Individual Intensive Care Units Research Articles

A Single Hospital-Wide Antibiogram is Insufficient to Account for Differences in Antibiotic Resistance Patterns Across Multiple ICUs.

Infection is a common cause of mortality within intensive care units (ICUs). Antibiotic resistance patterns and culture data are used to create antibiograms. Knowledge of antibiograms facilitates guiding empiric therapies and reduces mortality. Most major hospitals utilize data collection to create hospital-wide antibiograms. Previous studies have shown significant differences in susceptibility patterns between hospital wards and ICUs. We hypothesize that institutional or combined ICU antibiograms are inadequate to account for differences in susceptibility for patients in individual ICUs. Culture and susceptibility data were reviewed over a 1-year period for 13 bacteria in the following ICUs: Surgical/Trauma, Medical, Neuroscience, Burn, and Emergency department. Antibiotic management decisions are made by individual teams. Nine species had sufficient data for inclusion into an All-ICU antibiogram. E coli and S aureus were the most common isolates. Seven species had significant differences in susceptibility patterns between ICUs. E cloacae showed higher rates of resistance to multiple antibiotics in the STICU than other ICUs. P aeruginosa susceptibility rates in the NSICU and BICU were 88% and 92%, respectively, compared to 60% and 55% in the STICU and MICU. Cephalosporins and Aztreonam had reduced efficacy against E coli in the NSICU, however remain effective in other ICUs. The results of this study show that different ICUs do have variability in antibiotic susceptibility patterns within a single hospital. While this only represents a single institution, it shows that the use of hospital-wide antibiograms is inadequate for creating empiric antibiotic protocols within individual ICUs.

A pharmacogenetic precision medicine approach to analgesia and sedation optimization in critically ill adults.

Precision medicine is a growing field in critical care. Research increasingly demonstrated pharmacogenomic variability to be an important determinant of analgesic and sedative drug response in the intensive care unit (ICU). Genome-wide association and candidate gene finding studies suggest analgesic and sedatives tailored to an individual's genetic makeup, environmental adaptations, in addition to several other patient- and drug-related factors, will maximize effectiveness and help mitigate harm. However, the number of pharmacogenetic studies in ICU patients remains small and no prospective studies have been published using pharmacogenomic data to optimize analgesic or sedative therapy in critically ill patients. Current recommendations for treating ICU pain and agitation are based on controlled studies having low external validity, including the failure to consider pharmacogenomic factors affecting response. Use of a precision medicine approach to individualize pharmacotherapy focused on optimizing ICU patient comfort and safety may improve the outcomes of critically ill adults. Additionally, benefits and risks of analgesic and/or sedative therapy in an individual may be informed with large, standardized datasets. The purpose of this review was to describe a precision medicine approach focused on optimizing analgesic and sedative therapy in individual ICU patients to optimize clinical outcomes and reduce safety concerns.

Discrete-time survival analysis in the critically ill: a deep learning approach using heterogeneous data

Prediction of survival for patients in intensive care units (ICUs) has been subject to intense research. However, no models exist that embrace the multiverse of data in ICUs. It is an open question whether deep learning methods using automated data integration with minimal pre-processing of mixed data domains such as free text, medical history and high-frequency data can provide discrete-time survival estimates for individual ICU patients. We trained a deep learning model on data from patients admitted to ten ICUs in the Capital Region of Denmark and the Region of Southern Denmark between 2011 and 2018. Inspired by natural language processing we mapped the electronic patient record data to an embedded representation and fed the data to a recurrent neural network with a multi-label output layer representing the chance of survival at different follow-up times. We evaluated the performance using the time-dependent concordance index. In addition, we quantified and visualized the drivers of survival predictions using the SHAP methodology. We included 37,355 admissions of 29,417 patients in our study. Our deep learning models outperformed traditional Cox proportional-hazard models with concordance index in the ranges 0.72–0.73, 0.71–0.72, 0.71, and 0.69–0.70, for models applied at baseline 0, 24, 48, and 72 h, respectively. Deep learning models based on a combination of entity embeddings and survival modelling is a feasible approach to obtain individualized survival estimates in data-rich settings such as the ICU. The interpretable nature of the models enables us to understand the impact of the different data domains.

Characterizing intensive care unit rounding teams using meta-data from the electronic health record

PurposeTeamwork is an important determinant of outcomes in the intensive care unit (ICU), yet the nature of individual ICU teams remains poorly understood. We examined whether meta-data in the form of digital signatures in the electronic health record (EHR) could be used to identify and characterize ICU teams. MethodsWe analyzed EHR data from 27 ICUs over one year. We linked intensivist physicians, nurses, and respiratory therapists to individual patients based on selected EHR meta-data. We then characterized ICU teams by their members' overall past experience and shared past experience; and used network analysis to characterize ICUs by their network's density and centralization. ResultsWe identified 2327 unique providers and 30,892 unique care teams. Teams varied based on their average team member experience (median and total range: 262.2 shifts, 9.0–706.3) and average shared experience (median and total range: 13.2 shared shifts, 1.0–99.3). ICUs varied based on their network's density (median and total range: 0.12, 0.07–0.23), degree centralization (0.50, 0.35–0.65) and closeness centralization (0.45, 0.11–0.60). In a regression analysis, this variation was only partially explained by readily observable ICU characteristics. ConclusionsEHR meta-data can assist in the characterization of ICU teams, potentially providing novel insight into strategies to measure and improve team function in critical care.

Comparison of the Beacon and Quark indirect calorimetry devices to measure resting energy expenditure in ventilated ICU patients

Critically ill patients in the Intensive Care Unit (ICU) should receive nutritional support matched to their metabolic needs as both under- and overfeeding energy has been shown to increase mortality. Critical illness can significantly affect metabolism. Consequently, resting energy expenditure (REE) can vary markedly during critical illness. Therefore, indirect calorimetry to estimate REE is recommended to determine energy requirements in individual ICU patients and to guide optimal nutritional support. Currently, the Quark metabolic monitor is considered the gold standard in our ICU, but novel mechanical support devices are also equipped with indirect calorimetry functionalities. This study aimed to evaluate the performance of a currently unevaluated device. A cross-sectional analysis in mechanically ventilated patients was conducted in a mixed medical-surgical ICU. The primary outcome was a numerical and visual comparison of the performance of the Beacon indirect calorimeter to calculate REE compared to the Quark device using Bland Altman plots. Performance was evaluated using bias, precision, accuracy, and reliability. Secondary analysis included a comparison with REE estimated by predictive equations. Seventy-one measurements were obtained in 27 mechanically ventilated subjects. An underestimation by the Beacon device in calculated REE of-96.2kcal/day (4.5%) was found. There was a bias towards higher VCO2 and lower VO2 values with Beacon as compared to Quark. The reliability of the Beacon was good, with an absolute intraclass correlation coefficient of 0.897 (95%CI 0.751-0.955; p=0.000). There was a poor correlation (<0.40) between the separate indirect calorimetry devices and most predictive equations. Only the Faisy predictive equations had good reliability (ICC 0.687, p=0.002). Beacon indirect calorimetry accurately determined REE in mechanically ventilated critically ill patients compared to the gold standard in our ICU (Quark indirect calorimeter), although confidence intervals were wide. There was low bias and good reliability. On the other hand, predictive equations performed poorly compared to both devices, underestimating the true metabolic needs of mechanically ventilated ICU patients.

Are There Bad ICU Rooms? Temporal Relationship between Patient and ICU Room Microbiome, and Influence on Vancomycin-Resistant Enterococcus Colonization.

ABSTRACTThe gut microbiome of an individual can shape the local environmental surface microbiome. We sought to determine how the intensive care unit (ICU) patient gut microbiome shapes the ICU room surface microbiome, focusing on vancomycin-resistant Enterococcus (VRE), a common ICU pathogen. This was an ICU-based prospective cohort study. Rectal swabs were performed in adult ICU patients immediately at the time of ICU admission and environmental surface swabs were performed at five predetermined time points. All swabs underwent 16S rRNA gene sequencing and culture for VRE. 304 ICU patients and 24 ICU rooms were sampled (5 longitudinal samples per ICU room). Spatially adjacent ICU rooms were no more microbially similar than nonadjacent rooms. Microbial signatures within rooms diverged rapidly over time: in 14 days, ICU rooms were as similar to other ICU rooms as they were to their prior selves. This divergence over time was more pronounced in rooms with higher patient turnover. Examining VRE status by culture, patient VRE gut colonization had modest agreement with room surface VRE (kappa statistic 0.36). There were no ICU rooms that consistently cultured positive for VRE, including those that housed VRE positive patients. Individual ICU patients had a limited impact on ICU room surface microbiome, and rooms diverged similarly over time regardless of patients. Patient VRE gut colonization may have a modest influence on room surface VRE but there were no “bad rooms” that consistently cultured positive for VRE. These results may be useful in planning infection control measures.IMPORTANCE This study found that intensive care unit (ICU) room microbial signatures diverged from their baseline quickly: within 2 weeks, individual ICU rooms had lost distinguishing characteristics and were as similar to other ICU rooms as they were to their former selves. Patient turnover within rooms accelerated this drift. Patient gut colonization with vancomycin-resistant Enterococcus (VRE) was associated with ICU room surface contamination with VRE; again, within 2 weeks, this association was substantially diminished. These results provide dynamic information regarding how patients control the microbiota on local hospital room surfaces and may facilitate decision making for infection prevention and control measures targeting VRE or other organisms.

Association of patient-to-intensivist ratio with hospital mortality in Australia and New Zealand

PurposeThe impact of intensivist workload on intensive care unit (ICU) outcomes is incompletely described and assessed across healthcare systems and countries. We sought to examine the association of patient-to-intensivist ratio (PIR) with hospital mortality in Australia/New Zealand (ANZ) ICUs.MethodsWe conducted a retrospective study of adult admissions to ANZ ICUs (August 2016–June 2018) using two cohorts: “narrow”, based on previously used criteria including restriction to ICUs with a single daytime intensivist; and “broad”, refined by individual ICU daytime staffing information. The exposure was average daily PIR and the outcome was hospital mortality. We used summary statistics to describe both cohorts and multilevel multivariable logistic regression models to assess the association of PIR with mortality. In each, PIR was modeled using restricted cubic splines to allow for non-linear associations. The broad cohort model included non-PIR physician and non-physician staffing covariables.ResultsThe narrow cohort of 27,380 patients across 67 ICUs (predicted mortality: median 1.2% [IQR 0.4–1.4%]; mean 5.9% [sd 13.2%]) had a median PIR of 10.1 (IQR 7–14). The broad cohort of 91,206 patients across 73 ICUs (predicted mortality: 1.9% [0.6–6.5%]; 7.6% [14.9%]) had a median PIR of 7.8 (IQR 5.8–10.2). We found no association of PIR with mortality in either the narrow (PIR 1st spline term odds ratio [95% CI]: 1 [0.94, 1.06], Wald testing of spline terms p = 0.61) or the broad (1.02 [0.97, 1.07], p = 0.4) cohort.ConclusionWe found no association of PIR with hospital mortality across ANZ ICUs. The low cohort predicted mortality may limit external validity.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00134-021-06575-z.

Variation in severity-adjusted resource use and outcome in intensive care units

PurposeIntensive care patients have increased risk of death and their care is expensive. We investigated whether risk-adjusted mortality and resources used to achieve survivors change over time and if their variation is associated with variables related to intensive care unit (ICU) organization and structure.MethodsData of 207,131 patients treated in 2008–2017 in 21 ICUs in Finland, Estonia and Switzerland were extracted from a benchmarking database. Resource use was measured using ICU length of stay, daily Therapeutic Intervention Scoring System Scores (TISS) and purchasing power parity-adjusted direct costs (2015–2017; 17 ICUs). The ratio of observed to severity-adjusted expected resource use (standardized resource use ratio; SRUR) was calculated. The number of expected survivors and the ratio of observed to expected mortality (standardized mortality ratio; SMR) was based on a mortality prediction model covering 2015–2017. Fourteen a priori variables reflecting structure and organization were used as explanatory variables for SRURs in multivariable models.ResultsSMR decreased over time, whereas SRUR remained unchanged, except for decreased TISS-based SRUR. Direct costs of one ICU day, TISS score and ICU admission varied between ICUs 2.5–5-fold. Differences between individual ICUs in both SRUR and SMR were up to > 3-fold, and their evolution was highly variable, without clear association between SRUR and SMR. High patient turnover was consistently associated with low SRUR but not with SMR.ConclusionThe wide and independent variation in both SMR and SRUR suggests that they should be used together to compare the performance of different ICUs or an individual ICU over time.

The association of the COVID-19 pandemic and short-term outcomes of non-COVID-19 critically ill patients: an observational cohort study in Brazilian ICUs

PurposeTo assess whether intensive care unit (ICU) outcomes for patients not affected by coronavirus disease 2019 (COVID-19) worsened during the COVID-19 pandemic.MethodsRetrospective cohort study including prospectively collected information of patients admitted to 165 ICUs in a hospital network in Brazil between 2011 and 2020. Association between admission in 2020 and worse hospital outcomes was performed using different techniques, including assessment of changes in illness severity of admitted patients, a variable life-adjusted display of mortality during 2020, a multivariate mixed regression model with admission year as both fixed effect and random slope adjusted for SAPS 3 score, an analysis of trends in performance using standardized mortality ratio (SMR) and standardized resource use (SRU), and perturbation analysis.ResultsA total of 644,644 admissions were considered. After excluding readmissions and patients with COVID-19, 514,219 patients were available for analysis. Non-COVID-19 patients admitted in 2020 had slightly lower age and SAPS 3 score but a higher mortality (6.4%) when compared with previous years (2019: 5.6%; 2018: 6.1%). Variable-adjusted life display (VLAD) in 2020 increased but started to decrease as the number of COVID-19 cases increased; this trend reversed as number of COVID cases reduced but recurred on the second wave. After logistic regression, being admitted in 2020 was associated with higher mortality when compared to previous years from 2016 and 2019. Individual ICUs standardized mortality ratio also increased during 2020 (higher SMR) while resource use remained constant, suggesting worsening performance. A perturbation analysis further confirmed changes in ICU outcomes for non-COVID-19 patients.ConclusionHospital outcomes of non-COVID-19 critically ill patients worsened during the pandemic in 2020, possibly resulting in an increased number of deaths in critically ill non-COVID patients.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00134-021-06528-6.

Surge capacity and capability of intensive care units across a large healthcare system: An operational blueprint for regional integration

Many hospitals were unprepared for the surge of patients associated with the spread of coronavirus disease 2019 (COVID-19) pandemic. We describe the processes to develop and implement a surge plan framework for resource allocation, staffing, and standardized management in response to the COVID-19 pandemic across a large integrated regional healthcare system. A large academic medical center in the Cleveland metropolitan area, with a network of 10 regional hospitals throughout Northeastern Ohio with a daily capacity of more than 500 intensive care unit (ICU) beds. At the beginning of the pandemic, an equitable delivery of healthcare services across the healthcare system was developed. This distribution of resources was implemented with the potential needs and resources of the individual ICUs in mind, and epidemiologic predictions of virus transmissibility. We describe the processes to develop and implement a surge plan framework for resource allocation, staffing, and standardized management in response to the COVID-19 pandemic across a large integrated regional healthcare system. We also describe an additional level of surge capacity, which is available to well-integrated institutions called "extension of capacity." This refers to the ability to immediately have access to the beds and resources within a hospital system with minimal administrative burden. Large integrated hospital systems may have an advantage over individual hospitals because they can shift supplies among regional partners, which may lead to faster mobilization of resources, rather than depending on local and national governments. The pandemic response of our healthcare system highlights these benefits.

An evaluation of factors that may influence clinicians' decisions not to enroll eligible patients into randomized trials in critical care.

To determine the association between intensive care unit (ICU) characteristics and clinicians' decision to decline eligible patients for randomization into a multicentered pragmatic comparative-effectiveness controlled trial. Screening logs from the Adjunctive Glucocorticoid Therapy in Septic Shock Trial (ADRENAL) and site-level data from the College of Intensive Care Medicine and Australia New Zealand Intensive Care Society were examined. The effects of ICU characteristics such as tertiary academic status, research coordinator availability, number of admissions, and ICU affiliations on clinicians declining to randomize eligible patients were calculated using mixed effects logistic regression modelling. There were 21,818 patients screened for inclusion in the ADRENAL trial at 69 sites across five countries, out of which 5,501 were eligible, 3,800 were randomized and 659 eligible patients were declined for randomization by the treating clinician. The proportion of eligible patients declined by clinicians at individual ICUs ranged from 0 to41%. In the multivariable model, none of the ICU characteristics were significantly associated with higher clinician decline rate. Neither tertiary academic status, nor other site-level variables were significantly associated with increased rate of clinicians declining eligible patients.

Effectiveness of Contact Precautions to Prevent Transmission of Methicillin-Resistant Staphylococcus aureus and Vancomycin-Resistant Enterococci in Intensive Care Units.

Contact precautions for endemic methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant enterococci (VRE) are under increasing scrutiny, in part due to limited clinical trial evidence. We retrospectively analyzed data from the Strategies to Reduce Transmission of Antimicrobial Resistant Bacteria in Intensive Care Units (STAR*ICU) trial to model the use of contact precautions in individual intensive care units (ICUs). Data included admission and discharge times and surveillance test results. We used a transmission model to estimate key epidemiological parameters, including the effect of contact precautions on transmission. Finally, we performed multivariate meta-regression to identify ICU-level factors associated with contact precaution effects. We found that 21% of admissions (n = 2194) were placed on contact precautions, with most for MRSA and VRE. We found little evidence that contact precautions reduced MRSA transmission. The estimated change in transmission attributed to contact precautions was -16% (95% credible interval, -38% to 15%). VRE transmission was higher than MRSA transmission due to contact precautions, but not significantly. In our meta-regression, we did not identify associations between ICU-level factors and estimated contact precaution effects. Importation and transmission were higher for VRE than for MRSA, but clearance rates were lower for VRE than for MRSA. We found little evidence that contact precautions implemented during the STAR*ICU trial reduced transmission of MRSA or VRE. We did find important differences in the transmission dynamics between MRSA and VRE. Differences in organism and healthcare setting may impact the efficacy of contact precautions.

Personal protective equipment preparedness in Asia-Pacific intensive care units during the coronavirus disease 2019 pandemic: A multinational survey

BackgroundThere has been a surge in coronavirus disease 2019 admissions to intensive care units (ICUs) in Asia-Pacific countries. Because ICU healthcare workers are exposed to aerosol-generating procedures, ensuring optimal personal protective equipment (PPE) preparedness is important. ObjectiveThe aim of the study was to evaluate PPE preparedness across ICUs in six Asia-Pacific countries during the initial phase of the coronavirus disease 2019 pandemic, which is defined by the World Health Organization as guideline adherence, training healthcare workers, procuring stocks, and responding appropriately to suspected cases. MethodsA cross-sectional Web-based survey was circulated to 633 level II/III ICUs of Australia, New Zealand (NZ), Singapore, Hong Kong (HK), India, and the Philippines. FindingsTwo hundred sixty-three intensivists responded, representing 231 individual ICUs eligible for analysis. Response rates were 68–100% in all countries except India, where it was 24%. Ninety-seven percent of ICUs either conformed to or exceeded World Health Organization recommendations for PPE practice. Fifty-nine percent ICUs used airborne precautions irrespective of aerosol generation procedures. There were variations in negative-pressure room use (highest in HK/Singapore), training (best in NZ), and PPE stock awareness (best in HK/Singapore/NZ). High-flow nasal oxygenation and noninvasive ventilation were not options in most HK (66.7% and 83.3%, respectively) and Singapore ICUs (50% and 80%, respectively), but were considered in other countries to a greater extent. Thirty-eight percent ICUs reported not having specialised airway teams. Showering and “buddy systems” were underused. Clinical waste disposal training was suboptimal (38%). ConclusionsMany ICUs in the Asia-Pacific reported suboptimal PPE preparedness in several domains, particularly related to PPE training, practice, and stock awareness, which requires remediation. Adoption of low-cost approaches such as buddy systems should be encouraged. The complete avoidance of high-flow nasal oxygenation reported by several intensivists needs reconsideration. Consideration must be given to standardise PPE guidelines to minimise practice variations. Urgent research to evaluate PPE preparedness and severe acute respiratory syndrome coronavirus 2 transmission is required.

Prospective multicentre multifaceted before-after implementation study of ICU delirium guidelines: a process evaluation

ObjectiveWe aimed to explore: the exposure of healthcare workers to a delirium guidelines implementation programme; effects on guideline adherence at intensive care unit (ICU) level; impact on knowledge and barriers,...

Improving the Quality of Visualization Dashboards in Critical Care: A Mixed-Methods Study.

Intensive care units (ICUs) lack both standardized performance indicators to better understand the effectiveness of interventions and uniform platforms to present these indicators. The goal of this study was to identify ICU metrics meaningful to stakeholders to help guide the development of a local visualization dashboard. Individual ICU directors were interviewed to collate their input on metrics important to their units. These qualitative data were used to develop a dashboard draft, after which the authors surveyed 20 stakeholders from different hospital departments for feedback on its content and structure. The varied survey results reinforced the inherent difficulties of adapting previously developed measurement tools while also selecting ICU performance measures that are simultaneously widely accepted yet relevant to local practice. These results also call attention to the importance of interdisciplinary input in quality dashboard development, thereby enabling more successful implementation and utilization for ICU quality improvement.

External validation of a prognostic model for intensive care unit mortality: a retrospective study using the Ontario Critical Care Information System.

PurposeTo externally validate an intensive care unit (ICU) mortality prediction model that was created using the Ontario Critical Care Information System (CCIS), which includes the Multiple Organ Dysfunction Score (MODS).MethodsWe applied the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) recommendations to a prospective longitudinal cohort of patients discharged between 1 July 2015 and 31 December 31 2016 from 90 adult level-3 critical care units in Ontario. We used multivariable logistic regression with measures of discrimination, calibration-in-the-large, calibration slope, and flexible calibration plots to compare prediction model performance of the entire data set and for each ICU subtype.ResultsAmong 121,201 CCIS records with ICU mortality of 11.3%, the C-statistic for the validation data set was 0.805. The C-statistic ranged from 0.775 to 0.846 among the ICU subtypes. After intercept recalibration to adjust the baseline risk, the mean predicted risk of death matched actual ICU mortality. The calibration slope was close to 1 with all CCIS data and ICU subtypes of cardiovascular and community hospitals with low ventilation rates. Calibration slopes significantly less than 1 were found for ICUs in teaching hospitals and community hospitals with high ventilation rates whereas coronary care units had a calibration slope significantly higher than 1. Calibration plots revealed over-prediction in high risk groups to a varying degree across all cohorts.ConclusionsA risk prediction model primarily based on the MODS shows reproducibility and transportability after intercept recalibration. Risk adjusting models that use existing and feasible data collection can support performance measurement at the individual ICU level.Electronic supplementary materialThe online version of this article (10.1007/s12630-020-01686-5) contains supplementary material, which is available to authorized users.

Evaluating the influence of data collector training for predictive risk of death models: an observational study

BackgroundSeverity-of-illness scoring systems are widely used for quality assurance and research. Although validated by trained data collectors, there is little data on the accuracy of real-world data collection practices.ObjectiveTo evaluate...

Prevalence of ventilator associated pneumonia in adult at medico-surgical ICU of medical college hospital

Prevalence of ventilator associated pneumonia in adult at medico-surgical ICU of medical college hospital - IJIRM- Print ISSN No: - 2581-4214 Online ISSN No:- 2581-4222 Article DOI No:- 10.18231/j.ijirm.2019.037, IP Indian Journal of Immunology and Respiratory Medicine-IP Indian J Immunol Respir Med

Variability in catheter-associated asymptomatic bacteriuria rates among individual nurses in intensive care units: An observational cross-sectional study.

Catheter-associated asymptomatic bacteriuria (CAABU) is frequent in intensive care units (ICUs) and contributes to the routine use of antibiotics and to antibiotic-resistant infections. While nurses are responsible for the implementation of CAABU-prevention guidelines, variability in how individual nurses contribute to CAABU-free rates in ICUs has not been previously explored. This study’s objective was to examine the variability in CAABU-free outcomes of individual ICU nurses. This observational cross-sectional study used shift-level nurse-patient data from the electronic health records from two ICUs in a tertiary medical center in the US between July 2015 and June 2016. We included all adult (18+) catheterized patients with no prior CAABU during the hospital encounter and nurses who provided their care. The CAABU-free outcome was defined as a 0/1 indicator identifying shifts where a previously CAABU-free patient remained CAABU-free (absence of a confirmed urine sample) 24–48 hours following end of shift. The analytical approach used Value-Added Modeling and a split-sample design to estimate and validate nurse-level CAABU-free rates while adjusting for patient characteristics, shift, and ICU type. The sample included 94 nurses, 2,150 patients with 256 confirmed CAABU cases, and 21,729 patient shifts. Patients were 55% male, average age was 60 years. CAABU-free rates of individual nurses varied between 94 and 100 per 100 shifts (Wald test: 227.88, P<0.001) and were robust in cross-validation analyses (correlation coefficient: 0.66, P<0.001). Learning and disseminating effective CAABU-avoidance strategies from top-performers throughout the nursing teams could improve quality of care in ICUs.

Emergence of Burkholderia cepacia in ICU Setting.

ABSTRACTBackgroundB. cepacia is metabolically versatile organism which is not only resistant to many antibiotics but also disinfectants. This makes their survival easy even in restricted areas like intensive care unit (ICU) and management difficult.Aims and objectivesTo describe sudden emergence of Burkholderia at a tertiary care centre ICU setting in milieu of colistin usageMaterials and methodsCases were patients with culture proven B.cepacia. They were picked up as non-lactose fermenting, oxidase positive, motile, gram-negative bacilli which was resistant to colistin and aminoglycosides and sensitive to cotrimoxazole. These isolates were further confirmed by both VITEK-2 compact system (Biomerieux, France) and standard bacterial techniques.Colistin consumption data were retrospectively collected from medical store records of hospitals and individual ICU pharmacy records from January 2016 to June 2016, and were expressed as total dialy doses in a month per 1000 patient days (DDD/1000PD)ResultsAn increase was observed in B. cepacia infection linked to increased consumption of colistin in ICU.ConclusionBased on these results an increase was observed in B.cepacia infection which correlated with increased consumption of colistin in ICU. We speculate that extensive use of colistin may lead to selection of intrinsically resistant B. cepacia and may facilitate their spread as nosocomial pathogens.How to cite this articleMeena S, Bir R, Sood S, Das BK, Kapil A. Emergence of Burkholderia cepacia in ICU Setting. Indian J Crit Care Med 2019;23(9):423–426.