Background: One of the most frequent complications of spinal surgery is accidental dural tears (ADTs). Minimal access surgical techniques (MAST) have been described as a promising approach to minimizing such complications. ADTs have been studied extensively in connection with open spinal surgery, but there is less literature on minimally invasive spinal surgery (MISS).Materials and Methods: We reviewed 187 patients who had undergone degenerative lumbar spinal surgery using minimally invasive spinal fusions techniques. We analyzed the influence of age, Body Mass Index (BMI), smoking, diabetes, and previous surgery on the rate of ADTs in MISS.Results: Twenty-two patients (11.764%) suffered from an ADT. We recommended bed rest for two and a half to 5 days, depending on the type of repair required and the amount of cerebrospinal fluid (CSF) leakage. We could not find any statistically significant correlation between ADTs and age (p = 0.34,), BMI (p = 0.92), smoking (p = 0.46), and diabetes (p = 0.71). ADTs were significantly more frequent in cases of previous surgery (p < 0.001). None of the patients developed a transcutaneous CSF leak or post-operative infection.Conclusions: The frequency of ADTs in MISS appears comparable to that encountered when using open surgical techniques. Additionally, MAST produces less dead space along the corridor to the spine. Such reduced dead space may not be enough for pseudomeningocele to occur, cerebrospinal fluid to accumulate, and fistula to form. MAST, therefore, provides a certain amount of protection.

Study design: Monocentric, prospective, observational study. Objective: The clinical relevance of bacterial colonization of intervertebral discs is controversial. This study aimed to determine a possible relationship between bacterial and viral colonization and low-grade infection of the discs. Methods: We investigated 447 disc samples from 392 patients. Microbiological culture was used to examine the samples for bacterial growth, polymerase chain reaction (PCR) was used for detection of herpes simplex virus types 1 and 2 (HSV-1, HSV-2) and Cytomegalovirus (CMV), and histopathological analysis was used to detect signs of inflammation. The results were compared between subgroups organized according to gender, age, location of the samples, surgical approach, preoperative C-reactive protein (CRP), preoperative and 6 months postoperative Oswestry Disability Index (ODI) and Neck Disability Index (NDI), and Modic changes (MC) of the corresponding endplates. Also, we assessed the occurrence of postoperative infections within 6 months. Results: Microbiological culture was positive in 38.78% of the analyzed intervertebral discs. Altogether, 180 bacteria were isolated. Coagulase-negative staphylococci (CONS) (23.41%) and Cutibacterium acnes (18.05%) were the most frequently detected microorganisms. None of HSV-1, HSV-2, or CMV were detected. Male patients (p = 0.00036) and cervical segments (p = 0.00001) showed higher rates of positive culture results. Ventral surgical approaches ( p < 0.001) and Type 2 MC (p = 0.0127) were significantly associated with a positive microbiological result ( p< 0.001). Neither pre- nor postoperative ODI and NDI are associated with positive culture results. In 4 (1.02%) patients, postoperative spondylodiscitis occurred. Conclusions: With 447 segments from 392 patients, we present one of the largest studies to date. While disc degeneration caused by HSV-1, HSV-2, and CMV seems unlikely, we found positive microbiological culture results in 38.78% of all discs. The role of local skin flora and sample contamination should be the focus of further investigations. Level of Evidence: III. Trial registration: The study was registered at ClinicalTrials.gov (ID: NCT04712487, https://www.clinicaltrials.gov/ct2/show/study/NCT04712487).

Chronic posthemorrhagic hydrocephalus often arises following spontaneous subarachnoid hemorrhage (SAH). Timely identification of patients predisposed to develop chronic shunt-dependent hydrocephalus may significantly enhance clinical outcomes. We performed an analysis of 510 SAH-patients treated at our institution between 2013 and 2018. Clinical and radiological variables, including age, sex, Hunt & Hess grade, Fisher-Score, external ventricular drainage placement, central nervous system infection, aneurysm characteristics, and treatment modalities, were evaluated. Supervised machine learning models, trained and compared using Python and scikit-learn, were employed to predict chronic shunt-dependent hydrocephalus. Model performance was rigorously assessed through repeated cross-validation. To facilitate transparency and collaboration, we publicly released the dataset and code on GitHub (https://github.com/RISCSoftware/shuntclf) and developed an interactive web application (https://huggingface.co/spaces/risc42/shuntclf). Among the evaluated machine learning models, logistic regression exhibited superior performance, with an AUC-ROC of 0.819 and an AUC-PR of 0.482, along with the highest F1 score of 0.473. Although the balanced accuracy scores of the models were generally proximate, ranging from 0.735 to 0.764, logistic regression consistently outperform others in key metrics such as AUC-ROC and AUC-PR. Conversely, female gender and absence of aneurysm within the anterior communicating artery were associated with reduced shunt requirement likelihood. Machine learning models, including logistic regression, demonstrate strong predictive capability for early chronic shunt-dependent hydrocephalus following spontaneous SAH, which may potentially contribute to more timely shunt placement interventions. This predictive capability is supported by our web interface, which simplifies the application of these models, aiding clinicians in efficiently determining the need for shunt placement.

Background: The integration of artificial intelligence (AI), particularly large language models (LLMs) like ChatGPT-4, is transforming healthcare. ChatGPT’s potential to assist in decision-making for complex cases, such as spinal metastasis treatment, is promising but widely untested. Especially in cancer patients who develop spinal metastases, precise and personalized treatment is essential. This study examines ChatGPT-4’s performance in treatment planning for spinal metastasis cases compared to experienced spine surgeons. Materials and Methods: Five spine metastasis cases were randomly selected from recent literature. Consequently, five spine surgeons and ChatGPT-4 were tasked with providing treatment recommendations for each case in a standardized manner. Responses were analyzed for frequency distribution, agreement, and subjective rater opinions. Results: ChatGPT’s treatment recommendations aligned with the majority of human raters in 73% of treatment choices, with moderate to substantial agreement on systemic therapy, pain management, and supportive care. However, ChatGPT’s recommendations tended towards generalized statements, with raters noting its generalized answers. Agreement among raters improved in sensitivity analyses excluding ChatGPT, particularly for controversial areas like surgical intervention and palliative care. Conclusions: ChatGPT shows potential in aligning with experienced surgeons on certain treatment aspects of spinal metastasis. However, its generalized approach highlights limitations, suggesting that training with specific clinical guidelines could potentially enhance its utility in complex case management. Further studies are necessary to refine AI applications in personalized healthcare decision-making.

Over the last few decades, the number of lumbar interbody fusion surgeries performed has been constantly increasing, with transforaminal lumbar interbody fusion (TLIF) being one of the most common surgical techniques. Due to easy accessibility, patients frequently use YouTube to obtain information on health-related issues. Consequently, online video platforms may be a valuable tool for patient education. The aim of this study was to assess the quality, reliability, and comprehensiveness of online videos on TLIF. We screened 180 videos on YouTube, yielding a total of 30 videos that met the inclusion criteria. These videos were evaluated using Global Quality Scale, DISCERN reliability tool, and JAMA Benchmark Score, and assessed in regard to their comprehensiveness and coverage of relevant aspects. At the time of rating, the videos had between 9188 and 1,530,408 views and between 0 and 3344 likes. The median rater assessment for all videos was “moderate quality”. GQS and subjective grades showed a moderate to strong statistically significant association with views and likes. Considering this association of GQS and subjective grade with views and likes, these criteria could be used by laypersons to identify good-quality content. Nevertheless, there is an urgent need for peer-reviewed content that covers all of the relevant aspects.

Background: Cerebral vasospasm is a frequent and severe complication after aneurysmal subarachnoid hemorrhage (aSAH), often causing delayed cerebral ischemia (DCI) and poor outcomes. Despite progress in neurocritical care, early vasospasm prediction after aSAH remains challenging due to its multifactorial nature but is essential for timely intervention. Methods: We retrospectively analyzed 503 consecutive patients with spontaneous subarachnoid hemorrhage (SAH) treated between 2013 and 2018. Of these, 345 with angiographically confirmed aSAH were included in the primary analysis, and 158 SAH cases in a sensitivity analysis. We extracted demographic, clinical, and imaging parameters including age, sex, Hunt and Hess grade, Fisher scale, aneurysm and treatment features, external ventricular drainage (EVD), and central nervous system (CNS) infection. Seven supervised machine learning (ML) models, including logistic regression and gradient-boosted trees, were trained using nested cross-validation and evaluated by AUC-ROC, AUC-PR, accuracy, precision, sensitivity, specificity, and F1 score. Results: Over half of aSAH patients developed moderate to severe vasospasm. Independent predictors included younger age, higher Hunt and Hess and Fisher grades, and EVD placement (all p < 0.001). Logistic regression achieved the best discrimination (AUC-ROC 0.723), while tree-based models reached higher sensitivity (0.867) at the expense of specificity. Aneurysmal etiology further increased vasospasm risk (OR 4.72). Conclusions: Routinely available clinical and imaging parameters enable reliable ML-based vasospasm prediction after aSAH. Logistic regression provided the best balance between accuracy and interpretability, while tree-based models optimized sensitivity. This web-based, interpretable ML tool—one of the first using routine clinical data—may support the bedside prediction of vasospasm and requires prospective validation.