchers worked to develop a way to more accurately predict recovery from lumbar spine surgery.
The results, published 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,” says first author Ziqi Xu, a PhD student in the lab of Chenyang Lu, a professor in the McKelvey School of Engineering at Washington University in St. Louis.
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 says. Prior work training machine-learning algorithms focused on just one aspect of surgery outcome “but ignored the inherent multidimensional nature of surgery recovery,” she adds.
Researchers have used mobile health data from Fitbit devices to monitor and measure recovery and compare activity levels over time. But the new research has shown that activity data, plus longitudinal assessment data, is more accurate in predicting how the patient will do after surgery, says Jacob Greenberg, an assistant professor of neurosurgery at the School of Medicine.
The current work offers a “proof of principle” showing that, with multimodal machine learning, doctors can see a more accurate “big picture” of the interrelated factors that affect recovery. Before beginning this work, the team first laid out the statistical methods and protocol to ensure they were feeding the artificial intelligence system the right balanced diet of data.
Previously, the team had published work in the journal Neurosurgery showing for the first time that patient-reported and objective wearable measurements improve predictions of early recovery compared to traditional patient assessments.
In addition to Greenberg and Xu, Madelynn Frumkin, a PhD student studying psychological and brain sciences in Thomas Rodebaugh’s laboratory, was a co-first author on that work. Wilson “Zack” Ray, a professor of neurosurgery at the School of Medicine, was co-senior author, along with Rodebaugh and Lu. Rodebaugh is now at the University of North Carolina at Chapel Hill.
In that research, they show that Fitbit data can be correlated with multiple surveys that assess a person’s social and emotional state. They collected that data via “ecological momentary assessments” (EMAs) that employ smartphones to give patients frequent prompts to assess mood, pain levels, and behavior multiple times throughout day.
“We combine wearables, EMA, and clinical records to capture a broad range of information about the patients, from physical activities to subjective reports of pain and mental health, and to clinical characteristics,” Lu says.
Greenberg adds that state-of-the-art statistical tools that Rodebaugh and Frumkin have helped advance, such as “Dynamic Structural Equation Modeling,” were key in analyzing the complex, longitudinal EMA data.
For the most recent study, they took all those factors and developed a new machine-learning technique of “Multi-Modal Multi-Task Learning” to effectively combine these different types of data to predict multiple recovery outcomes.
In this approach, the AI learns to weigh the relatedness among the outcomes while capturing their differences from the multimodal data, Lu adds.
This method takes shared information on interrelated tasks of predicting different outcomes and then leverages the shared information to help the model understand how to make an accurate prediction, according to Xu.
It all comes together in the final package, producing a predicted change for each patient’s post-operative pain interference and physical function score.
Greenberg says the study is ongoing as the researchers continue to fine-tune their models so they can take more detailed assessments, predict outcomes and, most notably, “understand what types of factors can potentially be modified to improve longer-term outcomes.”
Funding for the study came from AO Spine North America, the Cervical Spine Research Society, the Scoliosis Research Society, the Foundation for Barnes-Jewish Hospital, Washington University/BJC Healthcare Big Ideas Competition, the Fullgraf Foundation, and the National Institute of Mental Health.
