Journal of Vascular Medicine & Surgery

New Findings on Predictive Methods for Stroke-Associated Pneumonia

Ya-ming Li^* Department of Neurology, Jiading District Central Hospital Affiliated Shanghai University of Medicine & Health Sciences, Shanghai, China
^*Correspondence: Ya-ming Li, Department of Neurology, Jiading District Central Hospital Affiliated Shanghai University of Medicine & Health Sciences, Shanghai, China, Email:

Received: 25-Apr-2022, Manuscript No. JVMS-22-16323; Editor assigned: 28-Apr-2022, Pre QC No. JVMS-22-16323(PQ); Reviewed: 11-May-2022, QC No. JVMS-22-16323; Revised: 13-May-2022, Manuscript No. JVMS-22-16323(R); Published: 25-May-2022, DOI: 10.35248/ 2329-6925.22.10.452

Description

We collected 2366 patients with acute ischemic stroke, and then divided them into the Stroke-Associated Pneumonia (SAP) group and non-SAP group. Of the 2366 patients, 459 were diagnosed with SAP. Subsequently, we discovered two new predictors of SAP. To the best of our knowledge, our study was the first to find that a high International Normalized Ratio (INR) (odds ratio=37.981; 95% confidence interval, 7.487-192.665; P<0.001) might predict SAP events in patients with acute ischemic stroke. In addition, we first proposed walking ability within 48 hours of admission (WA) (odds ratio=0.395; 95% confidence interval, 0.287-0.543; P<0.001) as a predictive indicator of SAP and found that this indicator was a protective factor. Furthermore, we designed a simple and practical SAP prediction model (AUC: 0.851) which included age, homocysteine, INR, history of chronic obstructive pulmonary disease, dysphagia, and WA. And, the model showed good accuracy.

Stroke-Associated Pneumonia (SAP) is a common cause of disability or death. Preventive antibiotic therapy does not improve functional outcomes in relatively unselected patients with stroke [1,2]. On the contrary, actively searching for signs of infections and prophylactic use of antibiotics are beneficial for patients at high risk of infection [3,4]. Factors that have been found to predict the occurrence of SAP include age, sex, smoking, pre-stroke Modified Rankin Scale (MRS), National Institutes of Health Stroke Scale (NIHSS), dysphagia, history of various diseases (e.g., atrial fibrillation, cardiac valve disease, chronic obstructive pulmonary disease, congestive heart failure, diabetes, etc.), and predictors in the blood (e.g., interleukin-6, interleukin-10, procalcitonin, C-reactive protein, leukocyte count, lymphocyte count, neutrophil-to-lymphocyte ratio, etc.) [5-9]. although so many predictors have been discovered, a method that can predict the occurrence of SAP with great accuracy has not yet been established. In this study, the authors discovered two new predictors of SAP and built a relatively accurate predictive model. They found for the first time that INR may be an independent predictor of SAP. In addition, INR is generally a routine examination index for admission of acute stroke patients, so it does not increase the economic burden on patients. Moreover, the items included in the predictive model are common and readily available. Therefore, the model has good economics and feasibility. Furthermore, it also has relatively high accuracy (AUC: 0.851). These findings may help identify high-risk patients with SAP and provide a reference for the timely use of preventive antibiotics. Subsequent studies should combine the above findings with proven reliable predictors to establish a model that can accurately predict the occurrence of SAP. Maybe it can be complicated, but if it is very accurate, it would be very meaningful to guide the use of prophylactic antibiotics, thereby reducing the occurrence of SAP. If possible, it is best to find out the key initiation factor for the occurrence of SAP, and try to prevent the initiation factor from functioning, thereby preventing the development of SAP in stroke patients and improving the outcomes of these patients.

Conclusion

We found that a higher INR and no WA could predict SAP in patients with acute ischemic stroke. In addition, we designed a simple and practical predictive model for SAP, which showed relatively good accuracy.

REFERENCES

1. Kalra L, Irshad S, Hodsoll J, Simpson M, Gulliford M, Smithard D, et al. Prophylactic antibiotics after acute stroke for reducing pneumonia in patients with dysphagia (STROKE-INF): a prospective, cluster-randomised, open-label, masked endpoint, controlled clinical trial. Lancet. 2015;386(10006):1835-1844.

   [Crossref] [Google Scholar] [Pubmed]

2. Ormseth CH, Sheth KN, Saver JL, Fonarow GC, Schwamm LH. The American Heart Association’s Get With the Guidelines (GWTG)-Stroke development and impact on stroke care. Stroke Vasc Neurol. 2017;2(2):94-105.

   [Crossref] [Google Scholar] [Pubmed]

3. Schwarz S, Al-Shajlawi F, Sick C, Meairs S, Hennerici MG. Effects of prophylactic antibiotic therapy with mezlocillin plus sulbactam on the incidence and height of fever after severe acute ischemic stroke: the Mannheim Infection in Stroke Study (MISS). Stroke. 2008;39(4):1220-1227.

   [Crossref] [Google Scholar] [Pubmed]

4. Harms H, Prass K, Meisel C, Klehmet J, Rogge W, Drenckhahn C, et al. Preventive antibacterial therapy in acute ischemic stroke: a randomized controlled trial. PloS one. 2008;3(5):e2158.

   [Crossref] [Google Scholar] [Pubmed]

5. Hotter B, Hoffmann S, Ulm L, Meisel C, Bustamante A, Montaner J, et al. External validation of five scores to predict stroke-associated pneumonia and the role of selected blood biomarkers. Stroke. 2021;52(1):325-330.

   [Crossref] [Google Scholar] [Pubmed]

6. Faura J, Bustamante A, Reverté S, García-Berrocoso T, Millán M, Castellanos M, et al. Blood biomarker panels for the early prediction of stroke-associated complications. J Am Heart Assoc. 2021;10(5):e018946.

   [Crossref] [Google Scholar] [Pubmed]

7. Kuo Y, Huang Y, Lee M, Lee T, Lee J. Risk stratification model for post-stroke pneumonia in patients with acute ischemic stroke. Eur J Cardiovasc Nurs. 2020;19(6):513-520.

   [Crossref] [Google Scholar] [Pubmed]

8. Zhang X, Wang F, Zhang Y, Ge Z. Risk factors for developing pneumonia in patients with diabetes mellitus following acute ischaemic stroke. Int. J Med Res. 2012;40(5):1860-1865.

   [Crossref] [Google Scholar] [Pubmed]

9. Yuan MZ, Li F, Tian X, Wang W, Jia M, Wang XF, et al. Risk factors for lung infection in stroke patients: a meta-analysis of observational studies. Expert Rev Anti-Infect Ther. 2015;13(10):1289-1298.

   [Crossref] [Google Scholar] [Pubmed]