About this trial
PAnDA-One is a prospective, multicenter, interventional study (10 centers, France) aimed at developing and validating a diagnostic support algorithm based on the ADx-One medical device, which non-invasively acquires thoracic vibrations using airborne ultrasound.
The study will enroll 2,500 patients presenting to the emergency department with acute dyspnea or non-traumatic chest pain, divided into a development cohort (N = 1,500) and an independent test cohort (N = 1,000). The deep learning algorithm will be trained to discriminate cardiovascular from non-cardiovascular origins of symptoms, and its performance will be assessed by AUROC, sensitivity, and specificity against a final diagnosis established by an expert adjudication committee.
Patient management will not be modified by study participation.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Presentation to the emergency department for one or more of the following symptoms of less than 14 days' duration: acute non-traumatic dyspnea, or recent worsening of chronic dyspnea, or non-traumatic chest pain.
Patient able to sit on a chair or on the edge of the bed
Affiliation to national health insurance
Able to receive study information, understand the study, and provide written informed consent
Disqualifiers
• Immediate need for life-saving intervention or clinical instability incompatible with study procedures.
Shock or severe hemodynamic instability, for example systolic blood pressure <90 mmHg for at least 30 minutes or associated signs of hypoperfusion.
Altered mental status or any condition preventing provision of valid informed consent.
Known cognitive impairment preventing informed consent.
Trial population
Patients presenting to the emergency department for one or more of the following symptoms of less than 14 days' duration: acute non-traumatic dyspnea, or recent worsening of chronic dyspnea, or non-traumatic chest pain.
Trial design
Cohort
Prospective
Treatments tested in this trial
All patients from both groups will be imaged with airborne ultrasound device
DevicePatients from both cohorts will follow their usual routine, except for stepping in front of the machine so that one or two images of their chest and back can be taken.
Treatment groups
Trial outcomes
Primary outcomes
Number of correct assignations for cardiovascular involvement by the algorithm trained on labeled data.
Diagnostic performance for distinguishing cardiovascular from non-cardiovascular involvement, including sensitivity, specificity, positive and negative predictive values, likelihood ratios, and AUROC.
Secondary outcomes
Number of correct assignations for acute heart failure with pulmonary edema by the algorithm.
Diagnostic performance for the diagnosis of acute heart failure with pulmonary edema, including sensitivity, specificity, positive and negative predictive values, likelihood ratios, and AUROC
Number of correct assignations for COPD exacerbation by the algorithm.
Diagnostic performance for the diagnosis of chronic obstructive pulmonary disease, including sensitivity, specificity, positive and negative predictive values, likelihood ratios, and AUROC.
Number of correct assignations by the algorithm for lower respiratory tract infection, defined as alveolar or interstitial involvement from an infectious cause.
Diagnostic performance for the diagnosis of lower respiratory tract infection, including sensitivity, specificity, positive and negative predictive values, likelihood ratios, and AUROC.
Number of correct assignations by the algorithm for absence of pulmonary and cardiovascular involvement.
Diagnostic performance for the diagnosis of absence of both pulmonary and cardiovascular involvement, including sensitivity, specificity, positive and negative predictive values, likelihood ratios, and AUROC.
Other outcomes
Number of correct assignations by the algorithm for pulmonary involvement, either isolated or combined with cardiovascular involvement.
Diagnostic performance for distinguishing pulmonary from non-pulmonary involvement, including sensitivity, specificity, positive and negative predictive values, likelihood ratios, and AUROC. Pulmonary involvement is defined as any diagnosis of pulmonary or parenchymal lesion or bronchial involvement, including (but not limited to): upper respiratory tract infection, lower respiratory tract infection, exacerbation of obstructive ventilatory disorder such as asthma or COPD.
Number of correct assignations by the algorithm for lung interstitial syndrome.
Diagnostic performance for the diagnosis of interstitial syndrome, including sensitivity, specificity, positive and negative predictive values, likelihood ratios, and AUROC.
Number of correct assignations by the algorithm for detecting an impaired left ventricular ejection fraction.
Diagnostic performance for the diagnosis of a left ventricular ejection fraction \< 55% at cardiac echography at any point in the month before or after the inclusion, including sensitivity, specificity, positive and negative predictive values, likelihood ratios, and AUROC.
Number of correct assignations by the algorithm for pulmonary embolism.
Diagnostic performance for the diagnosis of pulmonary embolism, diagnosed either at computed tomography pulmonary angiogram or V/Q scan, including sensitivity, specificity, positive and negative predictive values, likelihood ratios, and AUROC.
Sponsors and contacts
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