Different Algorithm Models to Predict Postoperative Pulmonary Complications in Elderly Patients

NCT ID: NCT05671939

Last Updated: 2023-01-05

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 10000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2023-01-31
• Study Completion Date: 2023-03-31

Brief Summary

Although a number of clinical predictive models were developed to predict postoperative pulmonary complications, few predictive models have been used in elderly patients. In this study, the researchers aim to compare different algorithms to predict postoperative pulmonary complications in elderly patients and to assess the risk of postoperative pulmonary complications in elderly patients.

Detailed Description

Postoperative pulmonary complications occur frequently, which is an important cause of death and morbidity. Age has been an important predictor of postoperative pulmonary complications. As the world's population ages, more and more older people are undergoing surgery as indications for surgery expand. In order to better assess the risk of postoperative pulmonary complications in elderly patients, we plan to use database information and different algorithms such as logistic regression, random forest, and other algorithms to build models respectively and evaluate the effects of the models.

Conditions

Postoperative Pulmonary Complications

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Study Groups

Training set

The whole cohort is randomly assigned to a training cohort and validation cohort.

No interventions assigned to this group

validation set

The whole cohort is randomly assigned to a training cohort and validation cohort.

No interventions assigned to this group

Eligibility Criteria

Inclusion Criteria

1. Age 65 years or older

2. receiving invasive ventilation during general anesthesia for surgery

Exclusion Criteria

1. preoperative mechanical ventilation

2. procedures related to a previous surgical complication

3. a second operation after surgery

4. organ transplantation

5. discharged within 24 hours after surgery

6. cardiac surgery

• Minimum Eligible Age: 65 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Union Hospital, Tongji Medical College, Huazhong University of Science and Technology

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Central Contacts

Qingping Wu, PhD

Role: CONTACT

wqp1968@163.com

13971605283

Other Identifiers

PPC20221201

Identifier Type: -

Identifier Source: org_study_id