Post-thrombolysis early neurological deterioration occurs with or without hemorrhagic transformation in acute cerebral infarction: risk factors, prediction model and prognosis

Influencing Factors (History of Alcohol Consumption) and Construction of a Nomogram Prediction Model for In-Hospital Gastrointestinal Bleeding Secondary to Acute Cerebral Hemorrhage in a Certain Hospital

Objective

To investigate the factors influencing acute cerebral hemorrhage (ACH) secondary to nosocomial gastrointestinal hemorrhage (GIH) and construct a nomogram prediction model.

Methods

A total of 500 ACH patients admitted to our hospital from August 2022 to August 2024 were retrospectively analyzed and divided into a modeling group (350 cases) and a validation group (150 cases) in a 7:3 ratio. Patients in the modeling group were further divided into the GIH and non-GIH groups. Clinical data were collected, and multivariate logistic regression was used to analyze risk factors. A nomogram model was constructed using R software. The predictive performance was evaluated using the ROC curve, calibration curve, and decision curve analysis (DCA).

Results

Among 500 patients, 78 (15.6%) developed GIH. In the modeling group (350 cases), 56 (16.0%) had GIH. There were significant differences in age, history of coronary heart disease, history of alcohol consumption, NIHSS score, systolic blood pressure, and hemorrhage volume between groups (P<0.05). Logistic regression analysis identified these factors as independent risk factors for secondary GIH (P<0.05). The Area Under Curve(AUC) was 0.798 in the modeling group and 0.978 in the validation group, with calibration curves showing good agreement between predicted and observed values (Hosmer-Lemeshow(H-L) test: modeling group, χ²=7.156, P=0.732; validation group, χ²=7.015, P=0.703). DCA indicated a high clinical application value when the probability ranged from 0.06 to 0.95.

Conclusion

Age, history of coronary heart disease, history of alcohol consumption, NIHSS score, systolic blood pressure, and hemorrhage volume are key risk factors for secondary GIH in ACH patients. The nomogram model constructed based on these factors demonstrates good predictive performance and clinical application value. It can help clinicians prevent early onset and reduce the risk of bleeding in patients.

Predictive Model for Early Neurological Deterioration in Acute Ischemic Stroke Utilizing Novel Thrombotic Biomarkers

BACKGROUND

Novel thrombotic molecular markers are significantly linked to acute ischemic stroke (AIS). However, the relationship between thrombin-antithrombin complex (TAT), tissue plasminogen activator-inhibitor complex (t-PAIC), plasmin-α2 plasmin inhibitor complex (PIC), thrombomodulin (TM), and early neurological deterioration (END) remains unclear. Therefore, we developed a prediction model for END based on these markers and evaluated its accuracy and clinical utility.

METHODS

Retrospective analysis of patients diagnosed with AIS in our hospital from 2023-2024. The above patients were divided into a training set (N = 577) and a test set (N = 246) in a 7:3 ratio. Least absolute shrinkage and selection of operator regression (LASSO) valid predictors were used. The coefficients of the predictors in logistic regression were used to develop a nomogram and to validate its differentiation, calibration, and clinical utility.

RESULTS

The prevalence of END in AIS patients was 24.3%. Predictors screened according to LASSO regression analysis included age, the National Institutes of Health Stroke Scale (NIHSS) score, t-PAIC, PIC, lymphocyte, and platelet. The resulting nomograms had the area under the curve (AUC) of 0.867 (95% CI, 0.834-0.9) and 0.825 (95% CI, 0.757-0.892) in the training and test sets, respectively, which had good differentiation. In addition, the calibration curve and decision curve analysis (DCA) showed that the model had good calibration and clinical utility.

CONCLUSION

A predictive model for END was developed using the serological markers t-PAIC (male >17.13 ng/mL;female >10.52 ng/mL), PIC >0.85 µg/mL, Lymph ≤ 3.2×10^9/L, NIHSS, age, and platelet. The model has significant predictive value for END occurrence in patients with AIS.

A Nomogram Incorporating Intracranial Atherosclerosis Score for Predicting Early Neurological Deterioration in Minor Stroke Patients With Type 2 Diabetes Mellitus

Purpose

Early neurological deterioration (END) frequently complicates acute ischemic stroke (AIS), worsening prognosis, particularly in patients with type 2 diabetes mellitus (T2DM), where hyperglycemia accelerates atherosclerosis, increasing both stroke risk and subsequent END. This study aimed to identify predictors of END in minor stroke patients with T2DM and develop a nomogram integrating these factors with intracranial atherosclerosis (ICAS) scores, evaluating its performance against various machine learning (ML) models.

Methods

We retrospectively analyzed clinical data from 473 minor stroke patients with T2DM treated at our hospital between January 2021 and December 2023. Utilizing LASSO and multivariate logistic regression, we identified characteristic predictors. The cohort was randomly allocated into training (n = 331) and validation (n = 142) groups. Six ML algorithms-SVM, LR, RF, CART, KNN, and Naive Bayes-were assessed, and nomograms were used to visualize the predictive model's performance, evaluated via Area Under the Curve (AUC), calibration plot, and Decision Curve Analysis (DCA).

Results

The ICAS score has been recognized as a pivotal determinant of END, alongside four other significant factors: NIHSS score, low-density lipoprotein cholesterol (LDL-C) levels, presence of branch atheromatous disease (BAD), and stenosis of the responsible vessel ≥50%. The model demonstrated robust predictive capabilities, achieving strong performance in training (AUC = 0.795) and validation (AUC = 0.799) sets. This advanced ML model, which integrates biochemical and imaging indicators, enables accurate risk assessment for END in minor stroke patients with T2DM.

Conclusion

By integrating the ICAS score with the NIHSS score, LDL-C levels, presence of BAD, and stenosis of responsible vessels ≥50%, we developed a clinical model for predicting END in patients with minor stroke and T2DM. This model provides critical decision support for clinicians, facilitating early identification of high-risk patients, personalized treatment, and improved outcomes.

Evaluating the interaction between hemorrhagic transformation and cerebral edema on functional outcome after ischemic stroke

BACKGROUND

Hemorrhagic transformation (HT) and cerebral edema (CED) are both major complications following ischemic stroke, but few studies have evaluated their overlap. We evaluated the frequency and predictors of CED/HT overlap and whether their co-occurrence impacts functional outcome more than each in isolation.

METHODS

892 stroke patients enrolled in a prospective study had follow-up CT imaging evaluated for HT and CED; the latter was quantified using the ratio of hemispheric CSF volumes (with hemispheric CSF ratio < 0.90 used as the CED threshold). The interaction between HT and CED on functional outcome (using modified Rankin Scale at 3 months) was compared to that for each condition separately.

RESULTS

Among the 275 (31%) who developed HT, 233 (85%) manifested hemispheric CSF ratio < 0.9 (CED/HT), with this overlap group representing half of the 475 with measurable CED. Higher baseline NIHSS scores and larger infarct volumes were observed in the CED/HT group compared with those with CED or HT alone. Functional outcome was worse in those with CED/HT [median mRS 3 (IQR 2-5)] than those with CED [median 2 (IQR 1-4)] or HT alone [median 1 (IQR 0-2), p < 0.0001]. Overlap of CED/HT independently predicted worse outcome [OR 1.89 (95% CI: 1.12-3.18), p = 0.02] while HT did not; however, CED/HT was no longer associated with worse outcome after adjusting for severity of CED [adjusted OR 0.35 (95% CI: 0.23, 0.51) per 0.21 lower hemispheric CSF ratio, p < 0.001].

CONCLUSIONS

Most stroke patients with HT also have measurable CED. The co-occurrence of CED and HT occurs in larger and more severe strokes and is associated with worse functional outcome, although this is driven by greater severity of stroke-related edema in those with HT.

Machine learning-based prediction of early neurological deterioration after intravenous thrombolysis for stroke: insights from a large multicenter study

Background

This investigation seeks to ascertain the efficacy of various machine learning models in forecasting early neurological deterioration (END) following thrombolysis in patients with acute ischemic stroke (AIS).

Methods

Employing data from the Shenyang Stroke Emergency Map database, this multicenter study compiled information on 7,570 AIS patients from 29 comprehensive hospitals who received thrombolytic therapy between January 2019 and December 2021. An independent testing cohort was constituted from 2,046 patients at the First People's Hospital of Shenyang. The dataset incorporated 15 pertinent clinical and therapeutic variables. The principal outcome assessed was the occurrence of END post-thrombolysis. Model development was executed using an 80/20 split for training and internal validation, employing classifiers like logistic regression with lasso regularization (lasso regression), support vector machine (SVM), random forest (RF), gradient-boosted decision tree (GBDT), and multi-layer perceptron (MLP). The model with the highest area under the curve (AUC) was utilized to delineate feature significance.

Results

Baseline characteristics showed variability in END incidence between the training (n = 7,570; END incidence 22%) and external validation cohorts (n = 2,046; END incidence 10%; p < 0.001). Notably, all machine learning models demonstrated superior AUC values compared to the reference model, indicating their enhanced predictive capacity. The lasso regression model achieved the highest AUC at 0.829 (95% CI: 0.799-0.86; p < 0.001), closely followed by the MLP model with an AUC of 0.828 (95% CI: 0.799-0.858; p < 0.001). The SVM, RF, and GBDT models also showed commendable AUCs of 0.753, 0.797, and 0.774, respectively. Decision curve analysis revealed that the SVM and MLP models demonstrated a high net benefit. Feature importance analysis emphasized "Onset To Needle Time" and "Admission NIHSS Score" as significant predictors.

Conclusion

Our research establishes the MLP and lasso regression as robust tools for predicting early neurological deterioration in acute ischemic stroke patients following thrombolysis. Their superior predictive accuracy, compared to traditional models, highlights the significant potential of machine learning approaches in refining prognosis and enhancing clinical decisions in stroke care management. This advancement paves the way for more tailored therapeutic strategies, ultimately aiming to improve patient outcomes in clinical practice.

Predictive value of glycoprotein DKK3 for early neurological deterioration after ischemic stroke

OBJECTIVE

To explore the value of serum Dickkopf-3 (sDKK3) in predicting Early Neurological Deterioration (END) and in-hospital adverse outcomes in acute ischemic stroke (AIS) patients.

METHODS

AIS patients (n = 200) were included and assessed by the National Institutes of Health Stroke Rating Scale. Serum Dkk3 levels were assessed by ELISA. END was defined as an increase of ≥ 4 points in NIHSS score within 72h. The biological threshold of sDKK3 level and END occurrence were predicted based on X-tile software. Primary outcomes were END and all-cause death, and the secondary outcome was ICU admission during hospitalization. The logistic regression model and Cox risk regression model were applied to evaluate the relationship between DKK3 level and END incidence, all-cause in-hospital mortality, and in-hospital adverse outcomes (ICU admission).

RESULTS

During hospitalization, the incidence of END in patients with AIS was 13.0 %, and the mortality rate within 7 days after END was 11.54 % (3/26). In patients below the serum DKK3 cutoff (93.0 pg/mL), the incidence of END was 43.5 % (20/48). Patients with lower sDKK3 levels were associated with a 1.188-fold increased risk of developing END (OR = 1.188, 95 % CI 1.055‒1.369, p < 0.0001). However, there was no significant association with admission to the ICU. sDKK3 below the threshold (93.0 pg/mL) was a risk factor for death.

CONCLUSION

Predictive threshold levels of serum DKK3 based on X-tile software may be a potential predictive biomarker of in-hospital END in patients with AIS, and low levels of DKK3 are independently associated with increased in-hospital mortality.

Optimizing early neurological deterioration prediction in acute ischemic stroke patients following intravenous thrombolysis: a LASSO regression model approach

Background

Acute ischemic stroke (AIS) remains a leading cause of disability and mortality globally among adults. Despite Intravenous Thrombolysis (IVT) with recombinant tissue plasminogen activator (rt-PA) emerging as the standard treatment for AIS, approximately 6-40% of patients undergoing IVT experience Early Neurological Deterioration (END), significantly impacting treatment efficacy and patient prognosis.

Objective

This study aimed to develop and validate a predictive model for END in AIS patients post rt-PA administration using the Least Absolute Shrinkage and Selection Operator (LASSO) regression approach.

Methods

In this retrospective cohort study, data from 531 AIS patients treated with intravenous alteplase across two hospitals were analyzed. LASSO regression was employed to identify significant predictors of END, leading to the construction of a multivariate predictive model.

Results

Six key predictors significantly associated with END were identified through LASSO regression analysis: previous stroke history, Body Mass Index (BMI), age, Onset to Treatment Time (OTT), lymphocyte count, and glucose levels. A predictive nomogram incorporating these factors was developed, effectively estimating the probability of END post-IVT. The model demonstrated robust predictive performance, with an Area Under the Curve (AUC) of 0.867 in the training set and 0.880 in the validation set.

Conclusion

The LASSO regression-based predictive model accurately identifies critical risk factors leading to END in AIS patients following IVT. This model facilitates timely identification of high-risk patients by clinicians, enabling more personalized treatment strategies and optimizing patient management and outcomes.

Prediction of early neurological deterioration in acute ischemic stroke patients treated with intravenous thrombolysis

A proportion of acute ischemic stroke (AIS) patients suffer from early neurological deterioration (END) within 24 hours following intravenous thrombolysis (IVT), which greatly increases the risk of poor prognosis of these patients. Therefore, we aimed to explore the predictors of early neurological deterioration of ischemic origin (ENDi) in AIS patients after IVT and develop a nomogram prediction model. This study collected 244 AIS patients with post-thrombolysis ENDi as the derivation cohort and 155 patients as the validation cohort. To establish a nomogram prediction model, risk factors were identified by multivariate logistic regression analysis. The results showed that neutrophil to lymphocyte ratio (NLR) (OR 2.616, 95% CI 1.640-4.175, P < 0.001), mean platelet volume (MPV) (OR 3.334, 95% CI 1.351-8.299, P = 0.009), body mass index (BMI) (OR 1.979, 95% CI 1.285-3.048, P = 0.002) and atrial fibrillation (AF) (OR 8.012, 95% CI 1.341-47.873, P = 0.023) were significantly associated with ENDi. The area under the curve of the prediction model constructed from the above four factors was 0.981 (95% CI 0.961-1.000) and the calibration curve was close to the ideal diagonal line. Therefore, this nomogram prediction model exhibited good discrimination and calibration power and might be a reliable and easy-to-use tool to predict post-thrombolysis ENDi in AIS patients.