Temporal trends in neurosurgical volume and length of stay in a public healthcare system: A decade in review with a focus on the COVID-19 pandemic.

Background: Over the past decade, neurosurgical interventions have experienced changes in operative frequency and postoperative length of stay (LOS), with the recent COVID-19 pandemic significantly impacting these metrics. Evaluating these trends in a tertiary National Health Service center provides insights into the impact of surgical practices and health policy on LOS and is essential for optimizing healthcare management decisions. Methods: This was a single tertiary center retrospective case series analysis of neurosurgical procedures from 2012 to 2022. Factors including procedure type, admission urgency, and LOS were extracted from a prospectively maintained database. Six subspecialties were analyzed: Spine, Neuro-oncology, Skull base (SB), Functional, Cerebrospinal fluid (CSF), and Peripheral nerve (PN). Mann-Kendall temporal trend test and exploratory data analysis were performed. Results: 19,237 elective and day case operations were analyzed. Of the 6 sub-specialties, spine, neuro-oncology, SB, and CSF procedures all showed a significant trend toward decreasing frequency. A shift toward day case over elective procedures was evident, especially in spine (P < 0.001), SB (tau = 0.733, P = 0.0042), functional (tau = 0.156, P = 0.0016), and PN surgeries (P < 0.005). Over the last decade, decreasing LOS was observed for neuro-oncology (tau = -0.648, P = 0.0077), SB (tau = -0.382, P = 0.012), and functional operations, a trend which remained consistent during the COVID-19 pandemic (P = 0.01). Spine remained constant across the decade while PN demonstrated a trend toward increasing LOS. Conclusion: Most subspecialties demonstrate a decreasing LOS coupled with a shift toward day case procedures, potentially attributable to improvements in surgical techniques, less invasive approaches, and increased pressure on beds. Setting up extra dedicated day case theaters could help deal with the backlog of procedures, particularly with regard to the impact of COVID-19.

Determining the risk of spinal pathology progression in neurofibromatosis type 1 patients - a national tertiary neurofibromatosis type 1 centre study.

BACKGROUND: Neurofibromatosis type 1 (NF1) gives rise to a variety of spinal pathologies that include dural ectasia (DE), vertebral malalignments (VMA), spinal deformities (SD), syrinx, meningoceles, spinal nerve root tumours (SNRT), and spinal plexiform tumours (SPT). The relationship between these and the progression of these pathologies has not been explored before in detail and this paper aims to address this. METHODS: Data was retrospectively collected from adult NF1 multi-disciplinary team meetings from 2016 to 2022 involving a total of 593 patients with 20 distinct predictor variables. Data were analyzed utilizing; Chi-Square tests, binary logistic regression, and Kaplan-Meier analysis. RESULTS: SNRT (19.9%), SD (18.6%), and (17.7%) of VMA had the highest rates of progression. SD was significantly associated (p < 0.02) with the presence and progression of all spinal pathologies except for SPT. Statistically significant predictors of SD progression included the presence of DVA, VMA, syrinx, meningocele, and SNRT. Kaplan-Meier analysis revealed no statistically significant difference between the times to progression for SD (85 days), SNRT (1196 days), and VMA (2243 days). CONCLUSION: This paper explores for the first time in detail, the progression of various spinal pathologies in NF1. The presence and progression of SD is a key factor that correlated with the progression of different spinal pathologies. Early identification of SD may help support clinical decision-making and guide radiological follow-up protocols and treatment.

Predicting neurosurgical referral outcomes in patients with chronic subdural hematomas using machine learning algorithms - A multi-center feasibility study.

Background: Chronic subdural hematoma (CSDH) incidence and referral rates to neurosurgery are increasing. Accurate and automated evidence-based referral decision-support tools that can triage referrals are required. Our objective was to explore the feasibility of machine learning (ML) algorithms in predicting the outcome of a CSDH referral made to neurosurgery and to examine their reliability on external validation. Methods: Multicenter retrospective case series conducted from 2015 to 2020, analyzing all CSDH patient referrals at two neurosurgical centers in the United Kingdom. 10 independent predictor variables were analyzed to predict the binary outcome of either accepting (for surgical treatment) or rejecting the CSDH referral with the aim of conservative management. 5 ML algorithms were developed and externally tested to determine the most reliable model for deployment. Results: 1500 referrals in the internal cohort were analyzed, with 70% being rejected referrals. On a holdout set of 450 patients, the artificial neural network demonstrated an accuracy of 96.222% (94.444-97.778), an area under the receiver operating curve (AUC) of 0.951 (0.927-0.973) and a brier score loss of 0.037 (0.022-0.056). On a 1713 external validation patient cohort, the model demonstrated an AUC of 0.896 (0.878-0.912) and an accuracy of 92.294% (90.952-93.520). This model is publicly deployed: https://medmlanalytics.com/neural-analysis-model/. Conclusion: ML models can accurately predict referral outcomes and can potentially be used in clinical practice as CSDH referral decision making support tools. The growing demand in healthcare, combined with increasing digitization of health records raises the opportunity for ML algorithms to be used for decision making in complex clinical scenarios.

Development and Validation of the Chronic Subdural HematOma Referral oUtcome Prediction Using Statistics (CHORUS) Score: A Retrospective Study at a National Tertiary Center.

BACKGROUND: Chronic subdural hematoma (CSDH) is a common neurosurgical condition with an increasing rate of patient referrals. CSDH referral decision-making is a subjective clinical process, and our aim was to develop a simple scoring system capable of acting as a decision support tool aiding referral triage. METHODS: A single tertiary center retrospective case series analysis of all CSDH patient referrals from 2015 to 2020 was conducted. Ten independent variables used in the referral process were analyzed to predict the binary outcome of either accepting or rejecting the CSDH referral. Following feature selection analysis, a multivariable scoring system was developed and evaluated. RESULTS: 1500 patient referrals were included. Stepwise multivariable logistic and least absolute shrinkage and selection operator regression identified age <85 years, the presence of headaches, dementia, motor weakness, radiological midline shift, a reasonable premorbid quality of life, and a large sized hematoma to be statistically significant predictors of CSDH referral acceptance (P <0.04). These variables derived a scoring system ranging from -9 to 6 with an optimal cut-off for referral acceptance at any score >1 (P <0.0001). This scoring system demonstrated optimal calibration (brier score loss = 0.0552), with a score >1 predicting referral acceptance with an area under the curve of 0.899 (0.876-0.922), a sensitivity of 83.838% (76.587-91.089), and a specificity of 96.000% (94.080-97.920). CONCLUSIONS: Certain patient specific clinical and radiological characteristics can predict the acceptance or rejection of a CSDH referral. Considering the precision of this scoring system, it has the potential for effectively triaging CSDH referrals.

Natural language processing for the automated detection of intra-operative elements in lumbar spine surgery.

Background: The aim of this study was to develop natural language processing (NLP) algorithms to conduct automated identification of incidental durotomy, wound drains, and the use of sutures or skin clips for wound closure, in free text operative notes of patients following lumbar surgery. Methods: A single-centre retrospective case series analysis was conducted between January 2015 and June 2022, analysing operative notes of patients aged >18 years who underwent a primary lumbar discectomy and/or decompression at any lumbar level. Extreme gradient-boosting NLP algorithms were developed and assessed on five performance metrics: accuracy, area under receiver-operating curve (AUC), positive predictive value (PPV), specificity, and Brier score. Results: A total of 942 patients were used in the training set and 235 patients, in the testing set. The average age of the cohort was 53.900 ± 16.153 years, with a female predominance of 616 patients (52.3%). The models achieved an aggregate accuracy of >91%, a specificity of >91%, a PPV of >84%, an AUC of >0.933, and a Brier score loss of ≤0.082. The decision curve analysis also revealed that these NLP algorithms possessed great clinical net benefit at all possible threshold probabilities. Global and local model interpretation analyses further highlighted relevant clinically useful features (words) important in classifying the presence of each entity appropriately. Conclusions: These NLP algorithms can help monitor surgical performance and complications in an automated fashion by identifying and classifying the presence of various intra-operative elements in lumbar spine surgery.