Evidence map›Paper›PMID 40145914›Full record

ArticleThe western journal of emergency medicine2025

Cardiac Computed Tomography Measurements in Pulmonary Embolism Associated with Clinical Deterioration.

Anthony J Weekes, Angela M Pikus, Parker L Hambright, Kelly L Goonan, Nathaniel O'Connell

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Article in The western journal of emergency medicine, 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

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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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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Anthony J WeekesAtrium Health's Carolinas Medical Center, Department of Emergency Medicine, Charlotte, North Carolina.
Angela M PikusAtrium Health's Carolinas Medical Center, Department of Emergency Medicine, Charlotte, North Carolina.
Parker L HambrightAtrium Health's Carolinas Medical Center, Department of Emergency Medicine, Charlotte, North Carolina.
Kelly L GoonanAtrium Health's Carolinas Medical Center, Department of Emergency Medicine, Charlotte, North Carolina.
Nathaniel O'ConnellWake Forest School of Medicine, Department of Biostatistics and Data Science, Winston-Salem, North Carolina.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Most pulmonary embolism response teams (PERT) use a radiologist-determined right ventricle to left ventricle ratio (RV:LV) cut-off of 1.0 to risk-stratify pulmonary embolism (PE) patients. Continuous measurements from computed tomography pulmonary angiograms (CTPAs) may improve risk stratification. We assessed associations of CTPA cardiac measurements with acute clinical deterioration and use of advanced PE interventions. Methods: This was a retrospective study of a PE registry used by eight affiliated emergency departments. We used an artificial intelligence (AI) algorithm to measure RV:LV on anonymized CTPAs from registry patients for whom the PERT was activated (2018-2023) by institutional guidelines. Primary outcome was in-hospital PE-related clinical deterioration defined as cardiac arrest, vasoactive medication use for hypotension, or rescue respiratory interventions. Secondary outcome was advanced intervention use. We used bivariable and multivariable analyses. For the latter, we used least absolute shrinkage and selection operator (LASSO) and random forest (RF) to determine associations of all candidate variables with the primary outcome (clinical deterioration), and the Youden index to determine RV:LV optimal cut-offs for primary outcome. Results: Artificial intelligence analyzed 1,467 CTPAs, with 88% agreement on RV:LV categorization with radiologist reports (kappa 0.36, 95% confidence interval [CI] 0.28-0.43). Of 1,639 patients, 190 (11.6%) had PE-related clinical deterioration, and 314 (19.2%) had advanced interventions. Mean RV:LV were 1.50 (0.39) vs 1.30 (0.32) for those with and without clinical deterioration and 1.62 (0.33) vs 1.35 (0.32) for those with and without advanced intervention use. The RV:LV cut-off of 1.0 by AI and radiologists had 0.02 and 0.53 Conclusion: Artifical intelligence-derived RV:LV measurements ≥1.5 on initial CTPA had strong associations with in-hospital clinical deterioration and advanced interventions in a large PERT database. This study points to the potential of capitalizing on immediately available CTPA RV:LV measurements for gauging PE severity and risk stratification.

Indexed as

Clinical DeteriorationComputed Tomography AngiographyHeart VentriclesPulmonary EmbolismTomography, X-Ray ComputedAgedArtificial IntelligenceEmergency Service, HospitalFemaleHumansMaleMiddle AgedRegistriesRetrospective StudiesRisk Assessment

Identifiers

PMID40145914
PMCPMC11931709

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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.