Evidence map›Paper›PMID 41757922›Full record

ReviewThe Journal of cardiovascular surgery2026

Risk stratification in pulmonary embolism: current evidence and future directions.

Jihane Jadi, Natalie D Sridharan

Abstract readReview
In one paragraph

Review in The Journal of cardiovascular surgery, 2026. 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

2 authors.

Jihane JadiDepartment of Vascular Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA, USA - jadij@upmc.edu.
Natalie D SridharanDepartment of Vascular Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.

Funding

Vascular Surgery Research Training (VascTrain) ProgramT32HL098036 · NHLBI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Edith Tzeng · 2010 to 2026
$7.2M
NHLBI NIH HHS T32 HL098036
6 · The paper itself

Abstract

Pulmonary embolism (PE) is associated with high cardiovascular morbidity and mortality. Timely diagnosis and accurate prognostication are extremely important for guiding patient management and treatment selection. Risk stratification and clinical scoring systems are crucial for rapid assessment, effective triage, and accurate outcome prediction. Traditional tools have demonstrated strong prognostic value, and the role of dynamic risk assessment is increasingly recognized. Pretest probability scores including the Wells clinical prediction rule and the revised Geneva score, act as first line assessments to determine the need for further diagnostic testing. The Hestia criteria, Pulmonary Embolism Severity Index (PESI) and simplified Pulmonary Embolism Severity Index (sPESI) help to identify patients appropriate for outpatient management. For hospitalized patients, several additional scoring systems, imaging findings, and biomarkers can be used for further risk stratification and treatment planning. The development of multidisciplinary pulmonary embolism response teams (PERTs) and the integration of hybrid predictive models are demonstrating a shift toward a multimodal approach to PE risk stratification. The objective of this review is to summarize current approaches to risk assessment in pulmonary embolism, evaluate existing and emerging prediction models, as well as discuss future directions aimed at improving individualized care in the management and treatment of pulmonary embolism.

Indexed as

Decision Support TechniquesPulmonary EmbolismClinical Decision-MakingHumansPredictive Value of TestsPrognosisRisk AssessmentRisk FactorsSeverity of Illness Index

Identifiers

PMID41757922
PMCPMC13454928

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

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.