A review of Big Data analytics and potential for implementation in the delivery of global neurosurgery

Big data for neuroscience in the context of predictive, preventive, and personalized medicine

Accurate and precise diagnosis made the medicine the hallmark of evidence-based medicine. While attaining absolute patient satisfaction may seem impossible in the aspect of disease recurrent, personalized their mecidal conditions to their responsive treatment approach may save the day. The last generation approaches in medicine require advanced technologies that will lead to evidence-based medicine. One of the trending fields in this is the use of big data in predictive, preventive, and personalized medicine (3PM). This review dwelled through the practical examples in which big data tools harness neuroscience to add more individualized apporahes to the medical conditions in a bid to confer a more personalized treatment strategies. Moreover, the known breakthroughs of big data in 3PM, big data and 3PM in neuroscience, AI and neuroscience, limitations of big data with 3PM in neuroscience, and the challenges are thoroughly discussed. Finally, the prospects of incorporating big data in 3PM are as well discussed. The review could point out that the implications of big data in 3PM are still in their infancy and will require a holistic approach. While there is a need to carefully sensitize the community, convincing them will come under interdisciplinary and, to some extent, inter-professional collaborations, capacity building for professionals, and optimal coordination of the joint systems.

Opportunities and Considerations for the Incorporation of Artificial Intelligence into Global Neurosurgery: A Generative Pretrained Transformer Chatbot-Based Approach

OBJECTIVE

Global neurosurgery is a public health focus in neurosurgery that seeks to ensure safe, timely, and affordable neurosurgical care to all individuals worldwide. Although investigators have begun to explore the promise of artificial intelligence (AI) for neurosurgery, its applicability to global neurosurgery has been largely hypothetical. We characterize opportunities and considerations for the incorporation of AI into global neurosurgery by synthesizing key themes yielded from a series of generative pretrained transformers (GPTs), discuss important limitations of GPTs and cautions when using AI in neurosurgery, and develop a framework for the equitable incorporation of AI into global neurosurgery.

METHODS

ChatGPT, Bing Chat/Copilot, You, Perplexity.ai, and Google Bard were queried with the prompt "How can AI be incorporated into global neurosurgery?" A layered ChatGPT-based thematic analysis was performed. The authors synthesized the results into opportunities and considerations for the incorporation of AI in global neurosurgery. A Pareto analysis was conducted to determine common themes.

RESULTS

Eight opportunities and 14 important considerations were synthesized. Six opportunities related to patient care, 1 to education, and another to public health planning. Four of the important considerations were deemed specific to global neurosurgery. The Pareto analysis included all 8 opportunities and 5 considerations.

CONCLUSIONS

AI may be incorporated into global neurosurgery in a variety of capacities requiring numerous considerations. The framework presented in this manuscript may facilitate the incorporation of AI into global neurosurgery initiatives while balancing contextual factors and the reality of limited resources.

Understanding Big Data in Neurosurgery

Big data refers to a large amount of data generated and distributed across diverse data sources from open, private, social, and Internet of Things (IoT). Understanding and harnessing the big data in the biomedical sciences, specifically in neurosurgery, is crucial as it can lead to breakthroughs in understanding complex neurological disorders, optimizing surgical interventions, evaluating long-term patient outcomes, and developing predictive models of disease progression, enabling personalized treatment plans tailored to the genetic, molecular, and environmental factors unique to each patient. Furthermore, Big data analytics can facilitate a deeper understanding of the socioeconomic factors contributing to neurological disorders, leading to more effective public health strategies and interventions. By analyzing large-scale patient data across diverse populations, researchers can uncover patterns and risk factors previously obscured in smaller studies, leading to more effective preventative measures and community health initiatives. This chapter provides an overview of big data and its applications in neurosurgery, the challenges associated with its implementation, and the opportunities it presents in transforming neurosurgical care.

Prediction of in-hospital hypokalemia using machine learning and first hospitalization day records in patients with traumatic brain injury

AIMS

Hypokalemia is a common complication following traumatic brain injury, which may complicate treatment and lead to unfavorable outcomes. Identifying patients at risk of hypokalemia on the first day of admission helps to implement prophylactic treatment, reduce complications, and improve prognosis.

METHODS

This multicenter retrospective study was performed between January 2017 and December 2020 using the electronic medical records of patients admitted due to traumatic brain injury. A propensity score matching approach was adopted with a ratio of 1:1 to overcome overfitting and data imbalance during subgroup analyses. Five machine learning algorithms were applied to generate a best-performed prediction model for in-hospital hypokalemia. The internal fivefold cross-validation and external validation were performed to demonstrate the interpretability and generalizability.

RESULTS

A total of 4445 TBI patients were recruited for analysis and model generation. Hypokalemia occurred in 46.55% of recruited patients and the incidences of mild, moderate, and severe hypokalemia were 32.06%, 12.69%, and 1.80%, respectively. Hypokalemia was associated with increased mortality, while severe hypokalemia cast greater impacts. The logistic regression algorithm had the best performance in predicting decreased serum potassium and moderate-to-severe hypokalemia, with an AUC of 0.73 ± 0.011 and 0.74 ± 0.019, respectively. The prediction model was further verified using two external datasets, including our previous published data and the open-assessed Medical Information Mart for Intensive Care database. Linearized calibration curves showed no statistical difference (p > 0.05) with perfect predictions.

CONCLUSIONS

The occurrence of hypokalemia following traumatic brain injury can be predicted by first hospitalization day records and machine learning algorithms. The logistic regression algorithm showed an optimal predicting performance verified by both internal and external validation.

Surgical Management of Chronic Subdural Hematoma in Older Adults: A Systematic Review

BACKGROUND

Chronic subdural hematoma (cSDH) is a form of intracranial hemorrhage common in older adults. Optimal treatment remains controversial. We conducted a systematic review to identify surgical thresholds, characterize outcomes, and delineate critical considerations in the surgical management of older adults in order to summarize the evidence supporting the best contemporary management of cSDH.

METHODS

A systematic review exploring surgical management of cSDH among individuals aged 65 years and older was conducting by searching the PubMed, Embase, and Scopus databases for articles in English. Abstracts from articles were read and selected for full-text review according to a priori criteria. Relevant full-text articles were analyzed for bibliographic data, aim, study design, population, interventions, and outcomes.

RESULTS

Of 1473 resultant articles, 21 were included. Surgery rationale was case-by-case for symptomatic patients with cSDH. Surgery was superior to conservative management and promoted equivalent neurologic outcomes and rates of complications. Recurrence and reoperation rates in older adults were similar to younger individuals. Some studies reported higher mortality rates for older adults, while others reported no difference. Anticoagulation or antiplatelet agent use did not seem to be associated with poorer outcomes in older adults.

CONCLUSIONS

Surgery for cSDH in older adults leads to favorable neurologic outcomes without increased risk of overall complications, recurrence, or reoperation compared to younger patients. However, older adults may be at increased risk for mortality after surgery. It is important to determine use of anticoagulant or antiplatelet agents in older adults to optimally manage patients with cSDH.

Monitored Anesthetic Care Combined with Scalp Nerve Block in Awake Craniotomy: An Effective Attempt at Enhanced Recovery After Neurosurgery

BACKGROUND

Enhanced recovery after surgery has been attempted in neurosurgery at a greater rate. However, concern exists regarding the feasibility of using enhanced recovery after neurosurgery (ERANS). How to manage available resources to safely perform ERANS and improve clinical outcomes has been the subject of much debate and discussion.

METHODS

Owing to the paucity of data available on the use of ERANS protocols, we performed the present feasibility study. We studied the outcomes of the protocols used within a tertiary referral neurosurgery center. Data from patients who had undergone awake craniotomy within an ERANS protocol were prospectively recorded in our institution from September 2017 to December 2018. We also evaluated the safety and effectiveness of the novel ERANS protocol.

RESULTS

A total of 20 patients (mean age, 49.5 ± 17.8 years) were included in the present study. Intraoperative hypertension, hypotension, and bradycardia were present in 4 (20%), 1 (5%), and 1 (5%) patient, respectively. The postoperative morbidities included epilepsy in 1 (5%), pain in 3 (15%), and nausea or vomiting in 2 (10%). No significant changes had occurred in the mean arterial pressure, heart rate, blood glucose, or lactic acid level throughout the procedure. The median length of intensive care unit stay and postoperative hospital stay were 1 and 9.5 days, respectively. No 30-day readmissions or reoperations occurred during the present study.

CONCLUSIONS

Applying an ERANS protocol was feasible, associated with a low incidence of complications, and acceptable intensive care unit and postoperative hospital lengths of stay. The findings from the present study might provide a new approach for the further research of ERANS.

Mapping Global Neurosurgery Research Collaboratives: A Social Network Analysis of the 50 Most Cited Global Neurosurgery Articles

Social network analysis of bibliometric data evaluates the relationships between the articles, authors, and themes of a research niche. The network can be visualized as maps composed of nodes and links. This study aimed to identify and evaluate the relationships between articles, authors, and keywords in global neurosurgery. The authors searched global neurosurgery articles on the Web of Science database from inception to June 18, 2020. The 50 most cited articles were selected and their metadata (document coupling, co-authorship, and co-occurrence) was exported. The metadata were analyzed and visualized with VOSViewer (Centre for Science and Technology Studies, Leiden University, The Netherlands). The articles were published between 1995 and 2020 and they had a median of 4.0 (interquartile range [IQR] = 5.0) citations. There were 5 clusters in the document coupling and 10 clusters in the co-authorship analysis. A total of 229 authors contributed to the articles and Kee B. Park contributed the most to articles (14 publications). Backward citation analysis was organized into 4 clusters and co-occurrence analysis into 7 clusters. The most common themes were pediatric neurosurgery, neurotrauma, and health system strengthening. The authors identified trends, contributors, and themes of highly cited global neurosurgery research. These findings can help establish collaborations and set the agenda in global neurosurgery research.

Current US neurosurgical resident involvement, interest, and barriers in global neurosurgery

OBJECTIVE

It is estimated that nearly 47 million preventable deaths occur annually due the current worldwide deficit in surgical care; subsequently, the World Health Organization resolved unanimously to endorse a decree to address this deficit. Neurosurgeons from industrialized nations can help address the needs of underserved regions. Exposure during training is critical for young neurosurgeons to gain experience in international work and to cultivate career-long interest. Here, the authors explore the opinions of current residents and interest in global neurosurgery as well as the current state of international involvement, opportunities, and barriers in North American residency training.

METHODS

An internet-based questionnaire was developed using the authors' university's REDCap database and distributed to neurosurgical residents from US ACGME (Accreditation Council for Graduate Medical Education)-approved programs. Questions focused on the resident's program's involvement and logistics regarding international rotations and the resident's interest level in pursuing these opportunities.

RESULTS

A 15% response rate was obtained from a broad range of training locations. Twenty-nine percent of respondents reported that their residency program offered elective training opportunities in developing countries, and 7.6% reported having participated in these programs. This cohort unanimously felt that the international rotation was a beneficial experience and agreed that they would do it again. Of those who had not participated, 81.3% reported interest or strong interest in international rotations.

CONCLUSIONS

The authors' results indicate that, despite a high level of desire for involvement in international rotations, there is limited opportunity for residents to become involved. Barriers such as funding and rotation approval were recognized. It is the authors' hope that governing organizations and residency programs will work to break down these barriers and help establish rotations for trainees to learn abroad and begin to join the cause of meeting global surgical needs. To meet overarching international neurosurgical needs, neurosurgeons of the future must be trained in global neurosurgery.

Increases in female academic productivity and female mentorship highlight sustained progress in previously identified neurosurgical gender disparities

OBJECTIVE

A meta-analysis was performed to understand disparities in the representation of female authorship within the neurosurgical literature and implications for career advancement of women in neurosurgery.

METHODS

Author names for articles published in 16 of the top neurosurgical journals from 2002 to 2019 were obtained from MEDLINE. The gender of each author was determined using automated prediction methods. Publication trends were compared over time and across subdisciplines. Female authorship was also compared to the proportionate composition of women in the field over time.

RESULTS

The metadata obtained from 16 major neurosurgical journals yielded 66,546 research articles. Gender was successfully determined for 96% (127,809/133,578) of first and senior authors, while the remainder (3.9%) were unable to be determined through prediction methods. Across all years, 13.3% (8826) of articles had female first authorship and 9.1% (6073) had female senior authorship. Female first authorship increased significantly over time from 5.8% in 2002 to 17.2% in 2019 (p < 0.001). Female senior authorship also increased significantly over time, from 5.5% in 2002 to 12.0% in 2019 (p < 0.001). The journals with the highest proportions of female first authors and senior authors were the Journal of Neurosurgery: Pediatrics (33.5%) and the Asian Journal of Neurosurgery (23.8%), respectively. Operative Neurosurgery had the lowest fraction of female first (12.4%) and senior (4.7%) authors. There was a significant difference between the year-by-year proportion of female neurosurgical trainees and the year-by-year proportion of female neurosurgical first (p < 0.001) and senior (p < 0.001) authors. Articles were also more likely to have a female first author if the senior author of the article was female (OR 2.69, CI 2.52-2.86; p < 0.001). From 1944 to 2019, the Journal of Neurosurgery showed a steady increase in female first and senior authorship, with a plateau beginning in the 1990s.

CONCLUSIONS

Large meta-analysis techniques have the potential to effectively leverage large amounts of bibliometric data to quantify the representation of female authorship in the neurosurgical literature. The proportion of female authors in major neurosurgical journals has steadily increased. However, the rate of increase in female senior authorship has lagged behind the rate of increase in first authorship, indicating a disparity in academic advancement in women in neurosurgery.

Machine learning models predict coagulopathy in spontaneous intracerebral hemorrhage patients in ER

AIMS

Coagulation abnormality is one of the primary concerns for patients with spontaneous intracerebral hemorrhage admitted to ER. Conventional laboratory indicators require hours for coagulopathy diagnosis, which brings difficulties for appropriate intervention within the optimal window. This study evaluates the possibility of building efficient coagulopathy prediction models using data mining and machine learning algorithms.

METHODS

A retrospective cohort enrolled 1668 cases with acute spontaneous intracerebral hemorrhage from three medical centers, excluding those under antithrombotic therapies. Coagulopathy-related clinical parameters were initially screened by univariate analysis. Two machine learning algorithms, the random forest and the support vector machine, were deployed via an approach of four-fold cross-validation to screen out the most important parameters contributing to the occurrence of coagulopathy. Model discrimination was assessed using metrics, including accuracy, precision, recall, and F1 score.

RESULTS

Albumin/globulin ratio, neutrophil count, lymphocyte percentage, aspartate transaminase, alanine transaminase, hemoglobin, platelet count, white blood cell count, neutrophil percentage, systolic and diastolic pressure were identified as major predictors to the occurrence of acute coagulopathy. Compared to support vector machine, the model based on the random forest algorithm showed better accuracy (93.1%, 95% confidence interval [CI]: 0.913-0.950), precision (92.4%, 95% CI: 0.897-0.951), F1 score (91.5%, 95% CI: 0.889-0.964), and recall score (93.6%, 95% CI: 0.909-0.964), and yielded higher area under the receiver operating characteristic curve (AU-ROC) (0.962, 95% CI: 0.942-0.982).

CONCLUSION

The constructed models exhibit good prediction accuracy and efficiency. It might be used in clinical practice to facilitate target intervention for acute coagulopathy in patients with spontaneous intracerebral hemorrhage.

Update on the global neurosurgery movement: A systematic review of international vernacular, research trends, and authorship

In 2015, key global and neurosurgical organizations increased collaboration to improve neurosurgical care access, delivery, and outcomes, particularly in low- to middle-income countries (LMICs); sparking what has been termed the global neurosurgery movement. The authors sought to assess trends in usage of the term 'global neurosurgery' in academic literature with particular focus on author affiliations, world regions most frequently discussed, and topics of research performed. A PubMed search for articles indexed as 'global neurosurgery' was completed yielding 277 articles which met inclusion criteria. It was found that over time, use of the term 'global neurosurgery' has increased, with increasing growth notable starting in the year 2008 and continuing into October 2019. Statistical comparisons showed authors with affiliated global neurosurgery centers were more likely to publish studies related to the continent of Africa (47.4% vs 15.9%, p < 0.001), and less likely to focus on countries in Asia (2.6% vs 20.9%, p = 0.023). Use of the term 'global neurosurgery' in the article abstract/title/keywords was associated with focus on LMICs (18.6% vs. 5.1%, p = 0.006). Use of the term 'global neurosurgery' was associated with workforce and capacity as research topics (41.9% vs 22.6%, p = 0.036). While fairly new, the global neurosurgery movement has seen a rapid increase in publications utilizing the term 'global neurosurgery.' Articles frequently have focused on collaborative, targeted workforce capacity building in LMICs. We encourage the development of more global neurosurgery academic centers, especially in non-USA countries, to continue this momentum.