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A simple and easily implemented risk model to predict 1-year ischemic stroke and systemic embolism in Chinese patients with atrial fibrillation.
Jiang, Chao; Chen, Tian-Ge; Du, Xin; Li, Xiang; He, Liu; Lai, Yi-Wei; Xia, Shi-Jun; Liu, Rong; Hu, Yi-Ying; Li, Ying-Xue; Jiang, Chen-Xi; Liu, Nian; Tang, Ri-Bo; Bai, Rong; Sang, Cai-Hua; Long, De-Yong; Xie, Guo-Tong; Dong, Jian-Zeng; Ma, Chang-Sheng.
Affiliation
  • Jiang C; Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, National Clinical Research Center for Cardiovascular Diseases, Beijing Advanced Innovation Center for Big Data-Based Precision Medicine for Cardiovascular Diseases, Beijing 100029, China.
  • Chen TG; Ping An Health Technology, Beijing 100035, China.
  • Du X; Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, National Clinical Research Center for Cardiovascular Diseases, Beijing Advanced Innovation Center for Big Data-Based Precision Medicine for Cardiovascular Diseases, Beijing 100029, China.
  • Li X; Heart Health Research Center, Beijing 100029, China.
  • He L; Ping An Health Technology, Beijing 100035, China.
  • Lai YW; Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, National Clinical Research Center for Cardiovascular Diseases, Beijing Advanced Innovation Center for Big Data-Based Precision Medicine for Cardiovascular Diseases, Beijing 100029, China.
  • Xia SJ; Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, National Clinical Research Center for Cardiovascular Diseases, Beijing Advanced Innovation Center for Big Data-Based Precision Medicine for Cardiovascular Diseases, Beijing 100029, China.
  • Liu R; Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, National Clinical Research Center for Cardiovascular Diseases, Beijing Advanced Innovation Center for Big Data-Based Precision Medicine for Cardiovascular Diseases, Beijing 100029, China.
  • Hu YY; Heart Health Research Center, Beijing 100029, China.
  • Li YX; Ping An Health Technology, Beijing 100035, China.
  • Jiang CX; Ping An Health Technology, Beijing 100035, China.
  • Liu N; Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, National Clinical Research Center for Cardiovascular Diseases, Beijing Advanced Innovation Center for Big Data-Based Precision Medicine for Cardiovascular Diseases, Beijing 100029, China.
  • Tang RB; Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, National Clinical Research Center for Cardiovascular Diseases, Beijing Advanced Innovation Center for Big Data-Based Precision Medicine for Cardiovascular Diseases, Beijing 100029, China.
  • Bai R; Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, National Clinical Research Center for Cardiovascular Diseases, Beijing Advanced Innovation Center for Big Data-Based Precision Medicine for Cardiovascular Diseases, Beijing 100029, China.
  • Sang CH; Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, National Clinical Research Center for Cardiovascular Diseases, Beijing Advanced Innovation Center for Big Data-Based Precision Medicine for Cardiovascular Diseases, Beijing 100029, China.
  • Long DY; Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, National Clinical Research Center for Cardiovascular Diseases, Beijing Advanced Innovation Center for Big Data-Based Precision Medicine for Cardiovascular Diseases, Beijing 100029, China.
  • Xie GT; Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, National Clinical Research Center for Cardiovascular Diseases, Beijing Advanced Innovation Center for Big Data-Based Precision Medicine for Cardiovascular Diseases, Beijing 100029, China.
  • Dong JZ; Ping An Health Technology, Beijing 100035, China.
  • Ma CS; Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, National Clinical Research Center for Cardiovascular Diseases, Beijing Advanced Innovation Center for Big Data-Based Precision Medicine for Cardiovascular Diseases, Beijing 100029, China.
Chin Med J (Engl) ; 134(19): 2293-2298, 2021 May 25.
Article in En | MEDLINE | ID: mdl-34039872
ABSTRACT

BACKGROUND:

Accurate prediction of ischemic stroke is required for deciding anticoagulation use in patients with atrial fibrillation (AF). Even though only 6% to 8% of AF patients die from stroke, about 90% are indicated for anticoagulants according to the current AF management guidelines. Therefore, we aimed to develop an accurate and easy-to-use new risk model for 1-year thromboembolic events (TEs) in Chinese AF patients.

METHODS:

From the prospective China Atrial Fibrillation Registry cohort study, we identified 6601 AF patients who were not treated with anticoagulation or ablation at baseline. We selected the most important variables by the extreme gradient boosting (XGBoost) algorithm and developed a simplified risk model for predicting 1-year TEs. The novel risk score was internally validated using bootstrapping with 1000 replicates and compared with the CHA2DS2-VA score (excluding female sex from the CHA2DS2-VASc score).

RESULTS:

Up to the follow-up of 1 year, 163 TEs (ischemic stroke or systemic embolism) occurred. Using the XGBoost algorithm, we selected the three most important variables (congestive heart failure or left ventricular dysfunction, age, and prior stroke, abbreviated as CAS model) to predict 1-year TE risk. We trained a multivariate Cox regression model and assigned point scores proportional to model coefficients. The CAS scheme classified 30.8% (2033/6601) of the patients as low risk for TE (CAS score = 0), with a corresponding 1-year TE risk of 0.81% (95% confidence interval [CI] 0.41%-1.19%). In our cohort, the C-statistic of CAS model was 0.69 (95% CI 0.65-0.73), higher than that of CHA2DS2-VA score (0.66, 95% CI 0.62-0.70, Z = 2.01, P = 0.045). The overall net reclassification improvement from CHA2DS2-VA categories (low = 0/high ≥1) to CAS categories (low = 0/high ≥1) was 12.2% (95% CI 8.7%-15.7%).

CONCLUSION:

In Chinese AF patients, a novel and simple CAS risk model better predicted 1-year TEs than the widely-used CHA2DS2-VA risk score and identified a large proportion of patients with low risk of TEs, which could potentially improve anticoagulation decision-making. TRIAL REGISTRATION www.chictr.org.cn (Unique identifier No. ChiCTR-OCH-13003729).
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Atrial Fibrillation / Brain Ischemia / Stroke / Embolism / Ischemic Stroke Type of study: Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limits: Female / Humans Country/Region as subject: Asia Language: En Journal: Chin Med J (Engl) Year: 2021 Document type: Article Affiliation country: China

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Atrial Fibrillation / Brain Ischemia / Stroke / Embolism / Ischemic Stroke Type of study: Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limits: Female / Humans Country/Region as subject: Asia Language: En Journal: Chin Med J (Engl) Year: 2021 Document type: Article Affiliation country: China