Variability and Post-op AEs: Does Preoperative CardioPulmonary Variability Assessment Identify Risk of Postoperative Adverse Events Following Thoracic Surgery

Not yet recruiting

• Conditions: Thoracic Cancer; Complication,Postoperative

• Registration Number: NCT07016022
• Lead Sponsor: Ottawa Hospital Research Institute

Brief Summary

Major thoracic surgery is high risk as it carries a significant risk of postoperative Adverse Events (AEs), where patients experience complications and do not recover as expected. These AEs can increase the risk of mortality, hospital length of stay, as well as healthcare costs. The investigators' aim is to improve surgical safety by pioneering a marked advance in preoperative prediction of postoperative AEs that will enable individualized targeted perioperative pathways to prevent postoperative AEs. Given that illness and stress are associated with a loss in physiologic variability (e.g. heart and respiration rate), the investigators will use heart and lung variability assessments to improve prediction of postoperative AEs. Therefore, this study aims to assess the feasibility of implementing a preoperative CardioPulmonary variability assessment; determine if preoperative CardioPulmonary variability is associated with postoperative AEs; and determine if this variability assessment is superior and complementary to existing measures of risk and frailty.

Detailed Description

Not available

Study Timeline

• Status Verified: 2025-06
• Study First Submitted: 2025-06-03
• Study First Submitted QC: 2025-06-03
• Study First Posted: 2025-06-11
• Last Update Submitted: 2025-06-16
• Last Update Posted: 2025-06-19
• Study Start: 2025-07-01
• Primary Completion: 2026-05-01
• Study Completion: 2026-07-01

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 130

Inclusion Criteria

• Adult patient (≥18 years of age)
• Patients undergoing major thoracic resection for lung, esophageal or gastric cancer or mediastinal tumour (at least lobectomy, pneumonectomy, esophagectomy, gastrectomy, or mediastinal tumour resection)

Exclusion Criteria

• Urgent/emergent cases
• Patients with pre-existing atrial fibrillation or arrhythmia (persistent/paroxysmal)
• Patients that are pacemaker dependent
• Patients unable to participate in preoperative testing protocol (CardioPulmonary Variability Assessment)
• Patients that are pregnant

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Feasibility of implementing CPVA testing in thoracic surgery patients	Upon study completion, 10 months after study initiation	Implementing CPVA testing in thoracic surgery patients will be considered feasible if: \>50% of identified eligible patients are enrolled and consented, \>90% of consented and enrolled patients complete CPVA testing protocol There is adequate data analysis from the CPVA in \>90% of patients that complete the CPVA protocol \>95% of CRFs are complete
Statistical association between preoperative HRV and RRV and major cardiac and pulmonary postoperative AEs	Upon study completion, 10 months after study initiation	The statistical association between preoperative HRV and RRV and major cardiac and pulmonary postoperative AEs will be determined by separating the patient's into subgroups based on (a) any AEs (any class), (b) serious AEs (Class III and above), (c) the type of AEs they experience (including POPCs, atrial fibrillation, air leak) and performing statistical analyses to determine if there is a significance between patients who experience AEs and those who don't based on the CPVA metrics.

Secondary Outcome Measures

Name	Time	Method
Assess predictive power of CPVA indices	Upon study completion, 10 months after study initiation	A simple univariate logistic regression model will be created for each variability metric and literature-based thresholds will be used for the CPVA indices to assess their predictive power, which will be compared to that of the current predictor for the 3 types of AEs (POPC, arrhythmia, air leak). For each AE, a multiple logistic regression using the original predictors and variability features will also be performed to assess the added value of the variability features.