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NCT06057688
Construction of Early Warning Model for Pulmonary Complications Risk of Surgical Patients Based on Multimodal Data Fusion
trial in Pulmonary Embolism in 1,770 participants. Status unknown.
1 October 2024
Quick facts
| Lead sponsor | Renrong Gong |
|---|---|
| Status | Status unknown |
| Study type | OBSERVATIONAL |
| Enrollment | 1,770 |
| Start date | 1 August 2023 |
| Primary completion | 1 October 2024 |
| Estimated completion | 31 December 2024 |
| Sites | 1 location across China |
Conditions studied
- Pulmonary Embolism — all drugs for Pulmonary Embolism →
- Respiratory Failure — all drugs for Respiratory Failure →
- Infection Complication — all drugs for Infection Complication →
- Cardiac Arrest — all drugs for Cardiac Arrest →
Sponsor
Renrong Gong
Who can join
Adults 14 to 90, any sex, with Pulmonary Embolism or Respiratory Failure. Patients with the condition only — healthy volunteers not accepted.
Sponsor's own description
The goal of this observational study is to establish an intelligent early warning system for acute and critical complications of the respiratory system such as pulmonary embolism and respiratory failure. Based on the electronic case database of the biomedical big data research center and the clinical real-world vital signs big data collected by wearable devices, the hybrid model architecture with multi-channel gated circulation unit neural network and deep neural network as the core is adopted, Mining the time series trends of multiple vital signs and their linkage change characteristics, integrating the structural nursing observation, laboratory examination and other multimodal clinical information to establish a prediction model, so as to improve patient safety, and lay the foundation for the later establishment of a higher-level and more comprehensive artificial intelligence clinical nursing decision support system. Issues addressed in this study 1. The big data of vital signs of patients collected in real-time by wearable devices were used to explore the internal relationship between the change trend of vital signs and postoperative complications (mainly including infection complications, respiratory failure, pulmonary embolism, cardiac arrest). Supplemented with necessary nursing observation, laboratory examination and other information, and use machine learning technology to build a prediction model of postoperative complications. 2. Develop the prediction model into software to provide auxiliary decision support for clinical medical staff, and lay the foundation for the later establishment of a higher-level and more comprehensive AI clinical decision support system.
Publications & conference data
No peer-reviewed publications indexed yet for this trial.
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Verify against primary sources
- ClinicalTrials.gov — authoritative US registry record
- WHO ICTRP — international registry index
- EU Clinical Trials Register
- Sponsor press releases (Google)
- Trial protocol + status: ClinicalTrials.gov NCT06057688 (US National Library of Medicine, public domain)
- Drug + disease cross-links: matched in real time against Drug Landscape's normalised drug + company + condition tables
- Sponsor: as reported to ClinicalTrials.gov by Renrong Gong
- Last refreshed: 28 September 2023
Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT06057688.
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