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| Name | Class |
|---|---|
| Beijing Anzhen Hospital | OTHER |
| First Affiliated Hospital of Xinjiang Medical University | OTHER |
| Qilu Hospital of Shandong University | OTHER |
| Second Affiliated Hospital, School of Medicine, Zhejiang University |
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The primary of this registry is to evaluate whether the availability of CTA/CT-FFR procedure could effectively optimize the flow of clinical practice of stable chest pain versus conventional clinical pathway in decision making, avoid the overuse of invasive procedure, finally improve clinical prognosis and reduce total medical expenditure. This registry is randomized, open labeled, prospective designed and will be performed in 6 Chinese hospitals. Approximately 1200 subjects will be enrolled and subsequently assigned to either routine clinically-indicated diagnostic care group (CID arm) or CTA/CT-FFR care group (CTA/CT-FFR arm) via computer-generated random numbers (1:1 ratio)
Based on the clinical fact that less stress myocardial perfusion scan are performed rather than stress exercise electrocardiogram (ECG) in China, more patients undergo coronary computed tomographic angiography (CTA) for determining whether they should be sent to catheter lab. However, nearly 30% of patients sent to catheter lab were found without obstructive coronary artery disease (CAD) and this invasive procedure was unnecessary and overused partly. Fortunately, fractional flow reserve (FFR) based non-invasive CT algorithm technology (CT-FFR) showed a great potential in detecting functional myocardial ischemia related to coronary specific lesion (Discovery-Flow, DEFACTO and NXT trial)[1-3]. Moreover, clinical care guided by CT-FFR could provide benefits with equivalent clinical outcomes and lower expenditure, compared with routine clinical care over 1-year follow-up (Platform trial). On the other aspect, ADVANCE trial revealed that CT-FFR modified treatment recommendation was associated with less negative invasive coronary angiography (ICA), predicted revascularization and identified subjects at low risk of adverse events through 90 days in real-world. However, these studies was not randomized designed and selection bias still existed. So our trial aims to evaluate whether CTA/CT-FFR outperforms the regular diagnostic care in ruling out patients without significantly obstructive CAD before catheter lab and improving clinical prognosis during follow-up in a randomized design.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| CTA/CT-FFR care group | Experimental | If the subjects are randomly allocated to CT-FFR arm, they will be examined by on-site DeepFFR for three major epicardial coronary 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. |
|
| Routine clinically-indicated diagnostic care group | No Intervention | 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. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| CT-FFR assessment | Diagnostic Test | When subjects are randomized to the CTA/CT-FFR arm, FFR based on the coronary CTA imaging will be measured. DEEPVESSEL FFR workstation is very dedicated software utilizing the original CTA imaging to meter simulated FFR values based on a machine learning algorithm. 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 DEEPVESSEL FFR, 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. |
| Measure | Description | Time Frame |
|---|---|---|
| Number of Participants With ICA Without Obstructive CAD or Intervention | 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. | 90 days |
| Measure | Description | Time Frame |
|---|---|---|
| Number of Participant With Major Adverse Cardiovascular Event | Major adverse cardiovascular event include death, myocardial infarction (MI), major complications from cardiovascular (CV) procedures or testing, and unstable angina hospitalization | 12 months |
| Medical Expenditure |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Yundai Chen, Ph.D. | Chinese PLA General Hospital | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Chinese PLA General Hospital | Beijing | Beijing Municipality | 100853 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 22922562 | Background | Min JK, Leipsic J, Pencina MJ, Berman DS, Koo BK, van Mieghem C, Erglis A, Lin FY, Dunning AM, Apruzzese P, Budoff MJ, Cole JH, Jaffer FA, Leon MB, Malpeso J, Mancini GB, Park SJ, Schwartz RS, Shaw LJ, Mauri L. Diagnostic accuracy of fractional flow reserve from anatomic CT angiography. JAMA. 2012 Sep 26;308(12):1237-45. doi: 10.1001/2012.jama.11274. | |
| 22032711 |
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| ID | Title | Description |
|---|---|---|
| FG000 | 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. |
| FG001 | 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. |
| Title | Milestones | Reasons Not Completed | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Overall Study |
|
Not provided
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| ID | Title | Description |
|---|---|---|
| BG000 | 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. |
| Units | Counts |
|---|---|
| Participants |
|
| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes |
|---|---|---|---|---|---|---|---|---|---|
| Age, Continuous | Mean |
| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Primary | Number of Participants With ICA Without Obstructive CAD or Intervention | 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. | 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. | Posted | Count of Participants | Participants | 90 days |
|
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.
Not provided
| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | 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. |
| Term | Organ System | Source Vocabulary | Assessment Type | Notes | Statistical Information |
|---|---|---|---|---|---|
| Nonfatal myocardiol infraction | Cardiac disorders | Non-systematic Assessment |
| Term | Organ System | Source Vocabulary | Assessment Type | Notes | Statistical Information |
|---|---|---|---|---|---|
| Hospitalization for unstable angina | Cardiac disorders | Non-systematic Assessment |
Not provided
| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Dr Junjie Yang | People's Liberation Army General Hospital | 13581662680 | 86 | fearlessyang@126.com |
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | Aug 20, 2020 | Dec 11, 2023 | Prot_SAP_000.pdf |
Not provided
| ID | Term |
|---|---|
| D003324 | Coronary Artery Disease |
| ID | Term |
|---|---|
| D003327 | Coronary Disease |
| D017202 | Myocardial Ischemia |
| D006331 | Heart Diseases |
| D002318 | Cardiovascular Diseases |
Not provided
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| OTHER |
| Tongji Hospital | OTHER |
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|
Overall cardiac medical expenditure by intention to treat at both 90 days and 12 months cumulatively |
| 12 months |
| Patient Reporting Outcomes | 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. | Study entry, 3 months, 6 months and12 months |
| Cumulative Radiation Exposure | 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 | 90 days, 12 months |
| Koo BK, Erglis A, Doh JH, Daniels DV, Jegere S, Kim HS, Dunning A, DeFrance T, Lansky A, Leipsic J, Min JK. Diagnosis of ischemia-causing coronary stenoses by noninvasive fractional flow reserve computed from coronary computed tomographic angiograms. Results from the prospective multicenter DISCOVER-FLOW (Diagnosis of Ischemia-Causing Stenoses Obtained Via Noninvasive Fractional Flow Reserve) study. J Am Coll Cardiol. 2011 Nov 1;58(19):1989-97. doi: 10.1016/j.jacc.2011.06.066. |
| 24486266 | Background | Norgaard BL, Leipsic J, Gaur S, Seneviratne S, Ko BS, Ito H, Jensen JM, Mauri L, De Bruyne B, Bezerra H, Osawa K, Marwan M, Naber C, Erglis A, Park SJ, Christiansen EH, Kaltoft A, Lassen JF, Botker HE, Achenbach S; NXT Trial Study Group. Diagnostic performance of noninvasive fractional flow reserve derived from coronary computed tomography angiography in suspected coronary artery disease: the NXT trial (Analysis of Coronary Blood Flow Using CT Angiography: Next Steps). J Am Coll Cardiol. 2014 Apr 1;63(12):1145-1155. doi: 10.1016/j.jacc.2013.11.043. Epub 2014 Jan 30. |
| 27470449 | Background | Douglas PS, De Bruyne B, Pontone G, Patel MR, Norgaard BL, Byrne RA, Curzen N, Purcell I, Gutberlet M, Rioufol G, Hink U, Schuchlenz HW, Feuchtner G, Gilard M, Andreini D, Jensen JM, Hadamitzky M, Chiswell K, Cyr D, Wilk A, Wang F, Rogers C, Hlatky MA; PLATFORM Investigators. 1-Year Outcomes of FFRCT-Guided Care in Patients With Suspected Coronary Disease: The PLATFORM Study. J Am Coll Cardiol. 2016 Aug 2;68(5):435-445. doi: 10.1016/j.jacc.2016.05.057. |
| 30165613 | Background | Fairbairn TA, Nieman K, Akasaka T, Norgaard BL, Berman DS, Raff G, Hurwitz-Koweek LM, Pontone G, Kawasaki T, Sand NP, Jensen JM, Amano T, Poon M, Ovrehus K, Sonck J, Rabbat M, Mullen S, De Bruyne B, Rogers C, Matsuo H, Bax JJ, Leipsic J, Patel MR. Real-world clinical utility and impact on clinical decision-making of coronary computed tomography angiography-derived fractional flow reserve: lessons from the ADVANCE Registry. Eur Heart J. 2018 Nov 1;39(41):3701-3711. doi: 10.1093/eurheartj/ehy530. |
| 27085447 | Background | Norgaard BL, Hjort J, Gaur S, Hansson N, Botker HE, Leipsic J, Mathiassen ON, Grove EL, Pedersen K, Christiansen EH, Kaltoft A, Gormsen LC, Maeng M, Terkelsen CJ, Kristensen SD, Krusell LR, Jensen JM. Clinical Use of Coronary CTA-Derived FFR for Decision-Making in Stable CAD. JACC Cardiovasc Imaging. 2017 May;10(5):541-550. doi: 10.1016/j.jcmg.2015.11.025. Epub 2016 Apr 13. |
| 28674617 | Background | Colleran R, Douglas PS, Hadamitzky M, Gutberlet M, Lehmkuhl L, Foldyna B, Woinke M, Hink U, Nadjiri J, Wilk A, Wang F, Pontone G, Hlatky MA, Rogers C, Byrne RA. An FFRCT diagnostic strategy versus usual care in patients with suspected coronary artery disease planned for invasive coronary angiography at German sites: one-year results of a subgroup analysis of the PLATFORM (Prospective Longitudinal Trial of FFRCT: Outcome and Resource Impacts) study. Open Heart. 2017 Mar 22;4(1):e000526. doi: 10.1136/openhrt-2016-000526. eCollection 2017. |
| 30312411 | Background | Collet C, Onuma Y, Andreini D, Sonck J, Pompilio G, Mushtaq S, La Meir M, Miyazaki Y, de Mey J, Gaemperli O, Ouda A, Maureira JP, Mandry D, Camenzind E, Macron L, Doenst T, Teichgraber U, Sigusch H, Asano T, Katagiri Y, Morel MA, Lindeboom W, Pontone G, Luscher TF, Bartorelli AL, Serruys PW. Coronary computed tomography angiography for heart team decision-making in multivessel coronary artery disease. Eur Heart J. 2018 Nov 1;39(41):3689-3698. doi: 10.1093/eurheartj/ehy581. |
| 30153968 | Background | Norgaard BL, Terkelsen CJ, Mathiassen ON, Grove EL, Botker HE, Parner E, Leipsic J, Steffensen FH, Riis AH, Pedersen K, Christiansen EH, Maeng M, Krusell LR, Kristensen SD, Eftekhari A, Jakobsen L, Jensen JM. Coronary CT Angiographic and Flow Reserve-Guided Management of Patients With Stable Ischemic Heart Disease. J Am Coll Cardiol. 2018 Oct 30;72(18):2123-2134. doi: 10.1016/j.jacc.2018.07.043. Epub 2018 Aug 25. |
| 28444153 | Background | Jensen JM, Botker HE, Mathiassen ON, Grove EL, Ovrehus KA, Pedersen KB, Terkelsen CJ, Christiansen EH, Maeng M, Leipsic J, Kaltoft A, Jakobsen L, Sorensen JT, Thim T, Kristensen SD, Krusell LR, Norgaard BL. Computed tomography derived fractional flow reserve testing in stable patients with typical angina pectoris: influence on downstream rate of invasive coronary angiography. Eur Heart J Cardiovasc Imaging. 2018 Apr 1;19(4):405-414. doi: 10.1093/ehjci/jex068. |
| 36870065 | Derived | Yang J, Shan D, Wang X, Sun X, Shao M, Wang K, Pan Y, Wang Z, Schoepf UJ, Savage RH, Zhang M, Dong M, Xu L, Zhou Y, Ma X, Hu X, Xia L, Zeng H, Liu Z, Chen Y. On-Site Computed Tomography-Derived Fractional Flow Reserve to Guide Management of Patients With Stable Coronary Artery Disease: The TARGET Randomized Trial. Circulation. 2023 May 2;147(18):1369-1381. doi: 10.1161/CIRCULATIONAHA.123.063996. Epub 2023 Mar 4. |
| 32819429 | Derived | Yang J, Shan D, Dong M, Wang Z, Ma X, Hu X, Zeng H, Chen Y. The effect of on-site CT-derived fractional flow reserve on the management of decision making for patients with stable chest pain (TARGET trial): objective, rationale, and design. Trials. 2020 Aug 20;21(1):728. doi: 10.1186/s13063-020-04649-9. |
| BG001 | 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. |
| BG002 | Total | Total of all reporting groups |
| years |
|
| Sex: Female, Male | Count of Participants | Participants |
|
| Race and Ethnicity Not Collected | Race and Ethnicity were not collected from any participant. | Count of Participants | Participants |
|
| Body mass index | Median | Inter-Quartile Range | kg/m² |
|
| OG001 | 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. |
|
|
|
| Secondary | Number of Participant With Major Adverse Cardiovascular Event | Major adverse cardiovascular event include death, myocardial infarction (MI), major complications from cardiovascular (CV) procedures or testing, and unstable angina hospitalization | 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. | Posted | Count of Participants | Participants | 12 months |
|
|
|
|
| Secondary | Medical Expenditure | Overall cardiac medical expenditure by intention to treat at both 90 days and 12 months cumulatively | 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. | Posted | Mean | Standard Deviation | ¥ | 12 months |
|
|
|
|
| Secondary | Patient Reporting Outcomes | 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. | 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. | Posted | Mean | Standard Deviation | scores on a scale | Study entry, 3 months, 6 months and12 months |
|
|
|
|
| Secondary | Cumulative Radiation Exposure | 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 | Not Posted | Jun 2024 | 90 days, 12 months | Participants |
| 2 |
| 587 |
| 14 |
| 587 |
| 39 |
| 587 |
| EG001 | 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. | 1 | 589 | 25 | 589 | 44 | 589 |
| Revascularization after 90 days | Cardiac disorders | Non-systematic Assessment |
|
Not provided
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| D001161 |
| Arteriosclerosis |
| D001157 | Arterial Occlusive Diseases |
| D014652 | Vascular Diseases |
| Seattle Angina Questionnaire-7(SAQ-7) Scale in 6 month |
|
| Seattle Angina Questionnaire-7(SAQ-7) Scale in 12 month |
|
| Physical limitation score in study entry |
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| Physical limitation score in 3 month |
|
| Physical limitation score in 6 month |
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| Physical limitation score in 12 month |
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| Quality of life score in study entry |
|
| Quality of life score in 3 month |
|
| Quality of life score in 6 month |
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| Quality of life score in 12 month |
|
| Angina frequency score in study entry |
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| Angina frequency score in 3 month |
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| Angina frequency score in 6 month |
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| Angina frequency score in 12 month |
|