RESUMEN
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.
RESUMEN
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.
