Thromb Haemost 2022; 122(01): 005-007 DOI: 10.1055/a-1508-7980
A New Paradigm of “Real-Time” Stroke Risk Prediction and Integrated Care Management in the Digital Health Era: Innovations Using Machine Learning and Artificial Intelligence Approaches
1
Department of Pulmonary Vessel and Thrombotic Disease, Medical School of Chinese PLA, Chinese PLA General Hospital, Beijing, China
› Institutsangaben
Reference
1
Krishnamurthi RV,
Ikeda T,
Feigin VL.
Global, regional and country-specific burden of ischaemic stroke, intracerebral haemorrhage and subarachnoid haemorrhage: a systematic analysis of the Global Burden of Disease Study 2017. Neuroepidemiology 2020; 54 (02) 171-179
2
Bos MJ,
Koudstaal PJ,
Hofman A,
Ikram MA.
Modifiable etiological factors and the burden of stroke from the Rotterdam study: a population-based cohort study. PLoS Med 2014; 11 (04) e1001634
3
O'Donnell MJ,
Chin SL,
Rangarajan S.
et al;
INTERSTROKE investigators.
Global and regional effects of potentially modifiable risk factors associated with acute stroke in 32 countries (INTERSTROKE): a case-control study. Lancet 2016; 388 (10046): 761-775
4
Guo Y,
Wang H,
Tian Y,
Wang Y,
Lip GY.
Multiple risk factors and ischaemic stroke in the elderly Asian population with and without atrial fibrillation. An analysis of 425,600 Chinese individuals without prior stroke. Thromb Haemost 2016; 115 (01) 184-192
5
Wolf PA,
D'Agostino RB,
Belanger AJ,
Kannel WB.
Probability of stroke: a risk profile from the Framingham Study. Stroke 1991; 22 (03) 312-318
6
Nobel L,
Mayo NE,
Hanley J,
Nadeau L,
Daskalopoulou SS.
MyRisk_Stroke Calculator: a personalized stroke risk assessment tool for the general population. J Clin Neurol 2014; 10 (01) 1-9
7
Parmar P,
Krishnamurthi R,
Ikram MA.
et al;
Stroke RiskometerTM Collaboration Writing Group.
The Stroke Riskometer(TM) App: validation of a data collection tool and stroke risk predictor. Int J Stroke 2015; 10 (02) 231-244
8
Hippisley-Cox J,
Coupland C,
Brindle P.
Derivation and validation of QStroke score for predicting risk of ischaemic stroke in primary care and comparison with other risk scores: a prospective open cohort study. BMJ 2013; 346: f2573
9
Lee HL,
Kim JT,
Lee JS.
et al.
CHA2DS2-VASc score in acute ischemic stroke with atrial fibrillation: results from the Clinical Research Collaboration for Stroke in Korea. Sci Rep 2021; 11 (01) 793
10
Yoon M,
Yang PS,
Jang E.
et al.
Dynamic changes of CHA2DS2-VASc score and the risk of ischaemic stroke in Asian patients with atrial fibrillation: a nationwide cohort study. Thromb Haemost 2018; 118 (07) 1296-1304
11
Chao TF,
Lip GY,
Liu CJ.
et al.
Validation of a modified CHA2DS2-VASc score for stroke risk stratification in Asian patients with atrial fibrillation: a nationwide cohort study. Stroke 2016; 47 (10) 2462-2469
12
Karlsson LO,
Nilsson S,
Bång M,
Nilsson L,
Charitakis E,
Janzon M.
A clinical decision support tool for improving adherence to guidelines on anticoagulant therapy in patients with atrial fibrillation at risk of stroke: a cluster-randomized trial in a Swedish primary care setting (the CDS-AF study). PLoS Med 2018; 15 (03) e1002528
13
Proietti M,
Rivera-Caravaca JM,
Esteve-Pastor MA,
Marín F,
Lip GYH.
Stroke and thromboembolism in warfarin-treated patients with atrial fibrillation: comparing the CHA2DS2-VASc and GARFIELD-AF risk scores. Thromb Haemost 2021; 121 (08) 1107-1114
14
Camelo-Castillo A,
Rivera-Caravaca JM,
Marín F,
Vicente V,
Lip GYH,
Roldán V.
Predicting adverse events beyond stroke and bleeding with the ABC-Stroke and ABC-bleeding scores in patients with atrial fibrillation: the Murcia AF project. Thromb Haemost 2020; 120 (08) 1200-1207
15
Chao TF,
Lip GYH,
Lin YJ.
et al.
Incident risk factors and major bleeding in patients with atrial fibrillation treated with oral anticoagulants: a comparison of baseline, follow-up and Delta HAS-BLED scores with an approach focused on modifiable bleeding risk factors. Thromb Haemost 2018; 118 (04) 768-777
16
Rivera-Caravaca JM,
Roldán V,
Esteve-Pastor MA.
et al.
Long-term stroke risk prediction in patients with atrial fibrillation: comparison of the ABC-Stroke and CHA2 DS2 -VASc scores. J Am Heart Assoc 2017; 6 (07) e006490
17
Esteve-Pastor MA,
Roldán V,
Rivera-Caravaca JM,
Ramírez-Macías I,
Lip GYH,
Marín F.
The use of biomarkers in clinical management guidelines: a critical appraisal. Thromb Haemost 2019; 119 (12) 1901-1919
18
Chao TF,
Liao JN,
Tuan TC.
et al.
Incident co-morbidities in patients with atrial fibrillation initially with a CHA2DS2-VASc score of 0 (males) or 1 (females): implications for reassessment of stroke risk in initially ‘low-risk’ patients. Thromb Haemost 2019; 119 (07) 1162-1170
19
Mortazavi BJ,
Bucholz EM,
Desai NR.
et al.
Comparison of machine learning methods with national cardiovascular data registry models for prediction of risk of bleeding after percutaneous coronary intervention. JAMA Netw Open 2019; 2 (07) e196835
20
Hill NR,
Ayoubkhani D,
McEwan P.
et al.
Predicting atrial fibrillation in primary care using machine learning. PLoS One 2019; 14 (11) e0224582
21
Lip GYH,
Tran G,
Genaidy A,
Marroquin P,
Estes C,
Landsheftl J.
Improving dynamic stroke risk prediction in non-anticoagulated patients with and without atrial fibrillation: comparing common clinical risk scores and machine learning algorithms. Eur Heart J Qual Care Clin Outcomes 2021;
22
Lip GYH,
Genaidy A,
Tran G,
Marroquin P,
Estes C.
Incident atrial fibrillation and its risk prediction in patients developing COVID-19: a machine learning based algorithm approach. Eur J Intern Med 2021; 91: 53-58
23
Lip GYH,
Tran G,
Genaidy A,
Marroquin P.
Revisiting the dynamic risk profile of cardiovascular/non-cardiovascular multimorbidity in incident atrial fibrillation patients and five cardiovascular/non-cardiovascular outcomes: a machine-learning approach. J Arrhythmia 2021; 22; 37 (04) 931-941
24
Tiwari P,
Colborn KL,
Smith DE,
Xing F,
Ghosh D,
Rosenberg MA.
Assessment of a machine learning model applied to harmonized electronic health record data for the prediction of incident atrial fibrillation. JAMA Netw Open 2020; 3 (01) e1919396
25
Khera R,
Haimovich J,
Hurley NC.
et al.
Use of machine learning models to predict death after acute myocardial infarction. JAMA Cardiol 2021; 6 (06) 633-641
26
Kilic A.
Artificial intelligence and machine learning in cardiovascular health care. Ann Thorac Surg 2020; 109 (05) 1323-1329
27
Lip GY,
Genaidy A,
Tran G,
Marroquin P,
Estes C,
Sloop S.
Improving stroke risk prediction in the general population: common clinical rules, a new multimorbid index and machine learning based algorithms. Thromb Haemost 2021;
28
Potpara TS,
Lip GYH,
Blomstrom-Lundqvist C.
et al.
The 4S-AF Scheme (Stroke Risk; Symptoms; Severity of Burden; Substrate): a novel approach to in-depth characterization (rather than classification) of atrial fibrillation. Thromb Haemost 2021; 121 (03) 270-278
29
Yoon M,
Yang PS,
Jang E.
et al.
Improved population-based clinical outcomes of patients with atrial fibrillation by compliance with the simple ABC (Atrial Fibrillation Better Care) pathway for integrated care management: a nationwide cohort study. Thromb Haemost 2019; 119 (10) 1695-1703
30
Jamthikar A,
Gupta D,
Saba L.
et al.
Cardiovascular/stroke risk predictive calculators: a comparison between statistical and machine learning models. Cardiovasc Diagn Ther 2020; 10 (04) 919-938
31
Guo Y,
Lane DA,
Wang L.
et al.
mAF-App II Trial Investigators. Mobile health technology to improve care for patients with atrial fibrillation. J Am Coll Cardiol 2020; 75 (13) 1523-1534
32
Guo Y,
Guo J,
Shi X.
et al;
mAF-App II Trial investigators.
Mobile health technology-supported atrial fibrillation screening and integrated care: a report from the mAFA-II trial Long-term Extension Cohort. Eur J Intern Med 2020; 82: 105-111