Left Atrial Strain Predicts Stroke Recurrence and Death in Patients With Cryptogenic Stroke

Left atrial strain (LAS) has been widely studied as a predictor of atrial fibrillation (AF) after cryptogenic stroke (CS). However, the evidence about its prognostic role in terms of stroke recurrence and death in this setting remains scarce. A total of 92 consecutive patients with ischemic stroke or transient ischemic attack with ABCD2 scale ≥4 of unknown etiology were prospectively recruited. Echocardiography, including LAS was performed during admission. The primary outcome measure was the composite of stroke recurrence or death. The mean age was 77.5 ± 7.7, and 58% of patients were female. After a median follow up of 28 months, the primary outcome measure occurred in 15 patients (16%). The primary outcome was more frequent in patients with diabetes (53% vs 21%, p = 0.02), chronic kidney disease (33% vs 10%, p = 0.034), and a history of heart failure (13% vs 0%, p = 0.025). LAS reservoir (LASr) and LAS conduit (LAScd) were lower in patients developing the primary outcome (21% ± 7% vs 28.8% ± 11%, p = 0.017 and 7.7% ± 3.9% vs 13.7% ± 7%, p = 0.007, respectively). On multivariate analysis, LASr (hazard ratio 0.9, 95% confidence interval 0.85 to 0.99, p = 0.048) and diabetes (hazard ratio 3.3, 95% confidence interval 1.03 to 10.4, p = 0.045) were associated with stroke recurrence or all-cause death after CS. On the log-rank test (using the discriminatory cut-off value of LASr <23%), LASr (p = 0.009) was associated with higher risk of the primary outcome. In conclusion, lower values of the LAS reservoir were associated with a higher risk of stroke recurrence or death after CS. LAS may identify patients at higher risk of thromboembolism and stress conditions.

Risk of recurrent stroke and mortality after cryptogenic stroke in diabetic patients

BACKGROUND

Diabetes mellitus is a highly prevalent and growing chronic disease that is associated with increased risk of recurrence among several stroke subtypes. However, evidence on the prognostic role of diabetes in the setting of cryptogenic stroke (CS) remains scarce.

METHODS

From April 2019 to November 2021, we recruited prospectively 78 consecutive patients with CS. Patients were classified according to the presence of diabetes. Main outcome was the composite of stroke recurrence and death. Secondary outcome was stroke recurrence.

RESULTS

Mean age of the cohort was 78 ± 7.7 years and 18 patients (23%) had diabetes. After a median clinical follow-up of 23 months the incidence of stroke recurrence and mortality [HR 5.8 (95% CI 1.9-19), p = 0.002] and the incidence of stroke recurrence [HR 16.6 (95% CI 1.8-149), p = 0.012], were higher in patients with diabetes. After adjusting for potential confounders diabetes was identified as an independent predictor of stroke recurrence and death in patients with CS [HR 33.8 (95% CI 2.1-551), p = 0.013]. Other independent predictors of stroke recurrence and mortality were hypertension [HR 31.4 (95% CI 1.8-550), p = 0.018], NTproBNP [HR 1.002 (95% CI 1.001-1.004), p = 0.013] and chronic kidney disease (CKD) [HR 27.4 (95% CI 1.4-549) p = 0.03]. Furthermore, diabetes was an independent predictor of stroke recurrence [HR 103 (95% CI 1.3-8261), p = 0.038].

CONCLUSION

Diabetic patients with CS are at higher risk of stroke recurrence and death. Hypertension CKD and NTproBNP are also independent predictors of stroke recurrence and death after CS.

Usefulness of Tissue Tracking by Cardiac Magnetic Resonance to Predict Events in Patients With Hypertrophic Cardiomyopathy

Hypertrophic cardiomyopathy (HC) is the most common cardiovascular inherited disease, and it is associated with arrhythmic events, heart failure, and death. Strain analysis by tissue tracking (TT) techniques on cardiac magnetic resonance (CMR) is a novel noninvasive diagnostic tool. However, the usefulness of CMR-TT to identify patients with HC at risk of adverse outcomes remains unknown. CMR strain parameters by CMR-TT were prospectively measured in a cohort of 136 consecutive patients with HC. Clinical (death or readmission for heart failure) and arrhythmic (any ventricular tachycardia) events during follow-up were prospectively recorded. Global radial systolic strain rate and global radial diastolic strain rate showed the best area under the receiver operating characteristic curve (ROC curve) to predict adverse clinical events. On Cox multivariate regression models, a global radial systolic strain rate value <1.4/s and a global radial diastolic strain rate value ≥ -1.38/s were independently associated with clinical events at follow-up (adjusted hazard ratio 6.57, 95% confidence interval [CI] 2.01 to 21.49, p = 0.002; adjusted hazard ratio 5.96, 95% CI 1.79 to 19.89, p = 0.004, respectively). Regarding arrhythmic events, global radial peak strain <27% showed the best area under the ROC curve and remained independently associated with ventricular tachycardia after adjustment for confounders (odds ratio 7.33, 95% CI 1.07 to 50.41, p = 0.043). CMR strain parameters by TT predict clinical and arrhythmic events in patients with HC.

Electrocardiogram and CMR to differentiate tachycardia-induced cardiomyopathy from dilated cardiomyopathy in patients admitted for heart failure

In patients admitted for heart failure (HF) with reduced left ventricular ejection fraction (LVEF) and a concomitant supraventricular tachyarrhythmia (SVT) it is a challenge to predict LVEF recovery and differentiate tachycardia-induced cardiomyopathy (TIC) from dilated cardiomyopathy (DCM). The role of the electrocardiogram (ECG) and cardiac magnetic resonance (CMR) and in this acute setting remains unsettled. Forty-three consecutive patients admitted for HF due to SVT and LVEF < 50% undergoing CMR in the acute phase, were retrospectively included. Those who had LVEF > 50% at follow up were classified as TIC and those with LVEF < 50% were classified as DCM. Clinical, CMR and ECG findings were analyzed to predict LVEF recovery. Twenty-five (58%) patients were classified as TIC. Patients with DCM had wider QRS (121.2 ± 26 vs 97.7 ± 17.35 ms; p = 0.003). On CRM the TIC group presented with higher LVEF (33.4 ± 11 vs 26.9 ± 6.4%; p = 0.019) whereas late gadolinium enhancement (LGE) was more frequent in DCM group (61 vs 16%; p = 0.004). On multivariate analysis, QRS duration ≥ 100 ms (p = 0.027), LVEF < 40% on CMR (p = 0.047) and presence of LGE (p = 0.03) were independent predictors of lack of LVEF recovery. Furthermore, during follow-up (median 60 months) DCM patients were admitted more frequently for HF (44 vs 0%; p < 0.001) than TIC patients. In patients with reduced LVEF admitted for HF due to SVT, QRS ≥ 100 ms, LVEF < 40% and LGE are independently associated with lack of LVEF recovery and worse clinical outcome.

A Comprehensive Model to Predict Atrial Fibrillation in Cryptogenic Stroke: The Decryptoring Score

OBJETIVE

Cryptogenic stroke (CS) represents up to 30% of ischemic strokes (IS). Since atrial fibrillation (AF) can be detected in up to 30% of CS, there is a clinical need for estimating the probability of underlying AF in CS to guide the optimal secondary prevention strategy. The aim of the study was to develop the first comprehensive predictive score including clinical conditions, biomarkers, and left atrial strain (LAS), to predict AF detection in this setting.

METHODS

Sixty-three consecutive patients with IS or transient ischemic attack with ABCD2 scale ≥ 4 of unknown etiology were prospectively recruited. Clinical, laboratory, and echocardiographic variables were collected. All patients underwent 15 days wearable Holter-ECG monitoring. Main objective was the Decryptoring score creation to predict AF in CS. Score variables were selected by a univariate analysis and, thereafter, score points were derived according to a multivariant analysis.

RESULTS

AF was detected in 15 patients (24%). Age > 75 (9 points), hypertension (1 point), Troponin T > 40 ng/L (8.5 points), NTproBNP > 200 pg/ml (0.5 points), LAS reservoir < 25.3% (24.5 points) and LAS conduct < 10.4% (0.5 points) were included in the score. The rate of AF detection was 0% among patients with a score of < 10 and 80% among patients with a score > 35. The comparison of the predictive validity between the proposed score and AF-ESUS score resulted in an AUC of 0.94 for Decryptoring score and of 0.65 for the AF-ESUS score(p < 0.001).

CONCLUSION

This novel score offers an accurate AF prediction in patients with CS; however these results will require validation in an independent cohort using this model before they may be translated into clinical practice.