Evidence map›Paper›PMID 40055274›Full record

ArticleEmergency radiology2025

Forecasting trends of rising emergency department chest imaging using machine learning.

Sishir Doddi, Oscar Salichs, Shruthi Karthika Varier, Ashish Khandelwal, Satheesh Krishna Jeyaraj, Sree Harsha Tirumani

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Article in Emergency radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Sishir DoddiUniversity of Toledo College of Medicine, Toledo, OH, USA. sishir.doddi@utoledo.edu.ORCID http://orcid.org/0000-0002-2818-6033
Oscar SalichsUniversity of Toledo College of Medicine, Toledo, OH, USA.
Shruthi Karthika VarierUniversity of Toledo College of Medicine, Toledo, OH, USA.
Ashish KhandelwalDepartment of Radiology, Mayo Clinic, 200 First St, Rochester, MN, 55902, USA.
Satheesh Krishna JeyarajDepartment of Medical Imaging, University of Toronto, 585 University Avenue, Toronto, ON, M5G 2N2, Canada.
Sree Harsha TirumaniDepartment of Radiology, University Hospitals Cleveland Medical Center, Case Western Reserve University School of Medicine, Cleveland, OH, USA.

Funding

Development of Magnetic Resonance Fingerprinting in Kidney for Evaluation of Renal Cell CarcinomaR01CA266702 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI CHEN, YONG, TIRUMANI, SREE HARSHA · 2022 to 2025
$2.0M
NIH HHS R01CA266702
6 · The paper itself

Abstract

introductionImaging studies in the acute care setting, such as the emergency room, have been increasing. In this report, we use the Centers for Medicare and Medicaid services (CMS) database to assess trends in ED chest CT and chest CTA imaging in ED from 2010 to 2021. In addition, we forecast the utilization of these imaging modalities until 2030 to predict volume of studies using machine learning models.

methodsCMS data regarding volume of procedures from 2010 to 2021 for CPT Codes: Chest CT: 71,250, 71,260, 71,270; and Chest CTA 71,275 was obtained. IBM SPSS Statistical Software was used to assess compound annual growth rate (CAGR) to identify trends in ED center (ED) imaging. For predictive time series forecast, and autoregressive integrated moving average (ARIMA) model was constructed using R Studio statistical software. Ljung-Box test was used to check the model's residuals.

resultsChest CT imaging in the ED setting has steadily increased from 273,063 in 2010 to 540,047 in 2021. Chest CTA imaging also saw an increase from 175,554 in 2010 to 705,727 in 2021. ED Chest CT imaging has increased from 2010 to 2021 with a CAGR of 7.5% (p < 0.001). Similarly, chest CTA imaging has also increased during this period with an average CAGR or 13.0% (p < 0.001). The ARIMA model forecast for ED CT chest imaging predicts a continued increase in volume of CT imaging reaching 614,995 in 2024 and 760,851 in 2030. Chest CTA is predicted to have an even higher increase up to 850,320 in 2024 and 1,139,505 in 2030 assuming the trend observed over the past decade is consistent.

conclusionFrom 2010 to 2021, we found an increasing trend in ED chest CT and CTA imaging with forecast predicting volume up to twice of volume in 2030 compared to a decade ago. Understanding the anticipated volumes of forthcoming ED imaging can aid in planning hospital infrastructure, personnel, and funding amongst other required healthcare resources. It is important to consider that while imaging holds a significant role in patient care, inappropriate utilization can result in unnecessary radiation exposure, prolonged wait times, and an increase in burden of healthcare resources.

Indexed as

Emergency Service, HospitalMachine LearningRadiography, ThoracicTomography, X-Ray ComputedComputed Tomography AngiographyDatabases, FactualForecastingHumansUnited StatesChest CTChest CTAEmergency imagingImaging volumeMachine learning

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.