Predictive Value of Cystatin C for Stroke Recurrence in Patients With Acute Ischemic Stroke

A one-year relapse prediction model for acute ischemic stroke (AIS) based on clinical big data

Objective

To develop and evaluate a nomogram prediction model for recurrence of acute ischemic stroke (AIS) within one year.

Method

Patients with AIS treated at the second affiliated hospital of Xuzhou Medical University from August 2017 to July 2019 were enrolled. Clinical data such as demographic data, risk factors, laboratory tests, TOAST etiological types, MRI features, and treatment methods were collected. Cox regression analysis was done to determine the parameters for entering the nomogram model. The performance of the model was estimated by receiver operating characteristic curves, decision curve analysis, calibration curves, and C-index.

Result

A total of 645 patients were enrolled in this study. Side of hemisphere (SOH, Bilateral, HR = 0.35, 95 % CI = 0.15-0.84, p = 0.018), homocysteine (HCY, HR = 1.38, 95 % CI = 1.29-1.47, p < 0.001), c-reactive protein (CRP, HR = 1.04, 95 % CI = 1.01-1.07, p = 0.013) and stroke severity (SS, HR = 3.66, 95 % CI = 2.04-6.57, p < 0.001) were independent risk factors. The C-index of the nomogram model was 0.872 (se = 0.016). The area under the receiver operating characteristic (ROC)curve at one-year recurrence was 0.900. Calibration curve, decision curve analysis showed good performance of the nomogram. The cutoff value for low or high risk of recurrence score was 1.73.

Conclusion

The nomogram model for stroke recurrence within one year developed in this study performed well. This useful tool can be used in clinical practice to provide important guidance to healthcare professionals.

A machine learning model for visualization and dynamic clinical prediction of stroke recurrence in acute ischemic stroke patients: A real-world retrospective study

Background and purpose

Recurrent stroke accounts for 25–30% of all preventable strokes, and this study was conducted to establish a machine learning-based clinical predictive rice idol for predicting stroke recurrence within 1 year in patients with acute ischemic stroke (AIS).

Methods

A total of 645 AIS patients at The Second Affiliated Hospital of Xuzhou Medical University were screened, included and followed up for 1 year for comprehensive clinical data. Univariate and multivariate logistic regression (LR) were used to screen the risk factors of stroke recurrence. The data set was randomly divided into training set and test set according to the ratio of 7:3, and the following six prediction models were established by machine algorithm: random forest (RF), Naive Bayes model (NBC), decision tree (DT), extreme gradient boosting (XGB), gradient boosting machine (GBM) and LR. The model with the strongest prediction performance was selected by 10-fold cross-validation and receiver operating characteristic (ROC) curves, and the models were investigated for interpretability by SHAP. Finally, the models were constructed to be visualized using a web calculator.

Results

Logistic regression analysis showed that right hemisphere, homocysteine (HCY), C-reactive protein (CRP), and stroke severity (SS) were independent risk factors for the development of stroke recurrence in AIS patients. In 10-fold cross-validation, area under curve (AUC) ranked from 0.777 to 0.959. In ROC curve analysis, AUC ranged from 0.887 to 0.946. RF model has the best ability to predict stroke recurrence, and HCY has the largest contribution to the model. A web-based calculator https://mlmedicine-re-stroke2-re-stroke2-baylee.streamlitapp.com/ has been developed accordingly.

Conclusion

This study identified four independent risk factors affecting recurrence within 1 year in stroke patients, and the constructed RF-based prediction model had good performance.

The value of cystatin C in evaluating the severity and prognosis of patients with severe fever with thrombocytopenia syndrome

Background

Severe fever with thrombocytopenia syndrome (SFTS) is a novel emerging viral infectious disease. We explore the value of cystatin C (CysC) level in the evaluation of disease severity and prognosis in patients with SFTS.

Methods

254 patients with SFTS were enrolled in this study. According to the classification and the outcome of the disease, the patients were divided into the general group and the severe group, the severe patients were divided into the fatal group and the non-fatal group. We compared the laboratory indexes by univariate and multivariate logistic regression analysis to explore the severity and prognostic risk factors of SFTS disease, ROC curve and Kaplan–Meier survival analysis curve were drawn to analyze the independent risk factors and the predictive value of disease severity and prognosis.

Results

Univariate analysis showed that the CysC level in severe group and fatal group was significantly higher than general group and non-fatal group (P < 0.05), respectively. Multivariate logistic regression showed that the CysC level was an independent risk factor for severe and death in SFTS patients, and it can effectively predict the risk of severe (AUC = 0.711, 95% CI: 0.645–0.777) and death (AUC = 0.814, 95% CI: 0.737–0.89). The risk of death in patients with cystatin C ≥ 1.23 mg/L was 5.487 times higher than that in patients with cystatin C < 1.23 mg/L.

Conclusions

The CysC level have good predictive value for disease severity and prognosis in patients with SFTS.

Trial registration Not applicable