Viewing Study NCT07409467


Ignite Creation Date: 2026-03-26 @ 3:18 PM
Ignite Modification Date: 2026-03-31 @ 7:35 AM
Study NCT ID: NCT07409467
Status: NOT_YET_RECRUITING
Last Update Posted: 2026-02-13
First Post: 2026-02-08
Is NOT Gene Therapy: True
Has Adverse Events: False

Brief Title: Risk Prediction Model for Older Adults Undergoing Noncardiac Surgery
Sponsor: Seoul National University Hospital
Organization:

Study Overview

Official Title: Development of a Surgical Risk Prediction Model for Older Adults Undergoing Major Noncardiac Surgery: A Prospective Cohort Study
Status: NOT_YET_RECRUITING
Status Verified Date: 2026-02
Last Known Status: None
Delayed Posting: No
If Stopped, Why?: Not Stopped
Has Expanded Access: False
If Expanded Access, NCT#: N/A
Has Expanded Access, NCT# Status: N/A
Acronym: None
Brief Summary: This prospective observational study aims to develop and evaluate predictive models for postoperative complications in patients aged 65 years or older scheduled for elective major surgery. The investigators will recruit 520 eligible patients visiting the preoperative assessment clinic.

The study involves the prospective collection of comprehensive preoperative data using specific validated tools:

* Physical Activity: Assessed using the Saltin-Grimby Physical Activity Scale (SGPAS) to categorize physical activity intensity.
* Nutritional Risk: Screened using the Nutritional Risk Screening 2002 (NRS-2002) tool.
* Psychological Status: Evaluated for anxiety and depression using the Hospital Anxiety and Depression Scale (HADS).
* Body Composition: Measured using a portable bioelectrical impedance analysis (BIA) device (BWA2.0S, InBody) to assess muscle mass, body fat/water, and phase angle.
* Physical Function: Assessed via the short physical performance battery (SPPB) using electronic measurement devices (AndanteFit, DYPHI) to calculate frailty index and physical age.

The primary endpoint is the occurrence and severity of postoperative complications within 30 days, evaluated using both the Clavien-Dindo Classification and the Comprehensive Complication Index (CCI). Using the collected dataset, the investigators will develop prediction models using both classical regression analysis and machine learning algorithms to compare their predictive performance.
Detailed Description: None

Study Oversight

Has Oversight DMC: False
Is a FDA Regulated Drug?: None
Is a FDA Regulated Device?: None
Is an Unapproved Device?: None
Is a PPSD?: None
Is a US Export?: None
Is an FDA AA801 Violation?: