[Acupoint selection rules of acupuncture and moxibustion for stroke-related pneumonia]

Using data mining technology, the rules of acupoint selection of acupuncture and moxibustion were explored in treatment of stroke-related pneumonia. The clinical articles of acupuncture and moxibustion in treatment of stroke-related pneumonia were retrieved from CNKI, SinoMed, Wanfang and VIP databases from their inception through to January l 2021, and then, the acupuncture-moxibustion prescription database was set up for stroke-related pneumonia. SPSS Modeler 18.0 Apriori algorithm was adopted to analyze the association rules of acupoints and draw complex network diagrams. SPSS26.0 was used in clustering analysis of acupoints. Finally, a total of 44 articles were included, with 51 acupoint prescriptions and 82 acupoints extracted. The total frequency of acupoints was 340 times. The high-frequency acupoints in treatment with acupuncture and moxibustion for stroke-related pneumonia were Feishu (BL 13), Fenglong (ST 40), Hegu (LI 4), etc. These acupoints were mainly distributed on the limbs and back and mostly from yang meridians. Of these extracted acupoints, the five-shu points, convergent points and back-shu points were selected specially. Regarding acupoint combination, the association of Quchi (LI 11) had the highest support with Hegu (LI 4) and Zusanli (ST 36). The core prescription of acupuncture-moxibustion treatment for stroke-related pneumonia could be composed of Quchi (LI 11), Hegu (LI 4), Zusanli (ST 36), Fenglong (ST 40) and Taichong (LR 3).

A Comparison of the National Institutes of Health Stroke Scale and the Gugging Swallowing Screen in Predicting Stroke-Associated Pneumonia

Background

There have been many scales to predict pneumonia in stroke patients, but they are so complex, making it difficult to apply in practice. Therefore, we conducted this study to assess the role of the National Institutes of Health Stroke Scale (NIHSS) and the Gugging Swallowing Screen (GUSS) in predicting stroke-associated pneumonia (SAP). These scales are routinely used in stroke patients. Therefore, their application in predicting SAP risk will be of high value in clinical practice. There has been no previous study evaluating the effectiveness of SAP risk prediction for each of these scales.

Aim

This study aimed to compare the value of NIHSS and GUSS in SAP prediction and their convenience in clinical practice.

Methods

It was a cohort study. The receiver operating characteristics (ROC) curves were constructed to assess the sensitivity (Se) and specificity (Sp) of the scales. Area under the curves (AUC) were calculated, and we compared them.

Results

NIHSS had a medium value of predictor of SAP with AUC 0.764 (95% CI 0.735–0.792), 65.4% Se, 76.5% Sp. GUSS had good value in predicting SAP with AUC 0.858 (95% CI 0.833–0.880), 80.5% Se, 80.1% Sp. Pairwise comparison of ROCs curves demonstrated that the difference between two AUCs was significant (p < 0.01). Performing GUSS required 24.5 ± 6.7 minutes, 2.5 times longer than NIHSS (9.9 ± 2.0 minutes).

Conclusion

GUSS had a better predictive value of SAP than NIHSS. But NIHSS was more convenient in clinical practice because of its simple instrument and quick performance.