Characterizing the Risk Profiles of Intensive Care Units

OBJECTIVE: To develop a new method to evaluate the performance of individual ICUs through the calculation and visualisation of risk profiles. METHODS: The study included 102,561 patients consecutively admitted to 77 ICUs in Austria. We customized the function which predicts hospital mortality (using SAPS II) for each ICU. We then compared the risks of hospital mortality resulting from this function with the risks which would be obtained using the original function. The derived risk ratio was then plotted together with point-wise confidence intervals in order to visualise the individual risk profile of each ICU over the whole spectrum of expected hospital mortality. MAIN MEASUREMENTS AND RESULTS: We calculated risk profiles for all ICUs in the ASDI data set according to the proposed method. We show examples how the clinical performance of ICUs may depend on the severity of illness of their patients. Both the distribution of the Hosmer-Lemeshow goodness-of-fit test statistics and the histogram of the corresponding P values demonstrated a good fit of the individual risk models. CONCLUSIONS: Our risk profile model makes it possible to evaluate ICUs on the basis of the specific risk for patients to die compared to a reference sample over the whole spectrum of hospital mortality. Thus, ICUs at different levels of severity of illness can be directly compared, giving a clear advantage over the use of the conventional single point estimate of the overall observed-to-expected mortality ratio.

Methicillin-resistant Staphylococcus aureus bloodstream infection: risk factors and clinical outcome in non-intensive-care units

INTRODUCTION: Methicillin-resistant Staphylococcus aureus (MRSA) is spread out in hospitals across different regions of the world and is regarded as the major agent of nosocomial infections, causing infections such as skin and soft tissue pneumonia and sepsis. The aim of this study was to identify risk factors for methicillin-resistance in Staphylococcus aureus bloodstream infection (BSI) and the predictive factors for death. METHODS: A retrospective cohort of fifty-one patients presenting bacteraemia due to S. aureus between September 2006 and September 2008 was analysed. Staphylococcu aureus samples were obtained from blood cultures performed by clinical hospital microbiology laboratory from the Uberlândia Federal University. Methicillinresistance was determined by growth on oxacillin screen agar and antimicrobial susceptibility by means of the disk diffusion method. RESULTS: We found similar numbers of MRSA (56.8%) and methicillin-susceptible Staphylococcus aureus (MSSA) (43.2%) infections, and the overall hospital mortality ratio was 47%, predominantly in MRSA group (70.8% vs. 29.2%) (p=0.05). Age (p=0.02) was significantly higher in MRSA patients as also was the use of central venous catheter (p=0.02). The use of two or more antimicrobial agents (p=0.03) and the length of hospital stay prior to bacteraemia superior to seven days (p=0.006) were associated with mortality. High odds ratio value was observed in cardiopathy as comorbidity. CONCLUSIONS: Despite several risk factors associated with MRSA and MSSA infection, the use of two or more antimicrobial agents was the unique independent variable associated with mortality.

Prevalence rates of infection in intensive care units of a tertiary teaching hospital

OBJECTIVE: To determine the prevalence rates of infections among intensive care unit patients, the predominant infecting organisms, and their resistance patterns. To identify the related factors for intensive care unit-acquired infection and mortality rates. DESIGN: A 1-day point-prevalence study. SETTING:A total of 19 intensive care units at the Hospital das Clínicas - University of São Paulo, School of Medicine (HC-FMUSP), a teaching and tertiary hospital, were eligible to participate in the study. PATIENTS: All patients over 16 years old occupying an intensive care unit bed over a 24-hour period. The 19 intensive care unit s provided 126 patient case reports. MAIN OUTCOME MEASURES: Rates of infection, antimicrobial use, microbiological isolates resistance patterns, potential related factors for intensive care unit-acquired infection, and death rates. RESULTS: A total of 126 patients were studied. Eighty-seven patients (69%) received antimicrobials on the day of study, 72 (57%) for treatment, and 15 (12%) for prophylaxis. Community-acquired infection occurred in 15 patients (20.8%), non- intensive care unit nosocomial infection in 24 (33.3%), and intensive care unit-acquired infection in 22 patients (30.6%). Eleven patients (15.3%) had no defined type. The most frequently reported infections were respiratory (58.5%). The most frequently isolated bacteria were Enterobacteriaceae (33.8%), Pseudomonas aeruginosa (26.4%), and Staphylococcus aureus (16.9%; [100% resistant to methicillin]). Multivariate regression analysis revealed 3 risk factors for intensive care unit-acquired infection: age > 60 years (p = 0.007), use of a nasogastric tube (p = 0.017), and postoperative status (p = 0.017). At the end of 4 weeks, overall mortality was 28.8%. Patients with infection had a mortality rate of 34.7%. There was no difference between mortality rates for infected and noninfected patients (p=0.088). CONCLUSION: The rate of nosocomial infection is high in intensive care unit patients, especially for respiratory infections. The predominant bacteria were Enterobacteriaceae, Pseudomonas aeruginosa, and Staphylococcus aureus (resistant organisms). Factors such as nasogastric intubation, postoperative status, and age ³60 years were significantly associated with infection. This study documents the clinical impression that prevalence rates of intensive care unit-acquired infections are high and suggests that preventive measures are important for reducing the occurrence of infection in critically ill patients.

Step towards a patient timeline in intensive care units

In Intensive Medicine, the presentation of medical information is done in many ways, depending on the type of data collected and stored. The way in which the information is presented can make it difficult for intensivists to quickly understand the patient's condition. When there is the need to cross between several types of clinical data sources the situation is even worse. This research seeks to explore a new way of presenting information about patients, based on the timeframe in which events occur. By developing an interactive Patient Timeline, intensivists will have access to a new environment in real-time where they can consult the patient clinical history and the data collected until the moment. The medical history will be available from the moment in which patients is admitted in the ICU until discharge, allowing intensivist to examine data regarding vital signs, medication, exams, among others. This timeline also intends to, through the use of information and models produced by the INTCare system, combine several clinical data in order to help diagnose the future patients’ conditions. This platform will help intensivists to make more accurate decision. This paper presents the first approach of the solution designed

Intelligent decision support to predict patient barotrauma risk in intensive care units

The occurrence of Barotrauma is identified as a major concern for health professionals, since it can be fatal for patients. In order to support the decision process and to predict the risk of occurring barotrauma Data Mining models were induced. Based on this principle, the present study addresses the Data Mining process aiming to provide hourly probability of a patient has Barotrauma. The process of discovering implicit knowledge in data collected from Intensive Care Units patientswas achieved through the standard process Cross Industry Standard Process for Data Mining. With the goal of making predictions according to the classification approach they several DM techniques were selected: Decision Trees, Naive Bayes and Support Vector Machine. The study was focused on identifying the validity and viability to predict a composite variable. To predict the Barotrauma two classes were created: “risk” and “no risk”. Such target come from combining two variables: Plateau Pressure and PCO2. The best models presented a sensitivity between 96.19% and 100%. In terms of accuracy the values varied between 87.5% and 100%. This study and the achieved results demonstrated the feasibility of predicting the risk of a patient having Barotrauma by presenting the probability associated.

Pervasive patient timeline for intensive care units

This research work explores a new way of presenting and representing information about patients in critical care, which is the use of a timeline to display information. This is accomplished with the development of an interactive Pervasive Patient Timeline able to give to the intensivists an access in real-time to an environment containing patients clinical information from the moment in which the patients are admitted in the Intensive Care Unit (ICU) until their discharge This solution allows the intensivists to analyse data regarding vital signs, medication, exams, data mining predictions, among others. Due to the pervasive features, intensivists can have access to the timeline anywhere and anytime, allowing them to make decisions when they need to be made. This platform is patient-centred and is prepared to support the decision process allowing the intensivists to provide better care to patients due the inclusion of clinical forecasts.

Optimization techniques to detect early ventilation extubation in intensive care units

The decision support models in intensive care units are developed to support medical staff in their decision making process. However, the optimization of these models is particularly difficult to apply due to dynamic, complex and multidisciplinary nature. Thus, there is a constant research and development of new algorithms capable of extracting knowledge from large volumes of data, in order to obtain better predictive results than the current algorithms. To test the optimization techniques a case study with real data provided by INTCare project was explored. This data is concerning to extubation cases. In this dataset, several models like Evolutionary Fuzzy Rule Learning, Lazy Learning, Decision Trees and many others were analysed in order to detect early extubation. The hydrids Decision Trees Genetic Algorithm, Supervised Classifier System and KNNAdaptive obtained the most accurate rate 93.2%, 93.1%, 92.97% respectively, thus showing their feasibility to work in a real environment.

A prospective randomised multi-centre controlled trial on tight glucose control by intensive insulin therapy in adult intensive care units: the Glucontrol study.

PURPOSE: An optimal target for glucose control in ICU patients remains unclear. This prospective randomized controlled trial compared the effects on ICU mortality of intensive insulin therapy (IIT) with an intermediate glucose control. METHODS: Adult patients admitted to the 21 participating medico-surgical ICUs were randomized to group 1 (target BG 7.8-10.0 mmol/L) or to group 2 (target BG 4.4-6.1 mmol/L). RESULTS: While the required sample size was 1,750 per group, the trial was stopped early due to a high rate of unintended protocol violations. From 1,101 admissions, the outcomes of 542 patients assigned to group 1 and 536 of group 2 were analysed. The groups were well balanced. BG levels averaged in group 1 8.0 mmol/L (IQR 7.1-9.0) (median of all values) and 7.7 mmol/L (IQR 6.7-8.8) (median of morning BG) versus 6.5 mmol/L (IQR 6.0-7.2) and 6.1 mmol/L (IQR 5.5-6.8) for group 2 (p < 0.0001 for both comparisons). The percentage of patients treated with insulin averaged 66.2 and 96.3%, respectively. Proportion of time spent in target BG was similar, averaging 39.5% and 45.1% (median (IQR) 34.3 (18.5-50.0) and 39.3 (26.2-53.6)%) in the groups 1 and 2, respectively. The rate of hypoglycaemia was higher in the group 2 (8.7%) than in group 1 (2.7%, p < 0.0001). ICU mortality was similar in the two groups (15.3 vs. 17.2%). CONCLUSIONS: In this prematurely stopped and therefore underpowered study, there was a lack of clinical benefit of intensive insulin therapy (target 4.4-6.1 mmol/L), associated with an increased incidence of hypoglycaemia, as compared to a 7.8-10.0 mmol/L target. (ClinicalTrials.gov # NCT00107601, EUDRA-CT Number: 200400391440).

Estimating attributable mortality due to nosocomial infections acquired in intensive care units.

BACKGROUND: The strength of the association between intensive care unit (ICU)-acquired nosocomial infections (NIs) and mortality might differ according to the methodological approach taken. OBJECTIVE: To assess the association between ICU-acquired NIs and mortality using the concept of population-attributable fraction (PAF) for patient deaths caused by ICU-acquired NIs in a large cohort of critically ill patients. SETTING: Eleven ICUs of a French university hospital. DESIGN: We analyzed surveillance data on ICU-acquired NIs collected prospectively during the period from 1995 through 2003. The primary outcome was mortality from ICU-acquired NI stratified by site of infection. A matched-pair, case-control study was performed. Each patient who died before ICU discharge was defined as a case patient, and each patient who survived to ICU discharge was defined as a control patient. The PAF was calculated after adjustment for confounders by use of conditional logistic regression analysis. RESULTS: Among 8,068 ICU patients, a total of 1,725 deceased patients were successfully matched with 1,725 control patients. The adjusted PAF due to ICU-acquired NI for patients who died before ICU discharge was 14.6% (95% confidence interval [CI], 14.4%-14.8%). Stratified by the type of infection, the PAF was 6.1% (95% CI, 5.7%-6.5%) for pulmonary infection, 3.2% (95% CI, 2.8%-3.5%) for central venous catheter infection, 1.7% (95% CI, 0.9%-2.5%) for bloodstream infection, and 0.0% (95% CI, -0.4% to 0.4%) for urinary tract infection. CONCLUSIONS: ICU-acquired NI had an important effect on mortality. However, the statistical association between ICU-acquired NI and mortality tended to be less pronounced in findings based on the PAF than in study findings based on estimates of relative risk. Therefore, the choice of methods does matter when the burden of NI needs to be assessed.

Early-onset ventilator-associated pneumonia incidence in intensive care units: a surveillance-based study.

ABSTRACT: BACKGROUND: The incidence of ventilator-associated pneumonia (VAP) within the first 48 hours of intensive care unit (ICU) stay has been poorly investigated. The objective was to estimate early-onset VAP occurrence in ICUs within 48 hours after admission. METHODS: We analyzed data from prospective surveillance between 01/01/2001 and 31/12/2009 in 11 ICUs of Lyon hospitals (France). The inclusion criteria were: first ICU admission, not hospitalized before admission, invasive mechanical ventilation during first ICU day, free of antibiotics at admission, and ICU stay >=48 hours. VAP was defined according to a national protocol. Its incidence was the number of events per 1,000 invasive mechanical ventilation-days. The Poisson regression model was fitted from day 2 (D2) to D8 to incident VAP to estimate the expected VAP incidence from D0 to D1 of ICU stay. RESULTS: Totally, 367 (10.8%) of 3,387 patients in 45,760 patient-days developed VAP within the first 9 days. The predicted cumulative VAP incidence at D0 and D1 was 5.3 (2.6-9.8) and 8.3 (6.1-11.1), respectively. The predicted cumulative VAP incidence was 23.0 (20.8-25.3) at D8. The proportion of missed VAP within 48 hours from admission was 11% (9%-17%). CONCLUSIONS: Our study indicates underestimation of early-onset VAP incidence in ICUs, if only VAP occurring [greater than or equal to]48 hours is considered to be hospital-acquired. Clinicians should be encouraged to develop a strategy for early detection after ICU admission.

Colonization with multiple Staphylococcus aureus strains among patients in European intensive care units.

Patients and healthy individuals intermittently and inconsistently carry different methicillin-resistant Staphylococcus aureus (MRSA) subtypes. In the present study, we assessed the clonality of methicillin-susceptible S. aureus (MSSA) and MRSA strains in patients admitted to 1 of 6 intensive care units (ICUs), using spa typing and multilocus variable number of tandem repeats analysis (MLVA).

Acquisition and cross-transmission of Staphylococcus aureus in European intensive care units

Objective. To study the acquisition and cross-transmission of Staphylococcus aureus in different intensive care units (ICUs). Methods. We performed a multicenter cohort study. Six ICUs in 6 countries participated. During a 3-month period at each ICU, all patients had nasal and perineal swab specimens obtained at ICU admission and during their stay. All S. aureus isolates that were collected were genotyped by spa typing and multilocus variable-number tandem-repeat analysis typing for cross-transmission analysis. A total of 629 patients were admitted to ICUs, and 224 of these patients were found to be colonized with S. aureus at least once during ICU stay (22% were found to be colonized with methicillin-resistant S. aureus [MRSA]). A total of 316 patients who had test results negative for S. aureus at ICU admission and had at least 1 follow-up swab sample obtained for culture were eligible for acquisition analysis. Results. A total of 45 patients acquired S. aureus during ICU stay (31 acquired methicillin-susceptible S. aureus [MSSA], and 14 acquired MRSA). Several factors that were believed to affect the rate of acquisition of S. aureus were analyzed in univariate and multivariate analyses, including the amount of hand disinfectant used, colonization pressure, number of beds per nurse, antibiotic use, length of stay, and ICU setting (private room versus open ICU treatment). Greater colonization pressure and a greater number of beds per nurse correlated with a higher rate of acquisition for both MSSA and MRSA. The type of ICU setting was related to MRSA acquisition only, and the amount of hand disinfectant used was related to MSSA acquisition only. In 18 (40%) of the cases of S. aureus acquisition, cross-transmission from another patient was possible. Conclusions. Colonization pressure, the number of beds per nurse, and the treatment of all patients in private rooms correlated with the number of S. aureus acquisitions on an ICU. The amount of hand disinfectant used was correlated with the number of cases of MSSA acquisition but not with the number of cases of MRSA acquisition. The number of cases of patient-to-patient cross-transmission was comparable for MSSA and MRSA.