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Aymeric Amelot, Maximilien Riche, Samuel Latreille, Vincent Degos, Alexandre Carpentier, Bertrand Mathon, and Anne-Marie Korinek

OBJECTIVE

The authors sought to evaluate the roles of perioperative antibiotic prophylaxis in noninstrumented spine surgery (NISS), both in postoperative infections and the impact on the selection of resistant bacteria. To the authors’ knowledge, only one prospective study recommending preoperative intravenous (IV) antibiotics for prophylaxis has been published previously.

METHODS

Two successive prospective IV antibiotic prophylaxis protocols were used: from 2011 to 2013 (group A: no prophylactic antibiotic) and from 2014 to 2016 (group B: prophylactic cefazolin). Patient infection rates, infection risk factors, and bacteriological status were determined.

RESULTS

In total, 2250 patients (1031 in group A and 1219 in group B) were followed for at least 1 year. The authors identified 72 surgical site infections, 51 in group A (4.9%) and 21 in group B (1.7%) (p < 0.0001). A multiple logistic regression hazard model identified male sex (HR 2.028, 95% CI 1.173–3.509; p = 0.011), cervical laminectomy (HR 2.078, 95% CI 1.147–3.762; p = 0.016), and postoperative CSF leak (HR 43.782, 95% CI 10.9–189.9; p < 0.0001) as independent predictive risk factors of infection. In addition, preoperative antibiotic prophylaxis was the only independent favorable factor (HR 0.283, 95% CI 0.164–0.488; p < 0.0001) that significantly reduced infections for NISS. Of 97 bacterial infections, cefazolin-resistant bacteria were identified in 26 (26.8%), with significantly more in group B (40%) than in group A (20.9%) (p = 0.02).

CONCLUSIONS

A single dose of preoperative cefazolin is effective and mandatory in preventing surgical site infections in NISS. Single-dose antibiotic prophylaxis has an immediate impact on cutaneous flora by increasing cefazolin-resistant bacteria.

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Mark W. Youngblood, Daniel Duran, Julio D. Montejo, Chang Li, Sacit Bulent Omay, Koray Özduman, Amar H. Sheth, Amy Y. Zhao, Evgeniya Tyrtova, Danielle F. Miyagishima, Elena I. Fomchenko, Christopher S. Hong, Victoria E. Clark, Maximilien Riche, Matthieu Peyre, Julien Boetto, Sadaf Sohrabi, Sarah Koljaka, Jacob F. Baranoski, James Knight, Hongda Zhu, M. Necmettin Pamir, Timuçin Avşar, Türker Kilic, Johannes Schramm, Marco Timmer, Roland Goldbrunner, Ye Gong, Yaşar Bayri, Nduka Amankulor, Ronald L. Hamilton, Kaya Bilguvar, Irina Tikhonova, Patrick R. Tomak, Anita Huttner, Matthias Simon, Boris Krischek, Michel Kalamarides, E. Zeynep Erson-Omay, Jennifer Moliterno, and Murat Günel

OBJECTIVE

Recent large-cohort sequencing studies have investigated the genomic landscape of meningiomas, identifying somatic coding alterations in NF2, SMARCB1, SMARCE1, TRAF7, KLF4, POLR2A, BAP1, and members of the PI3K and Hedgehog signaling pathways. Initial associations between clinical features and genomic subgroups have been described, including location, grade, and histology. However, further investigation using an expanded collection of samples is needed to confirm previous findings, as well as elucidate relationships not evident in smaller discovery cohorts.

METHODS

Targeted sequencing of established meningioma driver genes was performed on a multiinstitution cohort of 3016 meningiomas for classification into mutually exclusive subgroups. Relevant clinical information was collected for all available cases and correlated with genomic subgroup. Nominal variables were analyzed using Fisher’s exact tests, while ordinal and continuous variables were assessed using Kruskal-Wallis and 1-way ANOVA tests, respectively. Machine-learning approaches were used to predict genomic subgroup based on noninvasive clinical features.

RESULTS

Genomic subgroups were strongly associated with tumor locations, including correlation of HH tumors with midline location, and non-NF2 tumors in anterior skull base regions. NF2 meningiomas were significantly enriched in male patients, while KLF4 and POLR2A mutations were associated with female sex. Among histologies, the results confirmed previously identified relationships, and observed enrichment of microcystic features among “mutation unknown” samples. Additionally, KLF4-mutant meningiomas were associated with larger peritumoral brain edema, while SMARCB1 cases exhibited elevated Ki-67 index. Machine-learning methods revealed that observable, noninvasive patient features were largely predictive of each tumor’s underlying driver mutation.

CONCLUSIONS

Using a rigorous and comprehensive approach, this study expands previously described correlations between genomic drivers and clinical features, enhancing our understanding of meningioma pathogenesis, and laying further groundwork for the use of targeted therapies. Importantly, the authors found that noninvasive patient variables exhibited a moderate predictive value of underlying genomic subgroup, which could improve with additional training data. With continued development, this framework may enable selection of appropriate precision medications without the need for invasive sampling procedures.