Long-term Radiological and Clinical Outcome after Lumbar Spinal Fusion Surgery in Patients with Degenerative Spondylolisthesis: A Prospective 6-Year Follow-up Study

Preoperative Hounsfield Units Predict Pedicle Screw Loosening in Osteoporotic Patients Following Short-Segment Lumbar Fusion

STUDY DESIGN

Retrospective Cohort.

OBJECTIVE

(1) To determine if vertebral HU values obtained from preoperative CT predict postoperative outcomes following one to three level lumbar fusion and (2) to investigate whether decreased BMD values determined by HU predict cage subsidence and screw loosening.

SUMMARY OF BACKGROUND DATA

In light of suboptimal screening for osteoporosis, vertebral computerized tomography (CT) Hounsfield Units (HU), have been investigated as a surrogate for bone mineral density (BMD).

MATERIALS AND METHODS

In this retrospective study, adult patients who underwent one to three level posterior lumbar decompression and fusion (PLDF) or transforaminal lumbar interbody and fusion (TLIF) for degenerative disease between the years 2017 and 2022 were eligible for inclusion. Demographics and surgical characteristics were collected. Outcomes assessed included 90-day readmissions, 90-day complications, revisions, patient-reported outcomes (PROMs), cage subsidence, and screw loosening. Osteoporosis was defined as HU of ≤110 on preoperative CT at L1.

RESULTS

We assessed 119 patients with a mean age of 59.1, of whom 80.7% were white and 64.7% were nonsmokers. The majority underwent PLDF (63%) compared with TLIF (37%), with an average of 1.63 levels fused. Osteoporosis was diagnosed in 37.8% of the cohort with a mean HU in the osteoporotic group of 88.4 compared with 169 in nonosteoporotic patients. Although older in age, osteoporotic individuals did not exhibit increased 90-day readmissions, complications, or revisions compared with nonosteoporotic patients. A significant increase in the incidence of screw loosening was noted in the osteoporotic group with no differences observed in subsidence rates. On multivariable linear regression osteoporosis was independently associated with less improvement in visual analog scale (VAS) scores for back pain.

CONCLUSIONS

Osteoporosis predicts screw loosening and increased back pain. Clinicians should be advised of the importance of preoperative BMD optimization as part of their surgical planning and the utility of vertebral CT HU as a tool for risk stratification.

LEVEL OF EVIDENCE

3.

Predicting postoperative outcomes in lumbar spinal fusion: development of a machine learning model

BACKGROUND CONTEXT

Degenerative lumbar spondylolisthesis (DLS) is a prevalent spinal disorder, often requiring surgical intervention. Accurately predicting surgical outcomes is crucial to guide clinical decision-making, but this is challenging due to the multifactorial nature of postoperative results. Traditional risk assessment tools have limitations, and with the advent of machine learning, there is potential to enhance the precision and comprehensiveness of preoperative evaluations.

PURPOSE

We aimed to develop a machine-learning algorithm to predict surgical outcomes in patients with degenerative lumbar spondylolisthesis (DLS) undergoing spinal fusion surgery, only using preoperative data.

STUDY DESIGN

Retrospective cross-sectional study.

PATIENT SAMPLE

Patients with DLS undergoing lumbar spinal fusion surgery.

OUTCOME MEASURES

This study aimed to predict the occurrence of lower back pain (LBP) ≥4 on the numeric analogue scale (NAS) 2 years after surgery. LBP was evaluated as the average pain patients experienced at rest in the week before questioning. NAS ranges from 0 to 10, 0 representing no pain and 10 representing the worst pain imaginable.

METHODS

We conducted a retrospective analysis of prospectively enrolled patients who underwent spinal fusion surgery for degenerative lumbar spondylolistheses at our institution in the United States between January 2016 and December 2018. The initial patient characteristics to be included in the training of the model were chosen by clinical expertise and through a literature review and included demographic characteristics, comorbidities, and radiologic features. The data was split into a training and validation datasets using a 60/40 split. Four different machine learning models were trained, including the modern XGBoost model, logistic regression, random-forest, and support vector machine (SVM). The models were evaluated according to the area under the curve (AUC) of the receiver operating characteristics (ROC) curve. An AUC of 0.7 to 0.8 was considered fair, 0.8 to 0.9 good, and ≥ 0.9 excellent. Additionally, a calibration plot and the Brier score were calculated for each model.

RESULTS

A total of 135 patients (66% female) were included. A total of 38 (28%) patients reported LBP ≥ 4 after 2 years, representing the positive class. The XGBoost model demonstrated the best performance in the validation set with an AUC of 0.81 (95% CI 0.67-0.95). The other machine learning models performed significantly worse: with an AUC of 0.52 (95% CI 0.37-0.68) for the SVM, 0.56 (95% CI 0.37-0.76) for the logistic regression and an AUC of 0.56 (95% CI 0.37-0.78) for the random forest. In the XGBoost model age, composition of the erector spinae, and severity of lumbar spinal stenosis as were identified as the most important features.

CONCLUSIONS

This study represents a novel approach to predicting surgical outcomes in spinal fusion patients. The XGBoost demonstrated a better performance compared with classical models and highlighted the potential contributions of age and paraspinal musculature atrophy as significant factors. These findings have important implications for enhancing patient care through the identification of high-risk individuals and modifiable risk factors. As the incorporation of machine learning algorithms into clinical decision-making continues to gain traction in research and clinical practice, our insights reinforce this trajectory by showcasing the potential of these techniques in forecasting surgical results.

Foraminoplasty with Partial Pediculectomy in Degenerative Spondylolisthesis Cases with Nerve Compression

BACKGROUND

Degenerative spondylolisthesis refers to a forward displacement of a vertebra relative to the underlying vertebra. Patients with radicular pain and/or neurogenic claudication are considered suitable candidates for surgical intervention. The aim of this study was to present clinical results of dynamic stabilization applied after pediculectomy and neural foramen enlargement in patients with degenerative spondylolisthesis and normal spinal sagittal balance.

MATERIALS AND METHODS

All patients who underwent pediculectomy and dynamic stabilization for lumbar degenerative spondylolisthesis were retrospectively identified. Diagnosis was made with detailed neurological and radiological imaging examinations for the anatomical location of the pain. Demographic data, visual analog scale scores, and quality-of-life scores were obtained.

RESULTS

Patients included 3 (33.3%) men and 6 (66.7%) women with a mean age of 61.3 ± 13.0 years (range, 46-80 years) at initial symptom onset. Grade 1 degenerative spondylolisthesis was present in 5 (55.6%) patients, and grade 2 was present in 4 (44.4%) patients. Pathology was detected in L4-5 in 4 patients (44.4%) and L5-S1 in 5 patients (55.6%). In all patients, at the 3-month follow-up, statistically significant decrease in visual analog scale and Oswestry Disability Index scores was observed. At the last follow-up, no implant-related complications requiring revision were observed.

CONCLUSIONS

In patients without any pathology in the sagittal balance, the optimal physiological treatment modality can be created by applying posterior dynamic stabilization together with pediculectomy to support the posterior tension band.

Contralateral bridge fixation of freehand minimally invasive pedicle screws combined with unilateral MIS-TLIF vs. open TLIF in the treatment of multi-segmental lumbar degenerative diseases: A five years retrospective study and finite element analysis

OBJECTIVE

To evaluate the efficacy, safety, feasibility and biomechanical stability of contralateral bridge fixation of freehand minimally invasive pedicle screws (Freehand MIPS) combined with unilateral minimally invasive surgery-transforaminal lumbar interbody fusion (MIS-TLIF) (smile-face surgery) and open TLIF for the treatment of multi-segmental lumbar degenerative diseases (LDDs).

METHODS

From January 2013 to January 2016, clinical data of multi-segmental (2- or 3-level) LDDs receiving smile-face surgery or open TLIF were retrospectively collected and analyzed. The back and leg pain VAS and ODI were used to assess clinical outcomes preoperatively and postoperatively. The MacNab criteria were used to evaluate the satisfaction of patient. The disc height (DH), lumbar lordosis (LL) and segmental lordosis angle (SLA) were measured before and after surgery. We used patient's CT data to establish the finite element model of smile-face surgery and open TLIF, and analyze biomechanical stability of two methods.

RESULTS

Smile-face surgery group showed shorter operation time, shorter incision, less blood loss, shorter hospital stay than open TLIF (P < 0.05). The back VAS in smile-face surgery group was significantly lower than that in open TLIF immediately and 3 months after surgery, and no significant difference was observed 1 year, 2 years and 5 years after surgery. There was no significant difference in the leg pain VAS and ODI between both groups after surgery. No significant difference was observed between two groups in the DH, LL and SLA. At 5-year follow-up, grade I or II fusion was achieved in 99.00% (100/101) segments of smile-face surgery group and 97.67% (84/86) segments of open TLIF group according to Bridwell system. The complication rate of open TLIF was higher than that of smile-face surgery (24.32% vs. 0%, P < 0.01). After verification, the established finite element model can accurately simulate the biological structure of lumbar spine and there was no significant difference in biomechanical stability between two methods.

CONCLUSIONS

Smile-face surgery has some advantages over open TLIF including smaller aggression, less blood loss, and lower cost, indicating that it is a good choice of treatment for multi-segmental LDDs. Both methods can achieve good biomechanical stability.