Different Algorithm Models to Predict Postoperative Pneumonia in Elderly Patients

Huazhong University of Science and Technology

Status: Not yet enrolling

Conditions: Postoperative Pulmonary Complications

Study type: Observational

Funder types: Other

Identifiers

NCT05671926

PPC2021012

Details and patient eligibility

About

The researchers aim to compare different algorithms to predict postoperative pneumonia in elderly patients and to assess the risk of pneumonia in elderly patients.

Full description

Postoperative pneumonia is a common complication that increases the mortality and length of older patients. In order to better assess the risk of postoperative pneumonia in elderly patients, we plan to use database information and different algorithms, such as logistic regression, random forest, and other algorithms respectively to build models and evaluate the effects of the models.

Enrollment: 10,000 estimated patients

Sex: All

Ages: 65+ years old

Volunteers: No Healthy Volunteers

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 and thoracic surgery

Trial design

10,000 participants in 2 patient groups

Training set

Description:

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

validation set

Description:

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