title: A simple prediction score for developing a hospital-acquired infection after acute ischemic stroke.
abstract: BACKGROUND
  Hospital-acquired infections (HAIs) are a major cause of morbidity and mortality in acute ischemic stroke patients. Although prior scoring systems have been developed to predict pneumonia in ischemic stroke patients, these scores were not designed to predict other infections. We sought to develop a simple scoring system for any HAI.
  
  
  METHODS
  Patients admitted to our stroke center (July 2008-June 2012) were retrospectively assessed. Patients were excluded if they had an in-hospital stroke, unknown time from symptom onset, or delay from symptom onset to hospital arrival greater than 48 hours. Infections were diagnosed via clinical, laboratory, and imaging modalities using standard definitions. A scoring system was created to predict infections based on baseline patient characteristics.
  
  
  RESULTS
  Of 568 patients, 84 (14.8%) developed an infection during their stays. Patients who developed infection were older (73 versus 64, P < .0001), more frequently diabetic (43.9% versus 29.1%, P = .0077), and had more severe strokes on admission (National Institutes of Health Stroke Scale [NIHSS] score 12 versus 5, P < .0001). Ranging from 0 to 7, the overall infection score consists of age 70 years or more (1 point), history of diabetes (1 point), and NIHSS score (0-4 conferred 0 points, 5-15 conferred 3 points, >15 conferred 5 points). Patients with an infection score of 4 or more were at 5 times greater odds of developing an infection (odds ratio, 5.67; 95% confidence interval, 3.28-9.81; P < .0001).
  
  
  CONCLUSIONS
  In our sample, clinical, laboratory, and imaging information available at admission identified patients at risk for infections during their acute hospitalizations. If validated in other populations, this score could assist providers in predicting infections after ischemic stroke.
authors.affiliation: Stroke Program, Department of Neurology, Tulane University Hospital, New Orleans, LA.
authors.forename: Adam J
authors.initials: AJ
authors.lastname: Friedant
authors.affiliation: Stroke Program, Department of Neurology, Tulane University Hospital, New Orleans, LA.
authors.forename: Brittany M
authors.initials: BM
authors.lastname: Gouse
authors.affiliation: Gertrude H. Sergievsky Center, Department of Neurology, Columbia University, New York, NY; Department of Neurology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL.
authors.forename: Amelia K
authors.initials: AK
authors.lastname: Boehme
authors.affiliation: Stroke Program, Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA.
authors.forename: James E
authors.initials: JE
authors.lastname: Siegler
authors.affiliation: Department of Epidemiology, School of Public Health; Health Services and Outcomes Research Center for Outcome and Effectiveness Research and Education (COERE), Division of Preventive Medicine; Center of Excellence in Comparative Effectiveness Research for Eliminating Disparities (CERED) Minority Health & Health Disparities Research Center (MHRC).
authors.forename: Karen C
authors.initials: KC
authors.lastname: Albright
authors.affiliation: Stroke Program, Department of Neurology, Tulane University Hospital, New Orleans, LA.
authors.forename: Dominique J
authors.initials: DJ
authors.lastname: Monlezun
authors.affiliation: Stroke Program, Department of Neurology, Tulane University Hospital, New Orleans, LA.
authors.forename: Alexander J
authors.initials: AJ
authors.lastname: George
authors.affiliation: Section on Statistical Genetics, Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL.
authors.forename: Timothy Mark
authors.initials: TM
authors.lastname: Beasley
authors.affiliation: Stroke Program, Department of Neurology, Tulane University Hospital, New Orleans, LA. Electronic address: smartin2@tulane.edu.
authors.forename: Sheryl
authors.initials: S
authors.lastname: Martin-Schild
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D002545:Brain Ischemia / Q000150:complications* / Q000175:diagnosis; D016009:Chi-Square Distribution; D015897:Comorbidity; D003428:Cross Infection / Q000175:diagnosis / Q000209:etiology*; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D016017:Odds Ratio; D011237:Predictive Value of Tests; D011379:Prognosis; D012372:ROC Curve; D012042:Registries; D012189:Retrospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D020521:Stroke / Q000150:complications* / Q000175:diagnosis; D013997:Time Factors; D055815:Young Adult
keywords: Infection; acute ischemic stroke; modeling; outcome; risk factors