Intradiscal vacuum phenomenon matches lumbar spine degeneration patterns in an ageing population.

PURPOSE: Intra-Discal Vacuum phenomenon (IDVP) is well-recognised, yet poorly visualised and poorly understood radiological finding in disc degeneration, particularly with regard to its role in spinal alignment. CT analysis of the lumbar spine in an aging population aims to identify patterns associated with IDVP including lumbopelvic morphology and associated spinal diagnoses. METHODS: An analysis was performed of an over-60s population sample of 2020 unrelated abdominal CT scans, without acute spinal presentations. Spinal analysis included sagittal lumbopelvic reconstructions to assess for IDVP and pelvic incidence (PI). Subjects with degenerative pathologies, including previous vertebral fractures, auto-fusion, transitional vertebrae, and listhesis, were also selected out and analysed separately. RESULTS: The prevalence of lumbar spine IDVP was 50.3% (955/1898) and increased with age (125 exclusions). This increased in severity towards the lumbosacral junction (L1L2 8.3%, L2L3 10.9%, L3L4 11.5%, L4L5 23.9%, and L5S1 46.3%). A lower PI yielded a higher incidence of IDVP, particularly at L5S1 (p < 0.01). A total of 292 patients had IDVP with additional degenerative pathologies, which were more likely to occur at the level of isthmic spondylolisthesis, adjacent to a previous fracture or suprajacent to a lumbosacral transitional vertebra (p < 0.05). CONCLUSIONS: This study identified the prevalence and severity of IDVP in an aging population. Sagittal patterns that influence the pattern of IVDP, such as pelvic incidence and degenerative pathologies, provide novel insights into the function of aging spines.

Natural History of Intradiscal Vacuum Phenomenon and its Role in Advanced Disc Degeneration.

STUDY DESIGN: Observational serial computed tomography (CT) analysis of the lumbar spine in a normal-aging population. OBJECTIVE: To assess the natural history of the intradiscal vacuum phenomenon (IDVP) and its role in disc degeneration. BACKGROUND: The natural history of disc degeneration is well described but our understanding of the end stage of pathogenesis remains incomplete. Magnetic resonance imaging loses accuracy with advanced degeneration, becoming hyporesonant and indistinct. Cadaveric specimens display adaptive changes in the disc with loss of the hydrostatic capacity of the nucleus, increased intradiscal clefts, and endplate impermeability. IDVP is associated with advanced disc degeneration and CT is the optimal modality to visualize this, yet these insights remain unreported. PATIENTS AND METHODS: Patients only included historic CT abdomen scans of those over 60 years of age without acute or relevant spinal pathology, with a diagnosis of at least one level with IDVP on the original CT scan, and all of whom had a similar scan >7 years later. A history of clinically significant back pain was also recorded. RESULTS: CT scans included 360 levels in 29 males and 31 females (mean: 68.9 y), displaying 82 levels of IDVP, with a second scan included after a mean of 10.3 years. Most levels displayed the same level of severity (persisted, 45) compared with where some progressed (26), regressed (8), and fused (3; P < 0.01). There was also an increased incidence, 37/60 (62%) of developing IDVP at another level. Disc heights were reduced with increased severity of IDVP. A record of back pain was evident in 31/60 patients, which was not significantly worse in those with worsening severity or additional level involvement over the study period. CONCLUSION: As disc degeneration advances, the associated IDVP persists in most cases, displaying a plateauing of severity over long periods, but rarely with progression to autofusion.

A Quantum-Enhanced Precision Medicine Application to Support Data-Driven Clinical Decisions for the Personalized Treatment of Advanced Knee Osteoarthritis: The Development and Preliminary Validation of precisionKNEE_QNN.

Background Quantum computing and quantum machine learning (QML) are promising experimental technologies that can improve precision medicine applications by reducing the computational complexity of algorithms driven by big, unstructured, real-world data. The clinical problem of knee osteoarthritis is that, although some novel therapies are safe and effective, the response is variable, and defining the characteristics of an individual who will respond remains a challenge. In this study, we tested a quantum neural network (QNN) application to support precision data-driven clinical decisions to select personalized treatments for advanced knee osteoarthritis. Methodology After obtaining patients' consent and Research Ethics Committee approval, we collected the clinicodemographic data before and after the treatment from 170 patients eligible for knee arthroplasty (Kellgren-Lawrence grade ≥3, Oxford Knee Score (OKS) ≤27, age ≥64 years, and idiopathic aetiology of arthritis) treated over a two-year period with a single injection of microfragmented fat. Gender classes were balanced (76 males and 94 females) to mitigate gender bias. A patient with an improvement ≥7 OKS was considered a responder. We trained our QNN classifier on a randomly selected training subset of 113 patients to classify responders from non-responders (73 responders and 40 non-responders) in pain and function at one year. Outliers were hidden from the training dataset but not from the validation set. Results We tested our QNN classifier on a randomly selected test subset of 57 patients (34 responders, 23 non-responders) including outliers. The no information rate was 0.59. Our application correctly classified 28 responders out of 34 and 6 non-responders out of 23 (sensitivity = 0.82, specificity = 0.26, F1 Statistic = 0.71). The positive and negative likelihood ratios were 1.11 and 0.68, respectively. The diagnostic odds ratio was 2. Conclusions Preliminary results on a small validation dataset showed that QML applied to data-driven clinical decisions for the personalized treatment of advanced knee osteoarthritis is a promising technology to reduce computational complexity and improve prognostic performance. Our results need further research validation with larger, real-world unstructured datasets, as well as clinical validation with an artificial intelligence clinical trial to test model efficacy, safety, clinical significance, and relevance at a public health level.