High-Dimensional Neurology, Queen Square Institute of Neurology, University College London, London, UK.
Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK.
Pituitary. 2022 Dec;25(6):927-937. doi: 10.1007/s11102-022-01269-1. Epub 2022 Sep 9.
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.
A data-driven analysis was performed using all available electronic neurosurgical referrals (2014-2021) 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, Prophet and two standard baseline forecasting models. Median absolute, and median percentage error scoring metrics with cross-validation were employed to evaluate algorithm performance.
462 of 36,224 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, Prophet and baseline algorithms across scoring metrics, with standard accuracy being achieved for yearly predictions. Referral volumes significantly increased from the start of data collection with future projected increases (p < 0.001) and did not significantly reduce during the COVID-19 pandemic.
This work is the first to employ large-scale data and machine learning to describe and predict acute pituitary referral volumes, estimate future service demands, explore the impact of system stressors (e.g. COVID pandemic), and highlight areas for service improvement.
急性垂体疾病向神经外科转诊通常需要紧急护理。然而，缺乏对垂体急诊转诊模式的详细描述，包括其未来可能如何变化。本研究旨在评估历史和当前的垂体转诊模式，并利用最先进的机器学习工具来预测未来的服务使用情况。
使用英国最繁忙的垂体中心所有可用的电子神经外科转诊（2014-2021 年）进行数据驱动分析。使用自回归移动平均模型（带有季节性和趋势分解的 Loess 步长 (STL-ARIMA)）对垂体转诊进行特征描述和体积预测，并与卷积神经网络-长短期记忆 (CNN-LSTM) 算法、Prophet 和两种标准基线预测模型进行比较。使用中位数绝对误差和中位数百分比误差评分指标进行交叉验证，以评估算法性能。
共纳入 36224 例急诊转诊中的 462 例（转诊中心=48；患者平均年龄=56.7 岁，女性：男性=0.49:0.51）。急诊医学和内分泌学是转诊的主要原因（67%）。最常见的表现是头痛（47%）和视野缺损（32%）。病变主要包括肿瘤或出血（85%），涉及垂体或窝（70%）。STL-ARIMA 管道在评分指标上优于 CNN-LSTM、Prophet 和基线算法，标准准确率达到了每年的预测水平。转诊量从数据收集开始就显著增加，未来预计还会增加（p<0.001），并且在 COVID-19 大流行期间并没有显著减少。
这项工作首次利用大数据和机器学习来描述和预测急性垂体转诊量，估计未来的服务需求，探讨系统压力源（如 COVID 大流行）的影响，并突出服务改进的领域。
