Daniel J. Niven

Do intensivist staffing patterns influence hospital mortality following ICU admission? A systematic review and meta-analyses.

Objective:To determine the effect of different intensivist staffing models on clinical outcomes for critically ill patients. Data Sources:A sensitive search of electronic databases and hand-search of major critical care journals and conference proceedings was completed in October 2012. Study Selection:Comparative observational studies examining intensivist staffing patterns and reporting hospital or ICU mortality were included. Data Extraction:Of 16,774 citations, 52 studies met the inclusion criteria. We used random-effects meta-analytic models unadjusted for case-mix or cluster effects and quantified between-study heterogeneity using I2. Study quality was assessed using the Newcastle-Ottawa Score for cohort studies. Data Synthesis:High-intensity staffing (i.e., transfer of care to an intensivist-led team or mandatory consultation of an intensivist), compared to low-intensity staffing, was associated with lower hospital mortality (risk ratio, 0.83; 95% CI, 0.70–0.99) and ICU mortality (pooled risk ratio, 0.81; 95% CI, 0.68–0.96). Significant reductions in hospital and ICU length of stay were seen (–0.17 d, 95% CI, –0.31 to –0.03 d and –0.38 d, 95% CI, –0.55 to –0.20 d, respectively). Within high-intensity staffing models, 24-hour in-hospital intensivist coverage, compared to daytime only coverage, did not improved hospital or ICU mortality (risk ratio, 0.97; 95% CI, 0.89–1.1 and risk ratio, 0.88; 95% CI, 0.70–1.1). The benefit of high-intensity staffing was concentrated in surgical (risk ratio, 0.84; 95% CI, 0.44–1.6) and combined medical-surgical (risk ratio, 0.76; 95% CI, 0.66–0.83) ICUs, as compared to medical (risk ratio, 1.1; 95% CI, 0.83–1.5) ICUs. The effect on hospital mortality varied throughout different decades; pooled risk ratios were 0.74 (95% CI, 0.63–0.87) from 1980 to 1989, 0.96 (95% CI, 0.69–1.3) from 1990 to 1999, 0.70 (95% CI, 0.54–0.90) from 2000 to 2009, and 1.2 (95% CI, 0.84–1.8) from 2010 to 2012. These findings were similar for ICU mortality. Conclusions:High-intensity staffing is associated with reduced ICU and hospital mortality. Within a high-intensity model, 24-hour in-hospital intensivist coverage did not reduce hospital, or ICU, mortality. Benefits seen in mortality were dependent on the type of ICU and decade of publication.

Critical care transition programs and the risk of readmission or death after discharge from an ICU: a systematic review and meta-analysis.

Objective:To determine whether critical care transition programs reduce the risk of ICU readmission or death, when compared with standard care among adults who survived their incident ICU admission. Data Sources:MEDLINE, EMBASE, CENTRAL, CINAHL, and two clinical trial registries were searched from inception to October 2012. Study Selection:Studies that examined the effects of critical care transition programs on the risk of ICU readmission or death among patients discharged from ICU were selected for review. A critical care transition program included any rapid response team, medical emergency team, critical care outreach team, or ICU nurse liaison program that provided follow-up for patients discharged from ICU. Data Extraction:Two reviewers independently extracted data on study characteristics, transition program characteristics, and outcomes (number of ICU readmissions and in-hospital deaths following discharge from ICU). Data Synthesis:From 3,120 citations, nine before-and-after studies were included. The studies examined medical-surgical populations and described transition programs that were a component of a hospital’s outreach team (n = 6) or nurse liaison program (n = 3). Meta-analysis using a fixed-effect model demonstrated a reduced risk of ICU readmission (risk ratio, 0.87 [95% CI, 0.76–0.99]; p = 0.03; I2 = 0%) but no significant reduction in hospital mortality (risk ratio, 0.84 [95% CI, 0.66–1.05]; p = 0.1; I2 = 16%) associated with a critical care transition program. The risk of ICU readmission was similar whether the transition program was included within an outreach team or a nurse liaison program and did not depend on the presence of an intensivist. Conclusions:Critical care transition programs appear to reduce the risk of ICU readmission in patients discharged from ICU to a general hospital ward. Given methodological limitations of the included before-and-after studies, additional research should confirm these observations and explore the ideal model for these programs before recommending implementation.

Accuracy of Peripheral Thermometers for Estimating Temperature: A Systematic Review and Meta-analysis

Abnormalities in body temperature (that is, fever and hypothermia) are common (14). Such abnormalities are components of diagnostic criteria for certain disorders (58), influence clinical management decisions (9, 10), and are associated with increased mortality in certain patient populations (11, 12). For most adult and pediatric populations, fever is defined as a body temperature of 38.0C or higher (1315); this definition can vary according to certain patient characteristics (for example, acute brain injury [16, 17]), institutional preference (18), and different methods of assessing body temperature (Appendix Table 1). Hypothermia is also variably defined but generally accepted to occur when body temperature is less than 36.0C (19). Appendix Table 1. Temperature Measurement Sites and Methods Accurate assessment of temperature is important. Thermometers that measure temperature from an intravascular site are considered the gold standard (20). Guidelines in adults recommend measuring temperature from one of several central options (for example, the rectum) (21), whereas guidelines in children suggest measuring from a peripheral site (for example, the axilla) (22, 23, 24). Neither recommendation is based on systematic syntheses of available evidence pertaining to thermometer accuracy (15, 25, 26). Many small studies have examined the accuracy of peripheral thermometers and report generally discordant results. Systematic reviews that examined the accuracy of peripheral thermometers in adults were either focused on selectively chosen populations or did not use established meta-analytic methods (27, 28). Meta-analyses examining infrared ear and axillary thermometry compared with a rectal reference standard in children found wide limits of agreement (LOA) (25, 26). Given these drawbacks and the potential for advancements in thermometer technology (25, 26), the accuracy of peripheral thermometers is not well-defined. We therefore did a systematic review and meta-analysis to determine whether peripheral thermometers have clinically acceptable accuracy compared with central thermometers in adults and children and whether the type of peripheral thermometer is an important determinant of accuracy. Methods The primary objective was to determine the LOA in temperature between peripheral and central thermometers (29). The secondary objective was to determine the diagnostic accuracy (sensitivity, specificity, and positive and negative likelihood ratios) of peripheral thermometers for detecting fever or hypothermia. Methods for article inclusion and data analysis were prespecified in a protocol (not registered) and consistent with major systematic review reporting guidelines (3032). Data Sources and Searches We searched MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and CINAHL Plus for studies that compared the accuracy of peripheral thermometers with a central reference standard from inception to September 2014; we subsequently updated our search to 20 July 2015. In the MEDLINE and Cochrane Central Register of Controlled Trials searches, we used a combination of exploded Medical Subject Heading terms and text words that included synonyms for thermometer and validated filters for clinical trials (33), cohort studies (34), and diagnostic accuracy studies (Appendix Table 2) (35). The search strategy included similar combinations of terms and filters within the other databases and did not have any language restrictions. Additional citations were located using the related articles feature in PubMed and by hand-searching bibliographies of included studies, previously published reviews (15, 2528, 3640), and relevant societal guidelines (2124). Finally, 2 international clinical trial registries were searched to identify potentially relevant unpublished studies (41, 42). Appendix Table 2. MEDLINE Search Strategy* Selection Criteria Before reviewing all citations, 2 reviewers calibrated the citation screening form by reviewing 25 randomly selected citations. All titles and abstracts were then independently reviewed in duplicate. The full text of selected citations was then reviewed (in duplicate) against the inclusion and exclusion criteria. We used the following inclusion criteria: prospective study design (randomized clinical trial, controlled clinical trial, cohort study, or diagnostic accuracy study) of adults (aged 18 years) or children (aged <18 years) managed in acute care or ambulatory facilities; peripheral index thermometers included tympanic membrane, temporal artery, axillary (electronic, mercury, or chemical dot), or oral (electronic or mercury) thermometers (21, 23, 25, 26); central reference thermometers included pulmonary artery catheters or urinary bladder, esophageal, or rectal thermometers (21, 23, 25, 26); paired temperature measurements between index and reference thermometers were obtained within 5 minutes of one another; and mean difference and associated variance (SD or 95% CI) were reported for each indexreference comparison. The rectal thermometer was included as a central reference device because it is commonly viewed as invasive (22, 23, 26), frequently used in pediatric studies as the reference standard (25, 26), and listed in guidelines as being one of the most accurate thermometers (21). Exclusion criteria were as follows: casecontrol study, animal study, study population included healthy volunteers without medical concerns evaluated in nonclinical environments, or data presented in graphical format only. Noncontact infrared thermometers were excluded because they are not currently reported as a commonly used device for measuring temperature in clinical environments (18). Articles were excluded if they did not explicitly indicate that the mean difference estimates were derived using methods appropriate for repeated measures data (29). NonEnglish-language studies were translated and evaluated for inclusion. Agreement between authors at each stage of citation review was quantified using statistics. Data Extraction and Quality Assessment Data were abstracted independently and in duplicate, including the mean difference (and variance) in temperature between the index and reference thermometers and the data required to calculate the sensitivity and specificity for detecting fever and hypothermia (that is, the number of true-positive, false-positive, true-negative, and false-negative results). Data extraction that related to study quality included domains of the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2) tool (43), whether the index thermometer was calibrated, and whether users received training in proper index thermometer use. Corresponding authors of selected articles were contacted for missing information as required. Data Synthesis and Analysis The primary meta-analysis determined the 95% LOA between peripheral and central thermometers; this was calculated as the pooled estimate of the mean difference in temperature measurements (peripheral minus central) 2 times the pooled estimate of the SD of the difference (29, 44). The prespecified and clinically acceptable 95% LOA were0.5C (4547). Data were pooled using a random-effects model (48), reported as the pooled mean difference (95% CI), and converted to 95% LOA. Study weights were adjusted for several indexreference comparisons. When a study used the same population to examine the accuracy of multiple index thermometers, the total study population was divided by the number of different index comparisons for pooled analyses (26); for studies that compared a given index thermometer with several reference thermometers, we restricted analyses to comparisons with the pulmonary artery catheter. The validity of pooling data across the 4 reference thermometers was examined by determining the pooled 95% LOA between each nonvascular thermometer (bladder, esophageal, or rectal) and the pulmonary artery catheter (gold standard) (21). The secondary meta-analysis determined the diagnostic accuracy of peripheral thermometers for detecting fever or hypothermia using the analytic approach outlined by the Cochrane Diagnostic Test Accuracy Working Group (32). Individual study estimates of sensitivity and specificity were determined from 22 contingency tables and used to develop the summary receiver-operating characteristic curve. Pooled estimates of sensitivity, specificity, and positive and negative likelihood ratios were then determined via bivariate random-effects models (49, 50). Interstudy heterogeneity was evaluated using the Cochran Q test and the I 2 statistic, wherein a P value less than 0.05 and an I 2 statistic greater than 25% reflected statistical evidence of interstudy heterogeneity (51). Sources of heterogeneity were examined by stratified analyses and metaregression (51). Prespecified subgroups included those pertaining to the study participants (adult vs. pediatric and medical vs. surgical), study method (high vs. low risk of bias), thermometers (index measurement site, index method of operation, index thermometer brand, user training, thermometer calibration, and type of reference standard), and temperature (fever and hypothermia). Publication bias was assessed by inspecting funnel plots and doing the Begg (primary meta-analysis) and Deek (secondary meta-analysis) funnel plot asymmetry tests (P< 0.1 was considered evidence of publication bias) (52, 53). Analyses were done in Stata, version 13.1 (Stata Corp) (54, 55). Role of the Funding Source This study received no funding. Results Study Selection and Description From 2563 citations, 75 were included in the systematic review (= 0.8 for both stages of citation review) (Appendix Figure 1). The most common reasons for exclusion after full-text review were the lack of simultaneous index and reference thermometer measurements (36%) and data pertaining to the mean difference between index and reference thermometers (34%). Sixty-nine articles were included in the primary meta

Antipyretic therapy in febrile critically ill adults: A systematic review and meta-analysis ☆

PURPOSE To determine whether fever control with antipyretic therapy effects the mortality of febrile critically ill adults. METHODS Systematic review using MEDLINE, EMBASE, Cochrane Central Register for Controlled Trials, CINAHL, Google Scholar, and 2 clinical trial registries from inception to April 2012. Randomized clinical trials comparing treatment of fever with no treatment or comparing different thresholds for fever control in adults without acute neurological injury admitted to intensive care units (ICUs) were selected for review. The effect of fever control on all-cause ICU-mortality was determined using a random effects meta-analysis. RESULTS Five randomized clinical trials in 399 patients were included. The temperature threshold for treatment in the intervention group was commonly 38.3°C to 38.5°C, whereas it was typically 40.0°C for controls. Four studies used physical measures and 3 used pharmacologic measures for temperature control. There was no significant heterogeneity among the included studies (I(2) = 12.5%, P = .3). Fever control did not significantly effect ICU mortality with a pooled risk ratio of 0.98 (95% confidence interval 0.58-1.63, P = .9). CONCLUSIONS This meta-analysis found no evidence that fever treatment influences mortality in critically ill adults without acute neurological injury. However, studies were underpowered to detect clinically important differences.

Effect of Published Scientific Evidence on Glycemic Control in Adult Intensive Care Units

IMPORTANCE Little is known about the deadoption of ineffective or harmful clinical practices. A large clinical trial (the Normoglycemia in Intensive Care Evaluation and Survival Using Glucose Algorithm Regulation [NICE-SUGAR] trial) demonstrated that strict blood glucose control (tight glycemic control) in patients admitted to adult intensive care units (ICUs) should be deadopted; however, it is unknown whether deadoption occurred and how it compared with the initial adoption. OBJECTIVE To evaluate glycemic control in critically ill patients before and after the publication of clinical trials that initially suggested that tight glycemic control reduced mortality (Leuven I) but subsequently demonstrated that it increased mortality (NICE-SUGAR). DESIGN, SETTING, AND PARTICIPANTS Interrupted time-series analysis of 353,464 patients admitted to 113 adult ICUs from January 1, 2001, through December 31, 2012, in the United States using data from the Acute Physiology and Chronic Health Evaluation database. MAIN OUTCOMES AND MEASURES The physiologically most extreme blood glucose level on day 1 of ICU admission defined glycemic control as tight control (glucose level, 80-110 mg/dL; to convert to millimoles per liter, multiply by 0.0555), hypoglycemia (glucose level, <70 mg/dL), and hyperglycemia (glucose level, ≥180 mg/dL). Temporal changes in each marker were examined using mixed-effects segmented linear regression. RESULTS Before the publication of Leuven I, 17.2% (95% CI, 16.2%-18.2%) of ICU admissions had tight glycemic control, 3.0% (95% CI, 2.6%-3.5%) had hypoglycemia, and 40.2% (95% CI, 38.8%-41.5%) had hyperglycemia. After the publication of Leuven I, there were significant increases in the relative proportion of admissions with tight glycemic control (1.7% per quarter; 95% CI, 1.2%-2.3%; P<.001) and hypoglycemia (2.5% per quarter; 95% CI, 1.9%-3.2%; P<.001) and decreases in those with hyperglycemia (0.6% per quarter; 95% CI, 0.4%-0.9%; P<.001). Following the publication of NICE-SUGAR, there was no change in the proportion of patients with tight glycemic control or hyperglycemia. There was an immediate decrease in the relative proportion of patients with hypoglycemia (22.4%; 95% CI, 13.2%-30.1%; P<.001) but no subsequent change over time. CONCLUSIONS AND RELEVANCE Among patients admitted to adult ICUs in the United States, there was a slow steady adoption of tight glycemic control following publication of a clinical trial that suggested benefit, with little to no deadoption following a subsequent trial that demonstrated harm. There is an urgent need to understand and promote the deadoption of ineffective clinical practices.

Cost and outcomes of nosocomial bloodstream infections complicating major traumatic injury

The objective of this study was to assess the incidence, outcomes, and costs of trauma-related nosocomial bloodstream infection (BSI). This was a 3:1 matched cohort study in patients with severe trauma [defined by an injury severity score (ISS)≥12] admitted to adult or paediatric regional trauma centres over a four-year period. Case patients with nosocomial BSI were matched to controls without a BSI based on predetermined criteria. Outcomes of interest included mortality, length of stay (LOS), and cost attributable to nosocomial BSI. Fifty-seven cases were identified, among whom 51 were successfully matched to three controls. The mean ISS among cases was 30.3, and Staphylococcus aureus was the most commonly isolated pathogen (27%). Being a case was accompanied by a 27% relative increase in the hospital LOS (P=0.02). The odds ratio for 30 day mortality associated with being a case was 5.8 (95% confidence interval (CI): 1.1-30.8; P=0.04). Among survivor-matched groups, being a case was associated with 53% relative increase in the geometric mean total hospital cost [

Diagnosis and management of temperature abnormality in ICUs: a EUROBACT investigators' survey.

97,993 (95% CI:

Fever in adult ICUs: an interrupted time series analysis*.

70,143-136,899) for cases and

Patient and Family Member-Led Research in the Intensive Care Unit: A Novel Approach to Patient-Centered Research.

62,297 (95% CI:

Stakeholder Engagement to Identify Priorities for Improving the Quality and Value of Critical Care.

52,155-74,411) for controls, P<0.0001]. This is the first study to show that nosocomial BSI complicating severe trauma is associated with a substantial increase in hospital LOS and in total hospital cost. Our data provide justification to support efforts to reduce the adverse impact of BSI in trauma victims.