Objectives. To evaluate the effect of adding Di-tan decoction (DTD) and/or electroacupuncture (EA) to standard swallowing rehabilitation training (SRT) on improving PSD. Methods. In total, 80 PSD patients were enrolled and randomly assigned to the DTD, EA, DTD + EA or control group at a 1 : 1 : 1 : 1 ratio. All patients received basic treatment and standard SRT. The DTD group received DTD orally, the EA group received EA, the DTD + EA group received both DTD and EA simultaneously, and the control group received only basic treatment and standard SRT. The interventions lasted for 4 weeks. The outcome measurements included the Standardized Swallowing Assessment (SSA) and Swallowing-Quality of Life (SWAL-QOL), performed and scored from baseline to 2, 4, and 6 weeks after intervention, and the Videofluoroscopic Dysphagia Scale (VDS), scored at baseline and 4 weeks after intervention. Scores were compared over time by repeated-measures analysis of variance (ANOVA) among all groups. Interactions between interventions were explored using factorial design analysis. Results. (1) The effective rates (ERs) for PSD treatment were higher in the DTD, EA and DTD + EA groups than in the control group (all P  < 0.05). The ER was higher in the DTD + EA group than in the DTD or EA group (both P  < 0.05). (2) There were significant group effects, time effects and interactions for the SSA and SWAL-QOL scores (all P  < 0.05). All groups showed decreasing trends in SSA scores and increasing trends in SWAL-QOL scores over time from baseline to 6 weeks after intervention (all P  < 0.01). (3) Factorial design analysis for ΔVDS showed that there was a significant main effect for DTD intervention (F = 11.877, P  < 0.01) and for EA intervention (F = 29.357, P  < 0.01). However, there was no significant interaction effect between DTD and EA (F = 0.133, P  = 0.717). Multiple comparisons showed that the DTD, EA and DTD + EA groups all had higher ΔVDS values than the control group ( P  < 0.05). The DTD + EA group had a higher ΔVDS than the DTD or EA group (both P  < 0.05). (4) Most adverse reactions were mild and transient. Conclusions. Adding DTD or EA to SRT can better improve PSD than applying SRT alone. Adding DTD and EA simultaneously can accelerate and amplify the recovery of swallowing function versus DTD or EA alone, and both are effective and safe treatments, alone or jointly, for PSD and are a powerful supplement to routine treatments.

Background Malnutrition is a common complication after stroke and may worsen neurological outcomes for patients. There are still no uniform tools for screening nutritional status for the patients with stroke. We aimed to explore the relationship between the baseline geriatric nutritional risk index (GNRI) and neurological function at the convalescence stage for patients with stroke and assessed the predictive value of the GNRI for adverse neurological outcomes. Methods A total of 311 patients with stroke were enrolled retrospectively. Basic information and laboratory results on admission since onset of stroke were collected. The GNRI on admission was calculated and neurological outcomes evaluated by the Barthel index at 1 month after the onset of stroke. Statistical analyses, including correlation coefficient tests, multivariate regression analyses, and receiver operating characteristic (ROC) analyses, were applied in this study. Results Compared with the good outcome group, the poor outcome group showed a significantly lower GNRI on admission (P < 0.05). GNRI was associated with Barthel index (r = 0.702, P < 0.01). The GNRI was independently correlated with the Barthel index (Standardization β = 0.721, P < 0.01) and poor outcome 0.885 (95% CIs, 0.855–0.917, P < 0.01) after adjusting for covariates. Compared with no nutritional risk grades (Q4), the OR of GNRI to poor neurological outcome increased across increasing nutritional risk grades of GNRI (OR = 2.803, 95% CIs = 1.330–5.909 in Q3, 7.992, 95% CIs = 3.294–19.387 in Q2 and 14.011, 95% CIs = 3.972–49.426 in Q1, respectively, P for trend < 0.001). The area under ROC curves (AUC) of the GNRI was 0.804, which was larger than that of the NIHSS, BMI, or Albumin (P < 0.01), with an optimal cut-off value of 97.69, sensitivity of 69.51% and specificity of 77.27%. Combined GNRI with NIHSS gained the largest AUC among all the variables (all P < 0.05), with an AUC of 0.855, sensitivity of 84.75 and specificity of 72.73%. Conclusions For patients with stroke, higher nutritional risk grades at baseline indicated worse neurological function at the convalescence stage. Compared with NIHSS, BMI, and Albumin, GNRI was a competitive indicator for the risk of poor neurological outcome. The predictive property of GNRI for adverse neurological outcomes might be more powerful when combined with NIHSS.