Protocol for a Randomized Controlled Trial to Evaluate a Permissive Blood Pressure Target Versus Usual Care in Critically Ill Children with Hypotension (PRESSURE).

OBJECTIVES: Management of hypotension is a fundamental part of pediatric critical care, with cardiovascular support in the form of fluids or vasoactive drugs offered to every hypotensive child. However, optimal blood pressure (BP) targets are unknown. The PRotocolised Evaluation of PermiSSive BP Targets Versus Usual CaRE (PRESSURE) trial aims to evaluate the clinical and cost-effectiveness of a permissive mean arterial pressure (MAP) target of greater than a fifth centile for age compared with usual care. DESIGN: Pragmatic, open, multicenter, parallel-group randomized control trial (RCT) with integrated economic evaluation. SETTING: Eighteen PICUs across the United Kingdom. PATIENTS: Infants and children older than 37 weeks corrected gestational age to 16 years accepted to a participating PICU, on mechanical ventilation and receiving vasoactive drugs for hypotension. INTERVENTIONS: Adjustment of hemodynamic support to achieve a permissive MAP target greater than fifth centile for age during invasive mechanical ventilation. MEASUREMENTS AND MAIN RESULTS: Randomization is 1:1 to a permissive MAP target or usual care, stratified by site and age group. Due to the emergency nature of the treatment, approaching patients for written informed consent will be deferred until after randomization. The primary clinical outcome is a composite of death and days of ventilatory support at 30 days. Baseline demographics and clinical status will be recorded as well as daily measures of BP and organ support, and discharge outcomes. This RCT received Health Research Authority approval (reference 289545), and a favorable ethical opinion from the East of England-Cambridge South Research Ethics Committee on May 10, 2021 (reference number 21/EE/0084). The trial is registered and has an International Standard RCT Number (reference 20609635). CONCLUSIONS: Trial findings will be disseminated in U.K. national and international conferences and in peer-reviewed journals.

Relationship between patients' outcomes and the changes in serum creatinine and urine output and RIFLE classification in a large critical care cohort database.

We report the stepwise application of the RIFLE classification in 155,624 admissions in the UK Intensive Care National Audit & Research Centre Case Mix Programme database. The assumptions required to define RIFLE and their relationship with renal replacement therapy (RRT) and ICU mortality were assessed. Previous reports had not explored the method of estimating baseline creatinine, the position of class boundaries, or interactions between urine volume (AKI-U) and the peak/estimated baseline creatinine (AKI-Cr) within 24 h of ICU admission. The risk of developing AKI strongly depended on the assumed GFR increasing from 36 to 58% across the recommended range. AKI-U was often seen without AKI-Cr, and moderate oliguria (under 850 ml/24 h) was a stronger predictor of mortality than any degree of AKI-Cr partly because mortality fell when peak/estimated baseline creatinine ratios exceed fourfold. Mild oliguria (850-1500 ml/24 h) was common (38,928 admissions, 26%) and had a similar association with mortality (relative risk 1.6, 95% CI: 1.5-1.6) as did AKI-Cr defined Failure (risk ratio 1.5, 95% CI: 1.5-1.6). However, AKI-Cr was a strong predictor for RRT, which was used in 17,802 (11%) of admissions. Nearly half (48%) of the Failure patients never received RRT; nonetheless, most (66%) survived critical care. Thus, although the RIFLE classification may be attempted in large population cohorts, there is significant heterogeneity of both renal and, in particular, vital outcomes within each class.

Accounting for Heterogeneity in Relative Treatment Effects for Use in Cost-Effectiveness Models and Value-of-Information Analyses.

Cost-effectiveness analysis (CEA) models are routinely used to inform health care policy. Key model inputs include relative effectiveness of competing treatments, typically informed by meta-analysis. Heterogeneity is ubiquitous in meta-analysis, and random effects models are usually used when there is variability in effects across studies. In the absence of observed treatment effect modifiers, various summaries from the random effects distribution (random effects mean, predictive distribution, random effects distribution, or study-specific estimate [shrunken or independent of other studies]) can be used depending on the relationship between the setting for the decision (population characteristics, treatment definitions, and other contextual factors) and the included studies. If covariates have been measured that could potentially explain the heterogeneity, then these can be included in a meta-regression model. We describe how covariates can be included in a network meta-analysis model and how the output from such an analysis can be used in a CEA model. We outline a model selection procedure to help choose between competing models and stress the importance of clinical input. We illustrate the approach with a health technology assessment of intravenous immunoglobulin for the management of adult patients with severe sepsis in an intensive care setting, which exemplifies how risk of bias information can be incorporated into CEA models. We show that the results of the CEA and value-of-information analyses are sensitive to the model and highlight the importance of sensitivity analyses when conducting CEA in the presence of heterogeneity. The methods presented extend naturally to heterogeneity in other model inputs, such as baseline risk.

Effect of specialist retrieval teams on outcomes in children admitted to paediatric intensive care units in England and Wales: a retrospective cohort study.

BACKGROUND: Intensive care services for children have undergone substantial centralisation in the UK. Along with the establishment of regional paediatric intensive care units (PICUs), specialist retrieval teams were set up to transport critically ill children from other hospitals. We studied the outcome of children transferred from local hospitals to PICUs. METHODS: We analysed data that were gathered for a cohort of children (

Intensive care unit safety culture and outcomes: a US multicenter study.

OBJECTIVE: Safety culture may influence patient outcomes, but evidence is limited. We sought to determine if intensive care unit (ICU) safety culture is independently associated with outcomes. DESIGN: Cohort study combining safety culture survey data with the Project IMPACT Critical Care Medicine (PICCM) clinical database. SETTING: Thirty ICUs participating in the PICCM database. PARTICIPANTS: A total of 65 978 patients admitted January 2001-March 2005. INTERVENTIONS: None. MAIN OUTCOME MEASURES: Hospital mortality and length of stay (LOS). METHODS: From December 2003 to April 2004, we surveyed study ICUs using the Safety Attitudes Questionnaire-ICU version, a validated instrument that assesses safety culture across six factors. We calculated factor mean and percent-positive scores (% respondents with mean score > or =75 on a 0-100 scale) for each ICU, and generated case-mix adjusted, patient-level, ICU-clustered regression analyses to determine the independent association of safety culture and outcome. RESULTS: We achieved a 47.9% response (2103 of 4373 ICU personnel). Culture scores were mostly low to moderate and varied across ICUs (range: 13-88, percent-positive scores). After adjustment for patient, hospital and ICU characteristics, for every 10% decrease in ICU perceptions of management percent-positive score, the odds ratio for hospital mortality was 1.24 (95% CI: 1.07-1.44; P = 0.005). For every 10% decrease in ICU safety climate percent-positive score, LOS increased 15% (95% CI: 1-30%; P = 0.03). Sensitivity analyses for non-response bias consistently associated safety climate with outcome, but also yielded some counterintuitive results. CONCLUSION: In a multicenter study conducted in the USA, perceptions of management and safety climate were moderately associated with outcomes. Future work should further develop methods of assessing safety culture and association with outcomes.

Early observational research and registries during the 2009-2010 influenza A pandemic.

As a critical care community, we have an obligation to provide not only clinical care but also the research that guides initial and subsequent clinical responses during a pandemic. There are many challenges to conducting such research. The first is speed of response. However, given the near inevitability of certain events, for example, viral respiratory illness such as the 2009 pandemic, geographically circumscribed natural disasters, or acts of terror, many study and trial designs should be preplanned and modified quickly when specific events occur. Template case report forms should be available for modification and web entry; centralized research ethics boards and funders should have the opportunity to preview and advise on such research beforehand; and national and international research groups should be prepared to work together on common studies and trials for common challenges. We describe the early international critical care research response to the influenza A 2009 (H1N1) pandemic, including specifics of observational study case report form, registry, and clinical trial design, cooperation of international critical care research organizations, and the early results of these collaborations.

Perceptions of safety culture vary across the intensive care units of a single institution.

OBJECTIVE: To determine whether safety culture factors varied across the intensive care units (ICUs) of a single hospital, between nurses and physicians, and to explore ICU nursing directors' perceptions of their personnel's attitudes. DESIGN: Cross-sectional surveys using the Safety Attitudes Questionnaire-ICU version, a validated, aviation industry-based safety culture survey instrument. It assesses culture across six factors: teamwork climate, perceptions of management, safety climate, stress recognition, job satisfaction, and work environment. SETTING: Four ICUs in one tertiary care hospital. SUBJECTS: All ICU personnel. MEASUREMENTS AND MAIN RESULTS: We conducted the survey from January 1 to April 1, 2003, and achieved a 70.2% response rate (318 of 453). We calculated safety culture factor mean and percent-positive scores (percentage of respondents with a mean score of > or =75 on a 0-100 scale for which 100 is best) for each ICU. We compared mean ICU scores by ANOVA and percent-positive scores by chi-square. Mean and percent-positive scores by job category were modeled using a generalized estimating equations approach and compared using Wald statistics. We asked ICU nursing directors to estimate their personnel's mean scores and generated ratios of their estimates to the actual scores.Overall, factor scores were low to moderate across all factors (range across ICUs: 43.4-74.9 mean scores, 8.6-69.4 percent positive). Mean and percent-positive scores differed significantly (p < .0083, Bonferroni correction) across ICUs, except for stress recognition, which was uniformly low. Compared with physicians, nurses had significantly lower mean working conditions and perceptions of management scores. ICU nursing directors tended to overestimate their personnel's attitudes. This was greatest for teamwork, for which all director estimates exceeded actual scores, with a mean overestimate of 16%. CONCLUSIONS: Significant safety culture variation exists across ICUs of a single hospital. ICU nursing directors tend to overestimate their personnel's attitudes, particularly for teamwork. Culture assessments based on institutional level analysis or director opinion may be flawed.

A Colombian survey found intensive care mortality ratios were better in private vs. public hospitals.

BACKGROUND: Our main outcome was to identify organizational characteristics that help to evaluate the differences between the intensive care mortality ratios adjusted by APACHE II. We incorporated the variation associated with the ranking of institutions simulating its random effects under a binomial distribution. METHODS: A nationwide survey on structure, technology, and staffing resources available in Colombian intensive care units during 1997-1998 was conducted. We collected data on admissions from 20 randomly selected adult medical and surgical intensive care units. RESULTS: The mortality ratio from the 20 intensive care units ranged from 0.59 to 2.36; 80% of the intensive care units had a mortality ratio greater than 1. All four intensive care units with the lowest mortality ratio belonged to private institutions, while four of five institutions with the highest mortality belonged to the public sector. Intensive care units in private institutions also had fewer number of beds, lower median length of stay, lower occupancy rates, higher education training for specialists and nurses and fewer emergency nonelective surgical procedures. CONCLUSION: We successfully accounted for intensive care mortality baseline differences and random effects variations. There were substantial differences between intensive care units in institution type, bed availability, technology, staffing resources, and degree of training, which may have been associated with patient outcome. These results are of crucial importance to track, detect and assess future changes.