Early prediction of the 3-month outcome for individual acute ischemic stroke patients who received intravenous thrombolysis using the N2H3 nomogram model

Abstract

Background: The aim of this study was to establish a nomogram model for individualized early prediction of the 3-month prognosis in patients with acute ischemic stroke (AIS) who were treated with intravenous recombinant tissue plasminogen activator (rt-PA) thrombolysis. Methods: A total of 691 patients were included in this study; 564 patients were included in the training cohort, while 127 patients were included in the test cohort. The main outcome measure was a 3-month unfavorable outcome (modified Rankin Scale 3–6). To construct the nomogram model, stepwise logistic regression analysis was applied to select the significant predictors of the outcome. The discriminative performance of the model was assessed by calculating the area under the receiver operating characteristic curve (AUC-ROC). A decision curve analysis was used to evaluate prognostic value of the model. Results: The initial National Institutes of Health Stroke Scale [NIHSS, odds ratio (OR), 1.35; 95% confidence interval (CI), 1.28–1.44; p < 0.001], delta NIHSS (changes in the NIHSS score from baseline to 24 h, OR, 0.75; 95% CI, 0.70–0.79; p < 0.001), hypertension (OR, 2.07; 95% CI, 1.32–3.31; p = 0.002), hyperhomocysteinemia (Hhcy, OR, 2.18; 95% CI, 1.20–4.11; p = 0.013), and the ratio of high-density lipoprotein cholesterol (HDL-C) to low-density lipoprotein cholesterol (LDL-C) (HDL-C/LDL-C, OR, 3.29; 95% CI, 1.00–10.89; p = 0.049) (N2H3) were found to be independent predictors of a 3-month unfavorable outcome from multivariate logistic regression analysis and were incorporated in the N2H3 nomogram model. The AUC-ROC of the training cohort was 0.872 (95% CI, 0.841–0.902), and the AUC-ROC of the test cohort was 0.900 (95% CI, 0.848–0.953). Conclusion: The study presented the N2H3 nomogram model, with initial NIHSS score, delta NIHSS, hypertension, Hhcy, and HDL-C/LDL-C as predictors. It therefore provides an individualized early prediction of the 3-month unfavorable outcome in AIS patients treated with intravenous rt-PA thrombolysis.

Prognostic nomogram for the outcomes in acute stroke patients with intravenous thrombolysis

“…One group found the nomogram including initial NIHSS, delta NIHSS, hypertension, Hhcy, HDL-C/LDL-C hold a very high AUC-ROC value of the training cohort with 0.872 and 0.900 in the test cohort, which is higher than the predictive ability of our model. In their study, the authors included several novel blood markers such as: Hhcy, HDL-C/LDL-C, which indicates that these lipid markers might be very important in the pathology of stroke ( Lv et al, 2020 ). Similar results were obtained from Huan Tang et al, and the 3-month poor outcome is related to the baseline elevated SBP, baseline NIHSS, prior hyperlipemia, cardioembolic stroke ( Tang et al, 2021 ).…”

Delavatine A Attenuates OGD/R-Caused PC12 Cell Injury and Apoptosis through Suppressing the MKK7/JNK Signaling Pathway

“…Due to the narrow treatment window, thrombolysis and thrombectomy are commonly limited to a few patients, and these treatments fail to protect patients from ischemic injury. 26) Neuroprotective agents have the potential to extend the therapeutic time window of thrombolytic and thrombectomy therapies while also improving neuron survival in the ischemic area and restoring post-ischemic stroke neural function. 5) So, it is still very important for stroke treatment to discover novel neuroprotective agents.…”

The feasibility and accuracy of machine learning in improving safety and efficiency of thrombolysis for patients with stroke: Literature review and proposed improvements

“…Only two assessed both efficiency and safety. The clinical outputs of models predicting safety are all poststroke symptomatic intracerebral hemorrhage ( 16 – 33 ) while the clinical outputs of models predicting efficiency vary: the most common is the 3-month modified Rankin Scale(mRS) ( 25 , 34 – 38 ). Huang et al ( 39 ) used an even longer 6-month mRS. Saposnik et al ( 22 ) leveraged a composite 3-month outcome of mRS, National Institutes of Health Stroke Scale (NIHSS), and Barthel index and Glasgow Outcome Scale score.…”

“…Feature selection serves to decrease the number of input variables to both reduce the computational cost of modeling and avoid overfitting. Previous studies performed feature selection with a combination of clinical and statistical judgment: initially selected clinical features were identified by neurologists with clinical expertise or based on related studies, feature engineering was then adopted by some studies to transform raw data (we will explore feature engineering in details in the Section 6); stepwise model building ( 19 , 25 , 27 , 29 , 34 , 39 ), univariate analysis ( 17 , 20 , 28 , 30 , 33 , 38 , 43 , 48 ), multivariable analysis using logistic regression ( 16 , 21 , 24 , 26 , 31 , 32 ), plots displaying the pattern of predictors, and outcome ( 21 ), and Least Absolute Shrinkage and Selection Operator (LASSO) ( 25 , 40 ), was performed to further select statistically significant features among initially selected features and new features generated in feature engineering.…”