Contactless Ultrasound Data Acquisition in Emergency Departments to Discriminate the Origin of Dyspnea and Chest Pain (PAnDA-One)
Prospective Study for Data Acquisition in Emergency Departments to Develop a Discrimination Algorithm for the Origin of Dyspnea and Chest Pain Using the ADx-One Medical Device
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.
調査の概要
状態
詳細な説明
Acute dyspnea is a common reason for emergency department (ED) presentation, accounting for 7 to 12% of all consultations (Mockel et al., 2013). More than half of these patients are over 65 years of age - a population whose respiratory system is particularly vulnerable due to parenchymal degeneration and age-related decline in ventilatory and immune function (Boisguérin et Mauro, 2017). In the ED, the leading causes of acute dyspnea are acute heart failure, respiratory tract infections, exacerbations of asthma or chronic obstructive pulmonary disease, and pulmonary embolism (Ray et al., 2006). These episodes frequently lead to hospitalization and carry substantial mortality - reaching 10% in acute heart failure, for instance (Freund et al., 2020) - as well as a marked loss of autonomy, whether driven by the respiratory impairment itself or by the deconditioning that follows prolonged hospital stays.
Non-traumatic chest pain is another major reason for ED presentation, accounting for 5-10% of all visits, and shares with dyspnea the requirement for rapid etiological triage between life-threatening cardiovascular causes - acute coronary syndrome, pulmonary embolism, acute aortic syndrome, pericarditis, pneumothorax - and benign musculoskeletal or functional causes. Despite the ECG, high-sensitivity troponin and validated clinical scores, missed acute coronary syndrome at ED discharge has been reported in approximately 2% of patients (Pope et al., 2000), and the widespread use of "rule-out" strategies drives substantial use of coronary CTA and CT pulmonary angiography, with associated radiation exposure, costs and observation admissions. Diagnostic uncertainty is particularly pronounced in younger adults and in women, in whom the pretest probability of acute coronary syndrome is lower but pulmonary embolism, pneumothorax and pericarditis carry a non-negligible relative weight. Furthermore, several studies continue to highlight the increased use of irradiative imaging studies, with no clinical benefit in terms of diagnostic and prognostic (Roussel et al., 2023).
At present, the etiological work-up of acute dyspnea and chest pain relies on the combination of clinical examination, laboratory testing, and chest radiography (Olson et Davis, 2020; Miró et al., 2025). This strategy has well-documented limitations in the ED, particularly in older patients:
- Clinical examination lacks discriminative power. Signs and symptoms are rarely disease-specific, and they may be blunted - or altogether absent - in the elderly (Metlay, Kapoor et Fine, 1997; Lien et al., 2002).
- Chest radiography is often suboptimal. Films are frequently acquired with the patient supine, stooped, or unable to hold a full inspiration; the underlying parenchymal changes of ageing further compromise interpretation, and the radiographic signs themselves are notoriously nonspecific (Mueller-Lenke et al., 2006; Hawkins et al., 2009; Self et al., 2013).
- Interpretation is poorly reproducible, and the initial ED diagnosis proves incorrect in nearly one third of cases (Hopstaken et al., 2004; Claessens et al., 2015).
ADx-One is an airborne ultrasound device which relies on Surface Motion Camera technology.
The ADx-One device is based on the non-invasive acquisition of minute chest surface movements induced by cardiopulmonary activity (~100 µm), using airborne ultrasound. These vibrations, resulting from mechanical interactions between the heart, lungs, and chest wall, constitute a physical signature that integrates multiple pathophysiological determinants (Shirkovskiy et al., 2018).
In particular, any modification in intrathoracic composition or mechanical properties-whether due to the presence of fluid (congestion, effusion), solid tissue changes (consolidation, atelectasis), or abnormal air presence (pneumothorax)-is likely to alter these vibratory signatures.
The overall purpose of this clinical investigation is to collect ADx-One acquisition data in emergency department patients in order to develop and evaluate on distinct cohorts a diagnostic support algorithm intended to assist discrimination of the origin of acute cardiopulmonary symptoms.
研究の種類
入学 (推定)
連絡先と場所
研究連絡先
- 名前:Beatrice Spiluttini, PhD
- 電話番号:0033603491582
- メール:clinical-investigation@australdx.fr
研究場所
-
-
-
Paris、フランス、75013
- La Pitié-Salpétrière
-
-
参加基準
適格基準
就学可能な年齢
- 大人
- 高齢者
健康ボランティアの受け入れ
サンプリング方法
調査対象母集団
説明
Inclusion Criteria:
- 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
Exclusion Criteria:
• 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.
- Transfer to another care site before the ADx-One acquisition can be performed.
- Known pregnancy
- Minor, legally protected adult, or person deprived of liberty.
- Participation in another interventional clinical study judged incompatible with this study.
研究計画
研究はどのように設計されていますか?
デザインの詳細
コホートと介入
グループ/コホート |
介入・治療 |
|---|---|
|
training cohort
All patients will follow the same intervention but data from this cohort will only be used for training the algorithm.
|
Patients 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.
|
|
Test Cohort
All patients will follow the same intervention but data from this cohort will only be used for testing the algorithm.
|
Patients 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.
|
この研究は何を測定していますか?
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
|
Number of correct assignations for cardiovascular involvement by the algorithm trained on labeled data.
時間枠:Within the first hour after ED admission.
|
Diagnostic performance for distinguishing cardiovascular from non-cardiovascular involvement, including sensitivity, specificity, positive and negative predictive values, likelihood ratios, and AUROC.
|
Within the first hour after ED admission.
|
二次結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
|
Number of correct assignations for acute heart failure with pulmonary edema by the algorithm.
時間枠:Within the first hour after ED admission.
|
Diagnostic performance for the diagnosis of acute heart failure with pulmonary edema, including sensitivity, specificity, positive and negative predictive values, likelihood ratios, and AUROC
|
Within the first hour after ED admission.
|
|
Number of correct assignations for COPD exacerbation by the algorithm.
時間枠:Within the first hour after ED admission.
|
Diagnostic performance for the diagnosis of chronic obstructive pulmonary disease, including sensitivity, specificity, positive and negative predictive values, likelihood ratios, and AUROC.
|
Within the first hour after ED admission.
|
|
Number of correct assignations by the algorithm for lower respiratory tract infection, defined as alveolar or interstitial involvement from an infectious cause.
時間枠:Within the first hour after ED admission.
|
Diagnostic performance for the diagnosis of lower respiratory tract infection, including sensitivity, specificity, positive and negative predictive values, likelihood ratios, and AUROC.
|
Within the first hour after ED admission.
|
|
Number of correct assignations by the algorithm for absence of pulmonary and cardiovascular involvement.
時間枠:Within the first hour after ED admission.
|
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.
|
Within the first hour after ED admission.
|
その他の成果指標
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
|
Number of correct assignations by the algorithm for pulmonary involvement, either isolated or combined with cardiovascular involvement.
時間枠:Within the first hour after ED admission.
|
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.
|
Within the first hour after ED admission.
|
|
Number of correct assignations by the algorithm for lung interstitial syndrome.
時間枠:Within the first hour after ED admission.
|
Diagnostic performance for the diagnosis of interstitial syndrome, including sensitivity, specificity, positive and negative predictive values, likelihood ratios, and AUROC.
|
Within the first hour after ED admission.
|
|
Number of correct assignations by the algorithm for detecting an impaired left ventricular ejection fraction.
時間枠:Within the first hour after ED admission.
|
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.
|
Within the first hour after ED admission.
|
|
Number of correct assignations by the algorithm for pulmonary embolism.
時間枠:Within the first hour after ED admission.
|
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.
|
Within the first hour after ED admission.
|
|
Number of correct assignations by the algorithm for upper respiratory tract infection.
時間枠:Within the first hour after ED admission.
|
Diagnostic performance for the diagnosis of upper respiratory tract infection, including sensitivity, specificity, positive and negative predictive values, likelihood ratios, and AUROC.
|
Within the first hour after ED admission.
|
|
Number of correct assignations by the algorithm for atelectasis.
時間枠:Within the first hour after ED admission.
|
Diagnostic performance for the diagnosis of atelectasis, including sensitivity, specificity, positive and negative predictive values, likelihood ratios, and AUROC.
|
Within the first hour after ED admission.
|
|
Number of correct assignations by the algorithm for alveolar syndrome.
時間枠:Within the first hour after ED admission.
|
Diagnostic performance for the diagnosis of alveolar syndrome, including sensitivity, specificity, positive and negative predictive values, likelihood ratios, and AUROC.
|
Within the first hour after ED admission.
|
|
Number of correct assignations by the algorithm for pleural effusion.
時間枠:Within the first hour after ED admission.
|
Diagnostic performance for the diagnosis of pleural effusion, including sensitivity, specificity, positive and negative predictive values, likelihood ratios, and AUROC.
|
Within the first hour after ED admission.
|
|
Number of correct assignations by the algorithm for pneumothorax.
時間枠:Within the first hour after ED admission.
|
Diagnostic performance for the diagnosis of pneumothorax, including sensitivity, specificity, positive and negative predictive values, likelihood ratios, and AUROC
|
Within the first hour after ED admission.
|
協力者と研究者
スポンサー
捜査官
- 主任研究者:Yonathan Freund, Professor、Assistance Publique - Hôpitaux de Paris
研究記録日
主要日程の研究
研究開始 (推定)
一次修了 (推定)
研究の完了 (推定)
試験登録日
最初に提出
QC基準を満たした最初の提出物
最初の投稿 (実際)
学習記録の更新
投稿された最後の更新 (実際)
QC基準を満たした最後の更新が送信されました
最終確認日
詳しくは
本研究に関する用語
キーワード
その他の研究ID番号
- 2026-A01025-46
個々の参加者データ (IPD) の計画
個々の参加者データ (IPD) を共有する予定はありますか?
IPD プランの説明
医薬品およびデバイス情報、研究文書
米国FDA規制医薬品の研究
米国FDA規制機器製品の研究
この情報は、Web サイト clinicaltrials.gov から変更なしで直接取得したものです。研究の詳細を変更、削除、または更新するリクエストがある場合は、register@clinicaltrials.gov。 までご連絡ください。 clinicaltrials.gov に変更が加えられるとすぐに、ウェブサイトでも自動的に更新されます。