Step Back in Order to Go Forward: Willful Enlargement and Sandwich Closure Technique for Spinal Dura Repair after Several Unsuccessful Closure Attempts.

A CSF leak is a common complication in spine surgery which is usually closed effectivly by suture and/or epidural patches. There is currently no algorithm to treat a recurrent CSF leak that fails to be closed initially. We describe the case of a recurrent cerebrospinal fluid leak that we have successfully treated using an inlay-onlay dural repair technique.

Deep learning algorithm in detecting intracranial hemorrhages on emergency computed tomographies.

BACKGROUND: Highly accurate detection of intracranial hemorrhages (ICH) on head computed tomography (HCT) scans can prove challenging at high-volume centers. This study aimed to determine the number of additional ICHs detected by an artificial intelligence (AI) algorithm and to evaluate reasons for erroneous results at a level I trauma center with teleradiology services. METHODS: In a retrospective multi-center cohort study, consecutive emergency non-contrast HCT scans were analyzed by a commercially available ICH detection software (AIDOC, Tel Aviv, Israel). Discrepancies between AI analysis and initial radiology report (RR) were reviewed by a blinded neuroradiologist to determine the number of additional ICHs detected and evaluate reasons leading to errors. RESULTS: 4946 HCT (05/2020-09/2020) from 18 hospitals were included in the analysis. 205 reports (4.1%) were classified as hemorrhages by both radiology report and AI. Out of a total of 162 (3.3%) discrepant reports, 62 were confirmed as hemorrhages by the reference neuroradiologist. 33 ICHs were identified exclusively via RRs. The AI algorithm detected an additional 29 instances of ICH, missed 12.4% of ICH and overcalled 1.9%; RRs missed 10.9% of ICHs and overcalled 0.2%. Many of the ICHs missed by the AI algorithm were located in the subarachnoid space (42.4%) and under the calvaria (48.5%). 85% of ICHs missed by RRs occurred outside of regular working-hours. Calcifications (39.3%), beam-hardening artifacts (18%), tumors (15.7%), and blood vessels (7.9%) were the most common reasons for AI overcalls. ICH size, image quality, and primary examiner experience were not found to be significantly associated with likelihood of incorrect AI results. CONCLUSION: Complementing human expertise with AI resulted in a 12.2% increase in ICH detection. The AI algorithm overcalled 1.9% HCT. TRIAL REGISTRATION: German Clinical Trials Register (DRKS-ID: DRKS00023593).

Unique Indications for Covered Stent Grafts in Neuroradiology Departments of Level 1 Trauma Centers.

OBJECTIVE: To describe unique indications for covered stent grafts in trauma-associated cerebrovascular injuries. PATIENTS: Between 2006 and 2018, five patients with cerebrovascular injuries were treated with a covered stent graft. We present a retrospective analysis of technique and outcomes. RESULTS: In all cases stent deployment was successful. Endoleaks occurred in two cases requiring additional transvenous embolization of a carotid cavernous fistula (CCF) in one patient. Two cases of in-stent thrombosis were observed during intervention and 2 days postintervention in a patient with a long-segment dissection of the internal carotid artery (ICA) and another patient with a contained ICA rupture, both of which could not be prepared with dual antiplatelet therapy. Intravenous heparin and intra-arterial tirofiban dissolved in-stent thrombosis efficiently. One CCF and an iatrogenic vertebral artery injury were covered adequately with GraftMaster stent grafts. CONCLUSION: Patient selection with regard to individual anatomy and the site of vascular lesions is essential for an uncomplicated deployment of covered stent grafts and the success of therapy. Management of dual antiplatelet therapy, anticoagulation, and an escalation of medication in cases of in-stent thrombosis require expertise, a strict therapeutic regime, and an evaluation of individual risks in polytraumatized patients.