OBJECTIVES:Hyperglycemia during critical illness is common and is associated with increased mortality. Intensive insulin therapy has improved outcomes in some, but not all, intervention trials. It is ...unclear whether the benefits of treatment differ among specific patient populations. The purpose of the study was to determine the association between hyperglycemia and risk– adjusted mortality in critically ill patients and in separate groups stratified by admission diagnosis. A secondary purpose was to determine whether mortality risk from hyperglycemia varies with intensive care unit type, length of stay, or diagnosed diabetes.
DESIGN:Retrospective cohort study.
SETTING:One hundred seventy-three U.S. medical, surgical, and cardiac intensive care units.
PATIENTS:Two hundred fifty-nine thousand and forty admissions from October 2002 to September 2005; unadjusted mortality rate, 11.2%.
INTERVENTIONS:None.
MEASUREMENTS AND MAIN RESULTS:A two–level logistic regression model determined the relationship between glycemia and mortality. Age, diagnosis, comorbidities, and laboratory variables were used to calculate a predicted mortality rate, which was then analyzed with mean glucose to determine the association of hyperglycemia with hospital mortality. Hyperglycemia was associated with increased mortality independent of illness severity. Compared with normoglycemic individuals (70–110 mg/dL), adjusted odds of mortality (odds ratio, 95% confidence interval) for mean glucose 111–145, 146–199, 200–300, and >300 mg/dL was 1.31 (1.26–1.36), 1.82 (1.74–1.90), 2.13 (2.03–2.25), and 2.85 (2.58–3.14), respectively. Furthermore, the adjusted odds of mortality related to hyperglycemia varied with admission diagnosis, demonstrating a clear association in some patients (acute myocardial infarction, arrhythmia, unstable angina, pulmonary embolism) and little or no association in others. Hyperglycemia was associated with increased mortality independent of intensive care unit type, length of stay, and diabetes.
CONCLUSIONS:The association between hyperglycemia and mortality implicates hyperglycemia as a potentially harmful and correctable abnormality in critically ill patients. The finding that hyperglycemia–related risk varied with admission diagnosis suggests differences in the interaction between specific medical conditions and injury from hyperglycemia. The design and interpretation of future trials should consider the primary disease states of patients and the balance of medical conditions in the intensive care unit studied.
OBJECTIVES:To examine the effect of severity of acute kidney injury or renal recovery on risk-adjusted mortality across different intensive care unit settings. Acute kidney injury in intensive care ...unit patients is associated with significant mortality.
DESIGN:Retrospective observational study.
SETTING:There were 325,395 of 617,927 consecutive admissions to all 191 Veterans Affairs ICUs across the country.
PATIENTS:Large national cohort of patients admitted to Veterans Affairs ICUs and who developed acute kidney injury during their intensive care unit stay.
MEASUREMENTS AND MAIN RESULTS:Outcome measures were hospital mortality, and length of stay. Acute kidney injury was defined as a 0.3-mg/dL increase in creatinine relative to intensive care unit admission and categorized into Stage I (0.3 mg/dL to <2 times increase), Stage II (≥2 and <3 times increase), and Stage III (≥3 times increase or dialysis requirement). Association of mortality and length of stay with acute kidney injury stages and renal recovery was examined. Overall, 22% (n = 71,486) of patients developed acute kidney injury (Stage I17.5%; Stage II2.4%; Stage III2%); 16.3% patients met acute kidney injury criteria within 48 hrs, with an additional 5.7% after 48 hrs of intensive care unit admission. Acute kidney injury frequency varied between 9% and 30% across intensive care unit admission diagnoses. After adjusting for severity of illness in a model that included urea and creatinine on admission, odds of death increased with increasing severity of acute kidney injury. Stage I odds ratio = 2.2 (95% confidence interval, 2.17–2.30); Stage II odds ratio = 6.1 (95% confidence interval, 5.74, 6.44); and Stage III odds ratio = 8.6 (95% confidence interval, 8.07–9.15). Acute kidney injury patients with sustained elevation of creatinine experienced higher mortality risk than those who recovered.
INTERVENTIONS:None.
CONCLUSIONS:Admission diagnosis and severity of illness influence frequency and severity of acute kidney injury. Small elevations in creatinine in the intensive care unit are associated with increased risk-adjusted mortality across all intensive care unit settings, whereas renal recovery was associated with a protective effect. Strategies to prevent even mild acute kidney injury or promote renal recovery may improve survival.
In this report, implementation of a MRSA bundle (nasal surveillance for MRSA, contact precautions for patients with MRSA, hand hygiene, and an institutional culture change whereby infection control ...was everyone's responsibility) was associated with a significant decline in MRSA transmission.
Methicillin-resistant
Staphylococcus aureus
(MRSA) infections are a problem in the United States
1
and elsewhere. MRSA is one of the most common causes of ventilator-associated pneumonia, bloodstream infection associated with central venous catheters, and surgical-site infections.
1
,
2
In 2001, the Veterans Affairs (VA) Pittsburgh Healthcare System began working with the Pittsburgh Regional Healthcare Initiative and the Centers for Disease Control and Prevention (CDC) to eliminate health care–associated MRSA infections with the use of a “MRSA bundle.” The bundle, which was based on published guidelines, comprised universal nasal surveillance for MRSA colonization, contact precautions for patients who were carriers of MRSA, hand . . .
IMPORTANCE: The use of perioperative pharmacologic β-blockade in patients at low risk of myocardial ischemic events undergoing noncardiac surgery (NCS) is controversial because of the risk of stroke ...and hypotension. Published studies have not found a consistent benefit in this cohort. OBJECTIVE: To determine the effect of perioperative β-blockade on patients undergoing NCS, particularly those with no risk factors. DESIGN, SETTING, AND PARTICIPANTS: This is a retrospective observational analysis of patients undergoing surgery in Veterans Affairs hospitals from October 1, 2008, through September 31, 2013. METHODS: β-Blocker use was determined if a dose was ordered at any time between 8 hours before surgery and 24 hours postoperatively. Data from the Veterans Affairs electronic database included demographics, diagnosis and procedural codes, medications, perioperative laboratory values, and date of death. A 4-point cardiac risk score was calculated by assigning 1 point each for renal failure, coronary artery disease, diabetes mellitus, and surgery in a major body cavity. Previously validated linear regression models for all hospitalized acute care medical or surgical patients were used to calculate predicted mortality and then to calculate odds ratios (ORs). MAIN OUTCOMES AND MEASURES: The end point was 30-day surgical mortality. RESULTS: There were 326 489 patients in this cohort: 314 114 underwent NCS and 12 375 underwent cardiac surgery. β-Blockade lowered the OR for mortality significantly in patients with 3 to 4 cardiac risk factors undergoing NCS (OR, 0.63; 95% CI, 0.43-0.93). It had no effect on patients with 1 to 2 risk factors. However, β-blockade resulted in a significantly higher chance of death in patients (OR, 1.19; 95% CI, 1.06-1.35) with no risk factors undergoing NCS. CONCLUSIONS AND RELEVANCE: In this large series, β-blockade appears to be beneficial perioperatively in patients with high cardiac risk undergoing NCS. However, the use of β-blockers in patients with no cardiac risk factors undergoing NCS increased risk of death in this patient cohort.
The Veterans Affairs methicillin-resistant Staphylococcus aureus (MRSA) Prevention Initiative was implemented in its 133 long-term care facilities in January 2009. Between July 2009 and December ...2012, there were ∼12.9 million resident-days in these facilities nationwide. During this period, the mean quarterly MRSA admission prevalence increased from 23.3% to 28.7% ( P < .0001, Poisson regression for trend), but the overall rate of MRSA health care–associated infections decreased by 36%, from 0.25 to 0.16/1,000 resident-days ( P < .0001, Poisson regression for trend).
BackgroundElimination of hospital-acquired infections is an important patient safety goal.SettingAll 174 medical, cardiac, surgical and mixed Veterans Administration (VA) intensive care units ...(ICUs).InterventionA centralised infrastructure (Inpatient Evaluation Center (IPEC)) supported the practice bundle implementation (handwashing, maximal barriers, chlorhexidinegluconate site disinfection, avoidance of femoral catheterisation and timely removal) to reduce central line-associated bloodstream infections (CLABSI). Support included recruiting leadership, benchmarked feedback, learning tools and selective mentoring.Data collectionSites recorded the number of CLABSI, line days and audit results of bundle compliance on a secure website.AnalysisCLABSI rates between years were compared with incidence rate ratios (IRRs) from a Poisson regression and with National Healthcare Safety Network referent rates (standardised infection ratio (SIR)). Pearson's correlation coefficient compared bundle adherence with CLABSI rates. Semi-structured interviews with teams struggling to reduce CLABSI identified common themes.ResultsFrom 2006 to 2009, CLABSI rates fell (3.8–1.8/1000 line days; p<0.01); as did IRR (2007; 0.83 (95% CI 0.73 to 0.94), 2008; 0.65 (95% CI 0.56 to 0.76), 2009; 0.47 (95% CI 0.40 to 0.55)). Bundle adherence and CLABSI rates showed strong correlation (r=0.81). VA CLABSI SIR, January to June 2009, was 0.76 (95% CI 0.69 to 0.90), and for all FY2009 0.88 (95% CI 0.80 to 0.97). Struggling sites lacked a functional team, forcing functions and feedback systems.ConclusionCapitalising on a large healthcare system, VA IPEC used strategies applicable to non-federal healthcare systems and communities. Such tactics included measurement through information technology, leadership, learning tools and mentoring.
To examine the impact on infection rates and hospital rank for catheter-associated urinary tract infection (CAUTI), central line-associated bloodstream infection (CLABSI), and ventilator-associated ...pneumonia (VAP) using device days and bed days as the denominator
Retrospective survey from October 2010 to July 2013 SETTING: Veterans Health Administration medical centers providing acute medical and surgical care
Patients admitted to 120 Veterans Health Administration medical centers reporting healthcare-associated infections
We examined the importance of using device days and bed days as the denominator between infection rates and hospital rank for CAUTI, CLABSI, and VAP for each medical center. The relationship between device days and bed days as the denominator was assessed using a Pearson correlation, and changes in infection rates and device utilization were evaluated by an analysis of variance.
A total of 7.9 million bed days were included. From 2011 to 2013, CAUTI decreased whether measured by device days (2.32 to 1.64, P=.001) or bed days (4.21 to 3.02, P=.006). CLABSI decreased when measured by bed days (1.67 to 1.19, P=.04). VAP rates and device utilization ratios for CAUTI, CLABSI, and VAP were not statistically different across time. Infection rates calculated with device days were strongly correlated with infection rates calculated with bed days (r=0.79-0.94, P<.001). Hospital relative performance measured by ordered rank was also strongly correlated for both denominators (r=0.82-0.96, P<.001).
These findings suggest that device days and bed days are equally effective adjustment metrics for comparing healthcare-associated infection rates between hospitals in the setting of stable device utilization.
Implementation of a methicillin-resistant Staphylococcus aureus (MRSA) Prevention Initiative was associated with significant declines in MRSA transmission and MRSA health care-associated infection ...rates in Veterans Affairs acute care facilities nationwide in the 33-month period from October 2007 through June 2010. Here, we show continuing declines in MRSA transmissions ( P = .004 for trend, Poisson regression) and MRSA health care-associated infections ( P < .001) from July 2010 through June 2012. The Veterans Affairs Initiative was associated with these effects, sustained over 57 months, in a large national health care system.
BackgroundVeterans Health Administration (VA) intensive care units (ICUs) develop an infrastructure for quality improvement using information technology and recruiting leadership.MethodsSetting ...Participation by the 183 ICUs in the quality improvement program is required. Infrastructure includes measurement (electronic data extraction, analysis), quarterly web-based reporting and implementation support of evidence-based practices. Leaders prioritise measures based on quality improvement objectives. The electronic extraction is validated manually against the medical record, selecting hospitals whose data elements and measures fall at the extremes (10th, 90th percentile). Results are depicted in graphic, narrative and tabular reports benchmarked by type and complexity of ICU.ResultsThe VA admits 103 689±1156 ICU patients/year. Variation in electronic business practices, data location and normal range of some laboratory tests affects data quality. A data management website captures data elements important to ICU performance and not available electronically. A dashboard manages the data overload (quarterly reports ranged 106—299 pages). More than 85% of ICU directors and nurse managers review their reports. Leadership interest is sustained by including ICU targets in executive performance contracts, identification of local improvement opportunities with analytic software, and focused reviews.ConclusionLessons relevant to non-VA institutions include the: (1) need for ongoing data validation, (2) essential involvement of leadership at multiple levels, (3) supplementation of electronic data when key elements are absent, (4) utility of a good but not perfect electronic indicator to move practice while improving data elements and (5) value of a dashboard.
Introduction: Reliance on administrative data sources and a cohort with restricted age range (Medicare 65 y and above) may limit conclusions drawn from public reporting of 30-day mortality rates in 3 ...diagnoses acute myocardial infarction (AMI), congestive heart failure (CHF), pneumonia (PNA) from Center for Medicaid and Medicare Services. Methods: We categorized patients with diagnostic codes for AMI, CHF, and PNA admitted to 138 Veterans Administration hospitals (2006—2009) into 2 groups (less than 65 y or ALL), then applied 3 different models that predicted 30-day mortality Center for Medicaid and Medicare Services administrative (ADM), ADM+laboratory data (PLUS), and clinical (CLIN) to each age/diagnosis group. C statistic (CSTAT) and Hosmer Lemeshow Goodness of Fit measured discrimination and calibration. Pearson correlation coefficient (r) compared relationship between the hospitals' risk-standardized mortality rates (RSMRs) calculated with different models. Hospitals were rated as significantly different (SD) when confidence intervals (bootstrapping) omitted National RSMR. Results: The ≥ 65-year models included 57%—67% of all patients (78%—82% deaths). The PLUS models improved discrimination and calibration across diagnoses and age groups (CSTAT—CHF/65 y and above: 0.67 vs. 0. 773 vs. 0.761; ADM/PLUS/CLIN; Hosmer Lemeshow Goodness of Fit significant 4/6 ADM vs. 2/6 PLUS). Correlation of RSMR was good between ADM and PLUS (r—AMI 0.859; CHF 0.821; PNA 0.750), and 65 years and above and ALL (r > 0.90). SD ratings changed in 1%—12% of hospitals (greatest change in PNA). Conclusions: Performance measurement systems should include laboratory data, which improve model performance. Changes in SD ratings suggest caution in using a single metric to label hospital performance.
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