Natriuretic Peptides to Classify Risk of Atrial Fibrillation Detection After Stroke: Analysis of the BIOSIGNAL and PRECISE Cohort Studies.

BACKGROUND AND OBJECTIVES: Prolonged cardiac monitoring (PCM) increases atrial fibrillation (AF) detection after ischemic stroke, but access is limited, and it is burdensome for patients. Our objective was to assess whether midregional proatrial natriuretic peptide (MR-proANP) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) could classify people who are unlikely to have AF after ischemic stroke and allow better targeting of PCM. METHODS: We analyzed people from the Biomarker Signature of Stroke Aetiology (BIOSIGNAL) study with ischemic stroke, no known AF, and ≥3 days cardiac monitoring. External validation was performed in the Preventing Recurrent Cardioembolic Stroke: Right Approach, Right Patient (PRECISE) study of 28 days of cardiac monitoring in people with ischemic stroke or transient ischemic attack and no known AF. The main outcome is no AF detection. We assessed the discriminatory value of MR-proANP and NT-proBNP combined with clinical variables to identify people with no AF. A decision curve analysis was performed with combined data to determine the net reduction in people who would undergo PCM using the models based on a 15% threshold probability for AF detection. RESULTS: We included 621 people from the BIOSIGNAL study. The clinical multivariable prediction model included age, NIH Stroke Scale score, lipid-lowering therapy, creatinine, and smoking status. The area under the receiver-operating characteristic curve (AUROC) for clinical variables was 0.68 (95% CI 0.62-0.74), which improved with the addition of log10MR-proANP (0.72, 0.66-0.78; p = 0.001) or log10NT-proBNP (0.71, 0.65-0.77; p = 0.009). Performance was similar for the models with log10MR-proANP vs log10NT-proBNP (p = 0.28). In 239 people from the PRECISE study, the AUROC for clinical variables was 0.68 (0.59-0.76), which improved with the addition of log10NT-proBNP (0.73, 0.65-0.82; p < 0.001) or log10MR-proANP (0.79, 0.72-0.86; p < 0.001). Performance was better for the model with log10MR-proANP vs log10NT-proBNP (p = 0.03). The models could reduce the number of people who would undergo PCM by 30% (clinical and log10MR-proANP), 27% (clinical and log10NT-proBNP), or 20% (clinical only). DISCUSSION: MR-proANP and NT-proBNP help classify people who are unlikely to have AF after ischemic stroke. Measuring MR-proANP or NT-proBNP could reduce the number of people who need PCM by 30%, without reducing the amount of AF detected. TRIAL REGISTRATION INFORMATION: NCT02274727; clinicaltrials.gov/study/NCT02274727.

The role of single pill combination therapy in the prevention of ischaemic stroke.

BACKGROUND AND AIMS: The role of single pill combination therapy for stroke prevention remains to be established. We explored the perspectives of stroke survivors and healthcare professionals on single pill combination therapy for stroke prevention. METHODS: We conducted focus groups involving stroke survivors and healthcare professionals. RESULTS: We recruited six stroke survivors: four (67%) were female and mean age was 70 ± 12 years; and eight healthcare professionals (three Stroke Consultants, two Nurse Specialists, three General Practitioners). Improved adherence is the main perceived benefit of single pill combination therapy, although concerns exist surrounding less individualised care, unsuitability for use in the acute setting, reduced ability to titrate doses and difficulty identifying the cause of side effects. The clinical stability of patients, alongside single pill combination therapy efficacy, cost, side effect profile and evidence base for impact on risk factors and clinical outcomes are key factors influencing acceptability. Stroke survivors and healthcare professionals feel single pill combination therapy is most suitable for stable patients, although there is no evidence base for its use in this context. CONCLUSION: Stroke healthcare professionals and stroke survivors are most amenable to using single pill combination therapy for stable patients, although its role in this context should be evaluated in studies with risk factor targets and clinical outcomes as endpoints.

Stroke Risk in Patients With Reduced Ejection Fraction After Myocardial Infarction Without Atrial Fibrillation.

BACKGROUND: Stroke can occur after myocardial infarction (MI) in the absence of atrial fibrillation (AF). OBJECTIVES: This study sought to identify risk factors (excluding AF) for the occurrence of stroke and to develop a calibrated and validated stroke risk score in patients with MI and heart failure (HF) and/or systolic dysfunction. METHODS: The datasets included in this pooling initiative were derived from 4 trials: CAPRICORN (Effect of Carvedilol on Outcome After Myocardial Infarction in Patients With Left Ventricular Dysfunction), OPTIMAAL (Optimal Trial in Myocardial Infarction With Angiotensin II Antagonist Losartan), VALIANT (Valsartan in Acute Myocardial Infarction Trial), and EPHESUS (Eplerenone Post-Acute Myocardial Infarction Heart Failure Efficacy and Survival Study); EPHESUS was used for external validation. A total of 22,904 patients without AF or oral anticoagulation were included in this analysis. The primary outcome was stroke, and death was treated as a "competing risk." RESULTS: During a median follow-up of 1.9 years (interquartile range: 1.3 to 2.7 years), 660 (2.9%) patients had a stroke. These patients were older, more often female, smokers, and hypertensive; they had a higher Killip class; a lower estimated glomerular filtration rate; and a higher proportion of MI, HF, diabetes, and stroke histories. The final stroke risk model retained older age, Killip class 3 or 4, estimated glomerular filtration rate ≤45 ml/min/1.73 m2, hypertension history, and previous stroke. The models were well calibrated and showed moderate to good discrimination (C-index = 0.67). The observed 3-year event rates increased steeply for each sextile of the stroke risk score (1.8%, 2.9%, 4.1%, 5.6%, 8.3%, and 10.9%, respectively) and were in agreement with the expected event rates. CONCLUSIONS: Readily accessible risk factors associated with the occurrence of stroke were identified and incorporated in an easy-to-use risk score. This score may help in the identification of patients with MI and HF and a high risk for stroke despite their not presenting with AF.

Characteristic adverse events and their incidence among patients participating in acute ischemic stroke trials.

BACKGROUND AND PURPOSE: Adverse events (AE) in trial populations present a major burden to researchers and patients, yet most events are unrelated to investigational treatment. We aimed to develop a coherent list of expected AEs, whose incidence can be predicted by patient characteristics that will inform future trials and perhaps general poststroke care. METHODS: We analyzed raw AE data from patients participating in acute ischemic stroke trials. We identified events that occurred with a lower 99% confidence bound greater than nil. Among these, we applied receiver operating characteristic principles to select the fewest types of events that together represented the greatest number of reports. Using ordinal logistic regression, we modeled the incidence of these events as a function of patient age, sex, baseline National Institutes of Health Stroke Scale, and multimorbidity status, defining P<0.05 as statistically significant. RESULTS: We analyzed 5775 placebo-treated patients, reporting 21 217 AEs. Among 756 types of AEs, 132 accounted for 82.7%, of which 80% began within 10 days after stroke. Right hemisphere (odds ratio [OR], 1.67), increasing baseline National Institutes of Health Stroke Scale (OR, 1.11), multimorbidity status (OR, 1.09 per disease), patient age (OR, 1.01 per year), height (OR, 1.01 per centimeter), diastolic blood pressure (OR, 0.99 per mm Hg), and smoking (OR, 0.82) were independently associated with developing more AEs but together explained only 13% of the variation. CONCLUSIONS: A list of 132 expected AEs after acute ischemic stroke may be used to simplify interpretation and reporting of complications. AEs can be modestly predicted by patient characteristics, facilitating stratification of patients by risk for poststroke complications.