Individual Intensive Care Units Research Articles

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.

Providing Palliative Care in the Medical ICU: A Qualitative Study of MICU Physicians' Beliefs and Practices.

With the current and projected shortage of palliative care (PC) specialists, an integrative model of PC will be needed to meet the needs of patients in intensive care units (ICUs). Prior studies of PC interventions suggest that success depends upon meeting the needs of individual institutions or ICUs. The objective of this study was to qualitatively explore the beliefs and practices of one institution's medical ICU (MICU) physicians in regard to providing an integrative model of PC. This qualitative study used semistructured interviews of 17 physicians within a Department of Allergy, Pulmonary, and Critical Care Medicine at one large academic hospital. Interviews were conducted, transcribed, and coded according to qualitative research methods. Selected interviews were tested for interrater reliability and negotiated agreeability. All critical care physicians interviewed affirmed that providing PC was part of their job, and the majority expressed that providing PC gave them professional or personal satisfaction. Physicians also identified many ways that PC consultants enhance patient care in the MICU. They discussed several motivations for obtaining a PC consult, with the most frequently acknowledged motivation being lack of time. Developing an integrative model of PC in the ICU ought to take into account both studied interventions shown likely to be effective in the ICU setting and the specific needs and barriers to PC in that unit. This study demonstrates one way that needs and barriers can be identified and provides important findings that might be applicable to other institutions.

Clinician-Driven Design of VitalPAD-An Intelligent Monitoring and Communication Device to Improve Patient Safety in the Intensive Care Unit.

The pediatric intensive care unit (ICU) is a complex environment, in which a multidisciplinary team of clinicians (registered nurses, respiratory therapists, and physicians) continually observe and evaluate patient information. Data are provided by multiple, and often physically separated sources, cognitive workload is high, and team communication can be challenging. Our aim is to combine information from multiple monitoring and therapeutic devices in a mobile application, the VitalPAD, to improve the efficiency of clinical decision-making, communication, and thereby patient safety. We observed individual ICU clinicians, multidisciplinary rounds, and handover procedures for 54 h to identify data needs, workflow, and existing cognitive aid use and limitations. A prototype was developed using an iterative participatory design approach; usability testing, including general and task-specific feedback, was obtained from 15 clinicians. Features included map overviews of the ICU showing clinician assignment, patient status, and respiratory support; patient vital signs; a photo-documentation option for arterial blood gas results; and team communication and reminder functions. Clinicians reported the prototype to be an intuitive display of vital parameters and relevant alerts and reminders, as well as a user-friendly communication tool. Future work includes implementation of a prototype, which will be evaluated under simulation and real-world conditions, with the aim of providing ICU staff with a monitoring device that will improve their daily work, communication, and decision-making capacity. Mobile monitoring of vital signs and therapy parameters might help improve patient safety in wards with single-patient rooms and likely has applications in many acute and critical care settings.

Mobilization of Mechanically Ventilated Patients in Switzerland.

Growing evidence suggests that early mobilization benefits intensive care unit (ICU) patients. However, national practices and the culture of individual ICUs influence mobilization activities. In a 1-day, Swiss point prevalence study conducted in 35 ICUs (representing 45% of all ICUs), the highest level of mobilization for mechanically ventilated patients was characterized using the validated ICU Mobility Scale, along with data collection for potential safety events and mobilization barriers. Among 161 mechanically ventilated patients, a total of 33% (n = 53) had active mobilization, with walking achieved by only 2% (n = 4). More severe organ failure was associated with lower mobilization (respiratory Sequential Organ Failure Assessment score: P = .037, cardiac: P = .008, neurology: P < .001). Barriers to mobilization were reported in 71% (n = 115), with deep sedation significantly higher among patients receiving passive versus active mobilization (14% vs 0%, P = .005). Potential safety events occurred in 20% (n = 33) of patients without significant differences between passive and active mobilization. Availability of physiotherapists and appropriate equipment were not reported barriers. Mobilization during mechanical ventilation occurred infrequently with greater organ failure associated with lower mobilization. Addressing the identified modifiable barriers via structured efforts to achieve multidisciplinary culture change is essential to decrease the common use of bed rest in Swiss ICUs.

Electronic audit and feedback intervention with action implementation toolbox to improve pain management in intensive care: protocol for a laboratory experiment and cluster randomised trial

BackgroundAudit and feedback is often used as a strategy to improve quality of care, however, its effects are variable and often marginal. In order to learn how to design and deliver effective feedback, we need to understand their mechanisms of action. This theory-informed study will investigate how electronic audit and feedback affects improvement intentions (i.e. information–intention gap), and whether an action implementation toolbox with suggested actions and materials helps translating those intentions into action (i.e. intention–behaviour gap). The study will be executed in Dutch intensive care units (ICUs) and will be focused on pain management.Methods and designWe will conduct a laboratory experiment with individual ICU professionals to assess the impact of feedback on their intentions to improve practice. Next, we will conduct a cluster randomised controlled trial with ICUs allocated to feedback without or feedback with action implementation toolbox group. Participants will not be told explicitly what aspect of the intervention is randomised; they will only be aware that there are two variations of providing feedback. ICUs are eligible for participation if they submit indicator data to the Dutch National Intensive Care Evaluation (NICE) quality registry and agree to allocate a quality improvement team that spends 4 h per month on the intervention. All participating ICUs will receive access to an online quality dashboard that provides two functionalities: gaining insight into clinical performance on pain management indicators and developing action plans. ICUs with access to the toolbox can develop their action plans guided by a list of potential barriers in the care process, associated suggested actions, and supporting materials to facilitate implementation of the actions. The primary outcome measure for the laboratory experiment is the proportion of improvement intentions set by participants that are consistent with recommendations based on peer comparisons; for the randomised trial it is the proportion of patient shifts during which pain has been adequately managed. We will also conduct a process evaluation to understand how the intervention is implemented and used in clinical practice, and how implementation and use affect the intervention’s impact.DiscussionThe results of this study will inform care providers and managers in ICU and other clinical settings how to use indicator-based performance feedback in conjunction with an action implementation toolbox to improve quality of care. Within the ICU context, this study will produce concrete and directly applicable knowledge with respect to what is or is not effective for improving pain management, and under which circumstances. The results will further guide future research that aims to understand the mechanisms behind audit and feedback and contribute to identifying the active ingredients of successful interventions.Trial registrationClinicalTrials.gov NCT02922101. Registered 26 September 2016.

Association of Intensive Care Unit Patient-to-Intensivist Ratios With Hospital Mortality

The patient-to-intensivist ratio (PIR) across intensive care units (ICUs) is not standardized and the association of PIR with patient outcome is not well established. Understanding the impact of PIR on outcomes is necessary to optimize senior medical staffing and deliver high-quality care. To test the hypotheses that: (1) there is significant variation in the PIR across ICUs and (2) higher PIRs are associated with higher hospital mortality for ICU patients. Retrospective cohort analysis of patients (≥16 years) admitted to ICUs staffed by a single intensivist during daytime hours in the United Kingdom from 2010 to 2013. Patient-to-intensivist ratios, which we defined for each patient as the number of patients cared for by the intensivist each day averaged over the patient's stay. Using standard summary statistics, we evaluated PIR variation across ICUs. We used multivariable, mixed-effect, logistic regression analysis to evaluate the association between PIR and hospital mortality at ultimate discharge from acute hospital (primary outcome) and at ICU discharge. Among 49 686 adults in 94 ICUs, median age was 66 (interquartile range [IQR], 52-76) years, and 45.1% were women. The ultimate hospital mortality was 25.7%. The median PIR for patients was 8.5 (IQR, 6.9-10.8; full range, 1.0-23.5), and varied substantially among individual ICUs. The association between PIR and ultimate hospital mortality was U-shaped; there was a reduction in the odds of mortality associated with an increasing PIR up to 7.5 after which the odds of mortality increased again significantly (average patient mortality for lowest PIR, 22%; PIR of 7.5, 15%; highest PIR, 19%; P = .003). A similar U-shaped association was seen for PIR and mortality in the ICU (nadir of mortality at a PIR of 7.8, P < .001). PIR varied across UK ICUs. The optimal PIR in this cohort of UK ICU patients was 7.5, with significantly increased ICU and hospital mortality above and below this ratio. The number of patients cared for by 1 intensivist may impact patient outcomes.

Preliminary Identification of Coping Profiles Relevant to Surrogate Decision Making in the ICU

ObjectiveThe Intensive Care Unit (ICU) is a stressful environment for families of critically ill patients and these individuals are at risk to develop persistent psychological morbidity. Our study objective was to identify individual differences in coping with stress and information presentation preferences of respondents exposed to a simulated ICU experience.MethodsParticipants were recruited from a university and two community populations. Participants completed questionnaires that measured demographic information and characteristics that may be relevant to an individual’s ICU experience. Quality of life was measured by the EQ-5D, personality dimensions were examined with the abbreviated Big Five inventory, coping with stress was assessed with Brief COPE. Shared decision making preferences were assessed by the Degner Control Preferences Scale (CPS) and information seeking style was assessed with the Miller Behavioral Style Scale (MBSS). Social support was examined using an abbreviated version of the Social Relationship Index. Participants also completed a vignette-based simulated ICU experience, in which they made a surrogate decision on behalf of a loved one in the ICU.ResultsThree hundred forty-three participants completed the study. Three distinct coping profiles were identified: adaptive copers, maladaptive copers, and disengaged copers. Profiles differed primarily on coping styles, personality, quality of their closest social relationship, and history of anxiety and depression. Responses to the simulated ICU decision making experience differed across profiles. Disengaged copers (15%) were more likely to elect to refuse dialysis on behalf of an adult sibling compared to adaptive copers (7%) or maladaptive copers (5%) (p = 0.03). Notably, the MBSS and the CPS did not differ by coping profile.ConclusionDistinct coping profiles are associated with differences in responses to a simulated ICU experience. Tailoring communication and support to specific coping profiles may represent an important pathway to improving ICU experience for patients and families.

Association Between Overnight Extubations and Outcomes in the Intensive Care Unit

Little is known about the timing of extubations for patients in the intensive care unit (ICU) who undergo mechanical ventilation (MV) or whether overnight extubation is safe. To describe the frequency of overnight extubations and assess the association between overnight extubations and clinical outcomes. This retrospective cohort study included adults (aged ≥18 years) undergoing MV performed in US ICUs as part of the Project IMPACT database from October 1, 2000, to March 29, 2009. Data were analyzed from January 1, 2015, to July 5, 2016. Overnight extubation defined as occurring from 7 pm to 6:59 am. Multilevel multivariable regression analyses (clustered by individual ICU) were used to identify factors associated with overnight extubation. Propensity-matched pairs were created of patients undergoing overnight vs daytime extubation (separately for patients with MV duration <12 and ≥12 hours). Outcomes, including frequency of reintubation in the ICU, ICU and hospital mortality, and ICU and hospital length of stay (LOS), were assessed using χ2 and Mann-Whitney tests. The cohort consisted of 97 844 patients (40.8% men; 59.2% women; mean [SD] age, 58.3 [17.9] years) across 165 ICUs. Of these, 20.1% of patients underwent overnight extubation and the percentage decreased over time (23.3% in 2000-2001 vs 18.8% in 2009; P = .001). After multivariable adjustment, duration of MV of less than 12 hours had the greatest association with overnight extubation (compared with 12 hours to <1 day: adjusted odds ratio [AOR], 0.20 [95% CI, 0.19-0.21]; 1 to <2 days: AOR, 0.26 [95% CI, 0.24-0.28]; 2 to <7 days: AOR, 0.22 [95% CI, 0.21-0.24]; and ≥7 days: AOR, 0.24 [95% CI, 0.22-0.26]). In all, 4518 propensity-matched pairs had MV duration of less than 12 hours and 5761 had MV duration of at least 12 hours. For MV duration of less than 12 hours, reintubation rates were similar for overnight and daytime extubations (5.9% and 5.6%, respectively; P = .50), but mortality was increased for patients undergoing overnight extubation (ICU, 5.6% vs 4.6%, P = .03; hospital, 8.3% vs 7.0%, P = .01). The ICU LOS was shorter for overnight vs daytime extubations (median [interquartile range], 1.1 [0.8-2.3] vs 1.4 [0.9-2.5] days; P < .001), and hospital LOS was similar (median [interquartile range], 7.0 [4.0-12.0] vs 7.0 [3.0-12.0] days; P = .03). Patients with MV duration of at least 12 hours who underwent overnight extubation had more frequent reintubation in the ICU (14.6% vs 12.4%; P < .001) and higher mortality in the ICU (11.2% vs 6.1%; P < .001) and in the hospital (16.0% vs 11.1%; P < .001), with no differences in LOS. Approximately one-fifth of patients with MV in US ICUs undergo overnight extubation. These patients have higher rates of ICU and hospital mortality than patients undergoing extubation during the daytime. Further studies are needed to understand why overnight extubation results in poorer outcomes.

Multi-task transfer learning for in-hospital-death prediction of ICU patients.

Multi-Task Transfer Learning (MTTL) is an efficient approach for learning from inter-related tasks with small sample size and imbalanced class distribution. Since the intensive care unit (ICU) data set (publicly available in Physionet) has subjects from four different ICU types, we hypothesize that there is an underlying relatedness amongst various ICU types. Therefore, this study aims to explore MTTL model for in-hospital mortality prediction of ICU patients. We used single-task learning (STL) approach on the augmented data as well as individual ICU data and compared the performance with the proposed MTTL model. As a performance measurement metrics, we used sensitivity (Sens), positive predictivity (+Pred), and Score. MTTL with class balancing showed the best performance with score of 0.78, 0.73, o.52 and 0.63 for ICU type 1 (Coronary care unit), 2 (Cardiac surgery unit), 3 (Medical ICU) and 4 (Surgical ICU) respectively. In contrast the maximum score obtained using STL approach was 0.40 for ICU type 1 & 2. These results indicates that the performance of in-hospital mortality can be improved using ICU type information and by balancing the `non-survivor' class. The findings of the study may be useful for quantifying the quality of ICU care, managing ICU resources and selecting appropriate interventions.

Beyond the Intensive Care Unit (ICU): Countywide Impact of Universal ICU Staphylococcus aureus Decolonization.

A recent trial showed that universal decolonization in adult intensive care units (ICUs) resulted in greater reductions in all bloodstream infections and clinical isolates of methicillin-resistant Staphylococcus aureus (MRSA) than either targeted decolonization or screening and isolation. Since regional health-care facilities are highly interconnected through patient-sharing, focusing on individual ICUs may miss the broader impact of decolonization. Using our Regional Healthcare Ecosystem Analyst simulation model of all health-care facilities in Orange County, California, we evaluated the impact of chlorhexidine baths and mupirocin on all ICU admissions when universal decolonization was implemented for 25%, 50%, 75%, and 100% of ICU beds countywide (compared with screening and contact precautions). Direct benefits were substantial in ICUs implementing decolonization (a median 60% relative reduction in MRSA prevalence). When 100% of countywide ICU beds were decolonized, there were spillover effects in general wards, long-term acute-care facilities, and nursing homes resulting in median 8.0%, 3.0%, and 1.9% relative MRSA reductions at 1 year, respectively. MRSA prevalence decreased by a relative 3.2% countywide, with similar effects for methicillin-susceptible S. aureus. We showed that a large proportion of decolonization's benefits are missed when accounting only for ICU impact. Approximately 70% of the countywide cases of MRSA carriage averted after 1 year of universal ICU decolonization were outside the ICU.

Critical Care at the End of Life.

Intensive care unit (ICU) admission is common among patients approaching the end of their lives from acute as well as chronic life-limiting conditions. ICU providers are expected to have basic palliative care skills integrated into their routine practice. Palliative care skills can be applied to all ICU patients, regardless of prognosis, and may improve patient- and family-centered end of life (EOL) care in the ICU. Consultative palliative care models may be required for more complex palliative care needs including symptom management, medical decision making, and bereavement. This review discusses integrative and consultative palliative care models and the role of triggers for palliative care consultation, particularly when they are tailored to the needs of individual ICUs. We then review the evidence for providing several palliative care domains in the ICU and some of the ethical considerations surrounding EOL care in the ICU. Finally, we highlight the importance of self-care and peer support groups to mitigate the risk of burnout for clinicians providing EOL care in the ICU.

Variability Among US Intensive Care Units in Managing the Care of Patients Admitted With Preexisting Limits on Life-Sustaining Therapies.

Although the end-of-life care patients receive is known to vary across nations, regions, and centers, these differences are best explored within a group of patients with presumably similar care preferences. To examine the proportions of patients admitted to the intensive care unit (ICU) with limitations on life-sustaining treatments and the proportions of such patients who receive aggressive care across individual ICUs. Retrospective cohort study using the Project IMPACT database (from April 1, 2001, to December 31, 2008) including 141 ICUs in 105 hospitals in the United States and 277,693 ICU patient visits. We used logistic regression analysis models adjusted for available patient characteristics and clustered visits by individual ICU. The full analysis was last performed in October 2014. Outcomes included the provision of (1) cardiopulmonary resuscitation, (2) new forms of life support, and the (3) addition or (4) reversal of treatment limitations. Of the ICU admissions evaluated, 4.8% (95% CI, 4.7%-4.9%) had previously established treatment limitations. Patients admitted with treatment limitations were more likely to be older with more functional limitations and comorbidities. Among patients who survived to hospital discharge, more experienced reversals of existing treatment limitations during the ICU stay (17.8% [95% CI, 17.0%-18.7%]) than additions of new limits (11.7% [95% CI, 11.1%-12.4%]) (P < .01). Among patients who died, 15.7% (95% CI, 14.7-16.8%) had received cardiopulmonary resuscitation. After risk adjustment, ICUs varied widely in the proportions of patients admitted with treatment limitations (median, 4.0%; range, <1.0%-20.9%), the proportions of those who received cardiopulmonary resuscitation (37.7% [95% CI, 3.8%-92.4%]), the proportions of new forms of life support (30.0% [95% CI, 6.0%-84.2%]), and, among survivors, the proportion who had new treatment limitations established (11.2% [95% CI, 1.9%-57.3%]) and reversal of treatment limitations during or following ICU admission (20.2% [95% CI, 1.8%-76.2%]). The observed variability could not be consistently explained using measurable center-level characteristics. Intensive care units vary dramatically in how they manage care for patients admitted with treatment limitations. Among patients who survive, escalations in the aggressiveness of care are more common during the ICU stay than are de-escalations in aggressiveness. This study cannot directly measure whether care received was consistent with patients' preferences but suggests that ICU culture and physicians' practice styles contribute to the aggressiveness of care.

Intensive care unit capacity in low-income countries: a systematic review.

PurposeAccess to critical care is a crucial component of healthcare systems. In low-income countries, the burden of critical illness is substantial, but the capacity to provide care for critically ill patients in intensive care units (ICUs) is unknown. Our aim was to systematically review the published literature to estimate the current ICU capacity in low-income countries.MethodsWe searched 11 databases and included studies of any design, published 2004-August 2014, with data on ICU capacity for pediatric and adult patients in 36 low-income countries (as defined by World Bank criteria; population 850 million). Neonatal, temporary, and military ICUs were excluded. We extracted data on ICU bed numbers, capacity for mechanical ventilation, and information about the hospital, including referral population size, public accessibility, and the source of funding. Analyses were descriptive.ResultsOf 1,759 citations, 43 studies from 15 low-income countries met inclusion criteria. They described 36 individual ICUs in 31 cities, of which 16 had population greater than 500,000, and 14 were capital cities. The median annual ICU admission rate was 401 (IQR 234-711; 24 ICUs with data) and median ICU size was 8 beds (IQR 5-10; 32 ICUs with data). The mean ratio of adult and pediatric ICU beds to hospital beds was 1.5% (SD 0.9%; 15 hospitals with data). Nepal and Uganda, the only countries with national ICU bed data, had 16.7 and 1.0 ICU beds per million population, respectively. National data from other countries were not available.ConclusionsLow-income countries lack ICU beds, and more than 50% of these countries lack any published data on ICU capacity. Most ICUs in low-income countries are located in large referral hospitals in cities. A central database of ICU resources is required to evaluate health system performance, both within and between countries, and may help to develop related health policy.

Risk factors for Pseudomonas aeruginosa acquisition in intensive care units: a prospective multicentre study

Pseudomonas aeruginosa is a major nosocomial pathogen in intensive care units (ICUs); however, endogenous versus exogenous origin of contamination remains unclear. To identify individual and environmental ICU risk factors for P. aeruginosa acquisition. A five-month prospective multicentric study was performed in ten French ICUs. Adult patients hospitalized in ICU for ≥ 24 h were included and screened for P. aeruginosa colonization on admission, weekly and before discharge. P. aeruginosa acquisition was defined by a subsequent colonization or infection if screening swabs on admission were negative. Water samples were obtained weekly on water taps of the ICUs. Data on patient characteristics, invasive devices exposure, antimicrobial therapy, P. aeruginosa water and patient colonization pressures, and ICU characteristics were collected. Hazard ratios (HRs) were estimated using multivariate Cox model. Among the 1314 patients without P. aeruginosa on admission, 201 (15%) acquired P. aeruginosa during their ICU stay. Individual characteristics significantly associated with P. aeruginosa acquisition were history of previous P. aeruginosa infection or colonization, cumulative duration of mechanical ventilation and cumulative days of antibiotics not active against P. aeruginosa. Environmental risk factors for P. aeruginosa acquisition were cumulative daily ward 'nine equivalents of nursing manpower use score' (NEMS) [hazard ratio (HR): 1.47 for ≥ 30 points; 95% confidence interval (CI): 1.06-2.03] and contaminated tap water in patient's room (HR: 1.76; CI: 1.09-2.84). Individual risk factors and environmental factors for which intervention is possible were identified for P. aeruginosa acquisition.

Early prediction of new-onset physical disability after intensive care unit stay: a preliminary instrument.

IntroductionMany intensive care unit (ICU) survivors suffer from physical disability for months after ICU stay. There is no structured method to identify patients at risk for such problems. The purpose of the study was to develop a method for early in-ICU prediction of the patient’s individual risk for new-onset physical disability two months after ICU stay.MethodsIn total, 23 potential predictors for physical disability were assessed before individual ICU discharge. Two months after ICU discharge, out of 232 eligible patients, 148 ICU survivors (64%) completed the activity of daily living (ADL) staircase questionnaire to determine new-onset physical disability.ResultsA total of 95% percent of patients had no ADL reduction prior to ICU admission. Forty-seven percent (n = 69) of questionnaire responders suffered from worsened ADL. We identified four independent predictors for new-onset physical disability: Low educational level (odds ratio (OR) = 6.8), impaired core stability (OR = 4.6), fractures (OR = 4.5) and ICU length of stay longer than two days (OR = 2.6). The predictors were included in a screening instrument. The regression coefficient of each predictor was transformed into a risk score. The sum of risk scores was related to a predicted probability for physical disability in the individual patient. The cross-validated area under receiver operating characteristics curve (AUC) for the screening instrument was 0.80.ConclusionsEducational level is the single most important predictor for new-onset physical disability two months after ICU stay, followed by impaired core stability at ICU discharge, the presence of fractures and ICU stay longer than two days. A simple screening instrument based on these predictors can be used at ICU discharge to determine the risk for new-onset physical disability. This preliminary instrument may help clinicians to identify patients in need of support, but needs external validation prior to wider clinical use.Electronic supplementary materialThe online version of this article (doi:10.1186/s13054-014-0455-7) contains supplementary material, which is available to authorized users.

Preventing hospital acquired infections: A challenge we must accept

Preventing hospital acquired infections: A challenge we must accept

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Introduction: Empiric antibiotic therapy (EAT) depends on clinical evaluation of patient factors and antibiograms of individual intensive care units (ICU). Methods: Mechanically ventilated patients on the TICU service treated for suspected VAP were retrospectively reviewed to determine appropriate EAT. This data was presented to TICU physicians and implementation of the determined EAT was approved. A second cohort of TICU patients was retrospectively reviewed to determine its effect on clinical outcomes. Results: 434 patients were screened for the first cohort, with 136 patients meeting inclusion criteria. The mean age was 46.7 years and 80.1% of patients were male. Organism occurrence was stratified based on time of VAP onset. Early-onset VAPs (EVAPs), defined as onset on days 1-4, were more likely to be caused by community-acquired pathogens (Methicillin-sensitive S. aureus [MSSA] 22%, H. influenzae 16%, Streptococcus sp. 6%); or negative cultures (Mixed Upper Respiratory Flora [MURF] 30%, No growth [NG] 6%). Late-onset VAPs (LVAPs), defined as onset on day 5 or more, were more likely to be hospital-acquired pathogens (P. aeruginosa 10%, Methicillin-resistant S. aureus [MRSA] 10%, Enterobacter sp. 5%, Citrobacter sp. 5%). Ceftriaxone would have been appropriate for 85% of patients with EVAP and cefepime/vancomycin for 84% of patients with LVAP and were chosen as the determined EAT for TICU patients. 119 patients were screened for the second cohort, with 49 patients meeting inclusion criteria. EVAPs were similarly caused by sensitive pathogens (MSSA 24%, H. influenzae 14%, Streptococcus sp. 7%); or negative cultures (MURF 31%, NG 7%). LVAPs were more likely to be hospital-acquired (P. aeruginosa 12%, MRSA 2%, Enterobacter sp. 5%, S. marcescens 5%; Citrobacter sp. 3%). Mean age was 46.8 years and 81.6% of patients were male. 47% of TICU patients with EVAP received ceftriaxone and it was appropriate EAT for 90%. 48% of TICU patients with LVAP received cefepime/vancomycin and it was appropriate EAT for 88%. TICU length of stay was 25 days in the second cohort, versus 20 days in the first cohort (p=0.0045). All cause mortality was 12.2% in the second cohort, versus 21.3% in the first cohort (p=0.164). Conclusions: The use of targeted EAT for VAP resulted in more appropriate empiric therapy with no change in all cause mortality.

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Introduction: Intensive care acquired nosocomial infections are one of the major causes of increased morbidity, mortality, and costs in intensive care units (ICU). Methods: After Institutional Review Board approval the charts of patients who were admitted to the surgical ICU at Baskent University between January 2009 and July 2012, were reviewed. Sixty patients who developed CR-BSI, VAP, or CA-UTI during their ICU stay were identified and they were compared to 53 appropriate matches who did not have any nosocomial infections. Results: Out of 103 patients who were included in the final analyses, 60 (58%) had a nosocomial infection. Twenty five (24%) out of 60 infected patients had CR-BSI, 46 (45%) had VAP, and 20 (19%) had CA-UTI. Higher APACHE II scores (OR: 1.142; 95% CI: 1.007-1.294; p=0.039), presence of a comorbidity (OR: 39.488; 95% CI: 2.075-750.950; p=0.014), prolonged hospitalization prior to ICU (OR: 1.155; 95% CI: 1.015-7.953; p=0.029), and use of sedatives (OR: 10.980; 95% CI: 1.167-103.337; p=0.036) during the ICU course were identified as the risk factors for nosocomial infections. Advanced age (OR: 1.037; 95% CI: 1.001-1.073; p=0.042) was found to be the risk factor for CR-BSI. Presence of diabetes mellitus (OR: 12.048; %95 CI: 1.157-125.000; p=0.037), immunosupression therapy (OR: 16.949, %95 CI: 2.463-111.111; p=0.004), and presence of a open wound (OR: 5.714; 95% CI: 1.017-37.258; p=0.048), higher APACHE II scores (OR: 1.132; 95% CI: 1.022-1.254; p=0.018), and prolonged duration of mechanical ventilation (OR: 1.084; 95% CI: 1.002-1.171; p=0.043) were determined as risk factors for VAP. Prolonged hospitalization prior to ICU (OR: 1.037; 95% CI: 1.004-1.072; p=0.027) was found to be the risk factor for CA-UTI. Conclusions: In conclusion, in a cohort of surgical ICU patients we found that a higher APACHE II score, longer length of hospital stay, use of sedatives, and the presence of comorbidities were the predictors of nosocomial infections. Additionally, the respective predictors of individual ICU infections including CR-BSI; VAP; and CR-UTI were advanced age; presence of immunosuppressive state, open wounds, and length of mechanical ventilation; and length of hospitalization.

Explaining Matching Michigan: an ethnographic study of a patient safety program

BackgroundQuality and safety improvement initiatives in healthcare often display two disconcerting effects. The first is a failure to outperform the secular trend. The second is the decline effect, where an initially promising intervention appears not to deliver equally successful results when attempts are made to replicate it in new settings. Matching Michigan, a patient safety program aimed at decreasing central line infections in over 200 intensive care units (ICUs) in England, may be an example of both. We aimed to explain why these apparent effects may have occurred.MethodsWe conducted interviews with 98 staff and non-participant observation on 19 ICUs; 17 of these units were participating in Matching Michigan. We undertook further telephone interviews with 29 staff who attended program training events and we analyzed relevant documents.ResultsOne Matching Michigan unit transformed its practices and culture in response to the program; five boosted existing efforts, and 11 made little change. Matching Michigan’s impact may have been limited by features of program design and execution; it was not an exact replica of the original project. Outer and inner contexts strongly modified the program’s effects. The outer context included previous efforts to tackle central line infections superimposed on national infection control policies that were perceived by some as top-down and punitive. This undermined engagement in the program and made it difficult to persuade participants that the program was necessary. Individual ICUs’ histories and local context were also highly consequential: their past experience of quality improvement, the extent to which they were able to develop high quality data collection and feedback systems, and the success of local leaders in developing consensus and coalition all influenced the program’s impact on local practices.ConclusionsImproved implementation of procedural good practice may occur through many different routes, of which program participation is only one. The ‘phenotype’ of compliance may therefore arise through different ‘genotypes.’ When designing and delivering interventions to improve quality and safety, risks of decline effects and difficulties in demonstrating added value over the secular trend might be averted by improved understanding of program mechanisms and contexts of implementation.

Statistical process control of mortality series in the Australian and New Zealand Intensive Care Society (ANZICS) adult patient database: implications of the data generating process

BackgroundStatistical process control (SPC), an industrial sphere initiative, has recently been applied in health care and public health surveillance. SPC methods assume independent observations and process autocorrelation has been associated with increase in false alarm frequency.MethodsMonthly mean raw mortality (at hospital discharge) time series, 1995–2009, at the individual Intensive Care unit (ICU) level, were generated from the Australia and New Zealand Intensive Care Society adult patient database. Evidence for series (i) autocorrelation and seasonality was demonstrated using (partial)-autocorrelation ((P)ACF) function displays and classical series decomposition and (ii) “in-control” status was sought using risk-adjusted (RA) exponentially weighted moving average (EWMA) control limits (3 sigma). Risk adjustment was achieved using a random coefficient (intercept as ICU site and slope as APACHE III score) logistic regression model, generating an expected mortality series. Application of time-series to an exemplar complete ICU series (1995-(end)2009) was via Box-Jenkins methodology: autoregressive moving average (ARMA) and (G)ARCH ((Generalised) Autoregressive Conditional Heteroscedasticity) models, the latter addressing volatility of the series variance.ResultsThe overall data set, 1995-2009, consisted of 491324 records from 137 ICU sites; average raw mortality was 14.07%; average(SD) raw and expected mortalities ranged from 0.012(0.113) and 0.013(0.045) to 0.296(0.457) and 0.278(0.247) respectively. For the raw mortality series: 71 sites had continuous data for assessment up to or beyond lag40 and 35% had autocorrelation through to lag40; and of 36 sites with continuous data for ≥ 72 months, all demonstrated marked seasonality. Similar numbers and percentages were seen with the expected series. Out-of-control signalling was evident for the raw mortality series with respect to RA-EWMA control limits; a seasonal ARMA model, with GARCH effects, displayed white-noise residuals which were in-control with respect to EWMA control limits and one-step prediction error limits (3SE). The expected series was modelled with a multiplicative seasonal autoregressive model.ConclusionsThe data generating process of monthly raw mortality series at the ICU level displayed autocorrelation, seasonality and volatility. False-positive signalling of the raw mortality series was evident with respect to RA-EWMA control limits. A time series approach using residual control charts resolved these issues.