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Validation of an Algorithm to Predict the Ventilatory Threshold

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

Status
Completed
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT04929431
Enrollment
3000
Registered
2021-06-18
Start date
2021-03-01
Completion date
2021-04-01
Last updated
2021-06-18

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Cardiovascular Diseases

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

Interventions

None listed

Sponsors

University of Brasilia
CollaboratorOTHER
University of Bern
CollaboratorOTHER
Universitaire Ziekenhuizen KU Leuven
CollaboratorOTHER
University of Siena
CollaboratorOTHER
Technical University of Munich
CollaboratorOTHER
Hasselt University
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
RETROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No

Inclusion criteria

* CVD patients (eg obesity, diabetes, coronary heart disease, heart failure)

Exclusion criteria

* No present CVD

Design outcomes

Primary

MeasureTime frameDescription
Duration during cardiopulmonary exercise testingBaseline - day 1Test duration (min)
Workload during cardiopulmonary exercise testingBaseline - day 1Peak workload (watt)
Heart rate during cardiopulmonary exercise testingBaseline - day 1Peak heart rate (bpm)
Oxygen uptake during cardiopulmonary exercise testingBaseline - day 1Maximal oxygen uptake (ml/kg/min)
Heart rate in rest during cardiopulmonary exercise testingBaseline - day 1Resting heart rate (bpm)

Secondary

MeasureTime frameDescription
Medication intakeBaselineInformation regarding medication intake will be retrospectively extracted by personal communication with the subject
Cardiovascular surgeryBaselineInformation regarding cardiovascular surgery will be extracted by personal communication with the subject
Age in yearsBaselineAge in years will be retrospectively extracted from the databank
Hypertension (in mmHg)BaselineBlood pressure measurement with a automatic blood pressure cuff.
Length in metersBaselineLength in meter will be retrospectively extracted from the databank
Weight in kgBaselineWeight in kg will be retrospectively extracted from the databank
BMI (kg/m^2)BaselineWeight (in kg) and height (in m) will be combined to assess BMI (in kg/m\^2)
Gender (m/f)BaselineGender in m/f will be retrospectively extracted from the databank
Dyslipdemia (in mg/dl)BaselineBlood lipid concentration in mg/dl
Diabetes (mg/dl)BaselineGlucose concentration in the blood in mg/dl
ObesityBaselinePresence of obesity determined by the BMI (see below)
SmokingBaselinePresence of smoking by questionnaire (yes/no)

Countries

Belgium

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026