Nomogram Including Neutrophil-to-Lymphocyte Ratio for the Prediction of Stroke-Associated Infections

Stroke has been a leading cause of mortality in China. Stroke-associated infections (SAI) are common complications, occurring in 5–65% of stroke patients. Faced with SAI, clinicians often are placed in a considerable dilemma. On the one hand, preventive overuse of antibiotics will lead to the emergence of drug-resistant bacteria. On the other hand, treatment delay of the infection will likely result in a poor outcome. Therefore, it is necessary to determine the early predictors of post-stroke infection to screen patients with high infection risk for early clinical intervention, thereby promoting and improving survival rates. We assessed 257 patients with acute ischemic stroke from a consecutive retrospective cohort. Data of these patients were obtained from three hospitals (TongJi Hospital and its two branches) between August 2018 and June 2019. Of these patients, 59 (23.0%) developed SAI. SAI was defined according to the modified Centers for Disease Control and Prevention criteria. There were 38 patients (64.4%) who developed pneumonia, 11 with urinary tract infections (18.6%), and 10 with other infections (16.9%). We found that a higher neutrophil-to-lymphocyte ratio (adjusted odds ratio [aOR] = 1.16; 95% confidence interval [CI], 1.01–1.33; P = 0.034), National Institutes of Health Stroke Scale score (aOR = 1.18; CI, 1.09–1.27; p < 0.001), and dysphagia (aOR = 2.95; CI, 1.40–6.22; P = 0.004) were risk factors for SAI. Of note, hypertriglyceridemia (aOR = 0.35; CI, 0.13–0.90; P = 0.029) was a protective factor, lowering the risk of SAI. To this end, a reliable nomogram was constructed for the prediction of SAI in our study (mean C-index value ± standard deviation = 0.821 ± 0.03). It has the potential to be widely used and may help identify patients at high risk for SAI and make timely clinical decisions. Given our study was based on relatively small dataset, the results should be interpreted with care and external validation in independent datasets is very necessary.