Effect of Heart Rate on 1-Year Outcome for Patients With Acute Ischemic Stroke

Blood-brain barrier disruption and hemorrhagic transformation in acute stroke before endovascular reperfusion therapy

Background and purpose

Early blood-brain barrier (BBB) disruption in patients with acute ischemic stroke (AIS) can be detected on perfusion computed tomography (PCT) images before undergoing reperfusion therapy. In this study, we aimed to determine whether early disruption of the BBB predicts intracranial hemorrhage transformation (HT) in patients with AIS undergoing endovascular therapy and further identify factors influencing BBB disruption.

Methods

We retrospectively analyzed general clinical and imaging data derived from 159 consecutive patients with acute anterior circulation stroke who were admitted to the Department of Neurology of the First Hospital of Jilin University, and who underwent endovascular treatment between January 1, 2021, and March 31, 2023. We evaluated the relationship between BBB destruction and intracranial HT before endovascular reperfusion therapy and examined the risk factors for early BBB destruction.

Results

A total of 159 patients with assessable BBB leakage were included. The median (interquartile range, IQR) age was 63 (54-70) years, 108 (67.9%) patients were male, and the median baseline National Institutes of Health Stroke Scale (NHISS) score was 12 (10-15). Follow-up non-contrast computed tomography (NCCT) detected HT in 63 patients. After logistic regression modeling adjustment, we found that BBB leakage in the true leakage area was slightly more than 2-fold risk of HT (odds ratio [OR], 2.01; 95% confidence interval [CI] 1.02-3.92). Heart rate was also associated with HT (OR, 1.03, 95% CI, 1.00-1.05). High Blood-brain barrier permeability (BBBP) in the true leakage area was positively correlated with infarct core volume (OR, 1.03; 95% CI, 1.01-1.05).

Conclusion

Early BBB destruction before endovascular reperfusion therapy was associated with HT, whereas high BBBP correlated positively with infarct core volume.

Development and validation of a nomogram for predicting 28-day mortality in patients with ischemic stroke

BACKGROUND/AIM

We aimed to construct a validated nomogram model for predicting short-term (28-day) ischemic stroke mortality among critically ill populations.

MATERIALS AND METHODS

We collected raw data from the Medical Information Mart for Intensive Care IV database, a comprehensive repository renowned for its depth and breadth in critical care information. Subsequently, a rigorous analytical framework was employed, incorporating a 10-fold cross-validation procedure to ensure robustness and reliability. Leveraging advanced statistical methodologies, specifically the least absolute shrinkage and selection operator regression, variables pertinent to 28-day mortality in ischemic stroke were meticulously screened. Next, binary logistic regression was utilized to establish nomogram, then applied concordance index to evaluate discrimination of the prediction models. Predictive performance of the nomogram was assessed by integrated discrimination improvement (IDI) and net reclassification index (NRI). Additionally, we generated calibration curves to assess calibrating ability. Finally, we evaluated the nomogram's net clinical benefit using decision curve analysis (DCA), in comparison with scoring systems clinically applied under common conditions.

RESULTS

A total of 2089 individuals were identified and assigned into training (n = 1443) or validation (n = 646) cohorts. Various identified risk factors, including age, ethnicity, marital status, underlying metastatic solid tumor, Charlson comorbidity index, heart rate, Glasgow coma scale, glucose concentrations, white blood cells, sodium concentrations, potassium concentrations, mechanical ventilation, use of heparin and mannitol, were associated with short-term (28-day) mortality in ischemic stroke individuals. A concordance index of 0.834 was obtained in the training dataset, indicating that our nomogram had good discriminating ability. Results of IDI and NRI in both cohorts proved that our nomogram had positive improvement of predictive performance, compared to other scoring systems. The actual and predicted incidence of mortality showed favorable concordance on calibration curves (P > 0.05). DCA curves revealed that, compared with scoring systems clinically used under common conditions, the constructed nomogram yielded a greater net clinical benefit.

CONCLUSIONS

Utilizing a comprehensive array of fourteen readily accessible variables, a prognostic nomogram was meticulously formulated and rigorously validated to provide precise prognostication of short-term mortality within the ischemic stroke cohort.

Development of a nomogram to predict the incidence of acute kidney injury among ischemic stroke individuals during ICU hospitalization

Background

Limited clinical prediction models exist to assess the likelihood of acute kidney injury (AKI) occurrence in ischemic stroke individuals. In this retrospective study, our aim was to construct a nomogram that utilizes commonly available clinical features to predict the occurrence of AKI during intensive care unit hospitalization among this patient population.

Methods

In this study, the MIMIC-IV database was utilized to investigate potential risk factors associated with the incidence of AKI among ischemic stroke individuals. A predictive nomogram was developed based on these identified risk factors. The discriminative performance of the constructed nomogram was assessed. Calibration analysis was utilized to evaluate the calibration performance of the constructed model, assessing the agreement between predicted probabilities and actual outcomes. Furthermore, decision curve analysis (DCA) was employed to assess the clinical net benefit, taking into account the potential risks and benefits associated with different decision thresholds.

Results

A total of 2089 ischemic stroke individuals were included and randomly allocated into developing (n = 1452) and verification cohorts (n = 637). Risk factors for AKI incidence in ischemic stroke individuals, determined through LASSO and logistic regression. The constructed nomogram had good performance in predicting the occurrence of AKI among ischemic stroke patients and provided significant improvement compared to existing scoring systems. DCA demonstrated satisfactory clinical net benefit of the constructed nomogram in both the validation and development cohorts.

Conclusions

The developed nomogram exhibits robust predictive performance in forecasting AKI occurrence in ischemic stroke individuals.

Clinical outcome prediction using observational supervision with electronic health records and audit logs

OBJECTIVE

Audit logs in electronic health record (EHR) systems capture interactions of providers with clinical data. We determine if machine learning (ML) models trained using audit logs in conjunction with clinical data ("observational supervision") outperform ML models trained using clinical data alone in clinical outcome prediction tasks, and whether they are more robust to temporal distribution shifts in the data.

MATERIALS AND METHODS

Using clinical and audit log data from Stanford Healthcare, we trained and evaluated various ML models including logistic regression, support vector machine (SVM) classifiers, neural networks, random forests, and gradient boosted machines (GBMs) on clinical EHR data, with and without audit logs for two clinical outcome prediction tasks: major adverse kidney events within 120 days of ICU admission (MAKE-120) in acute kidney injury (AKI) patients and 30-day readmission in acute stroke patients. We further tested the best performing models using patient data acquired during different time-intervals to evaluate the impact of temporal distribution shifts on model performance.

RESULTS

Performance generally improved for all models when trained with clinical EHR data and audit log data compared with those trained with only clinical EHR data, with GBMs tending to have the overall best performance. GBMs trained with clinical EHR data and audit logs outperformed GBMs trained without audit logs in both clinical outcome prediction tasks: AUROC 0.88 (95% CI: 0.85-0.91) vs. 0.79 (95% CI: 0.77-0.81), respectively, for MAKE-120 prediction in AKI patients, and AUROC 0.74 (95% CI: 0.71-0.77) vs. 0.63 (95% CI: 0.62-0.64), respectively, for 30-day readmission prediction in acute stroke patients. The performance of GBM models trained using audit log and clinical data degraded less in later time-intervals than models trained using only clinical data.

CONCLUSION

Observational supervision with audit logs improved the performance of ML models trained to predict important clinical outcomes in patients with AKI and acute stroke, and improved robustness to temporal distribution shifts.

Association of electrocardiographic and echocardiographic variables with neurological outcomes after ischemic Stroke

Background

Cardiac dysfunction is often seen following neurological injury. Data regarding cardiac involvement after ischemic stroke is sparse. We investigated the association of electrocardiographic (ECG) and echocardiographic variables with neurological outcomes after an acute ischemic stroke.

Methods

We retrospectively collected baseline characteristics, stroke location, National Institute of Health Stroke Scale (NIHSS) at the time of admission, acute reperfusion treatment, ECG parameters, and echocardiographic data on 174 patients admitted with acute ischemic stroke. Outcomes of the stroke were based on cerebral performance category (CPC) with a CPC score of 1-2 indicating a good outcome and a CPC score of 3-5 indicating a poor outcome.

Results

Older age (75.31 ± 11.89 vs. 65.16 ± 15.87, p < 0.001, OR = 1.04, 95 % CI 1.01-1.07), higher heart rate (80.63 ± 18.69 vs. 74.45 ± 17.17 bpm, p = 0.024, OR = 1.02, 95 % CI 1.00-1.05) longer QTc interval (461.69 ± 39.94 vs. 450.75 ± 35.24, p = 0.024, OR = 1.01, 95 % CI 0.99-1.02), NIHSS score (60.9 % vs. 17.8 %, p < 0.001, OR = 14.90, 95 % CI 3.83-69.5), and thrombolysis (15 % vs. 5 %, p = 0.049, OR = 0.55, 95 % CI 0.10-2.55) were associated with poor neurological outcomes. However, when adjusted for age and NIHSS, heart rate and QTc were no longer statistically significant. None of the other ECG and echocardiographic variables were associated neurological outcomes.

Conclusions

Elevated heart rate and longer QTc intervals may potentially predict poor neurological outcomes. Further studies are needed for validation and possible integration of these variables in outcome predicting models.

Prediction of long-term mortality in patients with ischemic stroke based on clinical characteristics on the first day of ICU admission: An easy-to-use nomogram

Background

This study aimed to establish and validate an easy-to-use nomogram for predicting long-term mortality among ischemic stroke patients.

Methods

All raw data were obtained from the Medical Information Mart for Intensive Care IV database. Clinical features associated with long-term mortality (1-year mortality) among ischemic stroke patients were identified using least absolute shrinkage and selection operator regression. Then, binary logistic regression was used to construct a nomogram, the discrimination of which was evaluated by the concordance index (C-index), integrated discrimination improvement (IDI), and net reclassification index (NRI). Finally, a calibration curve and decision curve analysis (DCA) were employed to study calibration and net clinical benefit, compared to the Glasgow Coma Scale (GCS) and the commonly used disease severity scoring system.

Results

Patients who were identified with ischemic stroke were randomly assigned into developing (n = 1,443) and verification (n = 646) cohorts. The following factors were associated with 1-year mortality among ischemic stroke patients, including age on ICU admission, marital status, underlying dementia, underlying malignant cancer, underlying metastatic solid tumor, heart rate, respiratory rate, oxygen saturation, white blood cells, anion gap, mannitol injection, invasive mechanical ventilation, and GCS. The construction of the nomogram was based on the abovementioned features. The C-index of the nomogram in the developing and verification cohorts was 0.820 and 0.816, respectively. Compared with GCS and the commonly used disease severity scoring system, the IDI and NRI of the constructed nomogram had a statistically positive improvement in predicting long-term mortality in both developing and verification cohorts (all with p < 0.001). The actual mortality was consistent with the predicted mortality in the developing (p = 0.862) and verification (p = 0.568) cohorts. Our nomogram exhibited greater net clinical benefit than GCS and the commonly used disease severity scoring system.

Conclusion

This proposed nomogram has good performance in predicting long-term mortality among ischemic stroke patients.

Nutritional Biomarkers and Heart Rate Variability in Patients with Subacute Stroke

Malnutrition and autonomic dysfunction are associated with poor outcomes, mortality, and psychological problems after stroke. Relevant laboratory biomarkers include serum albumin, prealbumin, and transferrin. Heart rate variability (HRV), a noninvasive measurement, can objectively measure autonomic nervous system (ANS) function. The relationship between HRV and nutritional biomarkers in stroke patients has not been studied. This study aimed to examine the relationship between nutritional biomarkers and HRV parameters in stroke patients. We retrospectively recruited 426 patients with subacute stroke who were examined for nutritional biomarkers, such as serum albumin, prealbumin, and transferrin, and underwent 24 h ambulatory Holter electrocardiography. Patients were divided into groups according to their nutritional biomarker status. Differences in HRV parameters between nutritional biomarker-deficient and normal groups were assessed. Pearson's correlation and multiple regression analyses were used to verify the relationship between HRV parameters and nutritional biomarkers. HRV parameters were significantly lower in the nutritional biomarker-deficient groups. In addition, there was a significant association between HRV parameters and nutritional biomarkers. Serum albumin, prealbumin, and transferrin levels were associated with ANS function, as measured by HRV, and their deficiency may be a predictive factor for the severity of ANS dysfunction in stroke patients.

Effect of mean heart rate on 30-day mortality in ischemic stroke with atrial fibrillation: Data from the MIMIC-IV database

Background

The relationship of mean heart rate (MHR) with 30-day mortality in ischemic stroke patients with atrial fibrillation in the intensive care unit (ICU) remains unknown. This study aimed to investigate the association between MHR within 24 h of admission to the ICU and 30-day mortality among patients with atrial fibrillation and ischemic stroke.

Methods

This retrospective cohort study used data on US adults from the Medical Information Mart for Intensive Care-IV (MIMIC-IV, version 1.0) database. Patients with ischemic stroke who had atrial fibrillation for and first time in ICU admission were identified from the MIMIC-IV database. We used multivariable Cox regression models, a restricted cubic spline model, and a two-piecewise Cox regression model to show the effect of the MHR within 24 h of ICU admission on 30-day mortality.

Results

A total of 1403 patients with ischemic stroke and atrial fibrillation (mean [SD] age, 75.9 [11.4] years; mean [SD] heart rate, 83.8[16.1] bpm; 743 [53.0%] females) were included. A total of 212 (15.1%) patients died within 30 days after ICU admission. When MHR was assessed in tertials according to the 25th and 50th percentiles, the risk of 30-day mortality was higher in participants in group 1 (&lt; 72 bpm; adjusted hazard ratio, 1.23; 95% CI, 0.79–1.91) and group 3 (≥82 bpm; adjusted hazard ratio, 1.77; 95% CI, 1.23–2.57) compared with those in group 2 (72–82 bpm). Consistently in the threshold analysis, for every 1-bpm increase in MHR, there was a 2.4% increase in 30-day mortality (adjusted HR, 1.024; 95% CI, 1.01–1.039) in those with MHR above 80 bpm. Based on these results, there was a J-shaped association between MHR and 30-day mortality in ischemic stroke patients with atrial fibrillation admitted to the ICU, with an inflection point at 80 bpm of MHR.

Conclusion

In this retrospective cohort study, MHR within 24 h of admission was associated with 30-day mortality (nonlinear, J-shaped association) in patients with ischemic stroke and atrial fibrillation in the ICU, with an inflection point at about 80 bpm and a minimal risk observed at 72 to 81 bpm of MHR. This association was worthy of further investigation. If further confirmed, this association may provide a theoretical basis for formulating the target strategy of heart rate therapy for these patients.