Atrial fibrillation and stroke: A review and new insights

Escudero-Martínez, Irene; Morales-Caba, Lluis; Segura, Tomás

doi:10.1016/j.tcm.2021.12.001

Cited by 50 publications

(21 citation statements)

References 61 publications

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“…33 In the Acute Stroke Registry and Analysis of Lausanne registry, hemoglobin levels were inversely associated with stroke recurrence and mortality. 34 Atrial fibrillation is a well-recognized risk factor for stroke occurrence, 35 and even in patients with adequate treatment, the risk of recurrence is high. 36 In addition, anticoagulation is associated with an increased risk of ICH in these patients.…”

Section: Discussionmentioning

confidence: 99%

PRERISK: A Personalized, Artificial Intelligence–Based and Statistically–Based Stroke Recurrence Predictor for Recurrent Stroke

Colangelo,

Ribo,

Montiel

et al. 2024

Stroke

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BACKGROUND: Predicting stroke recurrence for individual patients is difficult, but individualized prediction may improve stroke survivors’ engagement in self-care. We developed PRERISK: a statistical and machine learning classifier to predict individual risk of stroke recurrence. METHODS: We analyzed clinical and socioeconomic data from a prospectively collected public health care–based data set of 41 975 patients admitted with stroke diagnosis in 88 public health centers over 6 years (2014–2020) in Catalonia-Spain. A new stroke diagnosis at least 24 hours after the index event was considered as a recurrent stroke, which was considered as our outcome of interest. We trained several supervised machine learning models to provide individualized risk over time and compared them with a Cox regression model. Models were trained to predict early, late, and long-term recurrence risk, within 90, 91 to 365, and >365 days, respectively. C statistics and area under the receiver operating characteristic curve were used to assess the accuracy of the models. RESULTS: Overall, 16.21% (5932 of 36 114) of patients had stroke recurrence during a median follow-up of 2.69 years. The most powerful predictors of stroke recurrence were time from previous stroke, Barthel Index, atrial fibrillation, dyslipidemia, age, diabetes, and sex, which were used to create a simplified model with similar performance, together with modifiable vascular risk factors (glycemia, body mass index, high blood pressure, cholesterol, tobacco dependence, and alcohol abuse). The areas under the receiver operating characteristic curve were 0.76 (95% CI, 0.74–0.77), 0.60 (95% CI, 0.58–0.61), and 0.71 (95% CI, 0.69–0.72) for early, late, and long-term recurrence risk, respectively. The areas under the receiver operating characteristic curve of the Cox risk class probability were 0.73 (95% CI, 0.72–0.75), 0.59 (95% CI, 0.57–0.61), and 0.67 (95% CI, 0.66–0.70); machine learning approaches (random forest and AdaBoost) showed statistically significant improvement ( P <0.05) over the Cox model for the 3 recurrence time periods. Stroke recurrence curves can be simulated for each patient under different degrees of control of modifiable factors. CONCLUSIONS: PRERISK is a novel approach that provides a personalized and fairly accurate risk prediction of stroke recurrence over time. The model has the potential to incorporate dynamic control of risk factors.

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Section: Discussionmentioning

confidence: 99%

PRERISK: A Personalized, Artificial Intelligence–Based and Statistically–Based Stroke Recurrence Predictor for Recurrent Stroke

Colangelo,

Ribo,

Montiel

et al. 2024

Stroke

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“…In the Acute Stroke Registry and Analysis of Lausanne (ASTRAL) registry, haemoglobin levels were inversely related with stroke recurrence and mortality 29 . Atrial fibrillation is a worldwide known risk factor for stroke occurrence 30 , and even in patients with adequate treatment, the risk of IS recurrence is high 31 . Furthermore, in these patients, anticoagulation also leads to an increased risk of ICH 32 .…”

Section: Discussionmentioning

confidence: 99%

PRERISK: A Personalized, daily and AI-based stroke recurrence predictor for patient awareness and treatment compliance

Colangelo

Ribó

Montiel³

et al. 2023

Preprint

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BACKGROUND The risk prediction of stroke recurrence for individual patients is a difficult task. Individualised prediction may enhance stroke survivors selfcare engagement. We have developed PRERISK: a statistical and Machine Learning (ML) classifier to predict individual stroke recurrence risk. METHODS We analysed clinical and socioeconomic data from a prospectively collected public healthcare-based dataset of 44623 patients admitted with stroke diagnosis in 88 public hospitals over 6 years in Catalonia-Spain. We trained several supervised-ML models to provide individualised risk along time and compared them with a Cox regression model. RESULTS Overall, 16% of patients presented a stroke recurrence along a median follow-up of 2.65 years. Models were trained for predicting early, late and long-term recurrence risk, within 90, 91-365 and >365 days, respectively. Most powerful predictors of stroke recurrence were time since index stroke, Barthel index, atrial fibrillation, dyslipidemia, haemoglobin and body mass index, which were used to create a simplified model with similar performance. The balanced AUROC were 0.77 (±0.01), 0.61 (±0.01) and 0.71 (±0.01) for early, late and long-term recurrence risk respectively (Cox risk class probability: 0.74(±0.01), 0.59(±0.01) and 0.68(±0.01), c-index 0.88). Overall, the ML approach showed statistically significant improvement over the Cox model. Stroke recurrence curves can be simulated for each patient under different degrees of control of modifiable factors. CONCLUSION PRERISK represents a novel approach that provides continuous, personalised and fairly accurate risk prediction of stroke recurrence along time according to the degree of modifiable risk factors control.

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“…Patients with AF are at increased risk for thromboembolic events, 2 and anticoagulation remains the primary method for decreasing thromboembolic risk in these patients. Surgical LAA exclusion has been employed concomitantly with other cardiac procedures to decrease embolic risk because the LAA has been shown to be the predominant source of thrombi in patients with AF.…”

Section: Discussionmentioning

confidence: 99%

“…Atrial fibrillation (AF) continues to increase in incidence and prevalence 1 and is estimated to be responsible for 15% of all strokes worldwide. 2 Although oral anticoagulation (OAC) remains a primary method of stroke prevention in AF patients, OAC has limitations including contraindication or intolerance in some patients. Concurrently, the left atrial appendage (LAA) has become a target for reducing thromboembolic event rates given its role in thrombus formation.…”

Section: Introductionmentioning

confidence: 99%

Thoracoscopic Left Atrial Appendage Exclusion for Atrial Fibrillation Patients Intolerant to Anticoagulation

Sharaf

Falasa

Jones

et al. 2023

Innovations�(Phila)

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Objective: Outcomes associated with isolated epicardial left atrial appendage (LAA) exclusion in atrial fibrillation (AF) patients with a contraindication or intolerance to anticoagulation are not clearly defined, especially in patients with prior stroke. This study evaluated the perioperative safety, medication use, and stroke outcomes for isolated thoracoscopic LAA exclusion for stroke prevention. Methods: A single-center retrospective study was performed of adults undergoing isolated thoracoscopic LAA exclusion with an epicardial exclusion device without a concomitant surgical procedure. Descriptive statistics were performed. Results: Twenty-five patients met the inclusion criteria. The cohort was 68% male ( n = 17) with a mean age of 76.4 ± 6.5 years, a mean preoperative CHA2DS2-VASc score of 4.2 ± 1.4, and a mean preoperative HAS-BLED score of 2.68 ± 1.03. Seventeen patients (68%) had nonparoxysmal AF. There were 11 patients with intolerance of anticoagulation due to intracranial hemorrhage (44%), 6 due to gastrointestinal bleeding (24%), and 4 due to genitourinary bleeding (16%). All procedures were performed thoracoscopically with 100% technical success—the mean LAA stump length was 5.5 ± 2.3 mm on intraoperative transesophageal echocardiography. The median hospital length of stay was 2 (interquartile range [IQR] 1, 6.5) days. The median follow-up time was 430 (IQR 125, 972) days. During follow-up, 1 patient with cerebral angiopathy had temporary neurologic deficits at an outside institution, with brain imaging showing no evidence of ischemic injury. There were no other thromboembolic events over the 38.8 postoperative patient-years examined. All patients were off anticoagulation at last follow-up. Conclusions: This study shows the perioperative safety, technical success, freedom from anticoagulation, and stroke outcomes of isolated thoracoscopic LAA exclusion in patients with AF at high risk for thromboembolic disease.

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Atrial fibrillation and stroke: A review and new insights

Cited by 50 publications

References 61 publications

PRERISK: A Personalized, Artificial Intelligence–Based and Statistically–Based Stroke Recurrence Predictor for Recurrent Stroke

PRERISK: A Personalized, Artificial Intelligence–Based and Statistically–Based Stroke Recurrence Predictor for Recurrent Stroke

PRERISK: A Personalized, daily and AI-based stroke recurrence predictor for patient awareness and treatment compliance

Thoracoscopic Left Atrial Appendage Exclusion for Atrial Fibrillation Patients Intolerant to Anticoagulation

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