Automated Surveillance for Ventilator-Associated Events

Stevens, Jennifer P.; Silva, George; Gillis, Jean; Novack, Victor; Talmor, Daniel; Klompas, Michael; Howell, Michael D.

doi:10.1378/chest.13-2255

Cited by 55 publications

(49 citation statements)

References 19 publications

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“…[10][11][12][13][14][15][16]19 The incidence of VAC and IVAC was reported to be approximately 5-10% and 3-5%, respectively, similar to our results (VAC 13.4%, IVAC 5.7%). 10,12,13,19 These previous studies consistently found that the relationship among VAC, IVAC, and con- ventional VAP was poor, and that VAE was associated with adverse outcomes. However, the duration of mechanical ventilation in these studies was shorter than the minimal requirement of 4 d in the VAE definition.…”

Section: Relationship To Previous Studiessupporting

confidence: 91%

The Impact of Ventilator-Associated Events in Critically Ill Subjects With Prolonged Mechanical Ventilation

et al. 2017

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VAP was defined according to the Centers for Disease Control and Prevention 2008 criteria and was identified from the surveillance data of the infection control team of our hospital.Clinical outcomes were studied in the VAC, IVAC, and VAP groups. Possible VAP was not investigated. RESULTS: Higher mortality was seen in VAC and IVAC subjects, but not in VAP subjects, compared with those without VAEs and VAP. By multivariable hazard analysis for hospital mortality, IVAC was independently associated with hospital mortality (hazard ratio 2.42, 95% CI 1.39 -4.20, P ‫؍‬ .002). VAC also tended to show a similar association with hospital mortality (hazard ratio 1.45, 95% CI 0.97-2.18, P ‫؍‬ .07). On the other hand, VAP did not increase a hazard of hospital death (hazard ratio 1.08, 95% CI 0.44 -2.66, P ‫؍‬ .87). CONCLUSIONS: We found that a VAE was related to hospital mortality in critically ill subjects with prolonged mechanical ventilation, and that VAP was not.

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Section: Relationship To Previous Studiessupporting

confidence: 91%

The Impact of Ventilator-Associated Events in Critically Ill Subjects With Prolonged Mechanical Ventilation

et al. 2017

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“…Automated surveillance has an accurate, clearly defined medical knowledge base, unlike conventional manual surveillance methods that are based on positive cultures alone and are prone to generate both under-diagnosis (false-negatives) and over-diagnosis (false-positives) [20][21][22]. Conventional manual surveillances have limitations and weakness related to time constraints and human factors such as stress and fatigue, which can all negatively influence decision making.…”

Section: Discussionmentioning

confidence: 99%

Use of personal digital assistants to detect healthcare-associated infections in a neonatal intensive care unit in Egypt

Saleh

El-Kholy

Sayed

et al. 2016

J Infect Dev Ctries

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Introduction: Personal digital assistants (PDAs) used in electronic laboratory-based surveillance are a promising alternative to conventional surveillance to detect healthcare-associated infections (HAIs). The aim of the study was to monitor, detect, and analyze HAIs using PDAs in a neonatal intensive care unit (NICU). Methodology: In this descriptive study, 1,053 neonates admitted to the NICU in the obstetrics and gynecology ward at the Cairo University hospital were included and evaluated for HAIs by collecting data using PDAs programmed by Naval Medical Research Unit 3, Cairo, with the definitions for HAIs provided by the National Healthcare Safety Network of the Centers for Disease Control and Prevention. Case records were reviewed three times a week over 19 months, from March 2012 to September 2013. Results: Of 124 suspected episodes of infection recorded in PDAs, 89 confirmed episodes of infection were identified. HAI and NICU infection rates were 7.4 and 2.72/1,000 patient-days, respectively. Primary bloodstream infection was detected in 81 episodes and pneumonia in 8 episodes. The majority of infections (62%) were acquired in the ward before NICU admission. Klebsiella spp. was isolated most frequently (42%), followed by coagulase-negative Staphylococci (31%). Conclusions: This study is the first to report the use of PDAs in surveillance to detect HAIs in the NICU in our hospital. The majority of infections were acquired at the obstetric care department, indicating the importance of implementing rigorous prevention and control programs and a more detailed surveillance to identify other risk factors for infections.

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“…They affect 5-10% of patients and have consistently been associated with longer duration of mechanical ventilation, longer hospital lengths of stay, more antibiotic usage, and higher mortality rates. [22][23][24][25][26][27][28][29][30][31][32] Multiple studies suggest that patients who develop VAEs are approximately twice as likely to die compared with similar patients who do not develop VAEs. [22][23][24][25][26][27][28][29][30][31][32] …”

Section: Clinical Importancementioning

confidence: 99%

“…Multiple hospitals have now published their experience successfully automating VAE case detection. [27][28][29]31,33 Workers from the Netherlands have pointed out that one can define daily minimum PEEP and F IO 2 in different ways and that this in turn can have a large effect on case detection. 28 Options include the lowest values derived from minute-to-minute ventilator settings, the lowest values derived from paper charts with hourly documentation, or the 10th percentile.…”

Section: Surveillance Efficiencymentioning

confidence: 99%

Should Ventilator-Associated Events become a Quality Indicator for ICUs?

Klompas,

Berra

2016

Respir Care

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in 2013. Controversy abounds as to whether VAE definitions are potentially suitable to serve as quality indicators for ICUs. On the pro side, VAE definitions overcome many of the weaknesses of traditional VAP surveillance. VAE definitions are objective, reproducible, electronically computable, and strongly predict poor outcomes for patients. There is an increasing amount of data on clinical correlates, risk factors, and approaches to prevent VAEs. Potential strategies to prevent VAEs are highly aligned with accepted best practices in critical care. VAE surveillance therefore has the potential to catalyze better care and to help hospitals track outcomes in ventilated patients more rigorously and more efficiently. On the con side, the complete VAE definition set with subtiers is complicated, neither sensitive nor specific for VAP, non-physiological compared with other ICU metrics, susceptible to gaming, and may bring about changes in clinician behavior that could paradoxically end up harming patients.

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Automated Surveillance for Ventilator-Associated Events

Cited by 55 publications

References 19 publications

The Impact of Ventilator-Associated Events in Critically Ill Subjects With Prolonged Mechanical Ventilation

The Impact of Ventilator-Associated Events in Critically Ill Subjects With Prolonged Mechanical Ventilation

Use of personal digital assistants to detect healthcare-associated infections in a neonatal intensive care unit in Egypt

Should Ventilator-Associated Events become a Quality Indicator for ICUs?

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