Five-year evolution of drug prescribing in a university adult intensive care unit

Introduction: Drug prescription is difficult in ICUs as prescribers are many, drugs expensive and decisions complex. In our ICU, specialist clinicians (SC) are entitled to prescribe a list of specific drugs, negotiated with intensive care physicians (ICP). The objective of this investigation was to assess the 5-year evolution of quantity and costs of drug prescription in our adult ICU and identify the relative costs generated by ICP or SC. Methods: Quantities and costs of drugs delivered on a quarterly basis to the adult ICU of our hospital between 2004 and 2008 were extracted from the pharmacy database by ATC code, an international five-level classification system. Within each ATC first level, drugs with either high level of consumption, high costs or large variations in quantities and costs were singled out and split by type of prescriber, ICP or SC. Cost figures used were drug purchase prices by the hospital pharmacy. Results: Over the 5-year period, both quantities and costs of drugs increased, following a nonsteady, nonparallel pattern. Four ATC codes accounted for 80% of both quantities and costs, with ATC code B (blood and haematopoietic organs) amounting to 63% in quantities and 41% in costs, followed by ATC code J (systemic anti-infective, 20% of the costs), ATC code N (nervous system, 11% of the costs) and ATC code C (cardiovascular system, 8% of the costs). Prescription by SC amounted to 1% in drug quantities, but 19% in drug costs. The rate of increase in quantities and costs was seven times larger for ICP than for SC (Figure 1 overleaf ). Some peak values in costs and quantities were related to a very limited number of patients. Conclusions: A 5-year increase in quantities and costs of drug prescription in an ICU is a matter of concern. Rather unexpectedly, total costs and cost increases were generated mainly by ICP. A careful follow-up is necessary to try influencing this evolution through an institutional policy co-opted by all professional categories involved in the process.

Impact of clinical practice guidelines on priorisation for intensive care beds allocation in high-risk acute coronary syndrome patients: does age play a role?

QUESTION UNDER STUDY: To assess which high-risk acute coronary syndrome (ACS) patient characteristics played a role in prioritising access to intensive care unit (ICU), and whether introducing clinical practice guidelines (CPG) explicitly stating ICU admission criteria altered this practice. PATIENTS AND METHODS: All consecutive patients with ACS admitted to our medical emergency centre over 3 months before and after CPG implementation were prospectively assessed. The impact of demographic and clinical characteristics (age, gender, cardiovascular risk factors, and clinical parameters upon admission) on ICU hospitalisation of high-risk patients (defined as retrosternal pain of prolonged duration with ECG changes and/or positive troponin blood level) was studied by logistic regression. RESULTS: Before and after CPG implementation, 328 and 364 patients, respectively, were assessed for suspicion of ACS. Before CPG implementation, 36 of the 81 high-risk patients (44.4%) were admitted to ICU. After CPG implementation, 35 of the 90 high-risk patients (38.9%) were admitted to ICU. Male patients were more frequently admitted to ICU before CPG implementation (OR=7.45, 95% CI 2.10-26.44), but not after (OR=0.73, 95% CI 0.20-2.66). Age played a significant role in both periods (OR=1.57, 95% CI 1.24-1.99), both young and advanced ages significantly reducing ICU admission, but to a lesser extent after CPG implementation. CONCLUSION: Prioritisation of access to ICU for high-risk ACS patients was age-dependent, but focused on the cardiovascular risk factor profile. CPG implementation explicitly stating ICU admission criteria decreased discrimination against women, but other factors are likely to play a role in bed allocation.

Detection of mixed microbial biofilms on central venous catheters removed from Intensive care Unit Patients

Cateteres venosos centrais inseridos em pacientes internados em unidade de terapia intensiva foram avaliados por métodos microbiológicos (cultura semi-quantitativa) e microscopia eletrônica de varredura a fim de detectar adesão microbiana e correlacionar com a cultura de sangue. Durante o período de estudo, foram avaliados 59 pacientes com cateter venoso central. A idade dos pacientes, sexo, sítio de inserção e tempo de permanência do cateter foram anotados. O cateter era de poliuretano não tunelizado e de único lúmen. O sangue para cultura foi coletado no momento da remoção do cateter. de 63 pontas de cateteres, 30 (47,6%) foram colonizadas e a infecção encontrada em 5 (23,8%) cateteres. A infecção foi mais prevalente em 26 pacientes (41,3%) com cateteres inseridos em veia subclávia do que nos 3 (3,2%) inseridos em veia jugular. A infecção foi observada com mais freqüência em cateteres com tempo de permanência maior do que sete dias. Os microrganismos isolados incluíram 32 estafilococos coagulase-negativa (29,7%), 61 bactérias Gram-negativas (52,9%), 9 estafilcocos coagulase-positiva (8,3%) e 3 leveduras (2,7%). Como agentes causais de infecções em unidade de terapia intensiva foram isolados E. aerogenes, P. aeruginosa, A. baumannii. Os antimicrobianos com maior atividade in vitro contra as bactérias Gram-negativas foram o imipenem e contra as Gram-positivas vancomicina, cefepime, penicilina, rifampicina e tetraciclina. As análises por microscopia eletrônica de varredura revelaram biofilmes sobre a superfície de todos os cateteres examinados.

Erythrocyte selenium concentration predicts intensive care unit and hospital mortality in patients with septic shock: a prospective observational study

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Point-of-care prothrombin time testing in paediatric intensive care: an observational study of the ease of use of two devices

Two major difficulties arise when taking blood samples in children: the challenge of venous access and the comparatively large amount of blood required.

Monitoring of emergency and intensive care patients: simple diagnostic aids

This article describes in short sections the use and interpretation of indirect blood pressure measurements, central venous pressure, body temperature, pulse oximetry, end tidal CO2 measurements, pulse and heart rate, urine production and emergency laboratory values. Most of these parameters are directly or indirectly linked to the perfusion of the patient. Optimizing these values are one of the most important goals in emergency and critical care medicine.

Prediction of sepsis in the burn intensive care unit patient

Sepsis is a significant cause for multiple organ failure and death in the burn patient, yet identification in this population is confounded by chronic hypermetabolism and impaired immune function. The purpose of this study was twofold: 1) determine the ability of the systemic inflammatory response syndrome (SIRS) and American Burn Association (ABA) criteria to predict sepsis in the burn patient; and 2) develop a model representing the best combination of clinical predictors associated with sepsis in the same population. A retrospective, case-controlled, within-patient comparison of burn patients admitted to a single intensive care unit (ICU) was conducted for the period January 2005 to September 2010. Blood culture results were paired with clinical condition: "positive-sick"; "negative-sick", and "screening-not sick". Data were collected for the 72 hours prior to each blood culture. The most significant predictors were evaluated using logistic regression, Generalized Estimating Equations (GEE) and ROC area under the curve (AUC) analyses to assess model predictive ability. Bootstrapping methods were employed to evaluate potential model over-fitting. Fifty-nine subjects were included, representing 177 culture periods. SIRS criteria were not found to be associated with culture type, with an average of 98% of subjects meeting criteria in the 3 days prior. ABA sepsis criteria were significantly different among culture type only on the day prior (p = 0.004). The variables identified for the model included: heart rate>130 beats/min, mean blood pressure<60 mmHg, base deficit<-6 mEq/L, temperature>36°C, use of vasoactive medications, and glucose>150 mg/d1. The model was significant in predicting "positive culture-sick" and sepsis state, with AUC of 0.775 (p < 0.001) and 0.714 (p < .001), respectively; comparatively, the ABA criteria AUC was 0.619 (p = 0.028) and 0.597 (p = .035), respectively. SIRS criteria are not appropriate for identifying sepsis in the burn population. The ABA criteria perform better, but only for the day prior to positive blood culture results. The time period useful to diagnose sepsis using clinical criteria may be limited to 24 hours. A combination of predictors is superior to individual variable trends, yet algorithms or computer support will be necessary for the clinician to find such models useful. ^

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

Dysfunction of respiratory muscles in critically ill patients on the intensive care unit.

Muscular weakness and muscle wasting may often be observed in critically ill patients on intensive care units (ICUs) and may present as failure to wean from mechanical ventilation. Importantly, mounting data demonstrate that mechanical ventilation itself may induce progressive dysfunction of the main respiratory muscle, i.e. the diaphragm. The respective condition was termed 'ventilator-induced diaphragmatic dysfunction' (VIDD) and should be distinguished from peripheral muscular weakness as observed in 'ICU-acquired weakness (ICU-AW)'. Interestingly, VIDD and ICU-AW may often be observed in critically ill patients with, e.g. severe sepsis or septic shock, and recent data demonstrate that the pathophysiology of these conditions may overlap. VIDD may mainly be characterized on a histopathological level as disuse muscular atrophy, and data demonstrate increased proteolysis and decreased protein synthesis as important underlying pathomechanisms. However, atrophy alone does not explain the observed loss of muscular force. When, e.g. isolated muscle strips are examined and force is normalized for cross-sectional fibre area, the loss is disproportionally larger than would be expected by atrophy alone. Nevertheless, although the exact molecular pathways for the induction of proteolytic systems remain incompletely understood, data now suggest that VIDD may also be triggered by mechanisms including decreased diaphragmatic blood flow or increased oxidative stress. Here we provide a concise review on the available literature on respiratory muscle weakness and VIDD in the critically ill. Potential underlying pathomechanisms will be discussed before the background of current diagnostic options. Furthermore, we will elucidate and speculate on potential novel future therapeutic avenues.

Fluid shifts have no influence on ciprofloxacin pharmacokinetics in intensive care patients with intra-abdominal sepsis

This study aimed to investigate whether fluid shifts alter ciprofloxacin pharmacokinetics in critically ill patients over time. Patients >= 18 years, with normal renal function, requiring intensive care treatment and parenteral antibiotics were enrolled. Group A (22 patients) included patients with documented intra-abdominal infections. Group B (18 patients) included patients with severe sepsis from other causes. All patients received intravenous ciprofloxacin 400 mg every 8 h infused over 60 min. Eight timed blood specimens were taken on days 0, 2 and 7. Ciprofloxacin plasma concentrations were determined using high performance liquid chromatography. There were no significant differences between the pharmacokinetics of the two groups or over time. Ciprofloxacin pharmacokinetics in critically ill patients do not change over time, and intra-abdominal sepsis does not alter ciprofloxacin pharmacokinetic parameters to a greater degree than sepsis from other causes in critically ill patients. (c) 2005 Elsevier B.V. and the International Society of Chemotherapy. All rights reserved.

Australian and New Zealand practice in intensive care (ANZPIC II: The spectrum of practice in the diagnosis and management of pneumonia in patients requiring mechanical ventilation

This study of ventilated patients investigated current clinical practice in 476 episodes of pneumonia (48% community-acquired pneumonia, 24% hospital-acquired pneumonia, 28% ventilator-associated pneumonia) using a prospective survey in 14 intensive care units (ICUs) within Australia and New Zealand. Diagnostic methods and confidence, disease severity, microbiology and antibiotic use were assessed. All pneumonia types had similar mortality (community-acquired pneumonia 33%, hospital-acquired pneumonia 37% and ventilator-associated pneumonia 24%, P = 0.15) with no inter-hospital differences (P = 0.08-0.91). Bronchoscopy was performed in 26%, its use predicted by admission hospital (one tertiary: OR 9.98, CI 95% 5.11-19.49, P < 0.001; one regional: OR 629, CI 95% 3.24-12.20, P < 0.001), clinical signs of consolidation (OR 3.72, CI 95% 2.09-662, P < 0.001) and diagnostic confidence (OR 2.19, CI 95% 1.29-3.72, P = 0.004). Bronchoscopy did not predict outcome (P = 0.11) or appropriate antibiotic selection (P = 0.69). Inappropriate antibiotic prescription was similar for all pneumonia types (11-13%, P = 0.12) and hospitals (0-16%, P = 0.25). Blood cultures were taken in 51% of cases. For community-acquired pneumonia, 70% received a third generation cephalosporin and 65% a macrolide. Third generation cephalosporins were less frequently used for mild infections (OR 0.38, CI 95% 0.16-0.90, P = 0.03), hospital-acquired pneumonia (OR 0.40, CI 95% 0.23-0.72, P < 0.01), ventilator-associated pneumonia (OR 0.04, CI 95% 0.02-0.13, P < 0.001), suspected aspiration (OR 0.20, CI 95% 0.04-0.92, P = 0.04), in one regional (OR 0.26, CI 95% 0.07-0.97, P = 0.05) and one tertiary hospital (OR 0.14, CI 95% 0.03-0.73, P = 0.02) but were more commonly used in older patients (OR 1.02, CI 95% 1.01-1.03, P = 0.01). There is practice variability in bronchoscopy and antibiotic use for pneumonia in Australian and New Zealand ICUs without significant impact on patient outcome, as the prevalence of inappropriate antibiotic prescription is low. There are opportunities for improving microbiological diagnostic work-up for isolation of aetiological pathogens.

The costs of caring for patients in a tertiary referral Australian intensive care unit

We determined the direct cost of an Intensive Care Unit (ICU) bed in a tertiary referral Australian ICU and the cost drivers thereof, by retrospectively analysing a number of prospectively designed Hospital- and Unit-specific electronic databases. The study period was a financial year, from 1 July 2002 to 30 June 2003. There were 1615 patients occupying 5692 fractional occupied bed days at a total cost of A$15,915,964, with an average length of stay of 3.69 days (range 0.5-77, median 1.06, interquartile range 2.33). The main cost driver not incorporated into this analysis was blood products (paid for centrally). The average costs of an ICU day and total stay per patient were A$2670 and A$9852 respectively. Staff-related charges were 68.76%, with consumables related expenditure making up 19.65%, clinical support services 9.55% and capital equipment 2.04%. Overtime charges and nursing agency staff were 19.4% of staff-related charges (2.9% for agency staff), 3.9% lower than expenditure associated with full-time employment charges, such as pension and leave. The emergency nature of ICU means it is difficult to accurately set a nursing establishment to cater for all admissions and therefore it is hard to decide what is an acceptable percentage difference between agency/overtime costs compared with the costs associated with full-time staff appointments. Consumable expenditure is likely to increase the most with new innovation and therapies. Using protocol driven practices may tighten and control costs incurred in ICU.

Antibiotic resistance in the intensive care unit: A primer in bacteriology

The clinical use of potent, well-tolerated, broad-spectrum antibiotics has been paralleled by the development of resistance in bacteria, and the prevalence of highly resistant bacteria in some intensive care units is despairingly commonplace. The intensive care community faces the realistic prospect of untreatable nosocomial infections and should be searching for new approaches to diagnose and manage resistant bacteria. In this review, we discuss some of the relevant underlying biology, with a particular focus on genetic transfer vehicles and the relationship of selection pressure to their movements. It is an attempt to demystify the relevant language and concepts for the anaesthetist and intensivist, to explain some of the reasons for the emergence of resistance in bacteria, and to provide a contextual basis for discussion of management approaches such as selective decontamination and antibiotic cycling.

Sedation in paediatric intensive care

Paediatric intensive care is an expanding specialty that has been shown to improve the quality of care provided to critically ill children. An important aspect of the management of critically ill children includes the provision of effective sedation to reduce stress and anxiety during their stay in intensive care. However, to achieve effective and safe sedation in these children, is recognised as a challenge that is not without risk. Often children receive too much or too little sedation resulting in over sedation or under sedation respectively. These problems have arisen owing to a lack of information regarding altered pharmacokinetics and pharmacodynamics of medicines administered to critically ill children. In addition there are few validated sedation scoring systems in practice with which to monitor level of sedation and titrate medication appropriately. This study consisted of two stages. Stage 1 investigated the reproducibility and practicality of two observational sedation assessment scales for use in critically ill children. The two scales were different in design, the first being simple in design requiring a single assessment of the patient. The second was more complex in design requiring assessment of five patient parameters to obtain an overall sedation score. Both scales were found to achieve good reproducibility (kappa values 0.50 and 0.62 respectively). Practicality of each sedation scale was undertaken by obtaining nursing staff opinion about both scales using questionnaire and interview technique. It was established that nursing staff preferred the second, more complex sedation scale mainly because it was perceived to give a more accurate assessment of level of sedation and anxiety rather than merely level of sedation. Stage 2 investigated the pharmacokinetics and pharmacodynamics of midazolam in critically ill children. 52 children, aged between 0 and 18 years were recruited to the study and 303 blood samples taken to analyse midazolam and its metabolites, I-hydroxyrnidazolam (I-OR) and 4-hydroxymidazolam (4-0H). Analysis of plasma was undertaken using high performance liquid chromatography. A significant correlation was found between midazolam plasma concentration and sedative effect (r=0.598, p=O.OI). It was found that a midazolam plasma concentration of 223ng/ml (±31.9) achieved a satisfactory level of sedation. Only a poor correlation was found between dose of midazolam and plasma concentration of midazolam. Similarly only a poor correlation was found between sedative effect and dose of midazolam. Clearance of midazolam was found to be 6.3mllkglmin (±0.36), which is lower than that reported in healthy children (9.Il-13.3mllkg/min). Age related differences in midazolam clearance were observed in the study. Neonates produced the lowest clearance values (l.63mllkg/min), compared to children aged 1 to 12 months (8.52mllkg/min) who achieved the highest clearance values. Clearance was found to decrease after the age of 12 months to values of 5.34mllkglmin in children aged 7 years and above. Patients with renal (n=5) and liver impairment (n~4) were found to have reduced midazolam clearance (1.37 and 0.74ml/kg/min respectively). Plasma concentrations of I-OH and 4-0H ranged from 0-5 1 89nglml and 0-27 Inglml respectively. All children were found to be capable of producing both metabolites irrespective of age, although no trend was established between age and extent of production of either metabolite. Disease state was found to affect production of l-OH. Patients with renal impairment (n=5) produced the lowest I-OH midazolam plasma ratio (0.059) compared to patients with head injury (0.858). Patients with severe liver impairment were found to be capable of manufacturing both metabolites despite having a severely damaged liver.

Risk Factors of Central Venous Catheter Related Infections in Intensive Care Patients

2002 Mathematics Subject Classification: 62P10.