Exploring the impact of intravenous thrombolysis on length of stay for acute ischemic stroke: a retrospective cohort study

Semantic Visualization in Functional Recovery Prediction of Intravenous Thrombolysis following Acute Ischemic Stroke in Patients by Using Biostatistics: An Exploratory Study

(1) Background: Intravenous thrombolysis following acute ischemic stroke (AIS) can reduce disability and increase the survival rate. We designed a functional recovery analysis by using semantic visualization to predict the recovery probability in AIS patients receiving intravenous thrombolysis; (2) Methods: We enrolled 131 AIS patients undergoing intravenous thrombolysis from 2011 to 2015 at the Medical Center in northern Taiwan. An additional 54 AIS patients were enrolled from another community hospital. A modified Rankin Score ≤2 after 3 months of follow-up was defined as favorable recovery. We used multivariable logistic regression with forward selection to construct a nomogram; (3) Results: The model included age and the National Institutes of Health Stroke Scale (NIHSS) score as immediate pretreatment parameters. A 5.23% increase in the functional recovery probability occurred for every 1-year reduction in age, and a 13.57% increase in the functional recovery probability occurred for every NIHSS score reduction. The sensitivity, specificity, and accuracy of the model in the validation dataset were 71.79%, 86.67%, and 75.93%, respectively, and the area under the receiver operating characteristic curve (AUC) was 0.867; (4) Conclusions: Semantic visualization-based functional recovery prediction models may help physicians assess the recovery probability before patients undergo emergency intravenous thrombolysis.

Risk factor identification and prediction models for prolonged length of stay in hospital after acute ischemic stroke using artificial neural networks

Background

Accurate estimation of prolonged length of hospital stay after acute ischemic stroke provides crucial information on medical expenditure and subsequent disposition. This study used artificial neural networks to identify risk factors and build prediction models for a prolonged length of stay based on parameters at the time of hospitalization.

Methods

We retrieved the medical records of patients who received acute ischemic stroke diagnoses and were treated at a stroke center between January 2016 and June 2020, and a retrospective analysis of these data was performed. Prolonged length of stay was defined as a hospital stay longer than the median number of days. We applied artificial neural networks to derive prediction models using parameters associated with the length of stay that was collected at admission, and a sensitivity analysis was performed to assess the effect of each predictor. We applied 5-fold cross-validation and used the validation set to evaluate the classification performance of the artificial neural network models.

Results

Overall, 2,240 patients were enrolled in this study. The median length of hospital stay was 9 days. A total of 1,101 patients (49.2%) had a prolonged hospital stay. A prolonged length of stay is associated with worse neurological outcomes at discharge. Univariate analysis identified 14 baseline parameters associated with prolonged length of stay, and with these parameters as input, the artificial neural network model achieved training and validation areas under the curve of 0.808 and 0.788, respectively. The mean accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of prediction models were 74.5, 74.9, 74.2, 75.2, and 73.9%, respectively. The key factors associated with prolonged length of stay were National Institutes of Health Stroke Scale scores at admission, atrial fibrillation, receiving thrombolytic therapy, history of hypertension, diabetes, and previous stroke.

Conclusion

The artificial neural network model achieved adequate discriminative power for predicting prolonged length of stay after acute ischemic stroke and identified crucial factors associated with a prolonged hospital stay. The proposed model can assist in clinically assessing the risk of prolonged hospitalization, informing decision-making, and developing individualized medical care plans for patients with acute ischemic stroke.

An equity evaluation in stroke inpatients in regard to medical costs in China: a nationwide study

Background

Stroke has always been a severe disease and imposed heavy financial burden on the health system. Equity in patients in regard to healthcare utilization and medical costs are recognized as a significant factor influencing medical quality and health system responsiveness. The aim of this study is to understand the equity in stroke patients concerning medical costs and healthcare utilization, as well as identify potential factors contributing to geographic variation in stroke patients’ healthcare utilization and costs.

Methods

Covering 31 provinces in mainland China, our main data were a 5% random sample of stroke claims from Urban Employees Basic Medical Insurance (UEBMI) and Urban Residents Basic Medical Insurance (URBMI) from 2013 to 2016. The Theil index was employed to evaluate the equity in stroke patients in regard to healthcare utilization and medical costs, and the random-effect panel model was used to explore the impact of province-level factors (health resource factors, enabling factors, and economic factors) on medical costs and health care utilization.

Results

Stroke patients’ healthcare utilization and medical costs showed significant differences both within and between regions. The UEBMI scheme had an overall lower Theil index value than the URBMI scheme. The intra-region Theil index value was higher than the inter-region Theil index, with the Theil index highest within eastern China, China’s richest and most developed region. Health resource factors and enabling factors (represented by reimbursement rate and education attainment years) were identified significantly associated with medical costs (P < 0.05), but have no impact on average length of stay.

Conclusions

China’s fragmented urban health insurance schemes require further reform to ensure better equity in healthcare utilization and medical costs for stroke patients. Improving education attainment, offering equal access to healthcare, allocating health resources reasonably and balancing health services prices in different regions also count.

Mean Platelet Volume, C-Reactive Protein, and Prognosis in Patients with Acute Ischemic Stroke Following Intravenous Thrombolytic Treatment

BACKGROUND The aim of this study was to investigate the association between mean platelet volume (MPV), C-reactive protein (CRP), and prognosis in patients with acute ischemic stroke (AIS) following intravenous (IV) thrombolytic treatment. MATERIAL AND METHODS A retrospective clinical study included 129 patients within 4.5 hours from the onset of AIS, who received IV thrombolytic treatment. Clinical data were retrieved from electronic medical records. MPV, CRP, and National Institutes of Health (NIH) Stroke Scale and the modified Rankin Scale (MRS) scores for physical disability were recorded. RESULTS Of the 129 patients, 65.9% were men, and more than half received IV thrombolytic treatment within between 3-4.5 hours. The NIH Stroke Scale scores at 24 hours and at three months after hospital admission were compared with the NIH Stroke Scale scores on hospital admission. A significant correlation was found between the MPV values at 24 hours (r=0.221; p=0.012) and at three months after hospital admission (r=196; p=0.026). There was a significant correlation between CRP values at 24 hours (r=0.224; p=0.021), the difference in NIH Stroke Scale score between 24 hours and three months (r=0.249; p=0.005), and the MPV score at three months (r=0.186; p=0.035). CONCLUSIONS MPV and CRP values were significantly associated with improvement in the NIH Stroke Scale and MRS scores in AIS when patients were treated with IV thrombolytic therapy within 4.5 hours of the onset of symptoms.

Impact of intravenous thrombolysis on length of hospital stay in cases of acute ischemic stroke

BACKGROUND

There are limited data available on factors associated with length of stay (LOS) in cases of acute ischemic stroke according to Poisson analysis, which is more appropriate than other methods.

MATERIALS AND METHODS

We retrospectively reviewed medical summary charts of patients with acute ischemic stroke in 30 hospitals across northeast Thailand, with the main outcome as LOS. Poisson regression was used to examine factors associated with LOS.

RESULTS

We included 898 patients in the analysis; 460 (51.2%) were male. The median age (interquartile; IQR) was 58 (67-75) years and the median LOS was 5 (4-7) days. The median National Institute of Health Stroke Scale (NIHSS [IQR]) was 8 (4-13). Results of the analysis showed that, after controlling for age, stroke severity, atrial fibrillation, and thrombolytic use, significant variables associated with LOS were moderate stroke (incidence rate ratio [IRR] 95% confidence interval [CI] =1.15 [range 1.01-1.30], P=0.040), severe stroke (IRR [95% CI] =1.27 [1.09-1.47], P=0.002), thrombolytic use (IRR [95% CI] =0.68 [0.60-0.76], P<0.001), and atrial fibrillation (IRR [95% CI] =1.15 [1.02-1.30], P=0.023). After adjusting for complications, thrombolytic use remained significantly associated with decreased LOS (IRR [95% CI] =0.74 [0.67-0.83], P=0.001). Other significant factors were atrial fibrillation (IRR [95% CI] =1.14 [1.02-1.28], P=0.018), pneumonia (IRR [95% CI] =1.48 [1.30-1.68], P<0.001), and urinary tract infection (IRR [95% CI] =1.41 [1.14-1.74], P=0.001).

CONCLUSION

According to Poisson analysis, intravenous thrombolysis, atrial fibrillation, pneumonia, and urinary tract infection are associated with LOS in cases of acute ischemic stroke, regardless of age, stroke severity, comorbidities, or complications.