Estimation of Extra Length of Stay Attributable to Hospital-Acquired Infections in Adult ICUs Using a Time-Dependent Multistate Model.

OBJECTIVES: The objective of the study was to estimate the length of stay of patients with hospital-acquired infections hospitalized in ICUs using a multistate model. DESIGN: Active prospective surveillance of hospital-acquired infection from January 1, 1995, to December 31, 2012. SETTING: Twelve ICUs at the University of Lyon hospital (France). PATIENTS: Adult patients age greater than or equal to 18 years old and hospitalized greater than or equal to 2 days were included in the surveillance. All hospital-acquired infections (pneumonia, bacteremia, and urinary tract infection) occurring during ICU stay were collected. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The competitive risks of in-hospital death, transfer, or discharge were considered in estimating the change in length of stay due to infection(s), using a multistate model, time of infection onset. Thirty-three thousand four-hundred forty-nine patients were involved, with an overall hospital-acquired infection attack rate of 15.5% (n = 5,176). Mean length of stay was 27.4 (± 18.3) days in patients with hospital-acquired infection and 7.3 (± 7.6) days in patients without hospital-acquired infection. A multistate model-estimated mean found an increase in length of stay by 5.0 days (95% CI, 4.6-5.4 d). The extra length of stay increased with the number of infected site and was higher for patients discharged alive from ICU. No increased length of stay was found for patients presenting late-onset hospital-acquired infection, more than the 25th day after admission. CONCLUSIONS: An increase length of stay of 5 days attributable to hospital-acquired infection in the ICU was estimated using a multistate model in a prospective surveillance study in France. The dose-response relationship between the number of hospitalacquired infection and length of stay and the impact of early-stage hospital-acquired infection may strengthen attention for clinicians to focus interventions on early preventions of hospital-acquired infection in ICU.

Can epidemic detection systems at the hospital level complement regional surveillance networks: case study with the influenza epidemic?

BACKGROUND: Early knowledge of influenza outbreaks in the community allows local hospital healthcare workers to recognise the clinical signs of influenza in hospitalised patients and to apply effective precautions. The objective was to assess intra-hospital surveillance systems to detect earlier than regional surveillance systems influenza outbreaks in the community. METHODS: Time series obtained from computerized medical data from patients who visited a French hospital emergency department (ED) between June 1st, 2007 and March 31st, 2011 for influenza, or were hospitalised for influenza or a respiratory syndrome after an ED visit, were compared to different regional series. Algorithms using CUSUM method were constructed to determine the epidemic detection threshold with the local data series. Sensitivity, specificity and mean timeliness were calculated to assess their performance to detect community outbreaks of influenza. A sensitivity analysis was conducted, excluding the year 2009, due to the particular epidemiological situation related to pandemic influenza this year. RESULTS: The local series closely followed the seasonal trends reported by regional surveillance. The algorithms achieved a sensitivity of detection equal to 100% with series of patients hospitalised with respiratory syndrome (specificity ranging from 31.9 and 92.9% and mean timeliness from -58.3 to 20.3 days) and series of patients who consulted the ED for flu (specificity ranging from 84.3 to 93.2% and mean timeliness from -32.3 to 9.8 days). The algorithm with the best balance between specificity (87.7%) and mean timeliness (0.5 day) was obtained with series built by analysis of the ICD-10 codes assigned by physicians after ED consultation. Excluding the year 2009, the same series keeps the best performance with specificity equal to 95.7% and mean timeliness equal to -1.7 day. CONCLUSIONS: The implementation of an automatic surveillance system to detect patients with influenza or respiratory syndrome from computerized ED records could allow outbreak alerts at the intra-hospital level before the publication of regional data and could accelerate the implementation of preventive transmission-based precautions in hospital settings.

Evaluation of syndromic algorithms for detecting patients with potentially transmissible infectious diseases based on computerised emergency-department data.

BACKGROUND: The objective of this study was to ascertain the performance of syndromic algorithms for the early detection of patients in healthcare facilities who have potentially transmissible infectious diseases, using computerised emergency department (ED) data. METHODS: A retrospective cohort in an 810-bed University of Lyon hospital in France was analysed. Adults who were admitted to the ED and hospitalised between June 1, 2007, and March 31, 2010 were included (N=10895). Different algorithms were built to detect patients with infectious respiratory, cutaneous or gastrointestinal syndromes. The performance parameters of these algorithms were assessed with regard to the capacity of our infection-control team to investigate the detected cases. RESULTS: For respiratory syndromes, the sensitivity of the detection algorithms was 82.70%, and the specificity was 82.37%. For cutaneous syndromes, the sensitivity of the detection algorithms was 78.08%, and the specificity was 95.93%. For gastrointestinal syndromes, the sensitivity of the detection algorithms was 79.41%, and the specificity was 81.97%. CONCLUSIONS: This assessment permitted us to detect patients with potentially transmissible infectious diseases, while striking a reasonable balance between true positives and false positives, for both respiratory and cutaneous syndromes. The algorithms for gastrointestinal syndromes were not specific enough for routine use, because they generated a large number of false positives relative to the number of infected patients. Detection of patients with potentially transmissible infectious diseases will enable us to take precautions to prevent transmission as soon as these patients come in contact with healthcare facilities.

Evaluation study of different strategies for detecting surgical site infections using the hospital information system at Lyon University Hospital, France.

OBJECTIVE: To evaluate different strategies for detecting surgical site infections (SSIs) using different sources (notification by the surgeon, bacteriological results, antibiotic prescription, and discharge diagnosis codes). BACKGROUND: Surveillance plays a role in reducing the risks of SSIs but the performance of case reports by surgeons is insufficient. Indirect methods of SSI detection are an alternative to increase the quality of surveillance. METHODS: A retrospective cohort study of 446 patients operated consecutively during the first half of 2007 was set up in a 56-bed general surgery unit in Lyon University Hospital, France. Patients were followed up 30 days after intervention. Different methods of detection were established by combining different data sources. The sensitivity and specificity of these methods were calculated by using, as reference method, the manual review of the medical records. RESULTS: The sensitivity and specificity of SSI detection were, respectively, 18.4% (95% confidence interval [CI]: 7.9-31.6) and 100% for surgeon notification; 63.2% (95% CI: 47.3-78.9) and 95.1% (95% CI: 92.9-97.1) for detection based on positive cultures; 68.4% (95% CI: 52.6-81.6) and 87.5% (95% CI: 84.3-90.7) using antibiotic prescription; 26.3% (95% CI: 13.2-42.1) and 99.5% (95% CI: 98.8-100) using discharge diagnosis codes. By combining the latter 3 sources, the sensitivity increased at 86.8% (95% CI: 76.3-97.4) and the specificity was lowered at 85.5% (95% CI: 82.1-89.0). CONCLUSIONS: SSI detection based on the combination of data extracted automatically from the hospital information system performed well. This strategy has been implemented gradually in Lyon University Hospital.

Factors associated with admission to intensive care units in COVID-19 patients in Lyon-France.

INTRODUCTION: A new respiratory virus, SARS-CoV-2, has emerged and spread worldwide since late 2019. This study aims at analysing clinical presentation on admission and the determinants associated with admission in intensive care units (ICUs) in hospitalized COVID-19 patients. PATIENTS AND METHODS: In this prospective hospital-based study, socio-demographic, clinical and biological characteristics, on admission, of adult COVID-19 hospitalized patients presenting from the community for their first admission were prospectively collected and analysed. Characteristics of patients hospitalized in medical ward to those admitted in ICU were compared using Mann-Whitney and Chi-square or Fisher exact test when appropriate. Univariate logistic regression was first used to identify variables on admission that were associated with the outcome i.e. admission to an ICU versus total hospital stay in a medical ward. Forward selection was then applied beginning with sex, age and temperature in the multivariable logistic regression model. RESULTS: Of the 412 patients included, 325 were discharged and 87 died in hospital. Multivariable regression showed increasing odds of ICU hospitalization with temperature (OR, 1.56 [95% CI, 1.06-2.28] per degree Celsius increase), oxygen saturation <90% (OR, 12.45 [95% CI, 5.27-29.4]), abnormal lung auscultation on admission (OR, 3.58 [95% CI, 1.58-8.11]), elevated level of CRP (OR, 2.7 [95% CI, 1.29-5.66for CRP>100mg/L vs CRP<10mg/L). and monocytopenia (OR, 3.28 [95% CI, 1.4-7.68]) were also associated with increasing odds of ICU hospitalization. Older patients were less likely to be hospitalized in ICU (OR, 0.17 [95%CI, 0.05-0.51]. CONCLUSIONS: Age and delay between onset of symptoms and hospital admission were associated with the risk of hospitalisation in ICU. Age being a fixed variable, interventions that shorten this delay would improve the prognosis of Covid-19 patients.

Trends in the proportion of resistant bacteria involved in ventilator-associated pneumonia as the first hospital-acquired infection in intensive care units between 2003 and 2016 in Lyon, France.

The aim of this study was to describe the proportion of multidrug-resistant microorganisms (MDROs) involved in ventilator-associated pneumonia (VAP) as the first hospital-acquired infection in 536 adults with restricted risk factors for MDRO-related infection. We found a significant decrease in the percentage of MDROs involved in VAP between 2003 and 2016 and this percentage increased when VAP occurred after day 10.

Determinants of Implementation of Isolation Precautions Against Infections by Multidrug-Resistant Microorganisms: A Hospital-Based, Multicenter, Observational Study.

OBJECTIVES We aimed to ascertain the factors associated with lack of isolation precautions (IP) in patients infected or colonized by third-generation cephalosporin-resistant Enterobacteriaceae (3GCR-E) and methicillin-resistant Staphylococcus aureus (MRSA) in hospital settings. DESIGN Prospective surveillance and audit of practices. SETTING The study included 4 university hospitals in Lyon, France. PARTICIPANTS All patients hospitalized between April and June in 2013 and 2015 were included. Case patients had ≥1 clinical sample positive for MRSA and/or 3GCR-E. METHODS Factors associated with the lack of IP implementation were identified using multivariate logistic regression. The incidence of MDRO infections was expressed per 10,000 patient days. RESULTS Overall, 57,222 patients accounting for 192,234 patient days of hospitalization were included, and 635 (1.1%) MDRO cases were identified. MRSA incidence was 2.5 per 10,000 patient days (95% confidence interval [95% CI], 2.1-3.0) and 3GCR-E incidence was 10.1 per 10,000 patient days (95% CI, 9.2-11.0), with no crude difference between 2013 and 2015 (P=.15 and P=.11, respectively). Among 3GCR-E, the main species were Escherichia coli (43.8%) and Klebsiella pneumoniae (31.0%). Isolation precautions were implemented in 78.5% of cases. Lack of IP implementation was independently associated with patient age, year, specialty, hospital, colonization compared with infection, and lack of medical prescription for IPs (adjusted odds ratio, 17.4; 95% CI, 8.5-35.8; P<.001). CONCLUSIONS MRSA and 3GCR-E infections and/or colonizations are frequent in healthcare settings, and IPs are implemented in most cases. When IPs are lacking, the main factor is the absence of medical prescription for IPs, underscoring the need for alerts to physicians by the microbiological laboratory and/or the infection control team. Infect Control Hosp Epidemiol 2017;38:1188-1195.

Heat map for data visualization in infection control epidemiology: An application describing the relationship between hospital-acquired infections, Simplified Acute Physiological Score II, and length of stay in adult intensive care units.

BACKGROUND: Hospital-acquired infections (HAIs) in intensive care units (ICUs) are associated with increased length of stay (LOS). The objective of this study was to graphically describe by heat mapping LOS of patients hospitalized in ICUs related to the occurrence of HAI and severity at admission measured by the Simplified Acute Physiological Score II (SAPSII). METHODS: Adult patients hospitalized in ICUs of Lyon University Hospitals (France) were included in an active standardized surveillance study of HAI from January 1, 1995-December 31, 2012. Surveillance included adult patients aged ≥18 years hospitalized ≥2 days. Patient follow-up ended at ICU discharge or death. LOS was calculated in days from differences between dates of entry and discharge from ICUs. HAIs recorded were pneumonia, bacteremia, and urinary tract infection. The heat map was designed with a spreadsheet software. RESULTS: A total of 34,694 patients were analyzed. Among infected patients, 72.3% had 1 infected site (IS), 23% had 2 ISs, and 4.7% had 3 ISs. Median LOS was 24 days in infected patients (20.4 days among patients with 1 IS, 34.2 days among patients with 2 ISs, and 45.3 days among patients with 3 ISs) and 5 days in noninfected patients (P < .001). Two groups of multi-infected patients with long LOSs were identified with the heat map. CONCLUSIONS: The heat map facilitated easy-to-implement semi-quantitative visualization of increasing LOS through the SAPSIIs and number of ISs.

Characterization of patients exposed to multiple devices but free of hospital-acquired infection at intensive care unit discharge.

Intensive care unit patients exposed to multiple devices but free of hospital-acquired infection (HAI) until discharge were identified through a surveillance network of HAIs in Lyon, France, between 2003 and 2011. Multiexposed patients were defined according to the tenth deciles of length of stay and exposures to invasive devices. Overall, 982 (5.0%) multiexposed patients were identified; 154 (15.7%) remained uninfected. Multiexposed infected patients differed from noninfected patients regarding length of exposures and mortality.