Trial Outcomes & Findings for Role of On-site CT-derived FFR in the Management of Suspect CAD Patients (NCT NCT03901326)

Last Updated: 2024-05-31

Results Overview

Number of those patients with planned ICA in whom no significant obstructive CAD (no stenosis≥70% by core lab quantitative analysis or invasive FFR≤0.8) is found or interventions (including stent implantation, balloon dilation and bypass graft) are performed during ICA within 90 days.

Recruitment status

COMPLETED

Study phase

Target enrollment

1216 participants

Primary outcome timeframe

90 days

Results posted on

2024-05-31

Participant Flow

Participant milestones

Participant milestones
Measure	CTA/CT-FFR Care Group If the subjects are randomly allocated to CT-FFR arm, they will be examined by DeepFFR for three major epicardial arteries. If the result of CT-FFR calculation is less than or equal to 0.8 in one or more major，coronary arteries, the patient will be referred to ICA directly; if the result of CT-FFR value is more than 0.8, optimal medical therapy will be recommended. The decision on the mode of revascularization is left to the treating cardiologists and depends on local practice standard. CT-FFR: DeepFFR workstation is very dedicated software utilizing the original CTA imaging to meter simulated FFR values in artificial intelligence model.The first step is to extract a 3D coronary artery model and generate coronary centerlines which are similar to the routine reconstruction of coronary CTA. The centerlines are extracted using a minimal path extraction filter. Then a novel path-based deep learning model, referred to DeepFFR, is used to predict the simulated FFR values on the vascular centerlines. Deep learning algorithm is used to establish characteristic sample database of coronary hemodynamics characteristic parameters. When deep training model is proved to be valid, it is applied to a new lesion-specific measurement. Lesion-specific CT-FFR is defined as simulated FFR value at distance of 20mm away from the lesion of interest.	Routine Clinically-indicated Diagnostic Care Group If the subjects are randomized to usual care arm, attending physicians will decide the next step of diagnosis and treatment, such as exercise ECG, stress cardiac echo, cardiac MR, and SPECT. According to the results of examination combined with risk factors assessment and clinical manifestations, physicians should provide recommendation whether the subjects would undergo ICA or not.
Overall Study STARTED	608	608
Overall Study COMPLETED	608	608
Overall Study NOT COMPLETED	0	0

Reasons for withdrawal

Withdrawal data not reported

Baseline Characteristics

Race and Ethnicity were not collected from any participant.

Baseline characteristics by cohort

Baseline characteristics by cohort
Measure	CTA/CT-FFR Care Group n=608 Participants If the subjects are randomly allocated to CT-FFR arm, they will be examined by DeepFFR for three major epicardial arteries. If the result of CT-FFR calculation is less than or equal to 0.8 in one or more major，coronary arteries, the patient will be referred to ICA directly; if the result of CT-FFR value is more than 0.8, optimal medical therapy will be recommended. The decision on the mode of revascularization is left to the treating cardiologists and depends on local practice standard. CT-FFR: DeepFFR workstation is very dedicated software utilizing the original CTA imaging to meter simulated FFR values in artificial intelligence model.The first step is to extract a 3D coronary artery model and generate coronary centerlines which are similar to the routine reconstruction of coronary CTA. The centerlines are extracted using a minimal path extraction filter. Then a novel path-based deep learning model, referred to DeepFFR, is used to predict the simulated FFR values on the vascular centerlines. Deep learning algorithm is used to establish characteristic sample database of coronary hemodynamics characteristic parameters. When deep training model is proved to be valid, it is applied to a new lesion-specific measurement. Lesion-specific CT-FFR is defined as simulated FFR value at distance of 20mm away from the lesion of interest.	Routine Clinically-indicated Diagnostic Care Group n=608 Participants If the subjects are randomized to usual care arm, attending physicians will decide the next step of diagnosis and treatment, such as exercise ECG, stress cardiac echo, cardiac MR, and SPECT. According to the results of examination combined with risk factors assessment and clinical manifestations, physicians should provide recommendation whether the subjects would undergo ICA or not.	Total n=1216 Participants Total of all reporting groups
Age, Continuous	60.0 years STANDARD_DEVIATION 8.3 • n=608 Participants	59.2 years STANDARD_DEVIATION 11.5 • n=608 Participants	59.6 years STANDARD_DEVIATION 10.0 • n=1216 Participants
Sex: Female, Male Female	210 Participants n=608 Participants	221 Participants n=608 Participants	431 Participants n=1216 Participants
Sex: Female, Male Male	398 Participants n=608 Participants	387 Participants n=608 Participants	785 Participants n=1216 Participants
Race and Ethnicity Not Collected	—	—	0 Participants Race and Ethnicity were not collected from any participant.
Body mass index	25.1 kg/m² n=608 Participants	25.3 kg/m² n=608 Participants	25.2 kg/m² n=1216 Participants

PRIMARY outcome

Timeframe: 90 days

Population: In the general population, patients with severe coronary stenosis maybe considered to be sent to catheter room, while the main endpoint is the negative findings during angiography. Therefore, for instance, the total population in the CT-FFR group is 608 (row1), of which 421 patients (row2, 3) enter the catheter room for coronary angiography. Therefore, the proportion of the three rows is different.

Outcome measures

Outcome measures
Measure	CTA/CT-FFR Care Group n=608 Participants If the subjects are randomly allocated to CT-FFR arm, they will be examined by DeepFFR for three major epicardial arteries. If the result of CT-FFR calculation is less than or equal to 0.8 in one or more major，coronary arteries, the patient will be referred to ICA directly; if the result of CT-FFR value is more than 0.8, optimal medical therapy will be recommended. The decision on the mode of revascularization is left to the treating cardiologists and depends on local practice standard. CT-FFR: DeepFFR workstation is very dedicated software utilizing the original CTA imaging to meter simulated FFR values in artificial intelligence model.The first step is to extract a 3D coronary artery model and generate coronary centerlines which are similar to the routine reconstruction of coronary CTA. The centerlines are extracted using a minimal path extraction filter. Then a novel path-based deep learning model, referred to DeepFFR, is used to predict the simulated FFR values on the vascular centerlines. Deep learning algorithm is used to establish characteristic sample database of coronary hemodynamics characteristic parameters. When deep training model is proved to be valid, it is applied to a new lesion-specific measurement. Lesion-specific CT-FFR is defined as simulated FFR value at distance of 20mm away from the lesion of interest.	Routine Clinically-indicated Diagnostic Care Group n=608 Participants If the subjects are randomized to usual care arm, attending physicians will decide the next step of diagnosis and treatment, such as exercise ECG, stress cardiac echo, cardiac MR, and SPECT. According to the results of examination combined with risk factors assessment and clinical manifestations, physicians should provide recommendation whether the subjects would undergo ICA or not.
Number of Participants With ICA Without Obstructive CAD or Intervention Number of patients undergoing ICA	421 Participants	483 Participants
Number of Participants With ICA Without Obstructive CAD or Intervention Number of patients with ICA without obstructive CAD or intervention	119 Participants	223 Participants
Number of Participants With ICA Without Obstructive CAD or Intervention Number of patients with ICA without obstructive CAD	88 Participants	184 Participants
Number of Participants With ICA Without Obstructive CAD or Intervention Number of patients with ICA without intervention	31 Participants	39 Participants

SECONDARY outcome

Timeframe: 12 months

Population: The primary endpoint analysis was performed in both groups as the primary endpoint was assessable at baseline. However, secondary endpoints were assessed after 1-year follow-up and due to loss to follow-up（21 in CT-FFR group; 19 in standard care group）, 587 and 589 patients from each group were available for final analysis of secondary endpoints.

Major adverse cardiovascular event include death, myocardial infarction (MI), major complications from cardiovascular (CV) procedures or testing, and unstable angina hospitalization

Outcome measures

Outcome measures
Measure	CTA/CT-FFR Care Group n=587 Participants If the subjects are randomly allocated to CT-FFR arm, they will be examined by DeepFFR for three major epicardial arteries. If the result of CT-FFR calculation is less than or equal to 0.8 in one or more major，coronary arteries, the patient will be referred to ICA directly; if the result of CT-FFR value is more than 0.8, optimal medical therapy will be recommended. The decision on the mode of revascularization is left to the treating cardiologists and depends on local practice standard. CT-FFR: DeepFFR workstation is very dedicated software utilizing the original CTA imaging to meter simulated FFR values in artificial intelligence model.The first step is to extract a 3D coronary artery model and generate coronary centerlines which are similar to the routine reconstruction of coronary CTA. The centerlines are extracted using a minimal path extraction filter. Then a novel path-based deep learning model, referred to DeepFFR, is used to predict the simulated FFR values on the vascular centerlines. Deep learning algorithm is used to establish characteristic sample database of coronary hemodynamics characteristic parameters. When deep training model is proved to be valid, it is applied to a new lesion-specific measurement. Lesion-specific CT-FFR is defined as simulated FFR value at distance of 20mm away from the lesion of interest.	Routine Clinically-indicated Diagnostic Care Group n=589 Participants If the subjects are randomized to usual care arm, attending physicians will decide the next step of diagnosis and treatment, such as exercise ECG, stress cardiac echo, cardiac MR, and SPECT. According to the results of examination combined with risk factors assessment and clinical manifestations, physicians should provide recommendation whether the subjects would undergo ICA or not.
Number of Participant With Major Adverse Cardiovascular Event	48 Participants	54 Participants

SECONDARY outcome

Timeframe: 12 months

Overall cardiac medical expenditure by intention to treat at both 90 days and 12 months cumulatively

Outcome measures

Outcome measures
Measure	CTA/CT-FFR Care Group n=587 Participants If the subjects are randomly allocated to CT-FFR arm, they will be examined by DeepFFR for three major epicardial arteries. If the result of CT-FFR calculation is less than or equal to 0.8 in one or more major，coronary arteries, the patient will be referred to ICA directly; if the result of CT-FFR value is more than 0.8, optimal medical therapy will be recommended. The decision on the mode of revascularization is left to the treating cardiologists and depends on local practice standard. CT-FFR: DeepFFR workstation is very dedicated software utilizing the original CTA imaging to meter simulated FFR values in artificial intelligence model.The first step is to extract a 3D coronary artery model and generate coronary centerlines which are similar to the routine reconstruction of coronary CTA. The centerlines are extracted using a minimal path extraction filter. Then a novel path-based deep learning model, referred to DeepFFR, is used to predict the simulated FFR values on the vascular centerlines. Deep learning algorithm is used to establish characteristic sample database of coronary hemodynamics characteristic parameters. When deep training model is proved to be valid, it is applied to a new lesion-specific measurement. Lesion-specific CT-FFR is defined as simulated FFR value at distance of 20mm away from the lesion of interest.	Routine Clinically-indicated Diagnostic Care Group n=589 Participants If the subjects are randomized to usual care arm, attending physicians will decide the next step of diagnosis and treatment, such as exercise ECG, stress cardiac echo, cardiac MR, and SPECT. According to the results of examination combined with risk factors assessment and clinical manifestations, physicians should provide recommendation whether the subjects would undergo ICA or not.
Medical Expenditure	47032 ¥ Standard Deviation 38533	51265 ¥ Standard Deviation 41462

SECONDARY outcome

Timeframe: Study entry, 3 months, 6 months and12 months

Patient reporting outcomes as measured by Seattle Angina Questionnaire-7(SAQ-7) Scale, use SAQ-7-item instrument that measures patient reported symptoms, function and quality of life for subjects with CAD within 12 months. The SAQ-7 score is calculated as the average of the physical limitation score, quality of life score and angina frequency score. The physical limitation score, quality of life score and angina frequency score range from 0 to 100 each. Therefore, the SAQ-7 score also ranges from 0 to 100.The higher the SAQ-7 socre, physical limitation score, quality of life score and angina frequency score are, the better the quality of life for patients with angina.

Outcome measures

Outcome measures
Measure	CTA/CT-FFR Care Group n=587 Participants If the subjects are randomly allocated to CT-FFR arm, they will be examined by DeepFFR for three major epicardial arteries. If the result of CT-FFR calculation is less than or equal to 0.8 in one or more major，coronary arteries, the patient will be referred to ICA directly; if the result of CT-FFR value is more than 0.8, optimal medical therapy will be recommended. The decision on the mode of revascularization is left to the treating cardiologists and depends on local practice standard. CT-FFR: DeepFFR workstation is very dedicated software utilizing the original CTA imaging to meter simulated FFR values in artificial intelligence model.The first step is to extract a 3D coronary artery model and generate coronary centerlines which are similar to the routine reconstruction of coronary CTA. The centerlines are extracted using a minimal path extraction filter. Then a novel path-based deep learning model, referred to DeepFFR, is used to predict the simulated FFR values on the vascular centerlines. Deep learning algorithm is used to establish characteristic sample database of coronary hemodynamics characteristic parameters. When deep training model is proved to be valid, it is applied to a new lesion-specific measurement. Lesion-specific CT-FFR is defined as simulated FFR value at distance of 20mm away from the lesion of interest.	Routine Clinically-indicated Diagnostic Care Group n=589 Participants If the subjects are randomized to usual care arm, attending physicians will decide the next step of diagnosis and treatment, such as exercise ECG, stress cardiac echo, cardiac MR, and SPECT. According to the results of examination combined with risk factors assessment and clinical manifestations, physicians should provide recommendation whether the subjects would undergo ICA or not.
Patient Reporting Outcomes Seattle Angina Questionnaire-7(SAQ-7) Scale in study entry	57.6 scores on a scale Standard Deviation 4.5	57.4 scores on a scale Standard Deviation 4.3
Patient Reporting Outcomes Seattle Angina Questionnaire-7(SAQ-7) Scale in 3 month	73.8 scores on a scale Standard Deviation 1.1	72.3 scores on a scale Standard Deviation 1.0
Patient Reporting Outcomes Seattle Angina Questionnaire-7(SAQ-7) Scale in 6 month	81.4 scores on a scale Standard Deviation 1.3	80.7 scores on a scale Standard Deviation 1.2
Patient Reporting Outcomes Seattle Angina Questionnaire-7(SAQ-7) Scale in 12 month	86.3 scores on a scale Standard Deviation 3.1	87 scores on a scale Standard Deviation 2.9
Patient Reporting Outcomes Physical limitation score in study entry	59 scores on a scale Standard Deviation 4.3	56.9 scores on a scale Standard Deviation 4.1
Patient Reporting Outcomes Physical limitation score in 3 month	73 scores on a scale Standard Deviation 1.1	70.1 scores on a scale Standard Deviation 0.9
Patient Reporting Outcomes Physical limitation score in 6 month	81.1 scores on a scale Standard Deviation 1.7	79.1 scores on a scale Standard Deviation 1.9
Patient Reporting Outcomes Physical limitation score in 12 month	88.9 scores on a scale Standard Deviation 3.3	88 scores on a scale Standard Deviation 2.9
Patient Reporting Outcomes Quality of life score in study entry	61.2 scores on a scale Standard Deviation 4.7	62.1 scores on a scale Standard Deviation 4.7
Patient Reporting Outcomes Quality of life score in 3 month	77.1 scores on a scale Standard Deviation 1.8	75 scores on a scale Standard Deviation 1.9
Patient Reporting Outcomes Quality of life score in 6 month	82 scores on a scale Standard Deviation 1.9	83 scores on a scale Standard Deviation 1.7
Patient Reporting Outcomes Quality of life score in 12 month	87.1 scores on a scale Standard Deviation 3.0	88.2 scores on a scale Standard Deviation 3.6
Patient Reporting Outcomes Angina frequency score in study entry	52.5 scores on a scale Standard Deviation 4.5	53.2 scores on a scale Standard Deviation 4.3
Patient Reporting Outcomes Angina frequency score in 3 month	71.2 scores on a scale Standard Deviation 1.9	72 scores on a scale Standard Deviation 1.1
Patient Reporting Outcomes Angina frequency score in 6 month	81 scores on a scale Standard Deviation 1.9	80 scores on a scale Standard Deviation 1.5
Patient Reporting Outcomes Angina frequency score in 12 month	82.9 scores on a scale Standard Deviation 3.0	84.9 scores on a scale Standard Deviation 2.4

SECONDARY outcome

Timeframe: 90 days, 12 months

Cumulative radiation exposure for any examination within 90 days and 12 months. Due to not enough data acquired, the investigators decided not to report at this time

Outcome measures

Outcome data not reported

Adverse Events

CTA/CT-FFR Care Group

Serious events: 14 serious events

Other events: 39 other events

Deaths: 2 deaths

Routine Clinically-indicated Diagnostic Care Group

Serious events: 25 serious events

Other events: 44 other events

Deaths: 1 deaths

Serious adverse events

Serious adverse events
Measure	CTA/CT-FFR Care Group n=587 participants at risk If the subjects are randomly allocated to CT-FFR arm, they will be examined by DeepFFR for three major epicardial arteries. If the result of CT-FFR calculation is less than or equal to 0.8 in one or more major，coronary arteries, the patient will be referred to ICA directly; if the result of CT-FFR value is more than 0.8, optimal medical therapy will be recommended. The decision on the mode of revascularization is left to the treating cardiologists and depends on local practice standard. CT-FFR: DeepFFR workstation is very dedicated software utilizing the original CTA imaging to meter simulated FFR values in artificial intelligence model.The first step is to extract a 3D coronary artery model and generate coronary centerlines which are similar to the routine reconstruction of coronary CTA. The centerlines are extracted using a minimal path extraction filter. Then a novel path-based deep learning model, referred to DeepFFR, is used to predict the simulated FFR values on the vascular centerlines. Deep learning algorithm is used to establish characteristic sample database of coronary hemodynamics characteristic parameters. When deep training model is proved to be valid, it is applied to a new lesion-specific measurement. Lesion-specific CT-FFR is defined as simulated FFR value at distance of 20mm away from the lesion of interest.	Routine Clinically-indicated Diagnostic Care Group n=589 participants at risk If the subjects are randomized to usual care arm, attending physicians will decide the next step of diagnosis and treatment, such as exercise ECG, stress cardiac echo, cardiac MR, and SPECT. According to the results of examination combined with risk factors assessment and clinical manifestations, physicians should provide recommendation whether the subjects would undergo ICA or not.
Cardiac disorders Nonfatal myocardiol infraction	1.2% 7/587 • 1 year The primary endpoint analysis was performed in both groups as the primary endpoint was assessable at baseline. However, secondary endpoints as well as adverse event were assessed after 1-year follow-up and due to loss to follow-up（21 in CT-FFR group; 19 in standard care group）, 587 and 589 patients from each group were available for final analysis of adverse event.	1.5% 9/589 • 1 year The primary endpoint analysis was performed in both groups as the primary endpoint was assessable at baseline. However, secondary endpoints as well as adverse event were assessed after 1-year follow-up and due to loss to follow-up（21 in CT-FFR group; 19 in standard care group）, 587 and 589 patients from each group were available for final analysis of adverse event.
Cardiac disorders Revascularization after 90 days	1.2% 7/587 • 1 year The primary endpoint analysis was performed in both groups as the primary endpoint was assessable at baseline. However, secondary endpoints as well as adverse event were assessed after 1-year follow-up and due to loss to follow-up（21 in CT-FFR group; 19 in standard care group）, 587 and 589 patients from each group were available for final analysis of adverse event.	2.7% 16/589 • 1 year The primary endpoint analysis was performed in both groups as the primary endpoint was assessable at baseline. However, secondary endpoints as well as adverse event were assessed after 1-year follow-up and due to loss to follow-up（21 in CT-FFR group; 19 in standard care group）, 587 and 589 patients from each group were available for final analysis of adverse event.

Other adverse events

Other adverse events
Measure	CTA/CT-FFR Care Group n=587 participants at risk If the subjects are randomly allocated to CT-FFR arm, they will be examined by DeepFFR for three major epicardial arteries. If the result of CT-FFR calculation is less than or equal to 0.8 in one or more major，coronary arteries, the patient will be referred to ICA directly; if the result of CT-FFR value is more than 0.8, optimal medical therapy will be recommended. The decision on the mode of revascularization is left to the treating cardiologists and depends on local practice standard. CT-FFR: DeepFFR workstation is very dedicated software utilizing the original CTA imaging to meter simulated FFR values in artificial intelligence model.The first step is to extract a 3D coronary artery model and generate coronary centerlines which are similar to the routine reconstruction of coronary CTA. The centerlines are extracted using a minimal path extraction filter. Then a novel path-based deep learning model, referred to DeepFFR, is used to predict the simulated FFR values on the vascular centerlines. Deep learning algorithm is used to establish characteristic sample database of coronary hemodynamics characteristic parameters. When deep training model is proved to be valid, it is applied to a new lesion-specific measurement. Lesion-specific CT-FFR is defined as simulated FFR value at distance of 20mm away from the lesion of interest.	Routine Clinically-indicated Diagnostic Care Group n=589 participants at risk If the subjects are randomized to usual care arm, attending physicians will decide the next step of diagnosis and treatment, such as exercise ECG, stress cardiac echo, cardiac MR, and SPECT. According to the results of examination combined with risk factors assessment and clinical manifestations, physicians should provide recommendation whether the subjects would undergo ICA or not.
Cardiac disorders Hospitalization for unstable angina	6.6% 39/587 • 1 year The primary endpoint analysis was performed in both groups as the primary endpoint was assessable at baseline. However, secondary endpoints as well as adverse event were assessed after 1-year follow-up and due to loss to follow-up（21 in CT-FFR group; 19 in standard care group）, 587 and 589 patients from each group were available for final analysis of adverse event.	7.5% 44/589 • 1 year The primary endpoint analysis was performed in both groups as the primary endpoint was assessable at baseline. However, secondary endpoints as well as adverse event were assessed after 1-year follow-up and due to loss to follow-up（21 in CT-FFR group; 19 in standard care group）, 587 and 589 patients from each group were available for final analysis of adverse event.

Additional Information

Dr Junjie Yang

People's Liberation Army General Hospital

Phone: 13581662680

Email: [email protected]

Results disclosure agreements

Principal investigator is a sponsor employee
Publication restrictions are in place