No abstract

No abstract

BackgroundStroke-associated pneumonia (SAP) is a serious and common complication in stroke patients.PurposeWe aimed to develop and validate an easy-to-use model for predicting the risk of SAP in acute ischemic stroke (AIS) patients.Patients and methodsThe nomogram was established by univariate and multivariate binary logistic analyses in a training cohort of 643 AIS patients. The prediction performance was determined based on the receiver operating characteristic curve (ROC) and calibration plots in a validation cohort (N=340). Individualized clinical decision-making was conducted by weighing the net benefit in each AIS patient by decision curve analysis (DCA).ResultsSeven predictors, including age, NIHSS score on admission, atrial fibrillation, nasogastric tube intervention, mechanical ventilation, fibrinogen, and leukocyte count were incorporated to construct the nomogram model. The nomogram showed good predictive performance in ROC analysis [AUROC of 0.845 (95% CI: 0.814–0.872) in training cohort, and 0.897 (95% CI: 0.860–0.927) in validation cohort], and was superior to the A2DS2, ISAN, and PANTHERIS scores. Furthermore, the calibration plots showed good agreement between actual and nomogram-predicted SAP probabilities, in both training and validation cohorts. The DCA confirmed that the SAP nomogram was clinically useful.ConclusionOur nomogram may provide clinicians with a simple and reliable tool for predicting SAP based on routinely available data. It may also assist clinicians with respect to individualized treatment decision-making for patients differing in risk level.

No abstract

PurposePopulation-based studies have revealed a high prevalence of cognitive impairment after stroke. We aimed to determine the impact of serum magnesium (Mg2+) levels on the occurrence of poststroke cognitive impairment (PSCI).Patients and methodsAcute ischemic stroke patients (n = 327) were enrolled in our study and serum Mg2+ levels were assessed on admission. The cognitive performance of each patient was evaluated using the Mini–Mental State Examination (MMSE) at a 1-month follow-up visit.ResultsOne hundred five (32.1%) patients were diagnosed with PSCI at 1-month poststroke. The serum Mg2+ levels in both the PSCI group and the non-PSCI group were significantly lower than those in normal control group (P<0.001). In addition, the PSCI group had lower levels of serum Mg2+ compared to the non-PSCI group (P=0.003). In the binary logistic regression analysis, a serum Mg2+ level of ≤0.82 mmol/L was significantly associated with an increased risk of developing PSCI by the 1-month follow-up (OR 2.236, 95% CI 1.232–4.058, P=0.008), as was age (OR 1.043, 95% CI 1.014–1.073, P=0.003).ConclusionOur results demonstrate the existence of a significant association between low levels of serum Mg2+ and the occurrence of PSCI 1-month poststroke, and these results suggest that low levels of serum Mg2+ on admission may serve as a risk factor for developing PSCI by 1-month poststroke.