Sex differences in in-hospital mortality following a first acute myocardial infarction: symptomatology, delayed presentation, and hospital setting

BACKGROUND: Women generally wait longer than men prior to seeking treatment for acute myocardial infarction (AMI). They are more likely to present with atypical symptoms, and are less likely to be admitted to coronary or intensive care units (CCU or ICU) compared to similarly-aged males. Women are more likely to die during hospital admission. Sex differences in the associations of delayed arrival, admitting ward, and mortality have not been thoroughly investigated.

METHODS: Focusing on presenting symptoms and time of presentation since symptom onset, we evaluated sex differences in in-hospital mortality following a first AMI in 4859 men and women presenting to three emergency departments (ED) from December 2008 to February 2014. Sex-specific risk of mortality associated with admission to either CCU/ICU or medical wards was calculated after adjusting for age, socioeconomic status, triage-assigned urgency of presentation, blood pressure, heart rate, presenting symptoms, timing of presentation since symptom onset, and treatment in the ED. Sex-specific age-adjusted attributable risks were calculated.

RESULTS: Compared to males, females waited longer before seeking treatment, presented more often with atypical symptoms, and were less likely to be admitted to CCU or ICU. Age-adjusted mortality in CCU/ICU or medical wards was higher among females (3.1 and 4.9 % respectively in CCU/ICU and medical wards in females compared to 2.6 and 3.2 % in males). However, after adjusting for variation in presenting symptoms, delayed arrival and other risk factors, risk of death was similar between males and females if they were admitted to CCU or ICU. This was in contrast to those admitted to medical wards. Females admitted to medical wards were 89 % more likely to die than their male counterparts. Arriving in the ED within 60 min of onset of symptoms was not associated with in-hospital mortality. Among males, 2.2 % of in-hospital mortality was attributed to being admitted to medical wards rather than CCU or ICU, while for females this age-adjusted attributable risk was 4.1 %.

CONCLUSIONS: Our study stresses the need to reappraise decision making in patient selection for admission to specialised care units, whilst raising awareness of possible sex-related bias in management of patients diagnosed with an AMI.

Organizational factors associated with decreased mortality among veterans affairs patients with an ICU stay

In-hospital mortality rates associated with an ICU stay are high and vary widely among units. This variation may be related to organizational factors such as staffing patterns, ICU structure, and care processes. We aimed to identify organizational factors associated with variation in in-hospital mortality for patients with an ICU stay. This was a retrospective observational cross-sectional study using administrative data from 34 093 patients from 171 ICUs in 119 Veterans Health Administration hospitals. Staffing and patient data came from Veterans Health Administration national databases. ICU characteristics came from a survey in 2004 of ICUs within the Veterans Health Administration. We conducted multilevel multivariable estimation with patient-, unit-, and hospital-level data. The primary outcome was in-hospital mortality. Of 34 093 patients, 2141 (6.3%)died in the hospital. At the patient level, risk of complications and having a medical diagnosis were significantly associated with a higher risk of mortality. At the unit level, having an interface with the electronic medical record was significantly associated with a lower risk of mortality. The finding that electronic medical records integrated with ICU information systems are associated with lower in-hospital mortality adds support to existing evidence on organizational characteristics associated with in-hospital mortality among ICU patients.

Model to predict inpatient mortality from information gathered at presentation to an emergency department: The Triage Information Mortality Model (TIMM)

OBJECTIVES: To derive and validate a mortality prediction model from information available at ED triage. METHODS: Multivariable logistic regression of variables from administrative datasets to predict inpatient mortality of patients admitted through an ED. Accuracy of the model was assessed using the receiver operating characteristic area under the curve (ROC-AUC) and calibration using the Hosmer-Lemeshow goodness of fit test. The model was derived, internally validated and externally validated. Derivation and internal validation were in a tertiary referral hospital and external validation was in an urban community hospital. RESULTS: The ROC-AUC for the derivation set was 0.859 (95% CI 0.856-0.865), for the internal validation set was 0.848 (95% CI 0.840-0.856) and for the external validation set was 0.837 (95% CI 0.823-0.851). Calibration assessed by the Hosmer-Lemeshow goodness of fit test was good. CONCLUSIONS: The model successfully predicts inpatient mortality from information available at the point of triage in the ED.

Toward a standard approach to measurement and reporting of perioperative mortality rate as a global indicator for surgery

INTRODUCTION: The proportion of patients who die during or after surgery, otherwise known as the perioperative mortality rate (POMR), is a credible indicator of the safety and quality of operative care. Its accuracy and usefulness as a metric, however, particularly one that enables valid comparisons over time or between jurisdictions, has been limited by lack of a standardized approach to measurement and calculation, poor understanding of when in relation to surgery it is best measured, and whether risk-adjustment is needed. Our aim was to evaluate the value of POMR as a global surgery metric by addressing these issues using 4, large, mixed, surgical datasets that represent high-, middle-, and low-income countries. METHODS: We obtained data from the New Zealand National Minimum Dataset, the Geelong Hospital patient management system in Australia, and purpose-built surgical databases in Pietermaritzburg, South Africa, and Port Moresby, Papua New Guinea. For each site, we calculated the POMR overall as well as for nonemergency and emergency admissions. We assessed the effect of admission episodes and procedures as the denominator and the difference between in-hospital POMR and POMR, including postdischarge deaths up to 30 days. To determine the need for risk-adjustment for age and admission urgency, we used univariate and multivariate logistic regression to assess the effect on relative POMR for each site. RESULTS: A total of 1,362,635 patient admissions involving 1,514,242 procedures were included. More than 60% of admissions in Pietermaritzburg and Port Moresby were emergencies, compared with less than 30% in New Zealand and Geelong. Also, Pietermaritzburg and Port Moresby had much younger patient populations (P < .001). A total of 8,655 deaths were recorded within 30 days, and 8-20% of in-hospital deaths occurred on the same day as the first operation. In-hospital POMR ranged approximately 9-fold, from 0.38 per 100 admissions in New Zealand to 3.44 per 100 admissions in Pietermaritzburg. In New Zealand, in-hospital 30-day POMR underestimated total 30-day POMR by approximately one third. The difference in POMR if procedures were used instead of admission episodes ranged from 7 to 70%, although this difference was less when central line and pacemaker insertions were excluded. Age older than 65 years and emergency admission had large, independent effects on POMR but relatively little effect in multivariate analysis on the relative odds of in-hospital death at each site. CONCLUSION: It is possible to collect POMR in countries at all level of development. Although age and admission urgency are strong, independent associations with POMR, a substantial amount of its variance is site-specific and may reflect the safety of operative and anesthetic facilities and processes. Risk-adjustment is desirable but not essential for monitoring system performance. POMR varies depending on the choice of denominator, and in-hospital deaths appear to underestimate 30-day mortality by up to one third. Standardized approaches to reporting and analysis will strengthen the validity of POMR as the principal indicator of the safety of surgery and anesthesia care.

Monitoring and evaluating surgical care: defining perioperative mortality rate and standardising data collection

BACKGROUND: Case volume per 100 000 population and perioperative mortality rate (POMR) are key indicators to monitor and strengthen surgical services. However, comparisons of POMR have been restricted by absence of standardised approaches to when it is measured, the ideal denominator, need for risk adjustment, and whether data are available. We aimed to address these issues and recommend a minimum dataset by analysing four large mixed surgical datasets, two from well-resourced settings with sophisticated electronic patient information systems and two from resource-limited settings where clinicians maintain locally developed databases. METHODS: We obtained data from the New Zealand (NZ) National Minimum Dataset, the Geelong Hospital patient management system in Australia, and purpose-built surgical databases in Pietermaritzburg, South Africa (PMZ) and Port Moresby, Papua New Guinea (PNG). Information was sought on inclusion and exclusion criteria, coding criteria, and completeness of patient identifiers, admission, procedure, discharge and death dates, operation details, urgency of admission, and American Society of Anesthesiologists (ASA) score. Date-related errors were defined as missing dates and impossible discrepancies. For every site, we then calculated the POMR, the effect of admission episodes or procedures as denominator, and the difference between in-hospital POMR and 30-day POMR. To determine the need for risk adjustment, we used univariate and multivariate logistic regression to assess the effect on relative POMR for each site of age, admission urgency, ASA score, and procedure type. FINDINGS: 1 365 773 patient admissions involving 1 514 242 procedures were included, among which 8655 deaths were recorded within 30 days. Database inclusion and exclusion criteria differed substantially. NZ and Geelong records had less than 0·1% date-related errors and greater than 99·9% completeness. PMZ databases had 99·9% or greater completeness of all data except date-related items (94·0%). PNG had 99·9% or greater completeness for date of birth or age and admission date and operative procedure, but 80-83% completeness of patient identifiers and date related items. Coding of procedures was not standardised, and only NZ recorded ASA status and complete post-discharge mortality. In-hospital POMR range was 0·38% in NZ to 3·44% in PMZ, and in NZ it underestimated 30-day POMR by roughly a third. The difference in POMR by procedures instead of admission episodes as denominator ranged from 10% to 70%. Age older than 65 years and emergency admission had large independent effects on POMR, but relatively little effect in multivariate analysis on the relative odds of in-hospital death at each site. INTERPRETATION: Hospitals can collect and provide data for case volume and POMR without sophisticated electronic information systems. POMR should initially be defined by in-hospital mortality because post-discharge deaths are not usually recorded, and with procedures as denominator because details allowing linkage of several operations within one patient's admission are not always present. Although age and admission urgency are independently associated with POMR, and ASA and case mix were not included, risk adjustment might not be essential because the relative odds between sites persisted. Standardisation of inclusion criteria and definitions is needed, as is attention to accuracy and completeness of dates of procedures, discharge and death. A one-page, paper-based form, or alternatively a simple electronic data collection form, containing a minimum dataset commenced in the operating theatre could facilitate this process. FUNDING: None.

A prospective observational study of prevalence and outcomes of patients with Gold Standard Framework criteria in a tertiary regional Australian Hospital

OBJECTIVES: Report the use of an objective tool, UK Gold Standards Framework (GSF) criteria, to describe the prevalence, recognition and outcomes of patients with palliative care needs in an Australian acute health setting. The rationale for this is to enable hospital doctors to identify patients who should have a patient-centred discussion about goals of care in hospital.

DESIGN: Prospective, observational, cohort study.

PARTICIPANTS: Adult in-patients during two separate 24 h periods.

MAIN OUTCOME MEASURES: Prevalence of in-patients with GSF criteria, documentation of treatment limitations, hospital and 1 year survival, admission and discharge destination and multivariate regression analysis of factors associated with the presence of hospital treatment limitations and 1 year survival.

RESULTS: Of 626 in-patients reviewed, 171 (27.3%) had at least one GSF criterion, with documentation of a treatment limitation discussion in 60 (30.5%) of those patients who had GSF criteria. Hospital mortality was 9.9%, 1 year mortality 50.3% and 3-year mortality 70.2% in patients with GSF criteria. One-year mortality was highest in patients with GSF cancer (73%), renal failure (67%) and heart failure (60%) criteria. Multivariate analysis revealed age, hospital length of stay and presence of the GSF chronic obstructive pulmonary disease criteria were independently associated with the likelihood of an in-hospital treatment limitation. Non-survivors at 3 years were more likely to have a GSF cancer (25% vs 6%, p=0.004), neurological (10% vs 3%, p=0.04), or frailty (45% vs 3%, p=0.04) criteria. After multivariate logistic regression GSF cancer criteria, renal failure criteria and the presence of two or more GSF clinical criteria were independently associated with increased risk of death at 3 years. Patients returning home to live reduced from 69% (preadmission) to 27% after discharge.

CONCLUSIONS: The use of an objective clinical tool identifies a high prevalence of patients with palliative care needs in the acute tertiary Australian hospital setting, with a high 1 year mortality and poor return to independence in this population. The low rate of documentation of discussions about treatment limitations in this population suggests palliative care needs are not recognised and discussed in the majority of patients.

TRIAL REGISTRATION NUMBER: 11/121.

Patient physiological status during emergency care and rapid response team or cardiac arrest team activation during early hospital admission

OBJECTIVES: The objective of this study was to examine the relationship between rapid response team (RRT) or cardiac arrest team (CAT) activation within 72 h of emergency admission and (i) physiological status in the emergency department (ED) and (ii) risk for ICU admission and in-hospital mortality.

METHODS: A retrospective matched cohort study was conducted in three hospitals in Melbourne, Australia. The exposed cohort (n=660) included randomly selected adults admitted to the medical or surgical ward through the ED who had RRT or CAT activation within 72 h of admission. Unexposed matched controls (n=1320) did not have RRT or CAT activation.

RESULTS: The exposed cohort was more likely to have physiological abnormalities fulfilling hospital RRT activation criteria during ED care (36.7 vs. 23.8%, P<0.001). After adjusting for confounders, tachypnoea (adjusted odds ratio=1.92, 95% confidence interval: 1.38-2.67) or hypotension (AOR=1.43, 95% confidence interval: 1.00-2.03), fulfilling RRT activation criteria during ED care, was associated with RRT or CAT activation within 72 h of admission. The exposed cohort had more in-hospital deaths (16.5 vs. 3.6%, P<0.001), more unexpected in-hospital deaths (2.05 vs. 0.2%, P<0.001), more ICU admissions (11.8 vs. 0.7%, P<0.001) and longer lengths of hospital stay (median=8 vs. 5 days, P<0.001).

CONCLUSION: CAT/RRT activations within 72 h of emergency admission are associated with higher mortality and increased length of stay. Factors associated with CAT/RRT activation in the wards are often identifiable when patients are in the ED. Further studies are required to determine whether early identification and intervention in patients at risk for RRT or CAT activation can improve their eventual outcomes.

Multi-task transfer learning for in hospital-death prediction for 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 hypothesizethat 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 singletask 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.

Visualising a state-wide patient data collection : a case study to expand the audience for healthcare data

This paper describes the application of existing and novel adaptations of visualisation techniques to routinely collected health data. The aim of this case study is to examine the capacity for visualisation approaches to quickly and e ectively inform clinical, policy, and scal decision making to improve healthcare provision. We demonstrate the use of interactive graphics, fluctuation plots, mosaic plots, time plots, heatmaps, and disease maps to visualise patient admission, transfer, in-hospital mortality, morbidity coding, execution of diagnosis and treatment guidelines, and the temporal and spatial variations of diseases. The relative e ectiveness of these techniques and associated challenges are discussed.

The timing of Rapid-Response Team activations: a multicentre international study

Background:
Most studies of Rapid-Response Teams (RRTs) assess their effect on outcomes of all hospitalised patients. Little information exists on RRT activation patterns or why RRT calls are needed. Triage error may necessitate RRT review of ward patients shortly after hospital admission. RRT diurnal activation rates may reflect the likely frequency of caregiver visits.

Objectives:
To study the timing of RRT calls in relation to time of day and day of week, and their frequency and outcomes in relation to days after hospital admission.

Methods:
We prospectively studied RRT calls over 1 month in seven hospitals during 2009, collecting data on patient age, sex, admitting unit, admission source, limitations of medical therapy (LOMTs), and admission and discharge dates. We assessed the timing of RRT calls in relation to hospital admission and circadian variation; and differences in characteristics and outcomes of calls occurring early (Days 0 and 1) versus late (after Day 7) after hospital admission.

Results:
There were 652 RRT calls for 518 patients. Calls were more likely on Mondays (P=0.018) and during work hours (P<0.0001) but less likely on weekends (P=0.003) or overnight (P<0.001). There were 177 early calls (27.1%) and 198 late calls (30.4%). Early calls involved younger patients (median ages, 67.5 years [early calls] v 73 years [late calls]; P= 0.01), fewer LOMTs (P=0.029), and lower in hospital mortality (12.8% [early calls] v 32.3% [late calls]; P<0.0001). The mortality difference remained in patients without LOMTs (5.6% [early calls] v 19.6% [late calls]; P=0.003).

Conclusions:
About one-quarter of RRT calls occurred shortly after hospital admission, and were more common when caregivers were around. Early calls may partially reflect suboptimal triage, though the associated mortality appeared low. Late calls may reflect suboptimal end-of-life care planning, and the associated mortality was high. There is a need to further assess the epidemiology of RRT calls at different phases of the hospital stay.

Emergency responses for clinical deterioration within and beyond 24 hours of emergency admission: Patient outcomes and ICU implications

Introduction: The National Emergency Access Target was implemented to ensure 90% of patients leave emergency departments (EDs) within 4h. The impact of time driven performance on the number of physiologically unstable ward-based patients is unknown. An increase in clinical deterioration episodes potentially leading to adverse events will have resource implications for intensive care units (ICUs).
Objectives: To compare the characteristics and outcomes of patients who required an emergency response for clinical deterioration (cardiac arrest team or rapid response system activation) within and beyond 24 h of emergency admission to general medical and surgical units.
Methods: A retrospective exploratory design was used. The study site was a 365 bed urban hospital in Melbourne. Emergency responses for clinical deterioration during 2012 were examined.
Results: Of 819 emergency responses for clinical deterioration, 587 patients were admitted via ED. The median time to first responsewas59h, 28.4% of patients required this <24 h after admission. One in eight patients required ICU admission. Comparison of patients requiring a response within and beyond 24h of admission showed no significant differences in age, gender, waiting times, ED length of stay or in-hospital mortality rates. Patients in whom first emergency response occurred <24h after admission were less likely to be admitted to ICU immediately following the emergency response (7.6% vs 13.9%, p-0.039), less likely to have recurrent emergency responses during their hospitalisation (9.7% vs 34.0%, p<0.001), and had shorter median hospital length of stay (7 vs 11 days, p<0.001).
Conclusions: Considerable ICU resources were utilised given one in eight patients required ICU admission following emergency response, and patients admitted via the ED constituted 55% of all rapid response system activations. Exploring potential antecedents to clinical deterioration in this cohort may assist in establishing risk management strategies to reduce utilisation of ICU resources.

Multisite analysis of the timing and outcomes of unplanned transfers from subacute to acute care

Objective The aim of the present study was to examine the timing and outcomes of patients requiring an unplanned transfer from subacute to acute care. Methods Subacute care in-patients requiring unplanned transfer to an acute care facility within four Victorian health services from 1 January to 31 December 2010 were included in the study. Data were collected using retrospective audit. The primary outcome was transfer within 24h of subacute care admission. Results In all, 431 patients (median age 81 years) had unplanned transfers; of these, 37.8% had a limitation of medical treatment (LOMT) order. The median subacute care length of stay was 43h: 29.0% of patients were transferred within 24h and 83.5% were transferred within 72h of subacute care admission. Predictors of transfer within 24h were comorbidity weighting (odds ratio (OR) 1.1, P≤0.02) and LOMT order (OR 2.1, P<0.01). Hospital admission occurred in 87.2% of patients and 15.4% died in hospital. Predictors of in-hospital mortality were comorbidity weighting (OR 1.2, P<0.01) and the number of physiological abnormalities in the 24h preceding transfer (OR 1.3, P<0.01). Conclusions There is a high rate of unplanned transfers to acute care within 24h of admission to subacute care. Unplanned transfers are associated with high hospital admission and in-hospital mortality rates. What is known about the topic? Subacute care is becoming a high acuity environment where many patients are at significant risk of clinical deterioration. Systems for recognising and responding to deteriorating patients are well developed in acute care, but still developing in subacute care. What does this paper add? This is the first Australian multisite study of clinical deterioration in patients situated in subacute care facilities. One-third of unplanned transfers occur within 24h of admission to subacute care. Patients who require unplanned transfer from subacute to acute care have unexpectedly high hospital admission rates and high in-hospital mortality rates. The frequency and completeness of physiological monitoring preceding transfer was low. What are the implications for practitioners? Patients in subacute care require regular physiological assessment and early escalation of care if there are physiological abnormalities. Risk of clinical deterioration should be a factor in the decision to admit patients to subacute care after an acute illness or injury. There is a need to improve systems for recognising and responding to deteriorating patients in subacute care settings.

Epidemiology of early Rapid Response Team activation after emergency department admission

BACKGROUND: Rapid Response Team (RRT) calls can often occur within 24h of hospital admission to a general ward. We seek to determine whether it is possible to identify these patients before there is a significant clinical deterioration. METHODS: Retrospective case-controlled study comparing patient characteristics, vital signs, and hospital outcomes in patients triggering RRT activation within 24h of ED admission (cases) with matched ED admissions not receiving a RRT call (controls). RESULTS: Over 12 months, there were 154 early RRT calls. Compared with controls, cases had a higher heart rate (HR) at triage (92 vs. 84beats/min; p=0.008); after 3h in the ED (91 vs. 80beats/min; p=0.0007); and at ED discharge (91 vs. 81beats/min; p=0.0005). Respiratory rate (RR) was also higher at triage (21.2 vs. 19.2breaths/min; p=0.001). On multiple variable analysis, RR at triage and HR before ward transfer predicted early RRT activation: OR 1.07 [95% CI 1.02-1.12] for each 1breath/min increase in RR; and 1.02 [95% CI 1.002-1.030] for each beat/minute increase in HR, respectively. Study patients required transfer to the intensive care in approximately 20% of cases and also had a greater mortality: (21% vs. 6%; OR 4.65 [95% CI 1.86-11.65]; p=0.0003) compared with controls. CONCLUSIONS: Patients that trigger RRT calls within 24h of admission have a fourfold increase in risk of in-hospital mortality. Such patients may be identified by greater tachycardia and tachypnoea in the ED.

Protocol-directed sedation versus non-protocol-directed sedation to reduce duration of mechanical ventilation in mechanically ventilated intensive care patients (review)

Background : The sedation needs of critically ill patients have been recognized as a core component of critical care and meeting these is vital to assist recovery and ensure humane treatment. There is growing evidence to suggest that sedation requirements are not always optimally managed. Sub-optimal sedation incorporates both under- and over-sedation and has been linked to both short-term (e.g. length of stay) and long-term (e.g. psychological recovery) outcomes. Various strategies have been proposed to improve sedation management and address aspects of assessment as well as delivery of sedation.

Objectives : To assess the effects of protocol-directed sedation management on the duration of mechanical ventilation and other relevant patient outcomes in mechanically ventilated intensive care unit (ICU) patients. We looked at various outcomes and examined the role of bias in order to examine the level of evidence for this intervention.

Search methods : We searched the Cochrane Central Register of Controlled trials (CENTRAL) (2013; Issue 11), MEDLINE (OvidSP) (1990 to November 2013), EMBASE (OvidSP) (1990 to November 2013), CINAHL (BIREME host) (1990 to November 2013), Database of Abstracts of Reviews of Effects (DARE) (1990 to November 2013), LILACS (1990 to November 2013), Current Controlled Trials and US National Institutes of Health Clinical Research Studies (1990 to November 2013), and reference lists of articles. We re-ran the search in October 2014. We will deal with any studies of interest when we update the review.

Selection criteria : We included randomized controlled trials (RCTs) conducted in adult ICUs comparing management with and without protocol-directed sedation.

Data collection and analysis : Two authors screened the titles and abstracts and then the full-text reports identified from our electronic search. We assessed seven domains of potential risk of bias for the included studies. We examined the clinical, methodological and statistical heterogeneity and used the random-effects model for meta-analysis where we considered it appropriate. We calculated the mean difference (MD) for duration of mechanical ventilation and risk ratio (RR) for mortality across studies, with 95% confidence intervals (CI).

Main results : We identified two eligible studies with 633 participants. Both included studies compared the use of protocol-directed sedation, specifically protocols delivered by nurses, with usual care. We rated the risk of selection bias due to random sequence generation low for one study and unclear for one study. The risk of selection bias related to allocation concealment was low for both studies. We also assessed detection and attrition bias as low for both studies while we considered performance bias high due to the inability to blind participants and clinicians in both studies. Risk due to other sources of bias, such as potential for contamination between groups and reporting bias, was considered unclear. There was no clear evidence of differences in duration of mechanical ventilation (MD -5.74 hours, 95% CI -62.01 to 50.53, low quality evidence), ICU length of stay (MD -0.62 days, 95% CI -2.97 to 1.73) and hospital length of stay (MD -3.78 days, 95% CI -8.54 to 0.97) between people being managed with protocol-directed sedation versus usual care. Similarly, there was no clear evidence of difference in hospital mortality between the two groups (RR 0.96, 95% CI 0.71 to 1.31, low quality evidence). ICU mortality was only reported in one study preventing pooling of data. There was no clear evidence of difference in the incidence of tracheostomy (RR 0.77, 95% CI 0.31 to 1.89). The studies reported few adverse event outcomes; one study reported self extubation while the other study reported re-intubation; given this difference in outcomes, pooling of data was not possible. There was significant heterogeneity between studies for duration of mechanical ventilation (I2 = 86%, P value = 0.008), ICU length of stay (I2 = 82%, P value = 0.02) and incidence of tracheostomy (I2 = 76%, P value = 0.04), with one study finding a reduction in duration of mechanical ventilation and incidence of tracheostomy and the other study finding no difference.

Authors' conclusions : There is currently insufficient evidence to evaluate the effectiveness of protocol-directed sedation. Results from the two RCTs were conflicting, resulting in the quality of the body of evidence as a whole being assessed as low. Further studies, taking into account contextual and clinician characteristics in different ICU environments, are necessary to inform future practice. Methodological strategies to reduce the risk of bias need to be considered in future studies.

Clinical review criteria and medical emergency teams: evaluating a two-tier rapid response system

OBJECTIVES: To assess the prevalence of patients fulfilling clinical review criteria (CRC), to determine activation rates for CRC assessments, to compare baseline characteristics and outcomes of patients who fulfilled CRC with patients who did not, and to identify the documented nursing actions in response to CRC values. DESIGN, SETTING AND PARTICIPANTS: A cross-sectional study using a retrospective medical record audit, in a universityaffiliated, tertiary referral hospital with a two-tier rapid response system in Melbourne, Australia. We used a convenience sample of hospital inpatients on general medical, surgical and specialist service wards admitted during a 24-hour period in 2013. MAIN OUTCOME MEASURES: Medical emergency team (MET) or code blue activation, unplanned intensive care unit admissions, hospital length of stay and inhospital mortality. For patients who fulfilled CRC or MET criteria during the 24- hour period, the specific criteria fulfilled, escalation treatments and outcomes were collected. RESULTS: Of the sample (N = 422), 81 patients (19%) fulfilled CRC on 109 occasions. From 109 CRC events, 66 patients (81%) had at least one observation fulfilling CRC, and 15 patients (18%) met CRC on multiple occasions. The documented escalation rate was 58 of 109 events (53%). The number of patients who fulfilled CRC and subsequent MET call activation criteria within 24 hours was significantly greater than the number who did not meet CRC (P < 0.001). CONCLUSIONS: About one in five patients reached CRC during the study period; these patients were about four times more likely to also fulfil MET call criteria. Contrary to hospital policy, escalation was not documented for about half the patients meeting CRC values. Despite the clarity of escalation procedures on the graphic observation chart, escalation remains an ongoing problem. Further research is needed on the impact on patient outcomes over time and to understand factors influencing staff response.