Utility of the CHADS-P2A2RC score for estimating net adverse clinical events in chronic coronary syndrome patients without atrial fibrillation: insights from the CLIDAS-PCI

Utility of the CHADS-P2A2RC score for estimating net adverse clinical events in chronic coronary syndrome patients without atrial fibrillation: insights from the CLIDAS-PCI | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var 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Shibata , View ORCID Profile Kenichiro Otsuka , View ORCID Profile Asahiro Ito , Hiroshi Okamura , Tetsuya Matoba , View ORCID Profile Takahide Kohro , Yusuke Oba , View ORCID Profile Tomoyuki Kabutoya , View ORCID Profile Yasushi Imai , View ORCID Profile Kazuomi Kario , View ORCID Profile Arihiro Kiyosue , View ORCID Profile Yoshiko Mizuno , View ORCID Profile Kotaro Nochioka , View ORCID Profile Masaharu Nakayama , View ORCID Profile Takamasa Iwai , View ORCID Profile Yoshihiro Miyamoto , View ORCID Profile Masanobu Ishii , Taishi Nakamura , View ORCID Profile Kenichi Tsujita , Hisahiko Sato , View ORCID Profile Naoyuki Akashi , View ORCID Profile Hideo Fujita , View ORCID Profile Ryozo Nagai CLIDAS Research Group doi: https://doi.org/10.1101/2025.03.21.25324437 Takenobu Shimada 1 Department of Cardiovascular Medicine, Osaka Metropolitan University Graduate School of Medicine , 1-4-3 Asahimachi Abenoku, Osaka 545-8585, Japan MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Takenobu Shimada For correspondence: takeboyism{at}gmail.com Daiju Fukuda 1 Department of Cardiovascular Medicine, Osaka Metropolitan University Graduate School of Medicine , 1-4-3 Asahimachi Abenoku, Osaka 545-8585, Japan MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Daiju Fukuda Atsushi Shibata 1 Department of Cardiovascular Medicine, Osaka Metropolitan University Graduate School of Medicine , 1-4-3 Asahimachi Abenoku, Osaka 545-8585, Japan MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Atsushi Shibata Kenichiro Otsuka 1 Department of Cardiovascular Medicine, Osaka Metropolitan University Graduate School of Medicine , 1-4-3 Asahimachi Abenoku, Osaka 545-8585, Japan MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kenichiro Otsuka Asahiro Ito 1 Department of Cardiovascular Medicine, Osaka Metropolitan University Graduate School of Medicine , 1-4-3 Asahimachi Abenoku, Osaka 545-8585, Japan MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Asahiro Ito Hiroshi Okamura 2 Department of Hematology, Osaka Metropolitan University Graduate School of Medicine , Osaka, Japan 3 Department of Laboratory Medicine and Medical Informatics, Osaka Metropolitan University Graduate School of Medicine , Osaka, Japan MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tetsuya Matoba 4 Department of Cardiovascular Medicine, Kyushu University Graduate School of Medical Sciences , Fukuoka, Japan MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Takahide Kohro 5 Department of Clinical Informatics, Jichi Medical University School of Medicine , Tochigi, Japan MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Takahide Kohro Yusuke Oba 6 Division of Cardiovascular Medicine, Department of Medicine, Jichi Medical University School of Medicine , Tochigi, Japan MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tomoyuki Kabutoya 6 Division of Cardiovascular Medicine, Department of Medicine, Jichi Medical University School of Medicine , Tochigi, Japan MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tomoyuki Kabutoya Yasushi Imai 7 Division of Clinical Pharmacology, Department of Pharmacology, Jichi Medical University , Tochigi, Japan MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yasushi Imai Kazuomi Kario 6 Division of Cardiovascular Medicine, Department of Medicine, Jichi Medical University School of Medicine , Tochigi, Japan MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kazuomi Kario Arihiro Kiyosue 8 Department of Cardiovascular Medicine, The University of Tokyo Hospital , Tokyo, Japan MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Arihiro Kiyosue Yoshiko Mizuno 8 Department of Cardiovascular Medicine, The University of Tokyo Hospital , Tokyo, Japan 9 Development Bank of Japan Inc. , Tokyo, Japan MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yoshiko Mizuno Kotaro Nochioka 10 Department of Cardiovascular Medicine, Tohoku University Graduate School of Medicine, Clinical Research, Innovation, and Education Center, Tohoku University Hospital , Sendai, Japan MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kotaro Nochioka Masaharu Nakayama 11 Department of Medical Informatics, Tohoku University Graduate School of Medicine , Sendai, Japan MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Masaharu Nakayama Takamasa Iwai 12 Department of Cardiovascular Medicine, National Cerebral and Cardiovascular Center , Osaka, Japan MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Takamasa Iwai Yoshihiro Miyamoto 13 Open Innovation Center, National Cerebral and Cardiovascular Center , Osaka, Japan MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yoshihiro Miyamoto Masanobu Ishii 14 Department of Cardiovascular Medicine, Graduate School of Medical Sciences, Kumamoto University , Kumamoto, Japan 15 Department of Medical Information Science, Graduate School of Medical Sciences, Kumamoto University , Kumamoto, Japan MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Masanobu Ishii Taishi Nakamura 15 Department of Medical Information Science, Graduate School of Medical Sciences, Kumamoto University , Kumamoto, Japan MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kenichi Tsujita 14 Department of Cardiovascular Medicine, Graduate School of Medical Sciences, Kumamoto University , Kumamoto, Japan MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kenichi Tsujita Hisahiko Sato 16 Precision Inc. , Tokyo, Japan MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Naoyuki Akashi 17 Division of Cardiovascular Medicine, Jichi Medical University Saitama Medical Center , Saitama, Japan MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Naoyuki Akashi Hideo Fujita 17 Division of Cardiovascular Medicine, Jichi Medical University Saitama Medical Center , Saitama, Japan MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Hideo Fujita Ryozo Nagai 18 Jichi Medical University School of Medicine , Tochigi, Japan MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ryozo Nagai Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Background There are few data verifying the utility of the CHADS-P 2 A 2 RC score in comparison with the CHADS 2 score for estimating net adverse clinical events (NACE) in real-world settings. Methods We performed analysis for a total of 3,985 chronic coronary syndrome (CCS) patients without atrial fibrillation (AF) who underwent percutaneous coronary intervention (PCI) between April 2013 and March 2019 for whom information was obtained from the CLIDAS (Clinical Deep Data Accumulation System)-PCI database. The primary endpoint was NACE defined as the composite of 3-point major adverse cardiovascular events (3P-MACE) (cardiovascular death, non-fatal myocardial infarction, and non-fatal stroke) and GUSTO moderate/severe bleeding events. Results Kaplan-Meier analysis showed that both the CHADS-P 2 A 2 RC and CHADS 2 scores stratified the risks. The incidences of NACE were stratified well by the very-high-risk category, which was uniquely defined as a CHADS-P 2 A 2 RC score of ≥6 (hazard ratio: 2.38, 95% CI=1.91-2.97, p-value <0.001). The area under the curve (AUC) in estimating NACE within 3 years was higher when the CHADS-P 2 A 2 RC score was used than when the CHADS 2 score was used (0.67 vs. 0.62, p=0.003). This was mainly due to the accuracy in estimating bleeding events (0.66 vs. 0.60, p=0.006). Conclusions The accuracy in estimating NACE after PCI for CCS patients without AF was higher when the CHADS-P 2 A 2 RC score was used than when the CHADS 2 score was used, mainly due to the accuracy in predicting bleeding risk. Higher incidences of endpoints were well-stratified by a very-high-risk category defined as a CHADS-P 2 A 2 RC score of ≥6. Introduction Administration of appropriate antithrombotic agents plays an important role in secondary prevention of coronary artery disease 1 – 4 . However, antithrombotic agents have the downside of increasing the risk of bleeding, and a simple risk model that can estimate net adverse clinical events (NACE) including both thrombotic and bleeding events is desirable. The CHADS-P 2 A 2 RC score was originally developed as a prognostic prediction model for assessing the thrombotic events in patients without atrial fibrillation (AF) 5 . Recently, it was reported that the CHADS-P 2 A 2 RC score can be used for assessing NACE in patients with chronic coronary syndrome (CCS) 6 , 7 . The CHADS-P 2 A 2 RC score has been increasingly recognized as a criterion for initiation of treatment with a low-dose direct oral anticoagulant (DOAC) in patients with CCS 6 . However, there are still no data regarding its utility in real-world settings in comparison with other risk models. The CHADS 2 score was originally developed as a tool for evaluating the risk of stroke and it is widely used as a criterion for the initiation of anticoagulant therapy in patients with AF 8 . Recently, the CHADS 2 score has also been shown to be useful for predicting prognosis in patients with CCS who do not have AF 9 – 11 . In this study, to verify the utility of the CHADS-P 2 A 2 RC score for estimating NACE including thrombotic and bleeding events, we compared its accuracy with that of the CHADS 2 score in CCS patients who underwent percutaneous coronary intervention (PCI) in real-world settings. Materials and methods Data source The analysis in this study was conducted retrospectively using the Clinical Deep Data Accumulation System (CLIDAS)-PCI database that was formerly referred to as CLIDAS 12 , 13 , which was started as the Japan Ischemic heart disease Multimodal Prospective data Acquisition for preCision Treatment (J-IMPACT) project launched in 2015 14 . The database is a retrospective all-comer registry including real-world data for a total of 9,690 patients with acute coronary syndrome (ACS) or CCS who underwent PCI between April 2013 and March 2019 at seven tertiary facilities in Japan (six university hospitals and one national cardiovascular center). This database directly extracted real-world clinical data electronically from the hospital information system (HIS) of each participating facility in standardized data formats for clinical studies. Briefly, Standardized Structured Medical Information eXchange version 2 (SS-MIX2) standard storage was used to collect essential patient information, prescriptions, and laboratory data; SS-MIX2 extended storage was used to collect echocardiographic parameters, electrocardiograms, and cardiac catheterization reports 15 . Researchers and data managers at each facility collected the nonstandardized information on patients’ backgrounds and follow-up data. After data collection, each facility’s data were sent to the central server after anonymization 12 – 15 . Study design The aim of this study was to determine the utility of CHADS-P 2 A 2 RC score in CCS patients without AF for estimating NACE in comparison with the CHADS 2 score. The CHADS-P 2 A 2 RC score was originally developed in a cohort without AF 5 , and its utility was previously reported in a CCS cohort 6 , 7 . Thus, CCS patients without AF were also analyzed in this study. Patients lacking any data among the factors of the CHADS-P 2 A 2 RC or CHADS 2 score were excluded. The study flow is shown in Figure 1 . Download figure Open in new tab Figure 1. Study flowchart. Information for CCS patients without atrial fibrillation were obtained from the CLIDAS-PCI database. Abbreviations: ACS, acute coronary syndrome; CCS, chronic coronary syndrome; PCI, percutaneous coronary intervention; CLIDAS, Clinical Deep Data Accumulation System In this study, we examined the baseline patient background characteristics and long-term adverse events according to the baseline calculated points using the CHADS-P 2 A 2 RC and CHADS 2 scores. The accuracy of the CHADS-P 2 A 2 RC score and the accuracy of the CHADS 2 score for estimating each endpoint were compared. This study complied with the Declaration of Helsinki for investigation in human beings and was conducted with the approval of the Jichi Medical University Bioethics Committee for Medical Research (23-158). The requirement for written informed consent was waived owing to the retrospective nature of the study, and patients were allowed to opt out of the study alternatively. Risk stratification using the CHADS-P 2 A 2 RC and CHADS 2 scores The CHADS-P 2 A 2 RC score assigns one point each to congestive heart failure, hypertension, age of 65–74 years, diabetes mellitus, active smoking, chronic kidney disease (CKD), and multi-vessel coronary artery disease (CAD) and it assigns two points each to peripheral arterial disease (PAD) and age ≥75 years 5 . The CHADS 2 score assigns one point each to congestive heart failure, hypertension, age ≥75 years, and diabetes mellitus and it assigns two points to stroke 8 . Furthermore, we categorized the study patients according to the baseline CHADS-P 2 A 2 RC and CHADS 2 scores. Classically, in the CHADS 2 score, patients with scores of 0–1 were classified as patients with low risk, patients with scores of 2–3 were classified as patients with intermediate risk, and patients with scores of 4–6 were classified as patients with high risk 8 . In a recent study focusing on the CHADS-P 2 A 2 RC score, patients were classified in the same manner as that for the CHADS 2 score: patients with scores of 0–1 were classified as patients with low risk, patients with scores of 2–3 were classified as patients with intermediate risk, and patients with 4 or higher were classified as patients with high risk 5 . However, the maximum CHADS-P 2 A 2 RC score is 11, we hypothesized that the high risk category for a CHADS-P 2 A 2 RC score of 4 or higher may include more patients than we want to identify who have a truly high risk for adverse events in real-world settings. Thus, we set a very-high-risk category uniquely based on the distribution of patients according to the points of the CHADS-P 2 A 2 RC scores. Endpoints and definitions The primary endpoint was NACE defined as the composite of 3-point major adverse cardiovascular events (3P-MACE) and bleeding event. 3P-MACE were defined as the composite of cardiovascular death, non-fatal myocardial infarction, and non-fatal stroke. Bleeding event was defined as a moderate or severe bleeding event according to the Global Utilization of Streptokinase and t-PA for Occluded Coronary Arteries Trial (GUSTO) classification 16 , 17 . Briefly, moderate bleeding is bleeding that requires blood transfusion but does not result in hemodynamic compromise, and severe bleeding is either intracranial hemorrhage or bleeding that causes hemodynamic compromise and requires intervention. The secondary endpoints were 3P-MACE and bleeding event, which were components of the primary endpoint, and the 3-year cumulative incidence of each endpoint. All baseline laboratory data were calculated as average values between 60 days before and 30 days after the index PCI. Estimated glomerular filtration rate (eGFR) was calculated by the Japanese equation using serum creatinine level, age, and sex as follows: eGFR (mL/min/1.73 m 2 ) = 194 × Cr −1.094 × age −0.287 (× 0.739 for women) 18 . We defined CKD as eGFR <60 ml/min per 1.73 m 2 , 19 . PAD was defined as a history of non-cardiac (aortic or peripheral vascular) vascular disease. The number of diseased vessels was defined as the number of coronary arteries with severe stenosis (≥75%) in the major epicardial coronary segments of the right coronary artery, left anterior descending artery, and left circumflex artery, including their branch lesions, in which PCI was performed. The diseased left main trunk (LMT), defined as ≥75% stenosis, was counted separately. Multi-vessel CAD was defined as non-single vessel disease 13 . Statistical analysis The baseline characteristics of the patients were summarized using medians [interquartile range (IQR)] for continuous variables and numbers (percentage) for categorical variables. For continuous data, the groups were compared using Wilcoxon’s rank-sum test. Categorical variables were compared using the chi-square test or Fisher’s exact test as appropriate. The effect of each factor of the CHADS-P 2 A 2 RC and CHADS 2 scores on each endpoint was analyzed using the Cox proportional hazard model and was expressed as the hazard ratio (HR) with 95% confidence interval (CI). The cumulative incidence of each endpoint was estimated by the Kaplan–Meier method and the cumulative incidences were compared by the log-rank test. The effect between each risk category was analyzed using the Cox proportional hazard model and was expressed as HR with the 95% CI. In the case of multiple comparisons, crude values were presented for interpretation using the Bonferroni method. To assess the model performance, we compared the areas under the receiver operating characteristic curves (AUC) by estimating the 3-year cumulative incidence of each endpoint. All statistical analyses were conducted using R version 4.3.1 (R Foundation for Statistical Computing). Results Study population A total of 3,985 CCS patients without AF who underwent PCI were included in this study. The baseline patient characteristics are shown in Table 1 . There were high prevalences of comorbidities including hypertension in 83.6% of the patients, diabetes mellitus in 48.1% of the patients, CKD in 48.2% of the patients, PAD in 11.4% of the patients, and malignancy in 11.8% of the patients. Multi-vessel CAD present in 52.0% of the patients. Regarding lipid management, the median LDL-C level was 88.6 [IQR:71.8-107.6] and the statin administration rate was less than 80%, which did not meet the current guidelines’ recommendation. However, the administration rates of RAS inhibitors and β-blocker were more than 50%, which may be high for CCS patients. The median follow-up duration was 951 [IQR:328-1695] days. View this table: View inline View popup Table 1. Baseline characteristics of the patient population Figure 2 shows the distributions of patients according to scores. The CHADS 2 score seemed to appropriately categorize the population according to the original criteria, by which 1,363 patients (34.2%) with scores of 0–1 were categorized as patients with low risk, 2,255 patients (56.6%) with scores of 2–3 points were categorized as patients with intermediate risk, and 367 patients (9.2%) with scores of 4–6 points were categorized as patients with high risk. On the other hand, in the CHADS-P 2 A 2 RC score, if all scores of 4 or more were categorized as high risk, more than half of the patients would be categorized as patients with high risk. Thus, based on the distribution of patients with each score, we decided to categorize patients with a score of 6 points or more as patients with a very high risk. By this categorization, 277 patients (7.0%) with scores of 0–1 were categorized as patients with low risk, 1,333 patients (33.5%) with scores of 2–3 were categorized as patients with intermediate risk, 1,746 patients (43.8%) with scores of 4–5 were categorized as patients with high risk, and 629 patients (15.8%) with scores of 6-11 points were categorized as patients with very high risk. Download figure Open in new tab Figure 2. Distributions of patients according to the CHADS-P 2 A 2 RC and CHADS 2 scores. The left side shows the numbers of patients categorized by the CHADS-P 2 A 2 RC scores, and the right side shows the numbers of patients categorized by the CHADS 2 scores. Classification using CHADS-P 2 A 2 RC and CHADS 2 scores The baseline characteristics of patients in each category of CHADS-P 2 A 2 RC and CHADS 2 scores are shown in Supplemental Table 1 and Supplemental Table 2, respectively. The backgrounds of patients in the categories differed significantly. The higher the risk categorization was, the higher was the proportion of poor background factors for all factors except for the LDL-C value and rate of statin administration. The percentage of patients with dyslipidemia was lowest in the low-risk category; however, LDL-C level and rate of statin administration were highest in the low-risk categories. The percentage of patients treated with clopidogrel was higher in the high-risk category, whereas the percentage of patients treated with prasugrel was higher in the low-risk category. These findings were the same for categories according to the CHADS-P 2 A 2 RC score and categories according to the CHADS 2 score. Table 2 shows the results of univariate Cox hazard regression analysis for NACE, 3P-MACE, and bleeding events according to the baseline factors of each CHADS-P 2 A 2 RC and CHADS 2 score. Stroke and diabetes mellitus were not associated with bleeding events and the active smoking was not associated with each endpoint; however, all of the other factors were significantly associated with each endpoint. View this table: View inline View popup Download powerpoint Table 2. Cox hazard regression analyses for clinical outcomes according to baseline factors Impact of CHADS-P 2 A 2 RC and CHADS 2 scores on each endpoint Kaplan-Meier curves for each endpoint according to baseline categories of CHADS-P 2 A 2 RC and CHADS 2 scores are shown in Figure 3 . There were significant differences in all endpoints according to the categorization by both the CHADS-P 2 A 2 RC score and CHADS 2 score. According to the features of these Kaplan-Meier curves, the incidences of endpoints were stratified well by the very-high-risk category according to the CHADS-P 2 A 2 RC score; however, the incidences of endpoints were not stratified well by the low-risk category according to the CHADS-P 2 A 2 RC score. On the other hand, the incidences of endpoints were stratified well by the low-risk category according to the CHADS 2 score. In the very-high-risk category according to the CHADS-P 2 A 2 RC score, the rates of NACE were 7.4% (95% CI=5.5-9.9%) at 1 year, 19.0% (95% CI=15.6-23.1%) at 3 years and 30.8% (95% CI=25.6-36.8%) at 6 years after the index PCI. In the low-risk category according to the CHADS 2 score, the rates of NACE were 2.0% (95% CI=1.4-3.0%) at 1 year, 4.6% (95% CI=3.4-6.2%) at 3 years and 9.9% (95% CI=7.5-13.0%) at 6 years after the index PCI. In the very-high-risk category according to the CHADS-P 2 A 2 RC score, in comparison with the other categories, there were significant differences in NACE (HR:2.38, 95% CI=1.91-2.97, p-value <0.001), 3P-MACE (HR:2.33, 95% CI=1.81-3.01, p-value <0.001), and bleeding events (HR:2.11, 95% CI=1.48-3.01, p-value <0.001). In the low-risk category according to the CHADS 2 score, in comparison with the other categories, there were significant differences in NACE (HR:0.41, 95% CI=0.32-0.54, p-value <0.001), 3P-MACE (HR:0.36, 95% CI=0.27-0.50, p-value <0.001), and bleeding events (HR:0.52, 95% CI=0.35-0.77, p-value <0.001). Download figure Open in new tab Figure 3. Kaplan-Meier curves for endpoints according to the baseline categorization by CHADS-P 2 A 2 RC and CHADS 2 scores. A and B, Kaplan-Meier curves for NACE; C and D, Kaplan-Meier curves for 3P-MACE; E and F, Kaplan-Meier curves for bleeding events. The left side shows Kaplan-Meier curves according to the CHADS-P 2 A 2 RC score and the right side shows Kaplan-Meier curves according to the CHADS 2 score. Abbreviations: NACE, net adverse clinical events; 3P-MACE, 3-point major adverse cardiovascular events Hazard ratios analyzed in comparison with the low-risk category according to both the CHADS-P 2 A 2 RC score and CHADS 2 score are shown in Supplemental Table 3. This analysis revealed that the low-risk category based on the CHADS-P 2 A 2 RC showed no significant differences from the intermediate-risk category in any endpoints. Accuracy of CHADS-P 2 A 2 RC and CHADS 2 scores for estimating each endpoint The results of ROC analyses for 3-year cumulative incidences of endpoints according to the CHADS-P 2 A 2 RC and CHADS 2 scores are shown in Figure 4 . The AUC in estimating NACE was higher when the CHADS-P 2 A 2 RC score was used than when the CHADS 2 score was used; this was mainly attributed to the accuracy in estimating bleeding events with no significant difference in estimation of 3P-MACE. Download figure Open in new tab Figure 4. Receiver operating characteristic curve analysis of 3-year cumulative incidences of endpoints. A, NACE. B, 3P-MACE, and C: Bleeding events. Abbreviations: NACE, net adverse clinical events; 3P-MACE, 3-point major adverse cardiovascular events; AUC, area under curve. Discussion The main findings of this study were as follows: (1) the risks for NACE including 3P-MACE and bleeding events were stratified well by both the CHADS-P 2 A 2 RC and CHADS 2 scores, (2) higher incidences of endpoints were stratified well by a very-high-risk category defined as a CHADS-P 2 A 2 RC score of ≥6, whereas lower incidences of endpoints were stratified well by the low-risk category of the CHADS 2 score, and (3) CHADS-P 2 A 2 RC score was superior to CHADS 2 score for estimating bleeding events and was therefore superior for estimating NACE. In this study, we evaluated the utility of the CHADS-P 2 A 2 RC score in comparison with the CHADS 2 score in real-world CCS patients without AF over a long-term period with a median follow-up period of 951 [IQR:328-1695] days. In terms of the utility of the CHADS-P 2 A 2 RC score, the results of this study were consistent with the results of previous studies showing that the CHADS-P 2 A 2 RC score can risk-stratify NACE 6 , 7 . In this study, we set a new very-high-risk category defined as a CHADS-P 2 A 2 RC score of ≥6, and patients in that category had high incidences of NACE. Supplemental Figure 1 shows Kaplan-Meier curves when the very-high-risk category was defined as a CHADS-P 2 A 2 RC score of ≥7. According to the features of these Kaplan-Meier curves, the risk stratification of 3P-MACE was better when the very-high-risk category was defined as a CHADS-P 2 A 2 RC score of ≥6 than when it was defined as a CHADS-P 2 A 2 RC score of ≥7. Furthermore, 629 patients (15.8%) were categorized as patients with a very high risk when the cut-off of the CHADS-P 2 A 2 RC score was set as ≥6, and only 240 patients (6.0%) were categorized as patients with a very high risk when the cut-off of the CHADS-P 2 A 2 RC score was set as ≥7. If the prognosis can be improved by performing some sort of intervention without increasing the risk of bleeding, it is better to include more patients. Thus, we believe that the very-high-risk category defined as a CHADS-P 2 A 2 RC score of ≥6 is more appropriate than that defined as a CHADS-P 2 A 2 RC score of ≥7. There are some well-known models for estimating both thrombotic and bleeding risks such as DAPT score 20 , PARIS score 21 , and CREDO-Kyoto risk score 22 , and there are some well-known models for estimating bleeding risk such as ARC-HBR criteria 23 and PRECISE-DAPT score 24 . Among these risk models, DAPT score is a model that can estimate both thrombotic and bleeding risks from a single calculation. However, the factors of the DAPT score include uncommon factors such as the use of paclitaxel-eluting stents and vein graft intervention, and it may not be a practical model 20 . The CHADS-P 2 A 2 RC score does not require any special factors and is easy to use in daily clinical settings. Among the factors of the CHADS-P 2 A 2 RC and CHADS 2 scores, CKD, active smoking, multi-vessel CAD, and PAD are factors that are included only in the CHADS-P 2 A 2 RC score, and stroke is a factor that is included only in the CHADS 2 score. CKD, active smoking, and PAD are included as factors in some of the well-known risk scores for estimating thrombotic or bleeding risk, and coronary complexity is also known to be associated with the cardiovascular outcomes 25 – 27 . On the other hand, stroke is only included as a minor criterion in the ARC-HBR criteria 23 . The difference in accuracy between the CHADS-P 2 A 2 RC score and the CHADS 2 score may have been affected by how many of these factors were included. In this study, the percentage of patients with dyslipidemia was lowest in the low-risk category, whereas the LDL-C level and rate of statin administration were highest in the low-risk category (Supplemental Table 1 and Supplemental Table 2). The reason of this phenomenon is unclear; however, it might be due to the nature of real-world lipid management in which young patients with high LDL-C levels prefer to receive strict lipid management and older patients with lower LDL-C levels tend to receive mild lipid management 28 . Currently, statin administration is recommended regardless of the presence of dyslipidemia, and the concept of “lower the better” is widely known 29 , so the results of this part may differ in current clinical practice. This might be due to the enrollment period and the nature of the non-randomized real-world observational cohort. In this study, high-risk patients tended to prefer to receive clopidogrel and low-risk patients tended to prefer prasugrel (Supplemental Table 1 and Supplemental Table 2). It has been reported that administration of prasugrel reduced cardiovascular events but also increased bleeding events compared to administration of clopidogrel 30 . East Asian people have a particularly high risk for bleeding 31 , and clopidogrel might have been preferred for safety concerns in patients who were considered to have a high risk for bleeding. On the other hand, administration of low-dose DOAC has been reported to reduce cardiovascular events while not significantly increasing major bleeding events 1 . Low-dose DOAC is not currently available in Japan, and future research focusing on the effectiveness and safety of low-dose DOAC in patients with a CHADS-P 2 A 2 RC score of ≥6 is needed. Limitations There are several limitations in our study. First, our study was a retrospective study using data from a non-randomized observational registry. Since the calculations of CHADS-P 2 A 2 RC and CHADS 2 scores were based on baseline information and the treatment provided to patients varied depending on the background of each patient, neither the baseline information nor the treatment provided could be adjusted, and all the results of this study were therefore just presented as crude data. Second, low-dose DOAC is not currently available in Japan, and the results of interventional analysis were therefore not included in this study. Only the utility and accuracy of the CHADS-P 2 A 2 RC and CHADS 2 scores were assessed in this study. The utility of CHADS-P 2 A 2 RC score as a criterion for initiation of treatment with low-dose DOAC in real-world settings has not been verified, especially in East Asian patients, and future investigation is required. Third, the rate of statin administration was insufficient. This might be due to changes in recommendations for lipid management, and this may differ from current clinical practice. Fourth, the very-high-risk category was uniquely determined on the basis of the distribution of patients in this cohort, and the cutoff may be different in other patient cohorts. Conclusion Long-term NACE in CCS patients without AF were stratified well by both the CHADS-P 2 A 2 RC and CHADS 2 scores. The accuracy of risk stratification was higher for the CHADS-P 2 A 2 RC score than for the CHADS 2 score due to the accuracy of the CHADS-P 2 A 2 RC score for predicting bleeding risk. Accurate identification of very-high-risk patients was achieved when the cut-off of the CHADS-P 2 A 2 RC score was set as ≥6. Data Availability The data underlying this article will be shared on reasonable request to the corresponding author. Disclosures DF has received research funding from BIOTRONIK Japan; honoraria from Novartis Pharma, Otsuka Pharmaceutical, and Kowa. TM has received lecture fees from Abbott Medical, Bayer, and MSD; research funding from Amgen, Bayer, and Kowa. TKabutoya has received scholarship funding from Abbott Medical. YI has received honoraria from Daiichi Sankyo, and Toa Eiyo. KK has received a research grant from Otsuka Pharmaceutical, Sanwa Kagaku Kenkyusho, Daiichi Sankyo, MSD, Astellas Pharma, Eisai, Taisho Pharmaceutical, Sumitomo Dainippon Pharma, Takeda Pharmaceutical, Mitsubishi Tanabe Pharma, Teijin Pharma, Boehringer Ingelheim Japan, Bristol-Myers Squibb, and Mochida Pharmaceutical; consulting fees from Kyowa Kirin, Sanwa Kagaku Kenkyusho, Mochida Pharmaceutical; honoraria from Otsuka Pharmaceutical, Daiichi Sankyo, Novartis Pharma, Mylan EPD; participation in Advisory Board of Daiichi Sankyo, and Novartis Pharma. AK has received honoraria from AstraZeneca, Eli Lilly, and Sumitomo Pharma. YMizuno has received research grants and consulting fees from Bayer. YMiyamoto has received research funds from Kowa, Tokyo Marine and Nichido Fire Insurance, Fujitsu, Softbank, Saraya, and Meiji Yasuda Life Insurance. KT received research grants from PPD-Shin Nippon Biomedical Laboratories and Alexion Pharmaceuticals; scholarship funds from Abbott Medical, Bayer, Boehringer Ingelheim, Daiichi Sankyo, ITI, Ono Pharmaceutical, Otsuka Pharmaceutical, and Takeda Pharmaceutical; affiliation with the endowed department from Abbott Medical, Boston Scientific, Cardinal Health, Fides-ONE, Fukuda Denshi, GM Medical, ITI, Japan Lifeline, Kaneka Medix, Medical Appliance, Medtronic, Nipro, and Terumo; honoraria from Abbott Medical, Amgen, AstraZeneca, Bayer, Daiichi Sankyo, Medtronic, Kowa, Novartis Pharma, Otsuka Pharmaceutical, Pfizer, and Janssen Pharmaceutical. HS reports stock or stock options in Precision. HF has received scholarship funds from Abbott Vascular, speaking honoraria from Novartis and Otsuka Pharmaceutical, and served as a consultant for Mehergen Group Holdings. RN has received honoraria from Kowa, Takeda Pharmaceutical, Tanabe-Mitsubishi Pharmaceutical, and Boehringer-Ingelheim. All other authors have no conflicts of interest to declare. Acknowledgements This work was supported by the Cross-ministerial Strategic Innovation Promotion Program (SIP) on “Integrated Health Care System” Grant Number JPJ012425. In addition, the authors thank the Kowa Company for funding the development of CLIDAS-PCI. Finally, the authors appreciate the contributions of all CLIDAS research group members and the authors are grateful to Yuri Matoba (Precision Inc., Tokyo, Japan) for helping them integrate the data. References 1. ↵ Eikelboom JW , Connolly SJ , Bosch J , Dagenais GR , Hart RG , Shestakovska O , Diaz R , Alings M , Lonn EM , Anand SS , et al. 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World Heart Federation expert consensus statement on antiplatelet therapy in East Asian patients with ACS or undergoing PCI . Nat Rev Cardiol . 2014 ; 11 : 597 – 606 . OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted March 23, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Utility of the CHADS-P2A2RC score for estimating net adverse clinical events in chronic coronary syndrome patients without atrial fibrillation: insights from the CLIDAS-PCI Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. 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Share Utility of the CHADS-P 2 A 2 RC score for estimating net adverse clinical events in chronic coronary syndrome patients without atrial fibrillation: insights from the CLIDAS-PCI Takenobu Shimada , Daiju Fukuda , Atsushi Shibata , Kenichiro Otsuka , Asahiro Ito , Hiroshi Okamura , Tetsuya Matoba , Takahide Kohro , Yusuke Oba , Tomoyuki Kabutoya , Yasushi Imai , Kazuomi Kario , Arihiro Kiyosue , Yoshiko Mizuno , Kotaro Nochioka , Masaharu Nakayama , Takamasa Iwai , Yoshihiro Miyamoto , Masanobu Ishii , Taishi Nakamura , Kenichi Tsujita , Hisahiko Sato , Naoyuki Akashi , Hideo Fujita , Ryozo Nagai medRxiv 2025.03.21.25324437; doi: https://doi.org/10.1101/2025.03.21.25324437 Share This Article: Copy Citation Tools Utility of the CHADS-P 2 A 2 RC score for estimating net adverse clinical events in chronic coronary syndrome patients without atrial fibrillation: insights from the CLIDAS-PCI Takenobu Shimada , Daiju Fukuda , Atsushi Shibata , Kenichiro Otsuka , Asahiro Ito , Hiroshi Okamura , Tetsuya Matoba , Takahide Kohro , Yusuke Oba , Tomoyuki Kabutoya , Yasushi Imai , Kazuomi Kario , Arihiro Kiyosue , Yoshiko Mizuno , Kotaro Nochioka , Masaharu Nakayama , Takamasa Iwai , Yoshihiro Miyamoto , Masanobu Ishii , Taishi Nakamura , Kenichi Tsujita , Hisahiko Sato , Naoyuki Akashi , Hideo Fujita , Ryozo Nagai medRxiv 2025.03.21.25324437; doi: https://doi.org/10.1101/2025.03.21.25324437 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Cardiovascular Medicine Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (299) Cardiovascular Medicine (4427) Dentistry and Oral Medicine (444) Dermatology (382) Emergency Medicine (608) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1508) Epidemiology (15224) Forensic Medicine (30) Gastroenterology (1124) Genetic and Genomic Medicine (6590) Geriatric Medicine (667) Health Economics (997) Health Informatics (4529) Health Policy (1368) Health Systems and Quality Improvement (1612) Hematology (540) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15910) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (145) Nephrology (667) Neurology (6590) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1143) Occupational and Environmental Health (956) Oncology (3331) Ophthalmology (971) Orthopedics (369) Otolaryngology (420) Pain Medicine (436) Palliative Medicine (129) Pathology (663) Pediatrics (1691) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5441) Public and Global Health (9222) Radiology and Imaging (2196) Rehabilitation Medicine and Physical Therapy (1370) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (711) Sports Medicine (529) Surgery (712) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a000730838188e2e',t:'MTc3OTUwMTQ0MQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();