A crucial risk factor for development of NTG in patients with shunt-treated NPH is the duration of optic nerve exposure to the lowering of ICP. Patients with NPH who are candidates for CSF shunting should be informed of the risk of incurring glaucoma. Longitudinal studies could provide estimates of tolerated times for a given ICP decrease.

Background Shunt-dependent hydrocephalus significantly complicates subarachnoid hemorrhage (SAH), and reliable prognosis methods have been sought in recent years to reduce morbidity and costs associated with delayed treatment or neglected onset. Machine learning (ML) defines modern data analysis techniques allowing accurate subject-based risk stratifications. We aimed at developing and testing different ML models to predict shunt-dependent hydrocephalus after aneurysmal SAH. Methods We consulted electronic records of patients with aneurysmal SAH treated at our institution between January 2013 and March 2019. We selected variables for the models according to the results of the previous works on this topic. We trained and tested four ML algorithms on three datasets: one containing binary variables, one considering variables associated with shunt-dependency after an explorative analysis, and one including all variables. For each model, we calculated AUROC, specificity, sensitivity, accuracy, PPV, and also, on the validation set, the NPV and the Matthews correlation coefficient (ϕ). Results Three hundred eighty-six patients were included. Fifty patients (12.9%) developed shunt-dependency after a mean follow-up of 19.7 (± 12.6) months. Complete information was retrieved for 32 variables, used to train the models. The best models were selected based on the performances on the validation set and were achieved with a distributed random forest model considering 21 variables, with a ϕ = 0.59, AUC = 0.88; sensitivity and specificity of 0.73 (C.I.: 0.39–0.94) and 0.92 (C.I.: 0.84–0.97), respectively; PPV = 0.59 (0.38–0.77); and NPV = 0.96 (0.90–0.98). Accuracy was 0.90 (0.82–0.95). Conclusions Machine learning prognostic models allow accurate predictions with a large number of variables and a more subject-oriented prognosis. We identified a single best distributed random forest model, with an excellent prognostic capacity (ϕ = 0.58), which could be especially helpful in identifying low-risk patients for shunt-dependency.

Purpose The aim of our retrospective study is to analyze how spinopelvic dissociations (SPDs) were treated in a single center trying to better understand how to improve surgical and non-surgical options. Methods Twenty patients of a single center surgically treated for SPDs between 2013 and 2021 were retrospectively included in this study. Three surgical techniques have been used: modified triangular stabilization, triangular stabilization and double iliac screws stabilization. Follow-up was assessed for up to 11.6 ± 9.9 months through ODI, MRS, NRS, IIEF or FSFI, a CT scan and whole spine X-ray examination. Results Twenty patients were admitted to our ER for traumatic spinopelvic dissociation. Surgical treatment for spinopelvic dissociation has been performed on average 11.5 ± 6.7 days after the trauma event. Eighteen fractures were C3 type and two C2 types. Neurological examination showed nerve root injury (N2) in 5 patients, incomplete spinal cord injury (N3) in 4 patients and cauda equina syndrome in two patients (N4). In case of neurologic deficits, routinary nerve decompression was performed. Three different surgical techniques have been used: 8 triangular fixations (Group 1), 6 modified triangular stabilization (Group 2) and 6 double iliac screws triangular fixation (Group 3). Conclusion In patients with post-traumatic neurological deficit, decompression surgery and fracture reduction seem to be associated with clinical improvement; however, sexual disorders seem to be less responsive to the treatment. Some open stabilization techniques, such as the double iliac screw, could help in restoring the sagittal balance in case of severe deformities.

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