Validity analysis of a unique infection surveillance system in the intensive care unit by analysis of a data warehouse built through a workflow-integrated software application.

BACKGROUND: An electronic decision support programme was developed within the intensive care unit (ICU) that provides an overview of all infection-related patient data, and allows ICU physicians to add clinical information during patient rounds, resulting in prospective compilation of a database. AIM: To assess the validity of computer-assisted surveillance (CAS) of ICU-acquired infection performed by analysis of this database. METHODS: CAS was compared with prospective paper-based surveillance (PBS) for ICU-acquired respiratory tract infection (RTI), bloodstream infection (BSI) and urinary tract infection (UTI) over four months at a 36-bed medical and surgical ICU. An independent panel reviewed the data in the case of discrepancy between CAS and PBS. FINDINGS: PBS identified 89 ICU-acquired infections (13 BSI, 18 UTI, 58 RTI) and CAS identified 90 ICU-acquired infections (14 BSI, 17 UTI, 59 RTI) in 876 ICU admissions. There was agreement between CAS and PBS on 13 BSI (100 %), 14 UTI (77.8 %) and 42 RTI (72.4 %). Overall, there was agreement on 69 infections (77.5%), resulting in a kappa score of 0.74. Discrepancy between PBS and CAS was the result of capture error in 11 and 14 infections, respectively. Interobserver disagreement on probability (13 RTI) and focus (two RTI, one UTI) occurred for 16 episodes. The time required to collect information using CAS is less than 30% of the time required when using PBS. CONCLUSION: CAS for ICU-acquired infection by analysis of a database built through daily workflow is a feasible surveillance method and has good agreement with PBS. Discrepancy between CAS and PBS is largely due to interobserver variability.

Towards automated generation and execution of clinical guidelines: engine design and implementation through the ICU Modified Schofield use case.

As the complexity and amount of medical information keeps increasing, it is difficult to maintain the same quality of care. Therefore, clinical guidelines are used to structure best practices and care, but they also support physicians and nurses in the diagnostic and treatment process. Currently, no standardized format exists to represent these guidelines. Translating guidelines into a computer interpretable format can overcome problems in the physicians' workflow and improve clinician's uptake. An engine is proposed to automatically translate and execute clinical guidelines. These guidelines are represented as flowcharts, expressed in either (i) a computer interpretable guideline format or (ii) a UML diagram. A detailed overview of the architecture is presented and algorithms, aiming at grouping several components and distributing the guidelines, are proposed to optimize the execution of the guidelines. The Modified Schofield guideline for the calculation of the calorie need for burn patients was used for evaluation. Results show that the execution of guidelines using the engine is very efficient. Using optimization algorithms the execution times can be lowered.

A novel time series analysis approach for prediction of dialysis in critically ill patients using echo-state networks.

BACKGROUND: Echo-state networks (ESN) are part of a group of reservoir computing methods and are basically a form of recurrent artificial neural networks (ANN). These methods can perform classification tasks on time series data. The recurrent ANN of an echo-state network has an 'echo-state' characteristic. This 'echo-state' functions as a fading memory: samples that have been introduced into the network in a further past, are faded away. The echo-state approach for the training of recurrent neural networks was first described by Jaeger H. et al. In clinical medicine, until this moment, no original research articles have been published to examine the use of echo-state networks. METHODS: This study examines the possibility of using an echo-state network for prediction of dialysis in the ICU. Therefore, diuresis values and creatinine levels of the first three days after ICU admission were collected from 830 patients admitted to the intensive care unit (ICU) between May 31 th 2003 and November 17th 2007. The outcome parameter was the performance by the echo-state network in predicting the need for dialysis between day 5 and day 10 of ICU admission. Patients with an ICU length of stay <10 days or patients that received dialysis in the first five days of ICU admission were excluded. Performance by the echo-state network was then compared by means of the area under the receiver operating characteristic curve (AUC) with results obtained by two other time series analysis methods by means of a support vector machine (SVM) and a naive Bayes algorithm (NB). RESULTS: The AUC's in the three developed echo-state networks were 0.822, 0.818, and 0.817. These results were comparable to the results obtained by the SVM and the NB algorithm. CONCLUSIONS: This proof of concept study is the first to evaluate the performance of echo-state networks in an ICU environment. This echo-state network predicted the need for dialysis in ICU patients. The AUC's of the echo-state networks were good and comparable to the performance of other classification algorithms. Moreover, the echo-state network was more easily configured than other time series modeling technologies.

Implementation of a real-time electronic alert based on the RIFLE criteria for acute kidney injury in ICU patients.

Acute kidney injury (AKI) is very common among critically-ill patients and is correlated with significant morbidity and mortality. The RIFLE criteria (an acronym comprising Risk, Injury, Failure, Loss and End-stage kidney disease), were developed by a panel of experts aiming at standardizing the definition of AKI and to subdivide AKI into different categories of severity. However, although these criteria are clear and easy to understand, they are still complex and labour-intensive, and therefore mostly used in retrospective. The use of an electronic alert based on the RIFLE criteria, which warns the physician in real-time when kidney function is deteriorating can help to implement these criteria in daily clinical practice. In this paper we describe the successful implementation of such an alert system. Not only were there technological barriers to solve; also acceptance of the alert by the end user was of pivotal importance. Further research is currently performed to investigate whether the implementation of real-time electronic RIFLE alerts induce faster therapeutic intervention, and to evaluate the impact of a more timely intervention on improved preservation of kidney function and patients' outcome.