Elder, Benjamin D

Assessing balance is crucial in neurological rehabilitation, yet while wearable sensors enable real-world monitoring, identifying reliable digital biomarkers remains challenging. This study utilized a high-fidelity instrumented walkway to determine which gait parameters best predict balance impairment, providing robust targets for future wearable applications. We analyzed 49 steady-state gait metrics from 140 individuals with diverse neurological conditions. Using statistical analysis and machine learning, we evaluated these parameters against objective force plate sway scores and clinical fall-history labels. Group analysis identified 16 parameters significantly distinguishing fallers from non-fallers, and a neural network classified fallers with an area under the curve of 0.75. Across all analytical approaches, overall gait variability, e.g., Stride Width S.D. and the Gait Variability Index, emerged as a universal predictor of balance impairment and fall risk. Furthermore, while traditional linear models emphasized spatial postural control, machine learning classification uniquely identified inter-limb asymmetry as a premier driver of fall prediction. These findings indicate that instrumented gait analysis effectively identifies digital biomarkers for balance deficits. Isolating these specific metrics provides a clear blueprint for meaningful metrics required for continuous objective monitoring and future development of personalized, adaptive rehabilitation strategies.

BACKGROUND AND OBJECTIVES: Anterior lumbar interbody fusion (ALIF) is a powerful procedure for sagittal correction and indirect decompression in the lumbar spine, though aggressive correction can cause stretch neuropraxia and implant subsidence. The aim of this study was to identify radiographic and clinical predictors of segmental correction, as well as to evaluate the degree to which segmental correction on supine intraoperative radiographs translates to postoperative upright alignment changes. METHODS: We identified patients treated with 1- or 2-level ALIF at a tertiary care center and extracted data on demographics, spinopelvic parameters, and surgical details. Patients treated with isolated ALIF and ALIF with supplementary posterior instrumentation were compared on pre- and postoperative intradiscal lordosis (IDL) and change in IDL. Multivariable linear regression was performed to identify independent predictors of postoperative IDL. RESULTS: We included 204 patients (median age 57 years, 55.4% male) treated at 258 levels, of whom 114 (55.9%) underwent supplementary posterior fixation and 150 (73.5%) underwent single-level implantation. Across all patients, postoperative IDL was significantly greater than preoperative segmental lordosis (25.4° vs 15.3°; P < .001). Standalone ALIF achieved less ΔIDL than circumferential fusion for L5/S1 procedures (7.4° vs 11.6°; P < .001), though ΔIDL for L3/4 and L4/5 was similar between the standalone and circumferential groups. Multivariable analysis showed postoperative IDL to be predicted by greater preoperative IDL (β = 0.26° per degree; [0.15, 0.36]; P < .001), L5/S1 [vs L4/5] implantation (β = 3.97°; [2.72, 5.21]; P < .001), use of supplementary posterior fixation (β = 1.80°; [0.70, 2.89]; P < .001), and greater implant lordosis (β = 0.62° per degree; [0.44, 0.79]; P < .001). CONCLUSION: Postoperative IDL after 1- or 2-level ALIF is best predicted by placement of larger interbody sizes, L5/S1 implantation, greater baseline IDL, and use of adjunct posterior fixation. Patients with higher preoperative lordosis experienced smaller postoperative increases in IDL, though this was likely influenced by a treatment bias.