Julie L. Chan
Daniel J. Hoh
Degenerative spine disease and low back pain are among the most prevalent causes of disability in the United States [
1,
2]. One common surgical treatment to reduce the risk of neurologic decline and pain includes lumbar fusion [
3]. Outcome following lumbar fusion is variable and may be impacted by the presence of heterogeneous preoperative factors. Multiple comorbidity indices have been developed to assess preoperative risk and morbidity of patients undergoing surgical procedures. Specifically, frailty has garnered interest as an index which may reliably predict postoperative outcome and provide insight when considering surgical intervention associated with higher complications and/or increased costs.
Here, Guo et al. [
4] utilize a novel metric to evaluate surgical risk stratification by combining Fried phenotype for frailty and posterior paraspinal muscle degeneration of the lumbosacral spine. Patients were subsequently classified into four frailty and muscle phenotypes. Fried frailty phenotype was obtained from clinical evaluation, while posterior frailty index was calculated from preoperative lumbar magnetic resonance imaging (MRI) with automated segmentation workflow to quantify muscle volume and fat infiltration. The four phenotypes are described as: nonfrail/nonsevere, frail/nonsevere, nonfrail/severe, and frail/severe. This manuscript describes a retrospective review of 248 patients, >75 years old who underwent 1-level transforaminal lumbar interbody fusion for degenerative disease, where the primary outcomes included inpatient complications and length of stay (LOS) >16 days.
Overall, Guo et al. [
4] found there was a difference in baseline characteristics across phenotypes, specifically more older patients were the higher risk phenotype. LOS was increased in higher risk phenotypes with nearly 80% of frail/severe patients staying in the hospital for over 16 days. Additionally, there were more in-hospital complications and worse postoperative patient reported outcome measures in the frail/severe patients.
Guo et al. [
4] are to be commended for utilizing a novel metric of frailty and paraspinal muscle phenotype to understand risk and predict outcome for older patients undergoing spinal fusion surgery for degenerative disease. This study demonstrates there is still much to learn regarding how these phenotypes relate to postoperative outcome. Guo et al. [
4] employed sound acquisition and automated segmentation workflow with manual correction for accuracy. But there may still be other factors that are not entirely captured by MRI that play a role in outcome following spinal surgery. These factors may have led to varying findings including the longer LOS and minor complications in the nonfrail/severe phenotype.
Overall, this study is a significant contribution to the literature and builds on prior studies demonstrating the utility of frailty indices in predicting postoperative outcome. Previous studies have shown that frailty is independently associated with postoperative complications in both short- and long-segment fusion [
5,
6]. Even further, the Fried phenotype in lumbar fusion patients demonstrates broader predictive value compared to the 11-item modified Frailty Index regarding endpoints such as nonhome discharge, LOS, and complications [
7]. By combining the Fried phenotype with paraspinous musculature parameters, there is potential for further strengthening the predictive value of additional frailty parameters beyond single factor indices. In a prior review, various frailty assessment tools such as 5-item modified Frailty Index, adult spinal deformity frailty index, adult cervical deformity index, and psoas muscle index were shown to be useful preoperative tools when assessing patients for complex spinal surgery [
8]. Specifically, enhanced recovery after surgery improves function after 1- or 2-level spinal fusion [
9], and may be further optimized with additional frailty indices such as Fried phenotype and paraspinous muscle as described by Guo et al. [
4] in this current study.
Guo et al. [
4] provide strong evidence that combining multiple indices may better predict outcomes compared to a single frailty index that may only address one dimension of a patient’s preoperative risk. As prediction models incorporate multiple indices, the complexity is likely to increase, further supporting the need for artificial intelligence (AI)-driven assessment tools. Taken together, a look into the future of advanced spinal care suggests large language models may play an integral role in improving patient outcomes. Specifically, AI may seamlessly incorporate multiple factors including clinical assessment, patient reported outcome measures, and radiographic data from electronic health records into a single model. Ultimately, the development of broader indices will likely lead to improved algorithms to drive enhanced patient-physician shared decision-making with real-time prediction for optimal intervention.
NOTES
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Conflict of Interest
The authors have nothing to disclose.
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