Peri-Surgical Inflammatory Profile Associated with Mini-Invasive or Standard Open Lumbar Interbody Fusion Approaches

BACKGROUND

Different surgical approaches are available for lumbar interbody fusion (LIF) to treat disc degeneration. However, a quantification of their invasiveness is lacking, and the definition of minimally invasive surgery (MIS) has not been biochemically detailed. We aimed at characterizing the inflammatory, hematological, and clinical peri-surgical responses to different LIF techniques.

METHODS

68 healthy subjects affected by single-level discopathy (L3 to S1) were addressed to MIS, anterior (ALIF, n = 21) or lateral (LLIF, n = 23), and conventional approaches, transforaminal (TLIF, n = 24), based on the preoperative clinical assessment. Venous blood samples were taken 24 h before the surgery and 24 and 72 h after surgery to assess a wide panel of inflammatory and hematological markers.

RESULTS

martial (serum iron and transferrin) and pro-angiogenic profiles (MMP-2, TWEAK) were improved in ALIF and LLIF compared to TLIF, while the acute phase response (C-reactive protein, sCD163) was enhanced in LLIF.

CONCLUSIONS

MIS procedures (ALIF and LLIF) associated with a reduced incidence of post-operative anemic status, faster recovery, and enhanced pro-angiogenic stimuli compared with TLIF. LLIF associated with an earlier activation of innate immune mechanisms than ALIF and TLIF. The trend of the inflammation markers confirms that the theoretically defined mini-invasive procedures behave as such.

Assessment and prediction of spine surgery invasiveness with machine learning techniques

BACKGROUND

The interest in Minimally Invasive Surgery (MIS) techniques has greatly increased in the recent years due to their significant advantages, both in terms of outcome improvement and cost reduction. Also in spine surgery, MIS is now applicable to several conditions and, above all, in low back pain (LBP) treatment. However, reliable and objective measures of invasiveness, necessary to compare different procedures, are still lacking.

METHODS

In this article we study the application of Machine Learning (ML) techniques to define an invasiveness score for LBP procedures based on biological markers and inflammatory profiles. In so doing, we can assess the invasiveness of surgical procedures. We also propose a predictive model for treatment planning based on the evaluation of invasiveness of surgical alternatives for specific patients, using their pre-surgery biomarkers. The data used in study was characterized by low sample size and high-dimensionality, thus we adopted a combination of feature selection, careful selection of ML models and conservative model selection choices in order to address these concerns. We also performed an external validation based on a statistically significantly different datasets in order to confirm the relevance of the findings.

RESULTS

We report the results of an experimental study on real-world data, for which we obtained promising results for both considered applications: we report an AUC of 0.87 for the task of invasiveness score definition, and an AUC of 0.76 for the invasiveness prediction task. The results obtained on the external validation were in agreement with the obtained results. Further, in both cases the performances were considered as excellent by the involved clinicians and the selected predictive features were biologically relevant and associated with invasiveness and biological impact in the relevant literature.

CONCLUSION

Our results show that ML techniques could be effectively employed not only for diagnosis or prognosis, but also for treatment planning, a task of fundamental importance toward personalized and value-based healthcare. These results also show that ML approaches could be effectively used even in scenarios (e.g. pilot studies) where only small samples are available.

Numerical simulation of lateral and transforaminal lumbar interbody fusion, two minimally invasive surgical approaches

The present study aims to compare spinal stability after two different minimally invasive techniques, the lateral lumbar interbody fusion (LLIF) and the transforaminal lumbar interbody fusion (TLIF) approaches. Two nonlinear three-dimensional finite element (FE) models of the L4-L5 functional spinal unit (FSU) were subjected to the loads that usually act on the lumbar spine. Findings show that the LLIF approach yields better results for torsion load case, due to the larger surface area of the implant. For extension, flexion and lateral bending loads, the TLIF approach presents smaller displacements probably due to the anterior placement of the cage and to the smaller damaged area of the annulus fibrosus.

Minimally invasive transforaminal and anterior lumbar interbody fusion surgery at level L5-S1

This paper presents a finite element analysis to investigate the biomechanical changes caused by transforaminal (TLIF) and anterior lumbar interbody fusion (ALIF) at the L5-S1 level, applying two different implants: T_PAL (TLIF) and SynFix (ALIF). The main objective is to determine which one is more stable for patients. Numerical simulations of segmental motion show that, in the early postoperative phase, displacements and rotation angles obtained in ALIF are greater than the corresponding ones obtained in TLIF, as well as the principal stress values on the ligaments. So, TLIF performed with T_PAL is more stable than ALIF, especially during the recovery phase.