Diagnosis-dependent misclassification of infections using administrative data variably affected incidence and mortality estimates in ICU patients

Objective: To determine the accuracy of hospital discharge diagnoses in identifying severe infections among intensive care unit (ICU) patients, and estimate the impact of misclassification on incidence and 1-year mortality. Study Design and Setting: Sepsis, pneumonia, and central nervous system (CNS) infections among 7,615 ICU admissions were identified using ICD-9 and ICD-10 diagnoses from the Swedish hospital discharge register (HDR). Sensitivity, specificity, and likelihood ratios were calculated using ICU database diagnoses as reference standard, with inclusion in sepsis trials (IST) as secondary reference for sepsis. Results: CNS infections were accurately captured (sensitivity 95.4% [confidence interval (CI) = 86.8-100] and specificity 99.6% [CI = 99.4-99.8]). Community- acquired sepsis (sensitivity 51.1 % [CI = 41.0-61.2] and specificity 99.4% [CI = 99.2-99.6]) and primary pneumonia (sensitivity 38.2% [CI = 31.2-45.2] and specificity 98.6% [CI = 98.2-99.0]) were more accurately detected than sepsis and pneumonia in general. One-year mortality was accurately estimated for primary pneumonia but underestimated for community-acquired sepsis. However, there were only small differences in sensitivity and specificity between HDR and ICU data in the ability to identify IST. ICD-9 appeared more accurate for sepsis, whereas ICD-10 was more accurate for pneumonia. Conclusion: Accuracy of hospital discharge diagnoses varied depending on diagnosis and case definition. The pattern of misclassification makes estimates of relative risk more accurate than estimates of absolute risk. (c) 2007 Elsevier Inc. All rights reserved.