Quality control of the surveillance programme of ICU-acquired infection (ENVIN–HELICS registry) in Spain

López-Pueyo, María Jesús; Olaechea-Astigarraga, Pedro; Palomar-Martínez, Mercedes; Insausti-Ordeñana, J.; Álvarez-Lerma, Francisco

doi:10.1016/j.jhin.2013.02.018

Cited by 27 publications

(21 citation statements)

References 21 publications

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“…The reliability and quality of the surveillance program has recently been investigated [13]. Data were prospectively collected on an individual patient level and also aggregate ICU level.…”

Section: Methodsmentioning

confidence: 99%

Multilevel competing risk models to evaluate the risk of nosocomial infection

et al. 2014

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IntroductionRisk factor analyses for nosocomial infections (NIs) are complex. First, due to competing events for NI, the association between risk factors of NI as measured using hazard rates may not coincide with the association using cumulative probability (risk). Second, patients from the same intensive care unit (ICU) who share the same environmental exposure are likely to be more similar with regard to risk factors predisposing to a NI than patients from different ICUs. We aimed to develop an analytical approach to account for both features and to use it to evaluate associations between patient- and ICU-level characteristics with both rates of NI and competing risks and with the cumulative probability of infection.MethodsWe considered a multicenter database of 159 intensive care units containing 109,216 admissions (813,739 admission-days) from the Spanish HELICS-ENVIN ICU network. We analyzed the data using two models: an etiologic model (rate based) and a predictive model (risk based). In both models, random effects (shared frailties) were introduced to assess heterogeneity. Death and discharge without NI are treated as competing events for NI.ResultsThere was a large heterogeneity across ICUs in NI hazard rates, which remained after accounting for multilevel risk factors, meaning that there are remaining unobserved ICU-specific factors that influence NI occurrence. Heterogeneity across ICUs in terms of cumulative probability of NI was even more pronounced. Several risk factors had markedly different associations in the rate-based and risk-based models. For some, the associations differed in magnitude. For example, high Acute Physiology and Chronic Health Evaluation II (APACHE II) scores were associated with modest increases in the rate of nosocomial bacteremia, but large increases in the risk. Others differed in sign, for example respiratory vs cardiovascular diagnostic categories were associated with a reduced rate of nosocomial bacteremia, but an increased risk.ConclusionsA combination of competing risks and multilevel models is required to understand direct and indirect risk factors for NI and distinguish patient-level from ICU-level factors.

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“…The reliability and quality of the surveillance program has recently been investigated [13]. Data were prospectively collected on an individual patient level and also aggregate ICU level.…”

Section: Methodsmentioning

confidence: 99%

Multilevel competing risk models to evaluate the risk of nosocomial infection

et al. 2014

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“…Moreover, only two studies were identi ed that compared VAP diagnostic algorithms [32,66]. HELICS was chosen as the reference standard due as it has been reported to have the best performance characteristics, is widely used internationally, and has been used as the reference standard for numerous other studies [2,[44][45][46][47]. CDC/NHSN and CPIS criteria were chosen as the other two most widely recognized and used criteria.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, data comparing techniques head-to-head is lacking, thus we conducted a prospective non-randomized study to determine if in patients with VAP, does application of the Johanson VAP diagnosis was made independently by the treating clinical team. Diagnostic criteria were according to HELICS criteria [28] in accordance with in institutional standard and other published studies [2,[44][45][46][47]. Chest radiograph interpretation was undertaken "off-line" and by a team of 3 physicians (1 radiology, 1 critical care, 1 pulmonology) who were independent of the treating team.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Diagnostic Algorithms and Clinical Parameters to Diagnose Ventilator-Associated Pneumonia: A Prospective Observational Study

Rahimi–Bashar

Miller²,

Yaghoobi

et al. 2020

Preprint

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BACKGROUND: Suspicion and clinical criteria continue to serve as the foundation for ventilator-associated pneumonia (VAP) diagnosis, however the criteria used to diagnose VAP vary widely. Data from head-to-head comparisons of clinical diagnostic algorithms is lacking, thus a prospective observational study was performed to determine the performance characteristics of the Johanson criteria, Clinical Pulmonary Infection Score (CPIS), and Centers for Disease Control and Prevention’s National Healthcare Safety Network (CDC/NHSN) criteria as compared to Hospital in Europe Link for Infection Control through Surveillance (HELICS) reference standard. METHODS: A prospective observational cohort study was performed in three mixed medical-surgical ICUs from one academic medical center from 1 October 2016 to 30 April 2018. VAP diagnostic criteria were applied to each patient including CDC/NHSN, CPIS, HELICS and Johanson criteria. Tracheal cultures and serum procalcitonin values were obtained for each patient. RESULTS: Eighty-five patients were enrolled (VAP 45, controls 41), mean age 46.94±18.9 years with a male predominance (72.94%). Using HELICS as the reference standard, the true positive (TP; sensitivity) and false negative (FN; miss rate) rates were CDC/NHSN (TP 44%; FN 0%), CPIS (TP 43%; FN 1%), Johnson (TP 43%; FN 1%). The highest true negative rate was seen with CPIS. CPIS had the highest Youden index; CDC/NHSN had the lowest. The positive tracheal culture rate was 81.2%. The sensitivity for positive tracheal culture with the serum procalcitonin level >0.5 ng/ml was 51.8%. CDC/NHSN had the highest false positive correlation with tracheal aspirate cultures. CONCLUSION: VAP remains a considerable source of morbidity and mortality in modern ICUs. The optimal diagnostic method remains unclear. Using HELICS criteria as the reference standard, CPIS displayed greater diagnostic accuracy compared to CDC/NHSN and Johanson criteria. Accuracy was improved with the addition of serum procalcitonin >0.5 ng/ml, but not positive quantitative endotracheal aspirate culture.TRIAL REGISTRATION: Not indicated for this study type.

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“…In recent years, the use of specific care bundles to control infection in the intensive care unit has been demonstrated to be effective, including ventilator-associated pneumonia (VAP) (Heck, 2012;López-Pueyo et al, 2013). Thus, zero VAP rates have been proposed as an indicator of quality in the Intensive Care Unit (ICU).…”

Section: Introductionmentioning

confidence: 99%

Ventilator-Associated Events: Definitions and Uses

Peña-López¹

2022

Encyclopedia of Respiratory Medicine

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Quality control of the surveillance programme of ICU-acquired infection (ENVIN–HELICS registry) in Spain

Cited by 27 publications

References 21 publications

Multilevel competing risk models to evaluate the risk of nosocomial infection

Multilevel competing risk models to evaluate the risk of nosocomial infection

A Comparison of Diagnostic Algorithms and Clinical Parameters to Diagnose Ventilator-Associated Pneumonia: A Prospective Observational Study

Ventilator-Associated Events: Definitions and Uses

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