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NCT Number: NCT05482269

Adverse Outcome of Acute Pulmonary Embolism by Artificial Intelligence System Based on CT Pulmonary Angiography

The investigators aim to build a predictive tool for Adverse Outcome of Acute Pulmonary Embolism by Artificial Intelligence System Based on CT Pulmonary Angiography.

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Key information

Conditions

Pulmonary Embolism and Thrombosis Artificial Intelligence Cardiovascular Diseases Clinical Deterioration Deterioration, Clinical Disease Attributes Disease Progression Embolism Embolism and Thrombosis Lung Diseases Pathologic Processes Pathological Conditions, Signs and Symptoms Pulmonary Embolism Respiratory Tract Diseases Thrombosis Vascular Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Shengjing Hospital

Shenyang, Liaoning, 110004, China

Location status: Recruiting

Location contact

YIZHUO GAO

CONTACT

[email protected]

86+18940257523

About this study

This study collected clinical, laboratory, and CT parameters of acute patients with acute pulmonary embolism from admission to predict adverse outcomes within 30 days after admission into hospital. The investigators aim to build a predictive tool for Adverse Outcome of Acute Pulmonary Embolism by Artificial Intelligence System Based on CT Pulmonary Angiography.

Eligible patients were randomized in some ratio into derivation and validation cohorts. The derivation cohort was used to develop and evaluate a multivariable logistic regression model for predicting the outcomes of interest. The discriminatory power was evaluated by comparing the nomogram to the established risk stratification systems. The consistency of the nomogram was evaluated using the validation cohort.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • age of ≥ 18 years and a pulmonary embolism diagnosis based on CT pulmonary angiography

Exclusion criteria

  • pregnancy
  • reception of reperfusion treatment before admission
  • missing data regarding CT parameters, echocardiography, cardiac troponin I (c-Tn I), and N-terminal-pro brain natriuretic peptide (NT-pro BNP) levels.

Treatment and study plan

No intervention

Other

no intervention

Primary outcomes

  1. Incidence of Treatment-Emergent Adverse Events

    Time frame: 30 days

    The outcomes of interest were defined as the occurrence of adverse outcomes within 30 days after admission. Adverse outcomes were defined as deaths, the need for mechanical ventilation, the need for cardiopulmonary resuscitation, and the need for life-saving vasopressor and reperfusion treatment.

Secondary outcomes

  1. Incidence of Treatment-Emergent Adverse Events

    Time frame: 2 years

    The outcomes of interest were defined as the occurrence of adverse outcomes within 2 years after admission. Adverse outcomes were defined as deaths, the need for mechanical ventilation, the need for cardiopulmonary resuscitation, and the need for life-saving vasopressor and reperfusion treatment.

Study contacts

Contact information is provided by the study sponsor or research team.

DONG JIA

CONTACT

[email protected]

+86-18940252800

YIZHUO GAO

CONTACT

[email protected]

+86-18940257523

Sponsors and collaborators

Lead sponsor

Shengjing Hospital

Other

Registry information

Official study title

Prediction of Adverse Outcome of Acute Pulmonary Embolism by Artificial Intelligence System Based on CT Pulmonary Angiography

Acronym: PEAICTPA

Important dates

Study start
2011
Primary completion
2026
Study completion
2026
First posted
Aug 1, 2022
Registry last updated
Mar 11, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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