Evidence map›Paper›PMID 41488093›Full record

ArticleFrontiers in medicine2025

A functionalized method for assessing the risk of adverse events in patients with pulmonary embolism.

Keyu Sun, Long Yu, Zichen Xie, Haisu Lu, Shengzhang Wang

Abstract read
In one paragraph

Article in Frontiers in 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.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Keyu SunInstitute of Biomechanics, College of Biomedical Engineering, Fudan University, Shanghai, China.
Long YuInstitute of Biomechanics, College of Biomedical Engineering, Fudan University, Shanghai, China.
Zichen XieEmergency Department, Minhang Hospital, Fudan University, Shanghai, China.
Haisu LuEmergency Department, Minhang Hospital, Fudan University, Shanghai, China.
Shengzhang WangInstitute of Biomechanics, College of Biomedical Engineering, Fudan University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Pulmonary embolism (PE) is a common and potentially fatal condition in emergency medicine, and accurate early risk stratification is critical for guiding clinical management. Existing prognostic models, such as the Pulmonary Embolism Severity Index (PESI) and PERFORM, rely on discrete scoring systems and subjective variables, which may introduce bias and limit predictive precision. Methods: In this study, we propose CON-PERFORM, a novel functional prognostic model that incorporates three readily available objective parameters: age, heart rate, and arterial partial pressure of oxygen (PaO Results: Compared with the PERFORM method, CON-PERFORM demonstrated superior diagnostic performance, with higher area under the curve (AUC), accuracy, and specificity, while maintaining comparable sensitivity. In the training cohort, CON-PERFORM achieved an AUC of 0.841 versus 0.793 for PERFORM, with consistent improvements observed in both the validation and prospective cohorts. Moreover, CON-PERFORM effectively stratified patients into high- and low-risk groups, which displayed distinct survival and discharge patterns over 30 days. Conclusion: In conclusion, CON-PERFORM provides a simple, objective, and robust tool for individualized risk assessment in PE. Its improved diagnostic performance and stability across cohorts highlight its potential for clinical application and resource optimization in emergency care.

Indexed as

function analysisphysiological parameterspulmonary embolismrisk assessmentweight coefficient

Identifiers

PMID41488093
PMCPMC12757274

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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.