Abstract
Accurate and early prediction of poststroke infections is important to improve antibiotic therapy guidance and/or to avoid unnecessary antibiotic treatment. We hypothesized that the combination of blood biomarkers with clinical parameters could help to optimize risk stratification during hospitalization. In this prospective observational study, blood samples of 283 ischemic stroke patients were collected at hospital admission within 72 h from symptom onset. Among the 283 included patients, 60 developed an infection during the first five days of hospitalization. Performance predictions of blood biomarkers (Serum Amyloid-A (SAA), C-reactive protein, procalcitonin (CRP), white blood cells (WBC), creatinine) and clinical parameters (National Institutes of Health Stroke Scale (NIHSS), age, temperature) for the detection of poststroke infection were evaluated individually using receiver operating characteristics curves. Three machine learning techniques were used for creating panels: Associative Rules Mining, Decision Trees and an internal iterative-threshold based method called PanelomiX. The PanelomiX algorithm showed stable performance when applied to two representative subgroups obtained as splits of the main subgroup. The panel including SAA, WBC and NIHSS had a sensitivity of 97% and a specificity of 45% to identify patients who did not develop an infection. Therefore, it could be used at hospital admission to avoid unnecessary antibiotic (AB) treatment in around half of the patients, and consequently, to reduce AB resistance.
Highlights
Stroke remains a main cause of disability and the second cause of death worldwide.While initial cerebral infarction is associated with elevated rates of morbidity and mortality, infections occurring during the acute phase of stroke have a major effect on patient’s long-term outcome [1]
The present study included a total of 283 patients, that were divided in two subgroups: the discovery group, with 40 patients (19 non-infected and 21 infected), and the validation subgroup, with 243 patients (204 non-infected and 39 infected)
The discovery of a biomarker or panel of biomarkers able to detect patients at low risk of infection development could importantly improve their hospital care and avoid antibiotic therapy. Results of this manuscript show that the panel combination of Serum Amyloid A (SAA), National Institutes of Health Stroke Scale (NIHSS) and white blood cells (WBC) obtained with PanelomiX, could be a promising tool to stratify patients at low risk of infection
Summary
Stroke remains a main cause of disability and the second cause of death worldwide.While initial cerebral infarction is associated with elevated rates of morbidity and mortality, infections occurring during the acute phase of stroke have a major effect on patient’s long-term outcome [1]. Pneumonia and urinary tract infections (UTI) are the most common complications, occurring in 20–60% of stroke survivors, prolonging hospital stays and being responsible of 30% of the poststroke deaths [2,3,4,5]. Physicians face uncertainty in the early diagnosis of infections caused by inconclusive clinical examination, or due to the lack of specific signs, symptoms and routine laboratory tests. Different blood biomarkers such as procalcitonin (PCT), C-reactive protein (CRP), or white blood cells (WBC) have been suggested as possible candidates to help in predicting/identifying patients at risk for poststroke infections [7,8,9]. To date, none of them has been applied in clinical practice
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