Historical and Future Trends in Emergency Pituitary Referrals: A Machine Learning Analysis

Abstract

Purpose Acute pituitary referrals to neurosurgical services frequently necessitate emergency care. Yet, a detailed characterisation of pituitary emergency referral patterns, including how they may change prospectively is lacking. This study aims to evaluate historical and current pituitary referral patterns and utilise state-of-the-art machine learning tools to predict future service use. Methods A data-driven analysis was performed using all available electronic neurosurgical referrals (2006-2022) to the busiest U.K. pituitary centre. Pituitary referrals were characterised and volumes were predicted using an auto-regressive moving average model with a preceding seasonal and trend decomposition using Loess step (STL-ARIMA), compared against a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) algorithm and Prophet. Mean absolute, and percentage error scoring metrics with cross-validation were employed to evaluate algorithm performance. Results 462 of 36224 emergency referrals were included (referring centres=48; mean patient age=56.7-years, female:male=0.49:0.51). Emergency medicine and endocrinology accounted for the majority of referrals (67%). The most common presentations were headache (47%) and visual field deficits (32%). Lesions mainly comprised tumours or haemorrhage (85%) and involved the pituitary gland or fossa (70%). The STL-ARIMA pipeline outperformed CNN-LSTM and Prophet algorithms across scoring metrics, with standard accuracy being achieved for 6-monthly and yearly predictions. Referral volumes significantly increased from the start of data collection with future projected increases (p<0.001). Conclusion This work is the first to employ large-scale data and machine learning to describe and predict acute pituitary referral volumes, estimating future demand and highlighting areas for service improvement.