Computer-assisted, high-frequency, hospital-wide point prevalence surveys of hospital-acquired infections in a tertiary care hospital, the Netherlands, 2013 to 2014.

BackgroundSurveillance of hospital-acquired infections (HAI) often relies on point prevalence surveys (PPS) to detect major deviations in the occurrence of HAI, supplemented with incidence measurements when more detailed information is needed. In a 1,320-bed university medical centre in the Netherlands, we evaluated an electronically assisted surveillance system based on frequently performed computer-assisted PPS (CAPPS).AimThe primary goals were to evaluate the performance of this method to detect trends and to determine how adjustments in the frequency with which the CAPPS are performed would affect this performance. A secondary goal was to evaluate the performance of the algorithm (nosocomial infection index (Nii)) used.MethodsWe analysed the data of 77 hospital-wide PPS, performed over a 2-year period (2013 and 2014) and including 25,056 patients.ResultsSix trends with statistical significance were detected. The probability to detect such trends rapidly decreased when PPS are performed at a lower frequency. The Nii and its dynamics strongly correlated with the presence of HAI.ConclusionPerforming computer-assisted, high frequency hospital-wide PPS, is a feasible method that will detect even subtle changes in HAI prevalence over time.

Internal and External Validation of a Computer-Assisted Surveillance System for Hospital-Acquired Infections in a 754-Bed General Hospital in the Netherlands.

OBJECTIVE To evaluate a computer-assisted point-prevalence survey (CAPPS) for hospital-acquired infections (HAIs). DESIGN Validation cohort. SETTING A 754-bed teaching hospital in the Netherlands. METHODS For the internal validation of a CAPPS for HAIs, 2,526 patients were included. All patient records were retrospectively reviewed in depth by 2 infection control practitioners (ICPs) to determine which patients had suffered an HAI. Preventie van Ziekenhuisinfecties door Surveillance (PREZIES) criteria were used. Following this internal validation, 13 consecutive CAPPS were performed in a prospective study from January to March 2013 to determine weekly, monthly, and quarterly HAI point prevalence. Finally, a CAPPS was externally validated by PREZIES (Rijksinstituut voor Volksgezondheid en Milieu [RIVM], Bilthoven, Netherlands). In all evaluations, discrepancies were resolved by consensus. RESULTS In our series of CAPPS, 83% of the patients were automatically excluded from detailed review by the ICP. The sensitivity of the method was 91%. The time spent per hospital-wide CAPPS was ~3 hours. External validation showed a negative predictive value of 99.1% for CAPPS. CONCLUSIONS CAPPS proved to be a sensitive, accurate, and efficient method to determine serial weekly point-prevalence HAI rates in our hospital. Infect Control Hosp Epidemiol 2016;1-6.

An automated algorithm to preselect patients to be assessed individually in point prevalence surveys for hospital-acquired infections in surgery.

In this pilot study, we evaluate an algorithm that uses predictive clinical and laboratory parameters to differentiate between patients with hospital-acquired infection (HAI) and patients without HAI. Seventy-four percent of the studied population of surgical patients could be reliably (negative predictive value of 98%) excluded from detailed assessment by the infection control practitioner.

Evaluation of an algorithm for electronic surveillance of hospital-acquired infections yielding serial weekly point prevalence scores.

Surveillance of hospital-acquired infections can be approximated by repeated surveys that are performed in a standardized, cost-effective manner. We developed an integrated software system for serial electronic hospital-wide point prevalence surveys using algorithms that proved highly sensitive and specific over a 5-year period in a large university medical center.