Analysis of risk factors and preventive strategies for intracranial infection after neuroendoscopic transnasal pituitary adenoma resection

Cerebrospinal Fluid Neutrophil Gelatinase-Associated Lipocalin as a Novel Biomarker for Postneurosurgical Bacterial Meningitis: A Prospective Observational Cohort Study

BACKGROUND AND OBJECTIVES

Postneurosurgical bacterial meningitis (PNBM) was a significant clinical challenge, as early identification remains difficult. This study aimed to explore the potential of neutrophil gelatinase-associated lipocalin (NGAL) as a novel biomarker for the early diagnosis of PNBM in patients who have undergone neurosurgery.

METHODS

A total of 436 postneurosurgical adult patients were enrolled in this study. Clinical information, cerebrospinal fluid (CSF), and blood samples were collected. After the screening, the remaining 267 patients were divided into the PNBM and non-PNBM groups, and measured CSF and serum NGAL levels to determine the diagnostic utility of PNBM. Subsequently, patients with PNBM were categorized into gram-positive and gram-negative bacterial infection groups to assess the effectiveness of CSF NGAL in differentiating between these types of infections. We analyzed the changes in CSF NGAL expression before and after anti-infection treatment in PNBM. Finally, an additional 60 patients were included as an independent validation cohort to further validate the diagnostic performance of CSF NGAL.

RESULTS

Compared with the non-PNBM group, CSF NGAL was significantly higher in the PNBM group (305.1 [151.6-596.5] vs 58.5 [30.7-105.8] ng/mL; P < .0001). The area under the curve of CSF NGAL for diagnosing PNBM was 0.928 (95% CI: 0.897-0.960), at a threshold of 119.7 ng/mL. However, there was no significant difference in serum NGAL between the 2 groups (142.5 [105.0-248.6] vs 161.9 [126.6-246.6] ng/mL, P = .201). Furthermore, CSF NGAL levels were significantly higher in patients with gram-negative bacterial infections than those with gram-positive bacteria (P = .023). In addition, CSF NGAL levels decrease after treatment compared with the initial stage of infection (P < .0001). Finally, in this validation cohort, the threshold of 119.7 ng/mL CSF NGAL shows good diagnostic performance with a sensitivity and specificity of 90% and 80%, respectively.

CONCLUSION

CSF NGAL holds promise as a potential biomarker for the diagnosis, early drug selection, and efficacy monitoring of PNBM.

Endoscopic Sellar Defect Reconstruction with Avascular Modified Gasket Seal Technique for Sellar Tumors

Watertight repair of the skull base defect is necessary during endonasal skull base surgery to avoid postoperative CSF leak (poCSFl) and consequent intracranial complications. Various techniques have been described for reconstructing sphenoid-sellar defects with varying success rates. We have described the immediate and long-term outcomes following the reconstruction of sphenoid-sellar defects with our technique. A retrospective analysis of the patients following transsphenoidal sellar surgery underwent barrier restoring reconstruction by multi-layered (inlay-overlay) with autologous thigh fat, fascia lata, fibrin glue, knitted collagen, and absorbable gelatin sponge (modified gasket seal technique). A total of 44 patients were included in the study (n = 44). Reconstruction with modified gasket seal technique was done for all patients. 26 (59.1%) had intraoperative CSF leak (ioCSFl), and 9 (20.4%) patients had grade 3 Esposito-Kelly ioCSFl requiring adjunct short-term pressure reducing procedure (Lumbar drain) intraoperatively. 11/44 (25%) had poCSFl, 7/11 patients with poCSFl were managed conservatively, and 4/11 patients required rescue second surgery and ventriculoperitoneal shunting. 1 (2.3%) had severe meningitis and succumbed to it. Pneumocephalus was seen in 6 (13.6%). Multivariate analysis showed that revision surgery, GH-secreting tumors, and defects extending to the suprasellar region had higher chances of poCSFl (p < 0.001). All 43 alive patients had no CSF leak on long-term follow-up. The modified gasket seal technique is a viable technique for endoscopic sellar reconstruction for ioCSFl with an immediate success rate of 79.6% and 97.72% in the long term in preventing the postoperative CSF leak with a 13.6% rate of meningitis.

Nomogram for Predicting Central Nervous System Infection Following Traumatic Brain Injury in the Elderly

OBJECTIVE

This study aims to identify risk factors for central nervous system (CNS) infection in elderly patients hospitalized with traumatic brain injury (TBI) and to develop a reliable predictive tool for assessing the likelihood of CNS infection in this population.

METHOD

We conducted a retrospective study on 742 elderly TBI patients treated at Tangdu Hospital, China. Clinical data was randomly split into training and validation sets (7:3 ratio). By conducting univariate and multivariate logistic regression analysis in the training set, we identified a list of variables to develop a nomogram for predicting the risk of CNS infection. We evaluated the performance of the predictive model in both cohorts respectively, using receiver operating characteristics curves, calibration curves, and decision curve analysis.

RESULTS

Results of the logistic analysis in the training set indicated that surgical intervention (P = 0.007), red blood cell count (P = 0.019), C-reactive protein concentration (P < 0.001), and cerebrospinal fluid leakage (P < 0.001) significantly predicted the occurrence of CNS infection in elderly TBI patients. The model constructed based on these variables had high predictive capability (area under the curve-training = 0.832; area under the curve-validation = 0.824) as well as clinical utility.

CONCLUSIONS

A nomogram constructed based on several key predictors reasonably predicts the risk of CNS infection in elderly TBI patients upon hospital admission. The model of the nanogram may contribute to timely interventions and improve health outcomes among affected individuals.

Graph neural network and machine learning analysis of functional neuroimaging for understanding schizophrenia

BACKGROUND

Graph representational learning can detect topological patterns by leveraging both the network structure as well as nodal features. The basis of our exploration involves the application of graph neural network architectures and machine learning to resting-state functional Magnetic Resonance Imaging (rs-fMRI) data for the purpose of detecting schizophrenia. Our study uses single-site data to avoid the shortcomings in generalizability of neuroimaging data obtained from multiple sites.

RESULTS

The performance of our graph neural network models is on par with that of our machine learning models, each of which is trained using 69 graph-theoretical measures computed from functional correlations between various regions of interest (ROI) in a brain graph. Our deep graph convolutional neural network (DGCNN) demonstrates a promising average accuracy score of 0.82 and a sensitivity score of 0.84.

CONCLUSIONS

This study provides insights into the role of advanced graph theoretical methods and machine learning on fMRI data to detect schizophrenia by harnessing changes in brain functional connectivity. The results of this study demonstrate the capabilities of using both traditional ML techniques as well as graph neural network-based methods to detect schizophrenia using features extracted from fMRI data. The study also proposes two methods to obtain potential biomarkers for the disease, many of which are corroborated by research in this area and can further help in the understanding of schizophrenia as a mental disorder.

Evaluating the Efficacy of a Novel Side-Support Surgical Tray Stand for Endoscopic Transnasal Skull Base Surgery: A Prospective Study

Objective Endoscopic transnasal skull base surgery is a valuable technique used in the surgical treatment of various skull base pathologies. In such surgeries, the reconstruction of the skull base is crucial for surgical success and minimizing complications. This study presents a new side-support surgical tray designed to improve the exposure of the lateral femoral surgical area during surgery, enhancing surgical efficiency and reducing the risk of surgical complications. The study compared this innovative tray stand with the conventional double-sided support tray stand to evaluate its impact on surgical procedures and complications. Materials and methods The study prospectively analyzed 248 endoscopic transnasal skull base surgeries requiring lateral femoral autologous tissue harvesting. One hundred fifty-eight cases were performed using the side-support surgical tray stand (experimental group), while 90 cases used the conventional double-sided support tray stand (control group). Various parameters were evaluated, including satisfaction scores of surgeons, circulating nurses, instrument nurses, and anesthetists, as well as objective outcomes such as surgical duration and the incidence of complications. Results Surgeons in the experimental group expressed higher satisfaction with the surgical field exposure and the portability of the surgical tray stand compared to the control group. Likewise, circulating nurses in the experimental group reported greater satisfaction with the installation and portability, surpassing that of the control group (p< 0.01). Although the stability of instrument nurses in the experimental group was slightly less than that of the control group, it had no discernible impact on surgical cooperation. Anaesthesiologists in the experimental group exhibited higher satisfaction regarding the convenience of intraoperative monitoring and management than their counterparts in the control group. The average duration required for intraoperative autologous tissue harvesting in the experimental group was significantly shorter than in the control group (p < 0.01). Furthermore, the incidence of postoperative wound infections and intracranial infections in the experimental group was notably lower than in the control group (would infections, p = 0.046; intracranial infection, p = 0.025). Conclusion The novel side-support surgical tray stand effectively improves surgical exposure, convenience, and safety while reducing the risk of surgical site and intracranial infections. It also shortens surgical duration and lowers complication rates, making it a suitable choice for clinical application.

Elucidating salient site-specific functional connectivity features and site-invariant biomarkers in schizophrenia via deep neural networks

Schizophrenia is a highly heterogeneous disorder and salient functional connectivity (FC) features have been observed to vary across study sites, warranting the need for methods that can differentiate between site-invariant FC biomarkers and site-specific salient FC features. We propose a technique named Semi-supervised learning with data HaRmonisation via Encoder-Decoder-classifier (SHRED) to examine these features from resting state functional magnetic resonance imaging scans gathered from four sites. Our approach involves an encoder-decoder-classifier architecture that simultaneously performs data harmonisation and semi-supervised learning (SSL) to deal with site differences and labelling inconsistencies across sites respectively. The minimisation of reconstruction loss from SSL was shown to improve model performance even within small datasets whilst data harmonisation often led to lower model generalisability, which was unaffected using the SHRED technique. We show that our proposed model produces site-invariant biomarkers, most notably the connection between transverse temporal gyrus and paracentral lobule. Site-specific salient FC features were also elucidated, especially implicating the paracentral lobule for our local dataset. Our examination of these salient FC features demonstrates how site-specific features and site-invariant biomarkers can be differentiated, which can deepen our understanding of the neurobiology of schizophrenia.

Predictive value of suprasellar extension for intracranial infection after endoscopic transsphenoidal pituitary adenoma resection

OBJECTIVE

To investigate the relationship between suprasellar extension (SSE) and intracranial infection after endoscopic endonasal transsphenoidal approach (EETA) for pituitary adenoma resection.

METHODS

We retrospectively analyzed 94 patients with suprasellar extended pituitary adenoma admitted to the Department of Neurosurgery of the Affiliated Hospital of Guilin Medical College from January 2018 to December 2021. We measured the preoperative magnetic resonance sagittal SSE and collected clinical data and divided the patients into groups according to the presence of postoperative intracranial infection. The critical value for the SSE was calculated by using a working characteristic curve for the subjects. The risk factors for intracranial infection after EETA resection of pituitary adenomas were analyzed by multivariate regression analysis.

RESULTS

Among the 94 patients, 12 cases (12.8%) were placed in the infection group and 82 cases (87.2%) in the non-infection group. The cut-off value for the SSE in the sagittal position was 15.6 mm, the sensitivity was 75%, the specificity was 87.8%, and the area under the curve (AUC) was 0.801. The coronary cut-off value for the SSE was 15.8 mm, the sensitivity was 66.7%, the specificity was 79.3%, and the AUC was 0.787. The SSE values in the sagittal and coronal positions were correlated with postoperative intracranial infection (P < 0.05). After univariate analysis, those with significant differences were included in the multivariate regression analysis. It was concluded that the extension distance of the tumor above the sella in the sagittal position was ≥ 15.6 mm, the tumor texture was hard, and the postoperative cerebrospinal fluid leakage were the independent risk factors for intracranial infection after EETA resection of suprasellar extended pituitary tumors (P < 0.05).

CONCLUSIONS

The value of SSE on sagittal MRI can predict intracranial infection in patients with suprasellar extended pituitary adenoma after endoscopic endonasal transsphenoidal resection. This finding recommends neurosurgeons pay more attention to the imaging characteristics of pituitary adenomas and select appropriate treatment plans in combination with the intraoperative conditions to reduce the incidence of intracranial infection.

Factors influencing the surveillance of re-emerging intracranial infections in elective neurosurgical patients: A single-center retrospective study

BACKGROUND

At present, many studies have reported the risk factors for postoperative intracranial reinfection, including age, sex, time to surgery, duration of postoperative catheterization, emergency procedures, type of disease and cerebrospinal fluid leakage, but the academic community has not reached a unified conclusion.

AIM

To find factors influencing the surveillance of re-emerging intracranial infections in elective neurosurgical patients.

METHODS

Ninety-four patients who underwent elective craniotomy from January 1, 2015 to December 31, 2022 in the Department of Neurosurgery, First Hospital of Jilin University, were included in this study. Of those, 45 patients were enrolled in the infection group, and 49 were enrolled in the control group. The clinical data of the patients were collected and divided into three categories, including preoperative baseline conditions, intraoperative characteristics and postoperative infection prevention. The data were analyzed using SPSS 26.0 software.

RESULTS

There were 23 males and 22 females in the infection group with a mean age of 52.8 ± 15.1 years and 17 males and 32 females in the control group with a mean age of 48.9 ± 15.2 years. The univariate analysis showed that the infection group had higher systolic blood pressures and postoperative temperatures, fewer patients who underwent a supratentorial craniotomy, more patients with a history of hypertension and higher initial postoperative white blood cell counts than the control group, with statistically significant differences (P < 0.05). The multifactorial logistic regression analysis showed that a history of hypertension and a high postoperative body temperature were independent risk factors for postoperative infection in neurosurgical patients.

CONCLUSION

The results obtained in this study indicated that a history of hypertension and a high postoperative body temperature were independent risk factors for postoperative neurological symptoms.

Development and Validation of a Nomograph Model for Post-Operative Central Nervous System Infection after Craniocerebral Surgery

PURPOSE

A nomograph model of predicting the risk of post-operative central nervous system infection (PCNSI) after craniocerebral surgery was established and validated.

METHODS

The clinical medical records of patients after cranial surgery in Renmin Hospital of Wuhan University from January 2020 to September 2022 were collected, of whom 998 patients admitted to Shouyi Hospital District were used as the training set and 866 patients admitted to Guanggu Hospital District were used as the validation set. Lasso regression was applied to screen the independent variables in the training set, and the model was externally validated in the validation set.

RESULTS

A total of 1864 patients after craniocerebral surgery were included in this study, of whom 219 (11.75%) had PCNSI. Multivariate logistic regression analysis showed that age > 70 years, a previous history of diabetes, emergency operation, an operation time ≥ 4 h, insertion of a lumbar cistern drainage tube ≥ 72 h, insertion of an intracranial drainage tube ≥ 72 h, intraoperative blood loss ≥ 400 mL, complicated with shock, postoperative albumin ≤ 30 g/L, and an ICU length of stay ≥ 3 days were independent risk factors for PCNSI. The area under the curve (AUC) of the training set was 0.816 (95% confidence interval (95%CI), 0.773-0.859, and the AUC of the validation set was 0.760 (95%CI, 0.715-0.805). The calibration curves of the training set and the validation set showed p-values of 0.439 and 0.561, respectively, with the Hosmer-Lemeshow test. The analysis of the clinical decision curve showed that the nomograph model had high clinical application value.

CONCLUSION

The nomograph model constructed in this study to predict the risk of PCNSI after craniocerebral surgery has a good predictive ability.

Prediction model of central nervous system infections in patients with severe traumatic brain injury after craniotomy

OBJECTIVE

The aim of this study was to develop and evaluate a nomogram to predict CNS infections in patients with severe traumatic brain injury (sTBI) after craniotomy.

METHODS

This retrospective study was conducted in consecutive adult patients with sTBI who were admitted to the neurointensive care unit (NCU) between January 2014 and September 2020. We applied the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analysis to construct the nomogram, and k-fold cross-validation (k=10) to validate it. The receiver operator characteristic area under the curve (AUC) and calibration curve were applied to evaluate the predictive effect of the nomogram. The clinical usefulness was investigated by decision curve analysis (DCA).

RESULTS

A total of 471 patients with sTBI who underwent surgical treatment were included, of whom 75 patients (15.7%) were diagnosed with CNS infections. The serum level of albumin, cerebrospinal fluid (CSF) otorrhoea at admission, CSF leakage, CSF sampling, and postoperative re-bleeding were associated with CNS infections and incorporated into the nomogram. The results showed that our model yielded satisfactory prediction performance with an AUC value of 0.962 in the training set and 0.942 in the internal validation. The calibration curve exhibited satisfactory concordance between the predicted and actual outcomes. The model had good clinical use since the DCA covered a large threshold probability.

CONCLUSION

We established a straightforward individualized nomogram for CNS infections in sTBI patients in the NCU, which could help physicians screen high-risk patients to perform early interventions to reduce the incidence of CNS infections in sTBI patients.

Impact of Penicillin Allergy-Based Alternative Antibiotics on the Risk of Postoperative Central Nervous System Infection: A Retrospective Cohort Study

BACKGROUND

Central nervous system (CNS) infection is one of the most serious complications after neurosurgery. This study aimed to analyze the effect of penicillin allergy (PA) and alternative prophylactic antibiotics on risk of postoperative CNS infection in patients undergoing neurosurgery.

METHODS

Data of patients who underwent neurosurgical procedures from January 2015 to December 2021 were analyzed retrospectively. Patients with PA were compared with patients without PA in a 1:1 ratio. A multivariate logistic regression model was used to examine whether PA was a risk factor for postoperative CNS infection.

RESULTS

Overall, 15,049 eligible neurosurgical records were reviewed, from which 578 surgical records of 556 patients with PA were matched to 578 records of 570 patients without PA. Patients with PA showed significantly lower probability to receive prophylactic cephalosporins (55.9% vs. 98.8%, P < 0.01), but significantly higher probability to receive clindamycin (41.86% vs. 1.03%, P < 0.01), than patients without PA. Multivariate analysis revealed that patients with PA were more likely to experience postoperative CNS infection than patients without PA (odds ratio = 2.03; 95% confidence interval, 1.15-3.56; P = 0.014). The incidence of postoperative CNS infection returned to a level comparable to that in general population when patients with suspected PA received prophylactic cephalosporins.

CONCLUSIONS

PA is associated with higher risk of postoperative CNS infection in patients undergoing neurosurgery. This may be attributed to the use of alternative prophylactic antibiotics other than cephalosporins, especially clindamycin.

Risk factors and management associated with postoperative cerebrospinal fluid leak after endoscopic endonasal surgery for pituitary adenoma

Objective

To determine risk factors and management for the development of a postoperative cerebrospinal fluid (CSF) leak after an endoscopic endonasal surgery (EES) for pituitary adenomas.

Methods

The clinical data of 400 patients who underwent EES for resection of pituitary adenomas from December 2018 to November 2019 in the Department of Neurosurgery of Beijing Tiantan Hospital were retrospectively reviewed. Age, gender, body mass index (BMI), tumor size, Knosp grade, suprasellar extension grade, sellar floor erosion grade, repeated transsphenoidal surgery, intraoperative CSF leak, use of pedicled nasoseptal flap and lumbar drain were collected and analyzed.

Results

Postoperative CSF leak occurred in 14 of 400 patients (3.5%). Age, gender, BMI, tumor size, Knosp grade and repeated transsphenoidal surgery were not risk factors for CSF leak. Suprasellar extension grade (≥B 6.0% vs. <B 1.4%; p = 0.024), sellar floor erosion grade (≥III 5.7% vs. <III 0.6%; p = 0.020) and intraoperative CSF leak (Yes 7.5% vs. No 2.0%; p = 0.009) were factors associated with an increased postoperative CSF leak rate.

Conclusions

Higher suprasellar extension grade, higher sellar floor erosion grade and intraoperative CSF leak were risk factors for postoperative CSF leak after endoscopic treatment of pituitary adenoma. Strict skull base reconstruction including use of a pedicled nasoseptal flap and perioperative lumbar drainage may avoid postoperative CSF leak.

Analysis of the Factors Related to Intracranial Infection after Brain Tumor Surgery

In order to explore the factors related to intracranial infection after brain tumor surgery, a retrospective analysis is conducted in this study. According to the patients with intracranial infection after brain tumor surgery in our hospital from January 2020 to October 2020, clinical data are divided into different groups and some indicators are put into the multiple regression model for multivariate analysis. The factors related to intracranial infection after brain tumor surgery are analyzed, and the clinical effect of a detailed management plan based on the abovementioned risk factors to prevent intracranial infection in patients after brain tumor surgery is observed. Multiple regression models demonstrate that complicated underlying diseases, operation time, and intraoperative blood loss are independent risk factors for postoperative intracranial infection.