Clinical trial NCT05969951: VTE Risk Prevention System Based on Big Data Analysis and Multimodule System
Official title: Construction of Perioperative Venous Thromboembolism Risk Prevention System Based on Big Data Analysis and Multimodule System
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Recruiting
Lead sponsor: Shengjing Hospital
Sponsor class: OTHER
First posted: 2023-08-01
Enrollment: 100000
Sex: ALL
Minimum age (years): 10
Maximum age (years): 90
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_CONTROL
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: All patients in the perioperative period between 10-90

Brief summary: Venous thromboembolism (VTE) is one of the most common complications in perioperative period and the most common cause of postoperative death. VTE includes deep vein thrombosis (DVT) and acute pulmonary thromboembolism (PTE). Since the embolus of PTE comes from the deep vein thrombosis, and not all PE patients can present obvious clinical symptoms, VTE is currently considered as a disease for research, prevention, diagnosis and treatment at home and abroad. Therefore, we urgently need to develop a more comprehensive and reliable perioperative VTE risk prevention system based on medical big data and multi-module computer in current clinical practice, so as to effectively guide the prevention of DVT/PE, and thus reduce the perioperative mortality.

Detailed description: Venous thromboembolism (VTE) is one of the most common complications in perioperative period and the most common cause of postoperative death. VTE includes deep vein thrombosis (DVT) and acute pulmonary thromboembolism (PTE). Since the embolus of PTE comes from the deep vein thrombosis, and not all PE patients can present obvious clinical symptoms, VTE is currently considered as a disease for research, prevention, diagnosis and treatment at home and abroad. Therefore, we urgently need to develop a more comprehensive and reliable perioperative VTE risk prevention system based on medical big data and multi-module computer in current clinical practice, so as to effectively guide the prevention of DVT/PE, and thus reduce the perioperative mortality.

As early as 1991, Caprini et al. collated and published the clinical scale of VTE risk assessment based on the study data of 538 patients. In 2010, Caprini et al. conducted retrospective verification of this scale again among 8216 patients, thus making this scale an important reference for many medical institutions to assess the risk of VTE occurrence in perioperative patients, and it has been used ever since. However, over the past 30 years, the disease spectrum, surgical methods, anesthesia methods and the cognition of thrombus formation susceptibility of surgical patients have all undergone great changes. Perioperative patients who are evaluated by Caprini scale and take preventive measures according to its suggested measures will still have postoperative VTE. Therefore, in recent years, scholars have published new VTE risk prediction methods, such as Kucher model, Padua model and so on. However, the study cases established by the model are almost only about 1000 cases, and the included research indicators are limited.

The new technology realizes for the first time the identification, reading and integration of VTE-related risk indicators under the guidance of informatization and digitalization, providing technical support for big data analysis; To realize forward-looking, big data, multi-variable, multi-module VTE-related risk assessment and prediction; The existing scoring system should be improved to provide a more reliable and operable scoring model for perioperative VTE prevention and treatment.

Eligibility criteria: Inclusion Criteria:

1. perioperative patients;
2. Age range from 10 to 90 years old, gender unlimited;
3. Sign informed consent.

Exclusion Criteria:

* (1) non-operative patients; (2) have developed deep vein thrombosis and/or pulmonary embolism before surgery; (3) emergency operation patients; (4) Patients considered unsuitable for inclusion in the study.

Primary outcomes:
measure: DVT incidence rate
timeFrame: 7±2days after operation
measure: PE incidence rate
timeFrame: 7±2days after operation
Secondary outcomes:
Other outcomes:
Locations:
city: Shenyang
country: China
facility: Shengjing Hospital of China Medical University
state: Liaoning
Study officials:
References:
citation: Cronin M, Dengler N, Krauss ES, Segal A, Wei N, Daly M, Mota F, Caprini JA. Completion of the Updated Caprini Risk Assessment Model (2013 Version). Clin Appl Thromb Hemost. 2019 Jan-Dec;25:1076029619838052. doi: 10.1177/1076029619838052.