Background and purpose To investigate the association of neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), and lymphocyte to monocyte ratio (LMR) with post-thrombolysis early neurological outcomes including early neurological improvement (ENI) and early neurological deterioration (END) in patients with acute ischemic stroke (AIS). Methods AIS patients undergoing intravenous thrombolysis were enrolled from April 2016 to September 2019. Blood cell counts were sampled before thrombolysis. Post-thrombolysis END was defined as the National Institutes of Health Stroke Scale (NIHSS) score increase of ≥ 4 within 24 h after thrombolysis. Post-thrombolysis ENI was defined as NIHSS score decrease of ≥ 4 or complete recovery within 24 h. Multinomial logistic regression analysis was performed to explore the relationship of NLR, PLR, and LMR to post-thrombolysis END and ENI. We also used receiver operating characteristic curve analysis to assess the discriminative ability of three ratios in predicting END and ENI. Results Among 1060 recruited patients, a total of 193 (18.2%) were diagnosed with END and 398 (37.5%) were diagnosed with ENI. Multinomial logistic model indicated that NLR (odds ratio [OR], 1.385; 95% confidence interval [CI] 1.238–1.551, P = 0.001), PLR (OR, 1.013; 95% CI 1.009–1.016, P = 0.001), and LMR (OR, 0.680; 95% CI 0.560–0.825, P = 0.001) were independent factors for post-thrombolysis END. Moreover, NLR (OR, 0.713; 95% CI 0.643–0.791, P = 0.001) served as an independent factor for post-thrombolysis ENI. Area under curve (AUC) of NLR, PLR, and LMR to discriminate END were 0.763, 0.703, and 0.551, respectively. AUC of NLR, PLR, and LMR to discriminate ENI were 0.695, 0.530, and 0.547, respectively. Conclusions NLR, PLR, and LMR were associated with post-thrombolysis END. NLR and PLR may predict post-thrombolysis END. NLR was related to post-thrombolysis ENI.

Background and purpose Acute ischaemic stroke (AIS) is a vital cause of mortality and morbidity in China. Many AIS patients develop early neurological deterioration (END). This study aimed to construct a nomogram to predict END in AIS patients. Methods Acute ischaemic stroke patients in Nanjing First Hospital were recruited as the training cohort. Additional patients in Nantong Third People’s Hospital were enrolled as the validation cohort. Multivariate logistic regression was utilized to establish the nomogram. Discrimination and calibration performance of the nomogram were tested by concordance index and calibration plots. Decision curve analysis was employed to assess the utility of the nomogram. Results In all, 1889 and 818 patients were recruited in the training and validation cohorts, respectively. Age [odds ratio (OR) 1.075; 95% confidence interval (CI) 1.059–1.091], diabetes mellitus (OR 1.673; 95% CI 1.181–2.370), atrial fibrillation (OR 3.297; 95% CI 2.005–5.421), previous antiplatelet medication (OR 0.473; 95% CI 0.301–0.744), hyper‐sensitive C‐reactive protein (OR 1.049; 95% CI 1.036–1.063) and baseline National Institutes of Health Stroke Scale (OR 1.071; 95% CI 1.045–1.098) were associated with END and incorporated in the nomogram. The concordance index was 0.826 (95% CI 0.785–0.885) and 0.798 (95% CI 0.749–0.847) in the training and validation cohorts. By decision curve analysis, the model was relevant between thresholds of 0.06 and 0.90 in the training cohort and 0.08 and 0.77 in the validation cohort. Conclusions The nomogram composed of hyper‐sensitive C‐reactive protein, age, diabetes mellitus, atrial fibrillation, previous antiplatelet medication and baseline National Institutes of Health Stroke Scale may predict the risk of END in AIS patients.

Background: A fraction of patients with penetrating artery infarction (PAI) experience progressive motor deficit deterioration (PMD). We sought to investigate the role of high-sensitivity C-reactive protein (hs-CRP) at admission in predicting PMD. Methods: From January 2015 to September 2018, consecutive patients with PAI from three centers were prospectively enrolled in this study. PMD was defined as worsening of motor function score by ≥1 point on the National Institutes of Health Stroke Scale during the first 5 days after admission. Multivariable logistic regression analyses were performed to explore the relationship between hs-CRP and PMD in patients with PAI. We also performed receiver operating characteristic curve analysis and constructed a nomogram to assess the overall discriminative ability of hs-CRP in predicting PMD. Results: We ultimately included 544 patients (mean age, 65.4 ± 11.8 years). A total of 85 (15.6%) patients were identified to have PMD. Multivariate logistic regression analysis showed that hs-CRP was independently associated with PMD (P = 0.001). The optimal cutoff value for hs-CRP as a predictor for PMD was 3.48 mg/L, with a sensitivity of 73.64% and a specificity of 82.35% (area under curve, 0.792). Moreover, the nomogram we constructed indicated that higher level of hs-CRP was an indicator of PMD (c-index = 0.780, P < 0.001). Conclusions: Our study suggested that hs-CRP might be a useful biomarker for predicting the risk of PMD in patients with PAI.

Background and Purpose: To investigate the association of neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR) and lymphocyte-monocyte ratio (LMR) with post-thrombolysis early neurological outcomes including early neurological improvement (ENI) and early neurological deterioration (END) in patients with acute ischemic stroke (AIS).Methods: AIS patients undergoing intravenous thrombolysis were enrolled from April 2016 to September 2019. Blood cell counts were sampled before thrombolysis. Post-thrombolysis END was defined as National Institutes of Health Stroke Scale (NIHSS) score increase of ≥4 within 24 hours after thrombolysis. Post-thrombolysis ENI was defined as NIHSS score decrease of ≥4 or complete recovery within 24 hours. Multivariable logistic regression analyses were performed to explore the relationship of NLR, PLR and LMR to post-thrombolysis END and ENI. We also used receiver operating characteristic curve analysis to assess the discriminative ability of three ratios in predicting END and ENI.Results: Among 1060 recruited patients, a total of 193 (18.2%) were diagnosed with ENI and 398 (37.5%) were diagnosed with END. Multivariable logistic models indicated that NLR (odds ratio [OR], 1.652; 95% confidence interval [CI] 1.510-1.807, P=0.001) and PLR (OR, 1.015; 95% CI 1.012-1.018, P=0.001) were independent factors for post-thrombolysis END. Moreover, NLR (OR, 0.686; 95% CI 0.631-0.745, P=0.001), PLR (OR, 0.997; 95% CI 0.994-0.999, P=0.006) and LMR (OR, 1.170; 95% CI 1.043-1.313, P=0.008) served as independent factors for post-thrombolysis ENI. Area under curve (AUC) of NLR, PLR and LMR to discriminate END were 0.763, 0.703 and 0.551, respectively. AUC of NLR, PLR and LMR to discriminate ENI were 0.695, 0.530 and 0.547, respectively.Conclusions: NLR and PLR were associated with and may predict post-thrombolysis END. NLR, PLR and LMR were related to post-thrombolysis ENI.

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