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.

We develop a patient level hierarchical regression model using administrative claims data to assess mortality outcomes for a national VA population. This model, which complements more traditional process driven performance measures, includes demographic variables and disease specific measures of risk classified by Diagnostic Cost Groups (DCGs). Results indicate some ability to discriminate survivors and non-survivors with an area under the Receiver Operating Characteristic Curve (C-statistic) of .86. Observed to expected mortality ranges from .86 to 1.12 across predicted mortality deciles while Risk Standardized Mortality Rates (RSMRs) range from .76 to 1.29 across 145 VA hospitals. Further research is necessary to understand mortality variation which persists even after adjusting for case mix differences. Future work is also necessary to examine the role of personal behaviors on patient outcomes and the potential impact on population survival rates from changes in treatment policy and infrastructure investment.

BACKGROUND:Millions of patients are discharged from intensive care units annually. These intensive care survivors and their families frequently report a wide range of impairments in their health status which may last for months and years after hospital discharge.OBJECTIVES:To report on a 2-day Society of Critical Care Medicine conference aimed at improving the long-term outcomes after critical illness for patients and their families.PARTICIPANTS:Thirty-one invited stakeholders participated in the conference. Stakeholders represented key professional organizations and groups, predominantly from North America, which are involved in the care of intensive care survivors after hospital discharge.DESIGN:Invited experts and Society of Critical Care Medicine members presented a summary of existing data regarding the potential long-term physical, cognitive and mental health problems after intensive care and the results from studies of postintensive care unit interventions to address these problems. Stakeholders provided reactions, perspectives, concerns and strategies aimed at improving care and mitigating these long-term health problems.MEASUREMENTS AND MAIN RESULTS:Three major themes emerged from the conference regarding: (1) raising awareness and education, (2) understanding and addressing barriers to practice, and (3) identifying research gaps and resources. Postintensive care syndrome was agreed upon as the recommended term to describe new or worsening problems in physical, cognitive, or mental health status arising after a critical illness and persisting beyond acute care hospitalization. The term could be applied to either a survivor or family member.CONCLUSIONS:Improving care for intensive care survivors and their families requires collaboration between practitioners and researchers in both the inpatient and outpatient settings. Strategies were developed to address the major themes arising from the conference to improve outcomes for survivors and families.

BACKGROUND:Critical care resource use accounts for almost 1% of US gross domestic product and varies widely among hospitals. However, we know little about the initial decision to admit a patient to the intensive care unit (ICU).METHODS:To describe hospital ICU admitting patterns for medical patients after accounting for severity of illness on admission, we performed a retrospective cohort study of the first nonsurgical admission of 289,310 patients admitted from the emergency department or the outpatient clinic to 118 Veterans Affairs acute care hospitals between July 1, 2009, and June 30, 2010. Severity (30-day predicted mortality rate) was measured using a modified Veterans Affairs ICU score based on laboratory data and comorbidities around admission. The main outcome measure was direct admission to an ICU.RESULTS:Of the 31,555 patients (10.9%) directly admitted to the ICU, 53.2% had 30-day predicted mortality at admission of 2% or less. The rate of ICU admission for this low-risk group varied from 1.2% to 38.9%. For high-risk patients (predicted mortality >30%), ICU admission rates also varied widely. For a 1-SD increase in predicted mortality, the adjusted odds of ICU admission varied substantially across hospitals (odds ratio = 0.85-2.22). As a result, 66.1% of hospitals were in different quartiles of ICU use for low- vs high-risk patients (weighted κ = 0.50).CONCLUSIONS:The proportion of low- and high-risk patients admitted to the ICU, variation in ICU admitting patterns among hospitals, and the sensitivity of hospital rankings to patient risk all likely reflect a lack of consensus about which patients most benefit from ICU admission.

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.

Veterans Health Administration Washington DC; and Division of Pulmonary/Critical Care/Sleep University of Cincinnati College of Medicine Cincinnati, OH *See also p. (INSERT CM START FOLIO). Supported by the Veterans Health Administration The author has not disclosed any potential conflicts of interest.

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)). Pearson's 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.

There is widespread belief that the US health care system could realize significant improvements in efficiency, savings, and patient outcomes if care were provided in a more integrated and accountable way. We examined efficiency and its relationship to quality of care for medical centers run by the Veterans Health Administration of the Department of Veterans Affairs (VA), a national, vertically integrated health care system that is accountable for a large patient population. After devising a statistical model to indicate efficiency, we found that VA medical centers were highly efficient. We also found only modest variation in the level of efficiency and cost across VA medical centers, and a positive correlation overall between greater efficiency and higher inpatient quality. These findings for VA medical centers suggest that efforts to drive integration and accountability in other parts of the US health care system might have important payoffs in reducing variations in cost without sacrificing quality. Policy makers should focus on what aspects of certain VA medical centers allow them to provide better care at lower costs and consider policies that incentivize other providers, both within and outside the VA, to adopt these practices.

BACKGROUND:Health care-associated infections with methicillin-resistant Staphylococcus aureus (MRSA) have been an increasing concern in Veterans Affairs (VA) hospitals.METHODS:A "MRSA bundle" was implemented in 2007 in acute care VA hospitals nationwide in an effort to decrease health care-associated infections with MRSA. The bundle consisted of universal nasal surveillance for MRSA, contact precautions for patients colonized or infected with MRSA, hand hygiene, and a change in the institutional culture whereby infection control would become the responsibility of everyone who had contact with patients. Each month, personnel at each facility entered into a central database aggregate data on adherence to surveillance practice, the prevalence of MRSA colonization or infection, and health care-associated transmissions of and infections with MRSA. We assessed the effect of the MRSA bundle on health care-associated MRSA infections.RESULTS:From October 2007, when the bundle was fully implemented, through June 2010, there were 1,934,598 admissions to or transfers or discharges from intensive care units (ICUs) and non-ICUs (ICUs, 365,139; non-ICUs, 1,569,459) and 8,318,675 patient-days (ICUs, 1,312,840; and non-ICUs, 7,005,835). During this period, the percentage of patients who were screened at admission increased from 82% to 96%, and the percentage who were screened at transfer or discharge increased from 72% to 93%. The mean (±SD) prevalence of MRSA colonization or infection at the time of hospital admission was 13.6±3.7%. The rates of health care-associated MRSA infections in ICUs had not changed in the 2 years before October 2007 (P=0.50 for trend) but declined with implementation of the bundle, from 1.64 infections per 1000 patient-days in October 2007 to 0.62 per 1000 patient-days in June 2010, a decrease of 62% (P<0.001 for trend). During this same period, the rates of health care-associated MRSA infections in non-ICUs fell from 0.47 per 1000 patient-days to 0.26 per 1000 patient-days, a decrease of 45% (P<0.001 for trend).CONCLUSIONS:A program of universal surveillance, contact precautions, hand hygiene, and institutional culture change was associated with a decrease in health care-associated transmissions of and infections with MRSA in a large health care system.

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.

Acute brain dysfunction, usually manifested as delirium, occurs in up to 80% of critically ill patients. Delirium increases costs of hospitalizations and affects short-term outcomes such as duration of mechanical ventilation, intensive care unit (ICU) length of stay, and the hospital length of stay. Long-term consequences-cognitive impairment and increased risk of death-can be devastating. For adequate recognition and management it is imperative to implement a successful delirium monitoring and assessment strategy. A liberation and animation strategy can reduce both the incidence and the duration of delirium. Liberation aims to reduce the harmful effects of sedative exposure through use of target-based sedation protocols, spontaneous awakening trials, and proper choice of sedative as well as liberation from the ventilator and the ICU. Animation refers to early mobilization, which reduces delirium and improves neurocognitive outcomes. Delirium is a serious problem with important consequences and can be prevented or improved using the information that we have learned in the last decade.

Introduction The Veterans Health Affairs (VA) began a national program to measure and report risk adjusted mortality and length of stay in its intensive care units (ICU) in October of 2004 expanding to all patients admitted for acute medical or surgical conditions in October of 2008. The program, the VA Inpatient Evaluation Center (IPEC), uses a validated risk measure [1-3]. The VA IPEC uses electronic data to measure, people resources to drive change. This brief paper provides an overview of the VA risk methods and lessons learned since implementation.

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.

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 Brier's score measured predictive validity. Coefficients from the 1997 model were applied to predictors (fixed) in a logistic regression model. A 10 x 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.