Classification of positive blood cultures: computer algorithms versus physicians' assessment - development of tools for surveillance of bloodstream infection prognosis using population-based laboratory databases
نویسندگان
چکیده
BACKGROUND Information from blood cultures is utilized for infection control, public health surveillance, and clinical outcome research. This information can be enriched by physicians' assessments of positive blood cultures, which are, however, often available from selected patient groups or pathogens only. The aim of this work was to determine whether patients with positive blood cultures can be classified effectively for outcome research in epidemiological studies by the use of administrative data and computer algorithms, taking physicians' assessments as reference. METHODS Physicians' assessments of positive blood cultures were routinely recorded at two Danish hospitals from 2006 through 2008. The physicians' assessments classified positive blood cultures as: a) contamination or bloodstream infection; b) bloodstream infection as mono- or polymicrobial; c) bloodstream infection as community- or hospital-onset; d) community-onset bloodstream infection as healthcare-associated or not. We applied the computer algorithms to data from laboratory databases and the Danish National Patient Registry to classify the same groups and compared these with the physicians' assessments as reference episodes. For each classification, we tabulated episodes derived by the physicians' assessment and the computer algorithm and compared 30-day mortality between concordant and discrepant groups with adjustment for age, gender, and comorbidity. RESULTS Physicians derived 9,482 reference episodes from 21,705 positive blood cultures. The agreement between computer algorithms and physicians' assessments was high for contamination vs. bloodstream infection (8,966/9,482 reference episodes [96.6%], Kappa = 0.83) and mono- vs. polymicrobial bloodstream infection (6,932/7,288 reference episodes [95.2%], Kappa = 0.76), but lower for community- vs. hospital-onset bloodstream infection (6,056/7,288 reference episodes [83.1%], Kappa = 0.57) and healthcare-association (3,032/4,740 reference episodes [64.0%], Kappa = 0.15). The 30-day mortality in the discrepant groups differed from the concordant groups as regards community- vs. hospital-onset, whereas there were no material differences within the other comparison groups. CONCLUSIONS Using data from health administrative registries, we found high agreement between the computer algorithms and the physicians' assessments as regards contamination vs. bloodstream infection and monomicrobial vs. polymicrobial bloodstream infection, whereas there was only moderate agreement between the computer algorithms and the physicians' assessments concerning the place of onset. These results provide new information on the utility of computer algorithms derived from health administrative registries.
منابع مشابه
Increasing the Reliability of Fully Automated Surveillance for Central Line-Associated Bloodstream Infections.
OBJECTIVE To increase reliability of the algorithm used in our fully automated electronic surveillance system by adding rules to better identify bloodstream infections secondary to other hospital-acquired infections. METHODS Intensive care unit (ICU) patients with positive blood cultures were reviewed. Central line-associated bloodstream infection (CLABSI) determinations were based on 2 sourc...
متن کاملبررسی فراوانی و الگوی مقاومت آنتیبیوتیکی جدایههای باکتریایی نمونههای کشت خون بیماران بستری در بیمارستان
Background: Bloodstream infections are the most important causes of morbidity and mortality in hospitalized patients. Blood culture plays an important role in identifying most of bacterial agents of bloodstream infections. Knowledge about bacterial agents of bloodstream infections and also antibiotic resistance of these bacteria are important. Antibiotic resistance among bacterial agents of blo...
متن کاملBody Mass Index Classification based on Facial Features using Machine Learning Algorithms for utilizing in Telemedicine
Background and Objectives: Due to the impact of controlling BMI on life, BMI classification based on facial features can be used for developing Telemedicine systems and eliminating the limitations of measuring tools, especially for paralyzed people. So that physicians can help people online during the Covid-19 pandemic. Method: In this study, new features and some previous work features were e...
متن کاملComputer Algorithms To Detect Bloodstream Infections
We compared manual and computer-assisted bloodstream infection surveillance for adult inpatients at two hospitals. We identified hospital-acquired, primary, central-venous catheter (CVC)-associated bloodstream infections by using five methods: retrospective, manual record review by investigators; prospective, manual review by infection control professionals; positive blood culture plus manual C...
متن کاملAgreement in classifying bloodstream infections among multiple reviewers conducting surveillance.
BACKGROUND Mandatory reporting of healthcare-associated infections (HAIs) is increasing. Evidence for agreement among different reviewers applying HAI surveillance criteria is limited. We aim to characterize agreement among infection preventionists (IPs) conducting surveillance for central line-associated bloodstream infection (CLABSI) with each other and as compared with simplified laboratory-...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره 12 شماره
صفحات -
تاریخ انتشار 2012