Kathy Rowan

Epidemiology of severe sepsis occurring in the first 24 hrs in intensive care units in england, Wales, and Northern Ireland

ObjectiveTo investigate the numbers, clinical characteristics, resource use, and outcomes of admissions who met precise clinical and physiologic criteria for severe sepsis (as defined in the PROWESS trial) in the first 24 hrs in the intensive care unit. DesignObservational cohort study, with retrospective analysis of prospectively collected data. SettingNinety-one adult general intensive care units in England, Wales, and Northern Ireland between 1995 and 2000. PatientsPatients were 56,673 adult admissions. InterventionsNone. Measurements and Main ResultsWe found that 27.1% of adult intensive care unit admissions met severe sepsis criteria in the first 24 hrs in the intensive care unit. Most were nonsurgical (67%), and the most common organ system dysfunctions were seen in the cardiovascular (88%) and respiratory (81%) systems. Modeling the data for England and Wales for 1997 suggested that 51 (95% confidence interval, 46–58) per 100,000 population per year were admitted to intensive care units and met severe sepsis criteria in the first 24 hrs.Of the intensive care unit admissions who met severe sepsis criteria in the first 24 hrs, 35% died before intensive care unit discharge and 47% died during their hospital stay. Hospital mortality rate ranged from 17% in the 16–19 age group to 64% in those >85 yrs. In England and Wales in 1997, an estimated 24 (95% confidence interval, 21–28) per 100,000 population per year died after intensive care unit admissions with severe sepsis in the first 24 hrs.For intensive care unit admissions who met severe sepsis criteria in the first 24 hrs, median intensive care unit length of stay was 3.56 days (interquartile range, 1.50–9.32) and median hospital length of stay was 18 days (interquartile range, 8–36 days). These admissions used 45% of the intensive care unit and 33% of the hospital bed days used by all intensive care unit admissions. ConclusionsSevere sepsis is common and presents a major challenge for clinicians, managers, and healthcare policymakers. Intensive care unit admissions meeting severe sepsis criteria have a high mortality rate and high resource use.

High-frequency oscillation for acute respiratory distress syndrome.

BACKGROUND Patients with the acute respiratory distress syndrome (ARDS) require mechanical ventilation to maintain arterial oxygenation, but this treatment may produce secondary lung injury. High-frequency oscillatory ventilation (HFOV) may reduce this secondary damage. METHODS In a multicenter study, we randomly assigned adults requiring mechanical ventilation for ARDS to undergo either HFOV with a Novalung R100 ventilator (Metran) or usual ventilatory care. All the patients had a ratio of the partial pressure of arterial oxygen (PaO) to the fraction of inspired oxygen (FiO) of 200 mm Hg (26.7 kPa) or less and an expected duration of ventilation of at least 2 days. The primary outcome was all-cause mortality 30 days after randomization. RESULTS There was no significant between-group difference in the primary outcome, which occurred in 166 of 398 patients (41.7%) in the HFOV group and 163 of 397 patients (41.1%) in the conventional-ventilation group (P=0.85 by the chi-square test). After adjustment for study center, sex, score on the Acute Physiology and Chronic Health Evaluation (APACHE) II, and the initial PaO:FiO ratio, the odds ratio for survival in the conventional-ventilation group was 1.03 (95% confidence interval, 0.75 to 1.40; P=0.87 by logistic regression). CONCLUSIONS The use of HFOV had no significant effect on 30-day mortality in patients undergoing mechanical ventilation for ARDS. (Funded by the National Institute for Health Research Health Technology Assessment Programme; OSCAR Current Controlled Trials number, ISRCTN10416500.).

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.

Use of consensus development to establish national research priorities in critical care

Abstract Objectives: To test the feasibility of using a nominal group technique to establish clinical and health services research priorities in critical care and to test the representativeness of the groups views. Design: Generation of topics by means of a national survey; a nominal group technique to establish the level of consensus; a survey to test the representativeness of the results. Setting: United Kingdom and Republic of Ireland. Subjects: Nominal group composed of 10 doctors (8 consultants, 2 trainees) and 2 nurses. Main outcome measure: Level of support (median) and level of agreement (mean absolute deviation from the median) derived from a 9 point Likert scale. Results: Of the 325 intensive care units approached, 187 (58%) responded, providing about 1000 suggestions for research. Of the 106 most frequently suggested topics considered by the nominal group, 37 attracted strong support, 48 moderate support and 21 weak support. There was more agreement after the group had met—overall mean of the mean absolute deviations from the median fell from 1.41 to 1.26. The groups views represented the views of the wider community of critical care staff (r=0.73, P<0.01). There was no significant difference in the views of staff from teaching or from non-teaching hospitals. Of the 37 topics that attracted the strongest support, 24 were concerned with organisational aspects of critical care and only 13 with technology assessment or clinical research. Conclusions: A nominal group technique is feasible and reliable for determining research priorities among clinicians. This approach is more democratic and transparent than the traditional methods used by research funding bodies. The results suggest that clinicians perceive research into the best ways of delivering and organising services as a high priority.

Recalibration of risk prediction models in a large multicenter cohort of admissions to adult, general critical care units in the United Kingdom.

Objective:To assess the performance of published risk prediction models in common use in adult critical care in the United Kingdom and to recalibrate these models in a large representative database of critical care admissions. Design:Prospective cohort study. Setting:A total of 163 adult general critical care units in England, Wales, and Northern Ireland, during the period of December 1995 to August 2003. Patients:A total of 231,930 admissions, of which 141,106 met inclusion criteria and had sufficient data recorded for all risk prediction models. Interventions:None. Measurements and Main Results:The published versions of the Acute Physiology and Chronic Health Evaluation (APACHE) II, APACHE II UK, APACHE III, Simplified Acute Physiology Score (SAPS) II, and Mortality Probability Models (MPM) II were evaluated for discrimination and calibration by means of a combination of appropriate statistical measures recommended by an expert steering committee. All models showed good discrimination (the c index varied from 0.803 to 0.832) but imperfect calibration. Recalibration of the models, which was performed by both the Cox method and re-estimating coefficients, led to improved discrimination and calibration, although all models still showed significant departures from perfect calibration. Conclusions:Risk prediction models developed in another country require validation and recalibration before being used to provide risk-adjusted outcomes within a new country setting. Periodic reassessment is beneficial to ensure calibration is maintained.

A new risk prediction model for critical care: The Intensive Care National Audit & Research Centre (ICNARC) model*

Objective:To develop a new model to improve risk prediction for admissions to adult critical care units in the UK. Design:Prospective cohort study. Setting:The setting was 163 adult, general critical care units in England, Wales, and Northern Ireland, December 1995 to August 2003. Patients:Patients were 216,626 critical care admissions. Interventions:None. Measurements and Main Results:The performance of different approaches to modeling physiologic measurements was evaluated, and the best methods were selected to produce a new physiology score. This physiology score was combined with other information relating to the critical care admission—age, diagnostic category, source of admission, and cardiopulmonary resuscitation before admission—to develop a risk prediction model. Modeling interactions between diagnostic category and physiology score enabled the inclusion of groups of admissions that are frequently excluded from risk prediction models. The new model showed good discrimination (mean c index 0.870) and fit (mean Shapiro’s R 0.665, mean Brier’s score 0.132) in 200 repeated validation samples and performed well when compared with recalibrated versions of existing published risk prediction models in the cohort of patients eligible for all models. The hypothesis of perfect fit was rejected for all models, including the Intensive Care National Audit & Research Centre (ICNARC) model, as is to be expected in such a large cohort. Conclusions:The ICNARC model demonstrated better discrimination and overall fit than existing risk prediction models, even following recalibration of these models. We recommend it be used to replace previously published models for risk adjustment in the UK.

ABC of intensive care: outcome data and scoring systems.

Intensive care has developed over the past 30 years with little rigorous scientific evidence about what is, or is not, clinically effective Without these data, doctors delivering intensive care often have to decide which patients can benefit most. Scoring systems have been developed in response to an increasing emphasis on the evaluation and monitoring of health services. These systems enable comparative audit and evaluative research of intensive care. ![][1] Although rigorous experiments or large randomised controlled trials are the gold standard for evaluating existing or new interventions, these are not always possible in intensive care. For example, it is unethical to randomly allocate severely ill patients to receive intensive care or general ward care. The alternative is to use observational methods that study the outcome of care patients receive as part of their natural treatment. However, before inferences can be drawn about outcomes of treatment in such studies the characteristics of the patients admitted to intensive care have to be taken into account This process is known as adjusting for case mix. Distribution of intensive care unit and hospital mortality across hospitals The death rate of patients admitted to intensive care units is much higher than that of other hospital patients. Data for 1995-8 on 22 057 patients admitted to 62 units in the case mix programme, the national comparative audit of patient outcome, showed an intensive care mortality of 20.6% and total hospital mortality of 30.9%. However, mortality across units varied more than threefold. Clearly, it is important to account for this variation. Given the relatively high mortality among intensive care patients, death is a sensitive, appropriate, and meaningful measure of outcome. However, death can result from many factors other than ineffective care Outcome depends not only on the input (equipment, staff) and the processes of care (type, skill, and … [1]: /embed/graphic-1.gif

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 (<or=16 years) admitted consecutively to 29 PICUs in England and Wales during 4 years (Jan 1, 2005, to Dec 31, 2008). We compared unplanned admissions from wards within the same hospital as the PICU and from other hospitals; interhospital transfers by non-specialist and specialist retrieval teams; and patients transferred to their nearest PICU and those who were not. Primary outcome measures were mortality rate in PICU and length of stay in PICU. We analysed data by use of logistic regression analysis. FINDINGS There were 57 997 admissions to PICUs during the study. Nearly half of unplanned admissions (17 649 [53%] of 33 492) were from other hospitals. Although children admitted from other hospitals were younger (median 10 months [IQR 1-55] vs 18 months [3-85]), sicker at admission (median predicted risk of mortality 6% [4-10] vs 4% [2-7]), stayed longer in PICUs (75 h [33-153] vs 43 h [18-116]), and had higher crude mortality rates (1384 [8%] of 17 649 vs 996 [6%] of 15 843; odds ratio 1.27, 95% CI 1.16-1.38), the risk-adjusted mortality rate in PICUs was lower than among children admitted from within the same hospital (0.65, 0.53-0.80). In a multivariable analysis, use of a specialist retrieval team for transfer was associated with improved survival (0.58, 0.39-0.87). INTERPRETATION These findings support the policy of combining centralisation of intensive care services for children with transfer by specialist retrieval teams. FUNDING National Clinical Audit and Patient Outcomes Programme through Healthcare Quality Improvement Partnership, Health Commission Wales Specialised Services, National Health Service (NHS) Lothian and National Service Division NHS Scotland, the Royal Belfast Hospital for Sick Children, and the Pan Thames PICU Commissioning Consortium.

Assessment and Optimization of Mortality Prediction Tools for Admissions to Pediatric Intensive Care in the United Kingdom

OBJECTIVE. To assess the Pediatric Risk of Mortality (PRISM, PRISM III-12, and PRISM III-24) systems and the Pediatric Index of Mortality (PIM and PIM2) systems for use in comparing the risk-adjusted mortality of children after admission for pediatric intensive care in the United Kingdom. METHODS. All PICUs in the United Kingdom were invited to participate. Predicted probability of PICU mortality was calculated using the published algorithms for PIM, PIM2, and PRISM and compared with observed mortality. These scores, along with PRISM III-12 and PRISM III-24, whose algorithms are not published, were optimized for the United Kingdom. RESULTS. Of 26 PICUs in the United Kingdom, 22 (85%) were recruited, and sufficient prospective data were collected from 18 (69%) units on 10197 (98%) of 10385 admissions between March 2001 and February 2002. All published tools were found to have poor calibration but provided good discriminatory power. After estimation of UK-specific coefficients, only PIM2, PRISM III-12, and PRISM III-24 had satisfactory calibration. All models provided good discriminatory power. Funnel plots for all of the recalibrated models indicated that the risk-adjusted mortality for all units was consistent with random variation. CONCLUSIONS. PIM2, PRISM III-12, and PRISM III-24 all were found to be suitable for use in a UK PICU setting. All tools provided similar conclusions in assessing the distribution of risk-adjusted mortality in UK PICUs. It now is important that these tools be used to monitor outcome and improve the quality of pediatric intensive care within the United Kingdom.

Evaluation of modernisation of adult critical care services in England: time series and cost effectiveness analysis

Objective To evaluate the impact and cost effectiveness of a programme to transform adult critical care throughout England initiated in late 2000. Design Evaluation of trends in inputs, processes, and outcomes during 1998-2000 compared with last quarter of 2000-6. Setting 96 critical care units in England. Participants 349 817 admissions to critical care units. Interventions Adoption of key elements of modernisation and increases in capacity. Units were categorised according to when they adopted key elements of modernisation and increases in capacity. Main outcome measures Trends in inputs (beds, costs), processes (transfers between units, discharge practices, length of stay, readmissions), and outcomes (unit and hospital mortality), with adjustment for case mix. Differences in annual costs and quality adjusted life years (QALYs) adjusted for case mix were used to calculate net monetary benefits (valuing a QALY gain at £20 000 (