Validation of an Algorithm to Predict the Ventilatory Threshold

Study Overview
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Brief Summary
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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
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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

Official Title
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Validation of an Algorithm to Predict the Ventilatory Threshold for Exercise Intensity Prescription in Patients With Cardiovascular Disease

Conditions
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Cardiovascular Diseases

Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: CVD patients (eg obesity, diabetes, coronary heart disease, heart failure) Exclusion Criteria: No present CVD

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Duration during cardiopulmonary exercise testing | Test duration (min) | Baseline - day 1 | 
| Workload during cardiopulmonary exercise testing | Peak workload (watt) | Baseline - day 1 | 
| Heart rate during cardiopulmonary exercise testing | Peak heart rate (bpm) | Baseline - day 1 | 
| Oxygen uptake during cardiopulmonary exercise testing | Maximal oxygen uptake (ml/kg/min) | Baseline - day 1 | 
| Heart rate in rest during cardiopulmonary exercise testing | Resting heart rate (bpm) | Baseline - day 1 | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Hypertension (in mmHg) | Blood pressure measurement with a automatic blood pressure cuff. | Baseline | 
| Dyslipdemia (in mg/dl) | Blood lipid concentration in mg/dl | Baseline | 
| Diabetes (mg/dl) | Glucose concentration in the blood in mg/dl | Baseline | 
| Obesity | Presence of obesity determined by the BMI (see below) | Baseline | 
| Smoking | Presence of smoking by questionnaire (yes/no) | Baseline | 
| Medication intake | Information regarding medication intake will be retrospectively extracted by personal communication with the subject | Baseline | 
| Cardiovascular surgery | Information regarding cardiovascular surgery will be extracted by personal communication with the subject | Baseline | 
| Age in years | Age in years will be retrospectively extracted from the databank | Baseline | 
| Gender (m/f) | Gender in m/f will be retrospectively extracted from the databank | Baseline | 
| Length in meters | Length in meter will be retrospectively extracted from the databank | Baseline | 
| Weight in kg | Weight in kg will be retrospectively extracted from the databank | Baseline | 
| BMI (kg/m^2) | Weight (in kg) and height (in m) will be combined to assess BMI (in kg/m^2) | Baseline |