Your browser doesn't support javascript.
Historical and future trends in emergency pituitary referrals: a machine learning analysis.
Pandit, A S; Khan, D Z; Hanrahan, J G; Dorward, N L; Baldeweg, S E; Nachev, P; Marcus, H J.
  • Pandit AS; High-Dimensional Neurology, Queen Square Institute of Neurology, University College London, London, UK.
  • Khan DZ; Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK.
  • Hanrahan JG; Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK.
  • Dorward NL; Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK.
  • Baldeweg SE; Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK.
  • Nachev P; Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK.
  • Marcus HJ; Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK.
Pituitary ; 25(6): 927-937, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-2014318
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 (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.

RESULTS:

462 of 36,224 emergency referrals were included (referring centres = 48; mean patient age = 56.7 years, femalemale = 0.490.51). 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.

CONCLUSION:

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.
Subject(s)
Keywords

Full text: Available Collection: International databases Database: MEDLINE Main subject: Pituitary Diseases / COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Female / Humans / Male / Middle aged Language: English Journal: Pituitary Journal subject: Endocrinology Year: 2022 Document Type: Article Affiliation country: S11102-022-01269-1

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: Pituitary Diseases / COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Female / Humans / Male / Middle aged Language: English Journal: Pituitary Journal subject: Endocrinology Year: 2022 Document Type: Article Affiliation country: S11102-022-01269-1