Complication,Postoperative, Thoracic Cancer
Conditions
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.
Interventions
OTHERCardioPulmonary Variability Assessment
The Preoperative CardioPulmonary Variability Assessment (CPVA) will assess baseline HRV \& RRV and the ability to tolerate mild physiologic stress. The assessment will involve a a Philips MP50 monitor that will collect continuous ECG (Lead II) and CO2-based respiratory waveform data with intermittent blood pressure (BP) measurements over a test of less than 60 minutes. We will measure the response to mild physiological stress which reflects adaptability, autonomic function and functional mobility by having the participant perform certain tasks such as sitting, standing, deep breathing, valsalva manoeuvres, and marching.
Sponsors
Ottawa Hospital Research Institute
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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
| Measure | Time frame | Description |
|---|---|---|
| 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. |
Countries
Canada
Contacts
CONTACTAndrew JE Seely, MD,PhD,FRSCS
CONTACTDaniel G Jones, MD,MPH,FRCSC
PRINCIPAL_INVESTIGATORAndrew JE Seely, MD, PhD, FRSCS
The Ottawa Hospital
Outcome results
None listed