Major cardiovascular events in patients presenting with acute stroke: a 5-year follow-up study in patients who had ischaemic stroke and stroke mimics

Prognostic model of in-hospital ischemic stroke mortality based on an electronic health record cohort in Indonesia

Stroke patients rarely have satisfactory survival, which worsens further if comorbidities develop in such patients. Limited data availability from Southeast Asian countries, especially Indonesia, has impeded the disentanglement of post-stroke mortality determinants. This study aimed to investigate predictors of in-hospital mortality in patients with ischemic stroke (IS). This retrospective observational study used IS medical records from the National Brain Centre Hospital, Jakarta, Indonesia. A theoretically driven Cox's regression and Fine-Gray models were established by controlling for age and sex to calculate the hazard ratio of each plausible risk factor for predicting in-hospital stroke mortality and addressing competing risks if they existed. This study finally included 3,278 patients with IS, 917 (28%) of whom had cardiovascular disease and 376 (11.5%) suffered renal disease. Bivariate exploratory analysis revealed lower blood levels of triglycerides, low density lipoprotein, and total cholesterol associated with in-hospital-stroke mortality. The average age of patients with post-stroke mortality was 64.06 ± 11.32 years, with a mean body mass index (BMI) of 23.77 kg/m2 and a median Glasgow Coma Scale (GCS) score of 12 and an IQR of 5. Cardiovascular disease was significantly associated with IS mortality risk. NIHSS score at admission (hazard ratio [HR] = 1.04; 95% confidence interval [CI]: 1.00-1.07), male sex (HR = 1.51[1.01-2.26] and uric acid level (HR = 1.02 [1.00-1.03]) predicted survivability. Comorbidities, such as cardiovascular disease (HR = 2.16 [1.37-3.40], pneumonia (HR = 2.43 [1.42-4.15] and sepsis (HR = 2.07 [1.09-3.94, had higher hazards for post-stroke mortality. Contrarily, the factors contributing to a lower hazard of mortality were BMI (HR = 0.94 [0.89-0.99]) and GCS (HReye = 0.66 [0.48-0.89]. In summary, our study reported that male sex, NIHSS, uric acid level, cardiovascular diseases, pneumonia, sepsis. BMI, and GCS on admission were strong determinants of in-hospital mortality in patients with IS.

Association between serum triglycerides and stroke type, severity, and prognosis. Analysis in 6558 patients

BACKGROUND AND OBJECTIVES

Hypertriglyceridemia (HT) may increase the risk of stroke. Limited studies have shown that stroke severity and infarction size are smaller in patients with HT. We explored the relationship between triglyceride levels and stroke risk factors, severity and outcome in a large prospective database.

DESIGN

Prospective Cross-sectional study.

SETTING

We retrospectively interrogated the Qatar Stroke Database in all patients admitted between 2014-2022 with acute ischemic stroke and evaluated the relationship between triglyceride, diabetes, stroke severity (measured on NIHSS), stroke type (TOAST classification) and the short- (mRS at 90 days) and long-term outcomes (MACE at 1 year) in patients with HT.

PARTICIPANTS

Six thousand five hundred fifty-eight patients ≥20 years were included in this study RESULTS: Six thousand five hundred fifty-eight patients with ischemic stroke [mean age 54.6 ± 12. 9; male 82.1%) were included. Triglyceride levels upon admission were low-normal (≤1.1 mmol/L) in 2019 patients, high-normal (1.2-1.7 mmol/L) in 2142 patients, borderline-high (1.8-2.2 mmol/L) in 1072 patients and high (≥2.3 mmol/L) in 1325 patients. Higher triglyceride levels were associated with stroke and increased likelihood of having diabetes, obesity, active smoking, and small vessel/lacunar stroke type. An inverse relationship was noted whereby higher triglyceride levels were associated with lower stroke severity and reduced likelihood of poorer outcome (mRS 3-6) at discharge and 90 days. Long-term MACE events were less frequent in patients with higher triglyceride levels. After adjusting age, gender, diabetes, prior stroke, CAD, and obesity, multivariate analysis showed that hypertension and triglyceride levels were higher in mild ischemic strokes patients.

CONCLUSIONS

Increasing triglycerides are associated with higher risk of small vessel disease and requires further prospective cohort studies for confirmation.

Association between HDL levels and stroke outcomes in the Arab population

Low HDL levels are associated with an increased stroke incidence and worsened long-term outcomes. The aim of this study was to assess the relationship between HDL levels and long-term stroke outcomes in the Arab population. Patients admitted to the Qatar Stroke Database between 2014 and 2022 were included in the study and stratified into sex-specific HDL quartiles. Long-term outcomes included 90-Day modified Rankin Score (mRS), stroke recurrence, and post-stroke cardiovascular complications within 1 year of discharge. Multivariate binary logistic regression analyses were performed to identify the independent effect of HDL levels on short- and long-term outcomes. On multivariate binary logistic regression analyses, 1-year stroke recurrence was 2.24 times higher (p = 0.034) and MACE was 1.99 times higher (p = 0.009) in the low-HDL compared to the high-HDL group. Mortality at 1 year was 2.27-fold in the low-normal HDL group compared to the reference group (p = 0.049). Lower sex-specific HDL levels were independently associated with higher adjusted odds of 1-year post-stroke mortality, stroke recurrence, and MACE (p < 0.05). In patients who suffer a stroke, low HDL levels are associated with a higher risk of subsequent vascular complication.

Appropriate use of antiplatelet medications following transient ischemic attacks and stroke: a 9-year study from the Middle East

Background and purpose

Guidelines recommend that patients with high-risk TIAs and minor strokes presenting within 1-3 days from onset should be offered dual antiplatelet therapy (DAPT). There are little data on real-world adherence to these recommendations. We evaluated the appropriateness of DAPT use in TIA and stroke patients in a prospective database.

Methods

The Qatar Stroke Database began the enrollment of patients with TIAs and acute stroke in 2014 and currently has ~16,000 patients. For this study, we evaluated the rates of guideline-adherent use of antiplatelet treatment at the time of discharge in patients with TIAs and stroke. TIAs were considered high-risk with an ABCD2 score of 4, and a minor stroke was defined as an NIHSS of 3. Patient demographics, clinical features, risk factors, previous medications, imaging and laboratory investigations, final diagnosis, discharge medications, and discharge and 90-day modified Rankin Scale (mRS) were analyzed.

Results

After excluding patients with ICH, mimics, and rare secondary causes, 8,082 patients were available for final analysis (TIAs: 1,357 and stroke: 6,725). In high-risk TIAs, 282 of 666 (42.3%) patients were discharged on DAPT. In patients with minor strokes, 1,207 of 3,572 (33.8%) patients were discharged on DAPT. DAPT was inappropriately offered to 238 of 691 (34.4%) low-risk TIAs and 809 of 3,153 (25.7%) non-minor stroke patients.

Conclusion

This large database of prospectively collected patients with TIAs and stroke shows that, unfortunately, despite several guidelines, a large majority of patients with TIAs and stroke are receiving inappropriate antiplatelet treatment at discharge from the hospital. This requires urgent attention and further investigation.

C-reactive protein levels are associated with early cardiac complications or death in patients with acute ischemic stroke: a propensity-matched analysis of a global federated health from the TriNetX network

The role of inflammation in predicting early cardiac complications among stroke patients is unclear. Electronic medical records from TriNetX, a global federated health research network, were used for this retrospective analysis. Patients with ischemic stroke and C-Reactive Protein (CRP) levels measured within 24 h post-stroke were categorized into three groups: (i) < 1 mg/L, (ii)1-3 mg/L and (iii) > 3 mg/L. The primary outcome was a composite outcome of cardiac complications (heart failure (HF), ischemic heart disease, atrial fibrillation (AF), ventricular arrhythmias and Takotsubo cardiomyopathy) or death at 30 days from the index event. Cox-regression analyses were used to produce hazard ratios (HRs) and 95% confidence intervals (CI) following 1:1 propensity score matching (PSM). Of the 104,741 patients enrolled, 51% were female and the mean age was 66 ± 16 years. After PSM, a new cardiac complication or death within 30 days occurred in 5624 (33.1%) patients with CRP > 3 mg/L, in 4243 (25.6%) patients with CRP 1-3 mg/L and in 3891 (23.5%) patients with CRP < 1 mg/L. Patients with CRP levels of 1-3 mg/L and > 3 mg/L had higher risk of the composite outcome (HR 1.10, 95%CI 1.05-1.52; HR 1.51, 95%CI 1.45-1.58), death (HR 1.43, 95%CI 1.24-1.64; HR 3.50, 95%CI 3.01-3.96), HF (HR 1.08, 95%CI 1.01-1.16; HR 1.51, 95%CI 1.41-1.61), AF (HR 1.10, 95% CI:1.02-1.18; HR 1.42, 95%CI 1.33-1.52) and ventricular arrhythmias (HR 1.25, 95%CI 1.02-1.52; HR 1.67, 95% CI 1.38-2.01) compared to those with CRP < 1 mg/L. Ischemic heart disease were more common among patients with CRP levels > 3 mg/L compared to those with CRP < 1 mg/L (HR:1.33, 95% CI:1.26-1.40), while no association with Takotsubo cardiomyopathy was found in all the analyses. CRP levels within the first 24 h of an ischemic stroke predict 30-day cardiac complications or death.

Deep learning-based personalised outcome prediction after acute ischaemic stroke

BACKGROUND

Whether deep learning models using clinical data and brain imaging can predict the long-term risk of major adverse cerebro/cardiovascular events (MACE) after acute ischaemic stroke (AIS) at the individual level has not yet been studied.

METHODS

A total of 8590 patients with AIS admitted within 5 days of symptom onset were enrolled. The primary outcome was the occurrence of MACEs (a composite of stroke, acute myocardial infarction or death) over 12 months. The performance of deep learning models (DeepSurv and Deep-Survival-Machines (DeepSM)) and traditional survival models (Cox proportional hazards (CoxPH) and random survival forest (RSF)) were compared using the time-dependent concordance index ([Formula: see text] index).

RESULTS

Given the top 1 to all 60 clinical factors according to feature importance, CoxPH and RSF yielded [Formula: see text] index of 0.7236-0.8222 and 0.7279-0.8335, respectively. Adding image features improved the performance of deep learning models and traditional models assisted by deep learning models. DeepSurv and DeepSM yielded the best [Formula: see text] index of 0.8496 and 0.8531 when images were added to all 39 relevant clinical factors, respectively. In feature importance, brain image was consistently ranked highly. Deep learning models automatically extracted the image features directly from personalised brain images and predicted the risk and date of future MACEs at the individual level.

CONCLUSIONS

Deep learning models using clinical data and brain images could improve the prediction of MACEs and provide personalised outcome prediction for patients with AIS. Deep learning models will allow us to develop more accurate and tailored prognostic prediction systems that outperform traditional models.