TY - JOUR
T1 - Improving dynamic stroke risk prediction in non-anticoagulated patients with and without atrial fibrillation
T2 - Comparing common clinical risk scores and machine learning algorithms
AU - Lip, Gregory Y H
AU - Tran, George
AU - Genaidy, Ash
AU - Marroquin, Patricia
AU - Estes, Cara
AU - Landsheftl, Jeremy
N1 - The Author(s) 2021. Published by Oxford University Press on behalf of the European Society of Cardiology.
PY - 2022/9
Y1 - 2022/9
N2 - AIMS: Diversified cardiovascular/non-cardiovascular multi-morbid risk and efficient machine learning algorithms may facilitate improvements in stroke risk prediction, especially in newly diagnosed non-anticoagulated atrial fibrillation (AF) patients where initial decision-making on stroke prevention is needed. Therefore the aims of this article are to study common clinical risk assessment for stroke risk prediction in AF/non-AF cohorts together with cardiovascular/ non-cardiovascular multi-morbid conditions; to improve stroke risk prediction using machine learning approaches; and to compare the improved clinical prediction rules for multi-morbid conditions using machine learning algorithms. METHODS AND RESULTS: We used cohort data from two health plans with 6 457 412 males/females contributing 14,188,679 person-years of data. The model inputs consisted of a diversified list of comorbidities/demographic/ temporal exposure variables, with the outcome capturing stroke event incidences. Machine learning algorithms used two parametric and two nonparametric techniques. The best prediction model was derived on the basis of non-linear formulations using machine learning criteria, with the highest c-index was obtained for logistic regression [0.892; 95% confidence interval (CI) 0.886-0.898] with consistency on external validation (0.891; 95% CI 0.882-0.9). These were significantly higher than those based on the conventional stroke risk scores (CHADS2: 0.7488, 95% CI 0.746-0.7516; CHA2DS2-VASc: 0.7801, 95% CI 0.7772-0.7831) and multi-morbid index (0.8508, 95% CI 0.8483-0.8532). The machine learning algorithm had good internal and external calibration and net benefit values. CONCLUSION: In this large cohort of newly diagnosed non-anticoagulated AF/non-AF patients, large improvements in stroke risk prediction can be shown with cardiovascular/non-cardiovascular multi-morbid index and a machine learning approach accounting for dynamic changes in risk factors.
AB - AIMS: Diversified cardiovascular/non-cardiovascular multi-morbid risk and efficient machine learning algorithms may facilitate improvements in stroke risk prediction, especially in newly diagnosed non-anticoagulated atrial fibrillation (AF) patients where initial decision-making on stroke prevention is needed. Therefore the aims of this article are to study common clinical risk assessment for stroke risk prediction in AF/non-AF cohorts together with cardiovascular/ non-cardiovascular multi-morbid conditions; to improve stroke risk prediction using machine learning approaches; and to compare the improved clinical prediction rules for multi-morbid conditions using machine learning algorithms. METHODS AND RESULTS: We used cohort data from two health plans with 6 457 412 males/females contributing 14,188,679 person-years of data. The model inputs consisted of a diversified list of comorbidities/demographic/ temporal exposure variables, with the outcome capturing stroke event incidences. Machine learning algorithms used two parametric and two nonparametric techniques. The best prediction model was derived on the basis of non-linear formulations using machine learning criteria, with the highest c-index was obtained for logistic regression [0.892; 95% confidence interval (CI) 0.886-0.898] with consistency on external validation (0.891; 95% CI 0.882-0.9). These were significantly higher than those based on the conventional stroke risk scores (CHADS2: 0.7488, 95% CI 0.746-0.7516; CHA2DS2-VASc: 0.7801, 95% CI 0.7772-0.7831) and multi-morbid index (0.8508, 95% CI 0.8483-0.8532). The machine learning algorithm had good internal and external calibration and net benefit values. CONCLUSION: In this large cohort of newly diagnosed non-anticoagulated AF/non-AF patients, large improvements in stroke risk prediction can be shown with cardiovascular/non-cardiovascular multi-morbid index and a machine learning approach accounting for dynamic changes in risk factors.
KW - Anticoagulants
KW - Atrial fibrillation
KW - Medical/pharmacy claims
KW - Stroke risk prediction
UR - http://www.scopus.com/inward/record.url?scp=85136909276&partnerID=8YFLogxK
U2 - 10.1093/ehjqcco/qcab037
DO - 10.1093/ehjqcco/qcab037
M3 - Journal article
C2 - 33999139
SN - 2058-1742
VL - 8
SP - 548
EP - 556
JO - European heart journal. Quality of care & clinical outcomes
JF - European heart journal. Quality of care & clinical outcomes
IS - 5
M1 - qcab037
ER -