Ron W. Freyberg

Hyperglycemia–related mortality in critically ill patients varies with admission diagnosis*

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.

Incidence and outcomes of acute kidney injury in intensive care units: a Veterans Administration study.

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 I: 17.5%; Stage II: 2.4%; Stage III: 2%); 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.

Variation in outcomes in Veterans Affairs intensive care units with a computerized severity measure.

Objective:To quantify the variability in risk-adjusted mortality and length of stay of Veterans Affairs intensive care units using a computer-based severity of illness measure. Design:Retrospective cohort study. Setting:A stratified random sample of 34 intensive care units in 17 Veterans Affairs hospitals. Participants:A consecutive sample of 29,377 first intensive care unit admissions from February 1996 through July 1997. Interventions:Standardized mortality ratio (observed/expected deaths) and observed minus expected length of stay (OMELOS) with 95% confidence intervals were estimated for each unit using a hierarchical logistic (standardized mortality ratio) or linear (OMELOS) regression model with Markov Chain Monte Carlo simulation. We adjusted for patient characteristics including age, admission diagnosis, comorbid disease, physiology at admission (from laboratory data), and transfer status. Measurements and Main Results:Mortality across the intensive care units for the 12,088 surgical and 17,289 medical cases averaged 11% (range, 2–30%). Length of stay in the intensive care units averaged 4.0 days (range, mean unit length of stay 3.0–5.9). Standardized mortality ratio of the intensive care units varied from 0.62 to 1.27; the standardized mortality ratio and 95% confidence interval were <1 for four intensive care units and >1.0 for seven intensive care units. OMELOS of the intensive care units ranged from −0.89 to 1.34 days. In a random slope hierarchical model, variation in standardized mortality ratio among intensive care units was similar across the range of severity, whereas variation in length of stay increased with severity. Standardized mortality ratio was not associated with OMELOS (Pearson’s r = .13). Conclusions:We identified intensive care units whose indicators for mortality and length of stay differ substantially using a conservative statistical approach with a severity adjustment model based on data available in computerized clinical databases. Computerized risk adjustment employing routinely available data may facilitate research on the utility of intensive care unit profiling and analysis of natural experiments to understand process and outcome links and quality efforts.

Veterans Affairs intensive care unit risk adjustment model: validation, updating, recalibration.

Background:A valid metric is critical to measure and report intensive care unit (ICU) outcomes and drive innovation in a national system. Objectives:To update and validate the Veterans Affairs (VA) ICU severity measure (VA ICU). Research Design:A validated logistic regression model was applied to two VA hospital data sets: 36,240 consecutive ICU admissions to a stratified random sample of moderate and large hospitals in 1999–2000 (cohort 1) and 81,964 cases from 42 VA Medical Centers in fiscal years 2002–2004 (cohort 2). The model was updated by adding diagnostic groups and expanding the source of admission variables. Measures:C statistic, Hosmer-Lemeshow goodness-of-fit statistic, and Briers score measured predictive validity. Coefficients from the 1997 model were applied to predictors (fixed) in a logistic regression model. A 10 × 10 table compared cases with both VA ICU and National Surgical Quality Improvement Performance metrics. The standardized mortality ratios divided observed deaths by the sum of predicted mortality. Results:The fixed model in both cohorts had predictive validity (cohort 1: C statistic = 0.874, Hosmer-Lemeshow goodness-of-fit C statistic chi-square = 72.5; cohort 2: 0.876, 307), as did the updated model (cohort 2: C statistic = 0.887, Hosmer-Lemeshow goodness-of-fit C statistic chi-square = 39). In 7,411 cases with predictions in both systems, the standardized mortality ratio was similar (1.04 for VA ICU, 1.15 for National Surgical Quality Improvement Performance), and 92% of cases matched (±1 decile) when ordered by deciles of mortality. The VA ICU standardized mortality ratio correlates with the National Surgical Quality Improvement Performance standardized mortality ratio (r2 = .74). Variation in discharge and laboratory practices may affect performance measurement. Conclusion:The VA ICU severity model has face, construct, and predictive validity.

Reduction of central line infections in Veterans Administration intensive care units: an observational cohort using a central infrastructure to support learning and improvement

Background Elimination of hospital-acquired infections is an important patient safety goal. Setting All 174 medical, cardiac, surgical and mixed Veterans Administration (VA) intensive care units (ICUs). Intervention A 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 collection Sites recorded the number of CLABSI, line days and audit results of bundle compliance on a secure website. Analysis CLABSI 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)). Pearsons correlation coefficient compared bundle adherence with CLABSI rates. Semi-structured interviews with teams struggling to reduce CLABSI identified common themes. Results From 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. Conclusion Capitalising 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.

Nationwide reduction of health care-associated methicillin-resistant Staphylococcus aureus infections in Veterans Affairs long-term care facilities.

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).

Infrastructure for quality transformation: measurement and reporting in veterans administration intensive care units

Background Veterans Health Administration (VA) intensive care units (ICUs) develop an infrastructure for quality improvement using information technology and recruiting leadership. Methods Setting 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. Results The 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. Conclusion Lessons 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.

Veterans Affairs methicillin-resistant Staphylococcus aureus prevention initiative associated with a sustained reduction in transmissions and health care-associated infections

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.

β-Blockade and Operative Mortality in Noncardiac Surgery: Harmful or Helpful?

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.

Degree of Acute Kidney Injury before Dialysis Initiation and Hospital Mortality in Critically Ill Patients

In a multicenter observational cohort of patients-admitted to intensive care units (ICU), we assessed whether creatinine elevation prior to dialysis initiation in acute kidney injury (AKI-D) further discriminates risk-adjusted mortality. AKI-D was categorized into four groups (Grp) based on creatinine elevation after ICU admission but before dialysis initiation: Grp I  > 0.3 mg/dL to <2-fold increase, Grp II ≥2 times but <3 times increase, Grp III ≥3-fold increase in creatinine, and Grp IV none or <0.3 mg/dl increase. Standardized mortality rates (SMR) were calculated by using a validated risk-adjusted mortality model and expressed with 95% confidence intervals (CI). 2,744 patients developed AKI-D during ICU stay; 36.7%, 20.9%, 31.2%, and 11.2% belonged to groups I, II, III, and IV, respectively. SMR showed a graded increase in Grp I, II, and III (1.40 (95% CI, 1.29–1.42), 1.84 (1.66–2.04), and 2.25 (2.07–2.45)) and was 0.98 (0.78–1.20) in Grp IV. In ICU patients with AKI-D, degree of creatinine elevation prior to dialysis initiation is independently associated with hospital mortality. It is the lowest in those experiencing minor or no elevations in creatinine and may represent reversible fluid-electrolyte disturbances.