Validation of an Algorithm to Predict the Ventilatory Threshold
- Conditions
- Cardiovascular Diseases
- Registration Number
- NCT04929431
- Lead Sponsor
- Hasselt University
- Brief Summary
The aim of the current study was to develop an algorithm which has the ability to accurately predict the first and second ventilatory threshold and in cardiovascular disease patients and to guide in proper exercise intensity determination. This would then help, at least in part, to overcome the lack of access to metabolic carts or cardiopulmonary exercise test, and/or methodological difficulties with ventilatory threshold determination in these patients.
- Detailed Description
Design
This study is composed out of two sub studies: 1. Generation/creation of VT prediction algorithm, and 2. Validation of this algorithm in independent laboratories.
Sub study 1: Generation/creation of VT prediction algorithm
From April 2015 up to July 2020, data from CVD (risk) patients (e.g. obesity, diabetes, coronary artery disease or heart failure) were collected from in light of research studies. All participants signed an informed consent explaining the nature and risks of CPET, and allowing us to use anonymized data for the analyses of their CPET at entry of cardiovascular rehabilitation or an exercise intervention. These data have been published in previous publications.
Sub study 2: Validation of the algorithm in independent laboratories
From April 2015 up to July 2020, data from CVD (risk) patients (e.g. obesity, diabetes, coronary artery disease or heart failure) were collected in light of research studies. All participants signed an informed consent (approved by the ethics committees of the local hospitals or research laboratories) explaining the nature and risks of CPET, and allowing us to use anonymized data for the analyses of their CPET at entry of CR or an exercise intervention. These data have been published in previous publications
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 3000
- CVD patients (eg obesity, diabetes, coronary heart disease, heart failure)
- No present CVD
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Workload during cardiopulmonary exercise testing Baseline - day 1 Peak workload (watt)
Duration during cardiopulmonary exercise testing Baseline - day 1 Test duration (min)
Oxygen uptake during cardiopulmonary exercise testing Baseline - day 1 Maximal oxygen uptake (ml/kg/min)
Heart rate during cardiopulmonary exercise testing Baseline - day 1 Peak heart rate (bpm)
Heart rate in rest during cardiopulmonary exercise testing Baseline - day 1 Resting heart rate (bpm)
- Secondary Outcome Measures
Name Time Method Cardiovascular surgery Baseline Information regarding cardiovascular surgery will be extracted by personal communication with the subject
Gender (m/f) Baseline Gender in m/f will be retrospectively extracted from the databank
Obesity Baseline Presence of obesity determined by the BMI (see below)
Diabetes (mg/dl) Baseline Glucose concentration in the blood in mg/dl
Smoking Baseline Presence of smoking by questionnaire (yes/no)
Length in meters Baseline Length in meter will be retrospectively extracted from the databank
Weight in kg Baseline Weight in kg will be retrospectively extracted from the databank
BMI (kg/m^2) Baseline Weight (in kg) and height (in m) will be combined to assess BMI (in kg/m\^2)
Hypertension (in mmHg) Baseline Blood pressure measurement with a automatic blood pressure cuff.
Dyslipdemia (in mg/dl) Baseline Blood lipid concentration in mg/dl
Medication intake Baseline Information regarding medication intake will be retrospectively extracted by personal communication with the subject
Age in years Baseline Age in years will be retrospectively extracted from the databank
Trial Locations
- Locations (1)
Hasselt University
🇧🇪Diepenbeek, Limburg, Belgium