Minimally Invasive Transforaminal Interbody Fusion Versus Microdiscectomy Without Fusion for Recurrent Lumbar Disk Herniation: A Prospective Comparative Study.

OBJECTIVE: The objective of this study was to compare the clinical outcome of minimally invasive transforaminal lumbar interbody fusion (MIS TLIF) versus standard revision diskectomy for recurrent lumbar disk herniation (RLDH). BACKGROUND: RLDH is the most common cause of redo surgery after a microdiscectomy. Commonly, in patients without evidence of spinal instability, many surgeons would simply redo microdiscectomy, while others proceed to a redo microdiscectomy with arthrodesis. According to the literature, there is no evidence of what the best management of an RLDH would be. METHODS: This study involved 90 patients who underwent lumbar microdiscectomy in the past and were now experiencing a new lumbar disk herniation for the first time. The patients were divided into two groups, each with 45 patients: group A received standard revision microdiscectomy, whereas group B received revision microdiscectomy with MIS TLIF.The Japanese Orthopaedic Association score, operating time, blood loss, duration of hospital stay, costs, and complications were all prospectively recorded in a database and examined. Back and leg discomfort were measured using the visual analog scale. RESULTS: The mean total postoperative Japanese Orthopaedic Association score across the groups exhibited no statistically significant difference, nor did the preoperative clinical and epidemiological data. Although postoperative leg pain was comparable in both groups, postoperative lower back pain in group A was much worse than that in group B. Additional revision surgery was necessary for six individuals in group A. Group A had higher rates of dural rupture and postoperative neurological impairment. Group A experienced much less intraoperative blood loss, longer operation times, and postoperative hospital stays. CONCLUSION: In patients with RLDH, revision microdiscectomy is effective. In comparison with conventional microdiscectomy, MIS TLIF reduces intraoperative risk of dural rupture or neural injury, postoperative incidence of mechanical instability or recurrence, and postoperative lower back pain. STUDY DESIGN: Prospective, randomized, multicenter, comparative study.

Machine learning model prediction of 6-month functional outcome in elderly patients with intracerebral hemorrhage.

Spontaneous intracerebral hemorrhage (ICH) has an increasing incidence and a worse outcome in elderly patients. The ability to predict the functional outcome in these patients can be helpful in supporting treatment decisions and establishing prognostic expectations. We evaluated the performance of a machine learning (ML) model to predict the 6-month functional status in elderly patients with ICH leveraging the predictive value of the clinical characteristics at hospital admission. Data were extracted by a retrospective multicentric database of patients ≥ 70 years of age consecutively admitted for the management of spontaneous ICH between January 1, 2014 and December 31, 2019. Relevant demographic, clinical, and radiological variables were selected by a feature selection algorithm (Boruta) and used to build a ML model. Outcome was determined according to the Glasgow Outcome Scale (GOS) at 6 months from ICH: dead (GOS 1), poor outcome (GOS 2-3: vegetative status/severe disability), and good outcome (GOS 4-5: moderate disability/good recovery). Ten features were selected by Boruta with the following relative importance order in the ML model: Glasgow Coma Scale, Charlson Comorbidity Index, ICH score, ICH volume, pupillary status, brainstem location, age, anticoagulant/antiplatelet agents, intraventricular hemorrhage, and cerebellar location. Random forest prediction model, evaluated on the hold-out test set, achieved an AUC of 0.96 (0.94-0.98), 0.89 (0.86-0.93), and 0.93 (0.90-0.95) for dead, poor, and good outcome classes, respectively, demonstrating high discriminative ability. A random forest classifier was successfully trained and internally validated to stratify elderly patients with spontaneous ICH into prognostic subclasses. The predictive value is enhanced by the ability of ML model to identify synergy among variables.