Dataset Information

CNS-LAND score: predicting early neurological deterioration after intravenous thrombolysis based on systemic responses and injury.

ABSTRACT:

Importance

Early neurological deterioration (END) is a critical complication in acute ischemic stroke (AIS) patients receiving intravenous thrombolysis (IVT), with a need for reliable prediction tools to guide clinical interventions.

Objective

This study aimed to develop and validate a rating scale, utilizing clinical variables and multisystem laboratory evaluation, to predict END after IVT.

Design setting and participants

The Clinical Trial of Revascularization Treatment for Acute Ischemic Stroke (TRAIS) cohort enrolled consecutive AIS patients from 14 stroke centers in China (Jan 2018 to Jun 2022).

Outcomes

END defined as NIHSS score increase >4 points or death within 24 h of stroke onset.

Results

1,213 patients (751 in the derivation cohort, 462 in the validation cohort) were included. The CNS-LAND score, a 9-point scale comprising seven variables (CK-MB, NIHSS score, systolic blood pressure, LDH, ALT, neutrophil, and D-dimer), demonstrated excellent differentiation of END (derivation cohort C statistic: 0.862; 95% CI: 0.796-0.928) and successful external validation (validation cohort C statistic: 0.851; 95% CI: 0.814-0.882). Risk stratification showed END risks of 2.1% vs. 29.5% (derivation cohort) and 2.6% vs. 31.2% (validation cohort) for scores 0-3 and 4-9, respectively.

Conclusion

CNS-LAND score is a reliable predictor of END risk in AIS patients receiving IVT.

SUBMITTER: Jin H

PROVIDER: S-EPMC10552779 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

CNS-LAND score: predicting early neurological deterioration after intravenous thrombolysis based on systemic responses and injury.

Jin Huijuan H Bi Rentang R Zhou Yifan Y Xiao Qinghui Q Li Min M Sun Shuai S Zhou Jinghua J Hu Jichuan J Huang Ming M Li Yanan Y Hong Candong C Chen Shengcai S Chang Jiang J Wan Yan Y Hu Bo B

Frontiers in neurology 20230921

<h4>Importance</h4>Early neurological deterioration (END) is a critical complication in acute ischemic stroke (AIS) patients receiving intravenous thrombolysis (IVT), with a need for reliable prediction tools to guide clinical interventions.<h4>Objective</h4>This study aimed to develop and validate a rating scale, utilizing clinical variables and multisystem laboratory evaluation, to predict END after IVT.<h4>Design setting and participants</h4>The Clinical Trial of Revascularization Treatment f ...[more]

PMID: 37808495

Similar Datasets

Project description:BackgroundThis 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).MethodsEmploying 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.ResultsBaseline 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.ConclusionOur 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.

Project description:ImportanceThe best reperfusion strategy in patients with acute minor stroke and large vessel occlusion (LVO) is unknown. Accurately predicting early neurological deterioration of presumed ischemic origin (ENDi) following intravenous thrombolysis (IVT) in this population may help to select candidates for immediate transfer for additional thrombectomy.ObjectiveTo develop and validate an easily applicable predictive score of ENDi following IVT in patients with minor stroke and LVO.Design, setting, and participantsThis multicentric retrospective cohort included 729 consecutive patients with minor stroke (National Institutes of Health Stroke Scale [NIHSS] score of 5 or less) and LVO (basilar artery, internal carotid artery, first [M1] or second [M2] segment of middle cerebral artery) intended for IVT alone in 45 French stroke centers, ie, including those who eventually received rescue thrombectomy because of ENDi. For external validation, another cohort of 347 patients with similar inclusion criteria was collected from 9 additional centers. Data were collected from January 2018 to September 2019.Main outcomes and measuresENDi, defined as 4 or more points' deterioration on NIHSS score within the first 24 hours without parenchymal hemorrhage on follow-up imaging or another identified cause.ResultsOf the 729 patients in the derivation cohort, 335 (46.0%) were male, and the mean (SD) age was 70 (15) years; of the 347 patients in the validation cohort, 190 (54.8%) were male, and the mean (SD) age was 69 (15) years. In the derivation cohort, the median (interquartile range) NIHSS score was 3 (1-4), and the occlusion site was the internal carotid artery in 97 patients (13.3%), M1 in 207 (28.4%), M2 in 395 (54.2%), and basilar artery in 30 (4.1%). ENDi occurred in 88 patients (12.1%; 95% CI, 9.7-14.4) and was strongly associated with poorer 3-month outcomes, even in patients who underwent rescue thrombectomy. In multivariable analysis, a more proximal occlusion site and a longer thrombus were independently associated with ENDi. A 4-point score derived from these variables-1 point for thrombus length and 3 points for occlusion site-showed good discriminative power for ENDi (C statistic = 0.76; 95% CI, 0.70-0.82) and was successfully validated in the validation cohort (ENDi rate, 11.0% [38 of 347]; C statistic = 0.78; 95% CI, 0.70-0.86). In both cohorts, ENDi probability was approximately 3%, 7%, 20%, and 35% for scores of 0, 1, 2 and 3 to 4, respectively.Conclusions and relevanceThe substantial ENDi rates observed in these cohorts highlights the current debate regarding whether to directly transfer patients with IVT-treated minor stroke and LVO for additional thrombectomy. Based on the strong associations observed, an easily applicable score for ENDi risk prediction that may assist decision-making was derived and externally validated.

Dataset Information

CNS-LAND score: predicting early neurological deterioration after intravenous thrombolysis based on systemic responses and injury.

Importance

Objective

Design setting and participants

Outcomes

Results

Conclusion

Publications

CNS-LAND score: predicting early neurological deterioration after intravenous thrombolysis based on systemic responses and injury.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets