Assessment of an Exhaled Breath Test Using High-Pressure Photon Ionization Time-of-Flight Mass Spectrometry to Detect Bronchiectasis
Conditions: Bronchiectasis
Sponsor: Shanghai Pulmonary Hospital, Shanghai, China

 
 
 
 
 
Study Protocol and Statistical Analysis Plan 
 
Official Title: Assessment of an Exhaled Breath Test Using High-Pressure Photon 
Ionization Time-of-Flight Mass Spectrometry to Detect Bronchiectasis 
 
NCT number: NCT05293314 
 
Document date: July 17, 2023 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Study Description 
To investigate whether the breath test is able to detect bronchiectasis using breathomics. This 
study was conducted with a prospective specimen collection, evaluator-blinded, case-
controlled clinical study designed to evaluate the accuracy of breathomics to diagnosis of 
bronchiectasis in adults. 
 
Study Design 
Study Population:  
 
The age of all subjects were all older than 18. The study population was recruited from 
Shanghai pulmonary hospital. Diagnosis of bronchiectasis was performed using chest HRCT 
scans in suspected patients with coughing and expectoration, or long durations of 
haemoptysis. Healthy participants were as control. Participants with Interstitial lung disease or 
Sarcoidosis were as unhealthy control. 
Criteria:  
 
Inclusion Criteria: 
Patients were recruited according to the following inclusion criteria: (1) Age>18 years; (2) the 
diagnosis of BE was according to European Respiratory Society guidelines for the 
management of adult bronchiectasis; (3) Willing to join in and sign the informed consent form. 
Healthy control subjects were recruited according to the following inclusion criteria:(1) 
Age>18 years; (2) No history of any lung disease (according to pulmonary imaging and 
physical examinations); (3) Willing to join in and sign the informed consent form. 
Unhealthy control subjects were recruited according to the following inclusion criteria: (1) 
Age>18 years; (2) The diagnosis of ILD was according to HRCT and clinical symptoms by two 
experts’ consensus; (3) The diagnosis of Sarcoidosis (SA) was according to American Thoracic 
Society Clinical Practice Guideline;(4) Willing to join in and sign the informed consent form. 
Exclusion Criteria: 
(1) Patients combined with serious comorbidities (chronic renal failure, hepatic disease, etc.); 
(2) Patients who are diagnosed with asthma, pneumonia, malignant tumor, and COPD; (3) 
Women who are pregnant or preparing for pregnancy or breastfeeding; (4) Participated in 
other clinical trials within three months; (5) Refused to join in and sign the informed consent 
form. 
 
Exhaled Breath collection: 
All exhaled breath samples were collected by trained investigators following the same 
protocol. All participants were asked to fast for eight hours and rinsed their mouths with clean 
water before collecting breath samples. To reduce potential confounding factors, all 
participants were asked to not ingest spicy food, alcohol, or coffee the night before exhaled 
breath collection. Participants were asked to perform a single deep nasal inhalation followed 
by complete exhalation via their mouth into an airbag with a volume of 1.2L made of 
polyether-ether-ketone (PEEK) bags until the bags were full of exhaled air. For each day of 
sample collection, ambient air was collected to make sure the environment was not polluted. 
The PEEK airbags were carried out to the laboratory within one week and analyzed by HPPI-

TOFMS analysis. 
 
Clinical Data Collection: 
The clinical information of hospitalized patients was collected through a medical record 
system and questionnaire survey, including chronic medical history, smoking history, chest 
HRCT imaging data, annual acute attack frequency, disease severity (BSI score), etc. 
The information of health examiners is obtained through a survey questionnaire, including 
age, chronic medical history, smoking history, family history of tumors, and chest HRCT. 
The imaging data were obtained from the Shanghai Shanghai pulmonary hospital. And all 
clinical data were extracted from the medical system by two individual researchers. 
 
VOCs detection: 
On the HPPI-TOFMS detection platform, soft ionization was achieved by a commercial VUV-
Kr lamp with a photon energy of 10.6 eV to predominantly produce radical cations (M+) which 
are formed as M + hγ → M+ + e, which can ionize most VOCs in the breath sample. 
Meanwhile, some protonated ions are also produced. The VOCs with mass-charge ratio(m/z) 
in the range of [20, 320] were detected on HPPI-TOFMS. In this study, the area of the 
strongest peak in the range of [x-0.5, x+0.5) was calculated, normalized, and regarded as the 
feature of VOC with m/z of x. Thus, three hundred kinds of VOC ions features were extracted 
via breath detection in HPPI-TOFMS. 
 
VOCs analysis: 
To select valuable breathomics features and avoid model over-fitting, a combined feature 
selection method was executed for breathomics features. Firstly, the breathomics features 
with no significant differences (p >0.05) between BE and controls were excluded. Then, the 
VOC ions with low intensity but highly correlated with other selected VOC ions (correlation 
coefficient >0.9) among all discovery samples were excluded. At last, a random forest (RF) 
model was constructed on the discovery data set, and the ten most important VOC ions were 
selected based on the feature coefficient in RF model.  
In this study, we constructed BE detection models via six popular ML methods, Random 
Forests (RF), Logistic regression (LR), Xtreme Gradient Boosting (XGB), K-Nearest Neighbor 
(KNN), Decision tree (DT) and the soft ensemble algorithm of RF, XGB, and LR, in discovery 
data set. To evaluate the performance of VOCs based BE detection models, the model 
detection results were evaluated and compared with the HRCT confirmed diagnosis results in 
the blinded test set. 
 
Statistical analysis 
In this study, descriptive statistics were described as frequencies (percentages) for categorical 
variables or medians (interquartile range [IQR]) for continuous variables. The Mann-Whitney 
U test for continuous variables and the chi-square test for categorical variables were 
calculated among the different groups. Sensitivity (SEN), specificity (SPE), and accuracy (ACC) 
were calculated to evaluate the BE detection performance. A receiver operating characteristic 
(ROC) curve was plotted, and the area under the ROC curve (AUC) was calculated to assess 
the overall performance of the VOCs based BE detection model. All statistical analyses were 

performed by SAS version 9.4 (SAS Institute Inc., Cary, NC, USA) and the Origin software 
(version 2018). A p-value<0.05 was considered statistically significant. All tests were two tailed. 
 
Study plan 
To construct an easy, fast, and non-invasive VOCs-based bronchiectasis detection mode  
This study plans to recruit 200 Bronchiectasis patients diagnosed by HRCT from the 
Department of Respiratory and Critical Care Medicine of Shanghai Pulmonary Hospital and 
200 healthy participants from July,2022 to March,2023. 
The exhaled gas of admitted patients was collected on the day of admission, that of 
outpatients on the day of outpatient service, and that of examinees on the day of physical 
examination. After the exhaled gas was collected, VOCs were detected by mass spectrometry 
according to the standard processing flow, and the full spectrum of exhaled VOCs of 
Bronchiectasis patients and healthy people were analyzed to explore the characteristics of 
exhaled VOCs of Bronchiectasis patients and healthy people, Analyze the correlation between 
VOCs and clinical disease severity and prognosis (number of acute exacerbations) 
characteristics. The data of 400 cases were randomly assigned in a 5:2:3 ratio to the discovery 
cohort, internal validation, and blinded test cohorts. The discovery and internal validation sets 
were used for potential VOC biomarkers selection and diagnostic model construction. 
 
To evaluate the VOCs-based bronchiectasis detection mode 
Participants were recruited as mentioned above. The exhaled gas was collected and analysed 
as previously described. The data of 400 cases were randomly assigned in a 5:2:3 ratio to the 
discovery cohort, internal validation, and blinded test cohorts. The blinded test set was used 
for model evaluation.