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Background Longitudinal analyses of large, detailed adult critical care datasets provide insights into practice trends and generate useful outcome and process benchmarks. Methods Data representing 991,571 consecutive critical care visits to 160 US adult ICUs from 2009 to 2013 from the eICU Research Institute clinical practice database were used to quantitate patient characteristics, APACHE IV–based acuity predictions, treatments, and outcomes. Analyses for changes over time were performed for patient characteristics, entry and discharge locations, primary admission diagnosis, treatments, adherence to consensus ICU best practices, length of stay (LOS), and inpatient mortality. Results We detected significant trends for increasing age, BMI, and risk of mortality, higher frequency of admission from an ED and stepdown unit, and more frequent hospital discharge to substance abuse centers and skilled nursing facilities. Significantly more patients were admitted for sepsis, emphysema, coma, congestive heart failure, diabetic ketoacidosis, and fewer were admitted for asthma, unspecified chest pain, coronary artery bypass graft, and stroke care. The frequency of noninvasive mechanical ventilation and adherence to critical care best practices significantly increased, whereas the duration of renal replacement therapies, frequency of transfusions, antimicrobial use, critical care complications, LOS, and inpatient mortality decreased. Conclusions Analyses of patients, practices, and outcomes from a large geographically dispersed sample of adult ICUs revealed trends of increasing age and acuity, higher rates of adherence to best practice, use of noninvasive mechanical ventilation, and decreased use of antimicrobials, transfusions, and duration of renal replacement therapies. Acuity-adjusted LOS and in hospital mortality decreased.

Part 2 of this review of ICU scoring systems examines how scoring system data should be used to assess ICU performance. There often are two different consumers of these data: lCU clinicians and quality leaders who seek to identify opportunities to improve quality of care and operational efficiency, and regulators, payors, and consumers who want to compare performance across facilities. The former need to know how to garner maximal insight into their care practices; this includes understanding how length of stay (LOS) relates to quality, analyzing the behavior of different subpopulations, and following trends over time. Segregating patients into low-, medium-, and high-risk populations is especially helpful, because care issues and outcomes may differ across this severity continuum. Also, LOS behaves paradoxically in high-risk patients (survivors often have longer LOS than nonsurvivors); failure to examine this subgroup separately can penalize ICUs with superior outcomes. Consumers of benchmarking data often focus on a single score, the standardized mortality ratio (SMR). However, simple SMRs are disproportionately affected by outcomes in high-risk patients, and differences in population composition, even when performance is otherwise identical, can result in different SMRs. Future benchmarking must incorporate strategies to adjust for differences in population composition and report performance separately for low-, medium- and high-acuity patients. Moreover, because many ICUs lack the resources to care for high-acuity patients (predicted mortality >50%), decisions about where patients should receive care must consider both ICU performance scores and their capacity to care for different types of patients.

This review examines the use of scoring systems to assess ICU performance. APACHE (Acute Physiology and Chronic Health Evaluation), MPM (mortality probability model), and SAPS (simplified acute physiology score) are the three major ICU scoring systems in use today. Central to all three is the use of physiologic data for severity adjustment. Differences in the size, nature, and time horizon of the data set translate into minor differences in accuracy and difficulty of data abstraction. APACHE IV provides ICU and hospital predictions for mortality and length of stay, whereas MPM and SAPS only provide hospital mortality predictions (although new algorithms generated from MPM data elements may predict ICU length of stay adequately). The primary use of scoring systems is for assessing ICU performance, with the ratio of actual-to-predicted outcomes in the study cohort providing performance comparisons to the reference ICUs. The reliability of scoring system predictions depends on the completeness and accuracy of the abstracted data; accordingly, ICUs must implement robust data quality control processes. CIs of the ratios are inversely related to sample size, and care must be taken to avoid overinterpreting changes in outcomes. ICU structural and process issues also can affect scoring system performance measures. Despite good discrimination and calibration, scoring systems are used in only 10% to 15% of US ICUs. Without ICU performance data, there is little hope of improving quality and reducing costs. Current demands for transparency and computerization of documentation are likely to drive future use of ICU scoring systems.

Severe sepsis remains a significant medical problem affecting up to 18 million individuals worldwide. Mortality remains high ranging between 28% and 50%. Owing to this and the time-sensitive nature of this disease state, early identification and prompt interventions are necessary to improve outcomes. Technology associated with telemedicine may help in screening, identifying, and monitoring the attainment of the severe sepsis bundle elements in a timely manner. However, the heterogeneity of systemic inflammatory response syndrome and clinical assessment necessary to diagnose and assess patients with severe sepsis makes technology alone insufficient to improve the outcomes in these patients.

The optimal approach for managing increased risk of VTE among critically ill adults is unknown.An observational study of 294,896 episodes of critical illness among adults was conducted in 271 geographically dispersed US adult ICUs. The primary outcomes were all-cause ICU and in-hospital mortality after adjustment for acuity and other factors among groups of patients assigned, based on clinical judgment, to prophylactic anticoagulation, mechanical devices, both, or neither. Outcomes of those managed with prophylactic anticoagulation or mechanical devices were compared in a separate paired, propensity-matched cohort.After adjustment for propensity to receive VTE prophylaxis, APACHE (Acute Physiology and Chronic Health Evaluation) IV scores, and management with mechanical ventilation, the group treated with prophylactic anticoagulation was the only one with significantly lower risk of dying than those not provided VTE prophylaxis (ICU, 0.81 [95% CI, 0.79-0.84]; hospital, 0.84 [95% CI, 0.82-0.86; P.0001). The mortality risk of those receiving mechanical device prophylaxis was not lower than that of patients without VTE prophylaxis. A study of 87,107 pairs of patients matched for propensity to receive VTE prophylaxis found that those managed with prophylactic anticoagulation therapy had significantly lower risk of death (ICU subhazard ratio, 0.82 [95% CI, 0.78-0.85]; hospital subhazard ratio, 0.82 [95% CI, 0.79-0.85]; P.001) than those receiving only mechanical device prophylaxis.These findings support a recommendation for prophylactic anticoagulation therapy in preference to mechanical device prophylaxis for critically ill adult patients who do not have a contraindication to anticoagulation.

The purpose of this study is to examine the impact of hypernatremia acquired after intensive care unit (ICU) admission on mortality and length of stay (LOS).Data for this observational study were collected from patients admitted between January 1, 2008, and September 30, 2010 to 344 ICUs in the eICU Research Institute.Of the 207702 eligible patients, 8896 (4.3%) developed hypernatremia (serum Na149 mEq/L). Hospital mortality was 32% for patients with hypernatremia and 11% for patients without hypernatremia (P.0001). Intensive care unit LOS was 13.7 ± 9.7 days for patients with hypernatremia and 5.1 ± 4.6 for patients without hypernatremia (P.0001). Multivariate analysis showed that hypernatremia was an independent risk factor for hospital mortality with a relative risk (RR) of 1.40 (95% confidence interval, 1.34-1.45) and ICU LOS with a rate ratio (RtR) of 1.28 (1.26-1.30). The RR for mortality and RtR for ICU LOS increased with increasing severity strata of hypernatremia, but the duration of hypernatremia was not associated with mortality.Hypernatremia developed following ICU admission in 4.3% of patients. Hypernatremia was independently associated with a 40% increase in risk for hospital mortality and a 28% increase in ICU LOS. Severity, but not duration of ICU-acquired hypernatremia was associated with hospital mortality.

Introduction Early discharge from the ICU is desirable because it shortens time in the ICU and reduces care costs, but can also increase the likelihood of ICU readmission and post-discharge unanticipated death if patients are discharged before they are stable. We postulated that, using eICU® Research Institute (eRI) data from >400 ICUs, we could develop robust models predictive of post-discharge death and readmission that may be incorporated into future clinical information systems (CIS) to assist ICU discharge planning. Methods Retrospective, multi-center, exploratory cohort study of ICU survivors within the eRI database between 1/1/2007 and 3/31/2011. Exclusion criteria: DNR or care limitations at ICU discharge and discharge to location external to hospital. Patients were randomized (2∶1) to development and validation cohorts. Multivariable logistic regression was performed on a broad range of variables including: patient demographics, ICU admission diagnosis, admission severity of illness, laboratory values and physiologic variables present during the last 24 hours of the ICU stay. Multiple imputation was used to address missing data. The primary outcomes were the area under the receiver operator characteristic curves (auROC) in the validation cohorts for the models predicting readmission and death within 48 hours of ICU discharge. Results 469,976 and 234,987 patients representing 219 hospitals were in the development and validation cohorts. Early ICU readmission and death was experienced by 2.54% and 0.92% of all patients, respectively. The relationship between predictors and outcomes (death vs readmission) differed, justifying the need for separate models. The models for early readmission and death produced auROCs of 0.71 and 0.92, respectively. Both models calibrated well across risk groups. Conclusions Our models for death and readmission after ICU discharge showed good to excellent discrimination and good calibration. Although prospective validation is warranted, we speculate that these models may have value in assisting clinicians with ICU discharge planning.

Nationwide benchmarks representing current critical care practice for the range of ICUs are lacking. This information may highlight opportunities for care improvement and allows comparison of ICU practice data.Data representing 243,553 adult admissions from 271 ICUs and 188 US nonfederal hospitals during 2008 were analyzed using the eICU Research Institute clinical practice database. Participating ICUs and hospitals varied widely regarding bed number, community size, academic status, geographic location, and organizational structure.More than one-half of these critically ill adults were65 years old, and most patients returned to their homes after hospital discharge. Most patients were admitted from an ED, had a medical admission diagnosis, and received antimicrobial therapy. Intensive treatment was common, including 27% who received mechanical ventilation, 7.5% who were supported with noninvasive ventilation, 24.3% who were treated with vasoactive infusions,20% who received a blood product, and 4.4% who agreed to a care limitation order during their ICU stay. Forty percent of cases had a10% mortality risk and did not have an intensive treatment documented.Admission to an ICU in 2008 involved active treatments that often included life support and counseling for those near the end of life and was associated with favorable outcomes for most patients.

Critically ill patients are particularly susceptible to adverse drug events (ADEs) due to their rapidly changing and unstable physiology, complex therapeutic regimens, and large percentage of medications administered intravenously. There are a wide variety of technologies that can help prevent the points of failure commonly associated with ADEs (i.e., the five "Rights": right patient; right drug; right route; right dose; right frequency). These technologies are often categorized by their degree of complexity to design and engineer and the type of error they are designed to prevent. Focusing solely on the software and hardware design of technology may over- or underestimate the degree of difficulty to avoid ADEs at the bedside. Alternatively, we propose categorizing technological solutions by identifying the factors essential for success. The two major critical success factors are: 1) the degree of clinical assessment required by the clinician to appropriately evaluate and disposition the issue identified by a technology; and 2) the complexity associated with effective implementation. This classification provides a way of determining how ADE-preventing technologies in the intensive care unit can be successfully integrated into clinical practice. Although there are limited data on the effectiveness of many technologies in reducing ADEs, we will review the technologies currently available in the intensive care unit environment. We will also discuss critical success factors for implementation, common errors made during implementation, and the potential errors using these systems.

Glycemic control is an important quality indicator in the management of intensive care unit patients. Tight glycemic control and/or insulin infusion protocols may reduce complications and improve outcomes in certain intensive care unit patients. Unfortunately, a consistent method of describing glycemic control has not been used for this population. A standardized metric is needed to adequately evaluate quality performance as well as interpret and apply the literature. The current glycemic control metrics such as mean, median, mean morning, hyperglycemic index, and time-weighted averages will be analyzed. The complexities associated with reporting glycemic control data for national quality performance will also be reviewed. The goal is to facilitate and propose the selection of a glycemic control metric for critically ill patients that can be universally applied in clinical trials and quality performance standards.

Although the use of low-molecular-weight heparins for treatment of acute coronary syndromes (ACS) has increased in recent years, unfractionated heparin (UFH) remains the drug of choice for many patients and institutions. One reason is that this agent is safe for patients with renal dysfunction as well as those who undergo percutaneous coronary intervention or coronary artery bypass graft. The use of UFH is complicated by the increased risk of bleeding due to concurrent administration of numerous antiplatelet drugs in most patients with ACS, the limited data regarding ideal therapeutic range, and the wide variability of patient response. Knowledge regarding the optimal therapeutic range and how to achieve it efficiently may enable clinicians to improve clinical outcomes in patients with ACS. We reviewed and analyzed the available evidence to clarify how to best manage UFH therapy in patients with ACS. Current data support the use of a lower and narrower therapeutic range for patients with ACS than the range that is used for venous thromboembolism. Many factors in addition to weight affect patient response to UFH, including age, sex, diabetes mellitus, smoking status, and obesity.