Segeroth M, Vosshenrich J, Breit HC, Wasserthal J, Heye T. Radiology weather forecast: A retrospective analysis of predictability of median daily polytrauma-CT occurrence based on weather data.
Eur J Radiol 2024;
170:111269. [PMID:
38142572 DOI:
10.1016/j.ejrad.2023.111269]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 12/05/2023] [Accepted: 12/14/2023] [Indexed: 12/26/2023]
Abstract
OBJECTIVES
Resource planning is a crucial component in hospitals, particularly in radiology departments. Since weather conditions are often described to correlate with emergency room visits, we aimed to forecast the amount of polytrauma-CTs using weather information.
DESIGN
All polytrauma-CTs between 01/01/2011 and 12/31/2022 (n = 6638) were retrieved from the radiology information system. Local weather data was downloaded from meteoblue.com. The data was normalized and smoothened. Daily polytrauma-CT occurrence was stratified into below median and above median number of daily polytrauma-CTs. Logistic regression and machine learning algorithms (neural network, random forest classifier, support vector machine, gradient boosting classifier) were employed as prediction models. Data from 2012 to 2020 was used for training, data from 2021 to 2022 for validation.
RESULTS
More polytrauma-CTs were acquired in summer compared with winter months, demonstrating a seasonal change (median: 2.35; IQR 1.60-3.22 vs. 2.08; IQR 1.36-3.03; p <.001). Temperature (rs = 0.45), sunshine duration (rs = 0.38) and ultraviolet light amount (rs = 0.37) correlated positively, wind velocity (rs = -0.57) and cloudiness (rs = -0.28) correlated negatively with polytrauma-CT occurrence (all p <.001). The logistic regression model for identification of days with above median number of polytrauma-CTs achieved an accuracy of 87 % on training data from 2011 to 2020. When forecasting the years 2021-2022 an accuracy of 65 % was achieved. A neural network and a support vector machine both achieved a validation accuracy of 72 %, whereas all classifiers regarded wind velocity and ultraviolet light amount as the most important parameters.
CONCLUSION
It is possible to forecast above or below median daily number of polytrauma-CTs using weather data.
CLINCICAL RELEVANCE STATEMENT
Prediction of polytrauma-CT examination volumes may be used to improve resource planning.
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