Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026
Development and validation of a nomogram to predict early neurological deterioration in patients with acute ischemic stroke and type 2 diabetes mellitus: the pivotal role of glycemic variability and thrombo-inflammation.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Zhong Xingju, Chen Zhuo, Cao Tianli, Fan Chaoli, Fu Wei, Jiang Yu, Wu Yong
Аннотация
BACKGROUND: Early neurological deterioration (END) is a devastating complication in acute ischemic stroke (AIS) patients with type 2 diabetes mellitus (T2DM). We aimed to develop and validate a clinical nomogram for individualized END risk prediction, focusing on acute glycemic variability and thrombo-inflammation.
METHODS: This retrospective study included 965 AIS patients with T2DM. END was defined as an NIHSS score increase of ≥ 2 points within 72 h. LASSO and multivariable logistic regression identified independent predictors to construct the nomogram. Model performance was evaluated using the area under the ROC curve (AUC), calibration plots, and decision curve analysis (DCA). Internal validation was performed via 1,000 bootstrap resamples, and the added predictive value was evaluated using the Net Reclassification Improvement (NRI).
RESULTS: END occurred in 152 patients (15.8%). Six independent predictors were identified: baseline NIHSS, admission HbA1c, 24-h capillary blood glucose standard deviation (24-h CBG-SD), fibrinogen, hs-CRP, and baseline DWI-ASPECTS. The nomogram exhibited excellent discrimination (AUC 0.815; 95% CI 0.772-0.858), with internal validation confirming minimal overfitting (optimism-corrected AUC 0.802). 24-h CBG-SD emerged as the strongest predictor. Integrating metabolic and thrombo-inflammatory markers provided significant added predictive value over basic clinical features (continuous NRI 0.485, P < 0.001). Calibration showed high concordance, and DCA demonstrated significant net clinical benefit across a 4%-75% threshold range.
CONCLUSIONS: Our nomogram provides a reliable and internally validated tool for early END risk stratification in T2DM patients. The findings highlight the critical impact of acute glycemic instability and thrombo-inflammation, suggesting that personalized glycemic management and intensive monitoring are warranted.
METHODS: This retrospective study included 965 AIS patients with T2DM. END was defined as an NIHSS score increase of ≥ 2 points within 72 h. LASSO and multivariable logistic regression identified independent predictors to construct the nomogram. Model performance was evaluated using the area under the ROC curve (AUC), calibration plots, and decision curve analysis (DCA). Internal validation was performed via 1,000 bootstrap resamples, and the added predictive value was evaluated using the Net Reclassification Improvement (NRI).
RESULTS: END occurred in 152 patients (15.8%). Six independent predictors were identified: baseline NIHSS, admission HbA1c, 24-h capillary blood glucose standard deviation (24-h CBG-SD), fibrinogen, hs-CRP, and baseline DWI-ASPECTS. The nomogram exhibited excellent discrimination (AUC 0.815; 95% CI 0.772-0.858), with internal validation confirming minimal overfitting (optimism-corrected AUC 0.802). 24-h CBG-SD emerged as the strongest predictor. Integrating metabolic and thrombo-inflammatory markers provided significant added predictive value over basic clinical features (continuous NRI 0.485, P < 0.001). Calibration showed high concordance, and DCA demonstrated significant net clinical benefit across a 4%-75% threshold range.
CONCLUSIONS: Our nomogram provides a reliable and internally validated tool for early END risk stratification in T2DM patients. The findings highlight the critical impact of acute glycemic instability and thrombo-inflammation, suggesting that personalized glycemic management and intensive monitoring are warranted.