832 infection control & hospital epidemiology july 2018, vol. 39, no. 7
on its performance compared to manual surveillance for VAE; rather, the algorithm was compared to manual surveillance for VAP. Nuckchady et al7 describe a partially automated surveillance algorithm that relied on extraction of mechanical ventilation data, temperature, and WBC—all entered manually into the EHR on a daily basis. In this study, antimicrobial admin- istrationwas not available for electronic review; thus, the algorithm couldonlyreportVAC andpossibleIVACbut notPVAP. Our study has several limitations. It was a single-center
study, and although 4 different adult ICUs were included, the results might not be generalizable to other facilities. During the study period, which occurred just prior to transition to a new EHR, temperature was not recorded in an extractable electronic format. To account for this limitation, the algorithm was programmed to not reject VAEs that did not meet the WBC criterion; these cases were allowed to flow through the algorithm during which the remainder of the VAE definition was applied, so that patients who did not meet the WBC criterion could be considered at the next stage of the VAE definition. Despite this limitation, no instances were observed in which the temperature criterion was required to detect a VAC. In all instances, the criterion was met using the WBC criterion. In fact, during this time, the work flow of IC staff performing manual surveillance was to first check the WBC because these data were readily available in the EHR. Only in instances in which the case did not meet theWBC criterion did the IC staff perform a manual review of clinical notes to identify temperature >38°C. With the caveat of a limited sample size, this observation suggests that WBC alone might be sufficiently sensitive and specific. Since the completion of the study, our institution has transitioned to a new EHR in which vital signs, including temperature, are documented electronically; thus, temperature data have been added to the algorithm. Interestingly, we observed an increase in the sensitivity of
manual surveillance between the development and validation cohorts. It is possible that this difference is due to accumulated experience by IC staff in conducting VAE surveillance, which was split temporally into the development and validation data sets, as well as the involvement of the IC staff in the adjudication discussions during the development period. This process likely improved IC staff knowledge with respect to VAE application as they accumulated experience and received feedback on the types of errors generated. Our study relied on specialized software for the continuous
monitoring of ventilator data (BedMaster; Excel Medical, Jupiter, FL). Similar software systems are not part of the standard of care at present, which limits the number of hospitals that will be able to duplicate the implementation of an automated surveillance system like ours. On the other hand, the high-temporal resolution ventilator data that our system provides is not strictly necessary for the VAE detection, which summarizes all ventilator data by the daily minimum (ie, the lowest PEEP and FiO2 values that were maintained for at least 1 hour). Thus, it should be possible to achieve similar automated surveillance results using ventilator data from an
definition to include only objective elements available in EHRs, completely automated surveillance for these events became possible. The extent to which facilities have the resources to implement automated surveillance, however, remains to be determined.10–15 Automated surveillance presents many advan- tages.16,17 These advantages include consistent application of the NHSN criteria, which can be updated through recoding as definitions change over time, without the possibility of human errors, and reduced time and effort required for manual surveil- lance. Electronic approaches to surveillance require maintenance: In addition to definition changes, any alterations in coding of components of the definition in the local EHRmust be known in advance and updated to maintain a robust surveillance tool. In the case of the algorithm described here, a further advantage
afforded by utilization of live-streaming clinical data is that sur- veillance can be conducted in real time to enhance both timing of reporting as well as, and perhaps more importantly, to use these data to identify opportunities to target quality improvement interventions, and to assess the impact of these interventions. Traditionalmanual surveillance, and even automated surveillance that relies on retrospective data from theEHR, results in reporting well after patients leave the ICU, limiting the impact of feedback to clinicians who are no longer caring for the affected patients. The automated surveillance can be programmed to generate alerts to frontline providers and can be expanded to include opportunities for improving clinical care. For example, the automated surveil- lancealgorithm canbeconfigured to generate electronic alerts to providers based on changes in PEEP and FiO2 that precede the establishment of a VAE, prompting clinicians to re-evaluate the patient’s ventilator settings and clinical status. Another possible intervention could include automated alerts at the time anti- microbials are initiated to provide evidence-based recommenda- tions for whether antimicrobials are indicated as the VAE surveillance definition captures noninfectious events, as well as guidance on the choice of empiric antimicrobials. At this time, implementation of the algorithm to perform hospital-wide VAE surveillance, which will require validation on new EHR data streams, has been prioritized, and it will include an assessment of the impact of automated surveillance on IC workflow. Prior stu- dies have demonstrated substantial improvements in the infection-preventionist time-effort using even partially automated surveillance tools.18 Initial discussions have begun regarding potential quality improvement interventions using the algorithm. In summary, we have developed a fully automated VAE sur-
veillance system with opportunities for increased accuracy of surveillance, the potential to improve patient care processes and outcomes, and the assessment of interventions aimed at enhan- cing care and reducing complications of mechanical ventilation.
EHR, collected as hourly “snapshots.” This work is currently in process at our institution to deploy automated surveillance at other hospitals in our hospital network. Finally, the failure of automated surveillance during a brief period of data loss highlights the reliance of the algorithm on data streams and the importance of data archiving. With the revision of VAP surveillance in 2013 to the VAE
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