Mortality risk prediction in sepsis.
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APACHE↗
Biomedical subjects
W A Knaus
Publications and source records attributed to W A Knaus.
The expanding role of ICU medical directors: from patient management to unit management.
Over the past three decades the focus of physicians delivering intensive care has been on patient management whereas the management of critical care units has been done primarily by nurses. This article reviews existing literature and the arguments supporting a more active role for physicians in the management of critical care units.
Communication↗
The use of risk predictions to identify candidates for intermediate care units. Implications for intensive care utilization and cost.
OBJECTIVE: To develop a predictive equation that estimates the probability of life-supporting therapy among ICU monitor admissions and to explore its potential for reducing cost and improving ICU utilization. DESIGN: Prospective inception cohort analysis. PARTICIPANTS: Forty-two ICUs in 40 US hospitals with more than 200 beds and a consecutive sample of 17,440 ICU admissions. INTERVENTIONS: A multivariate equation was developed to estimate the probability of life support for ICU monitoring admissions during an entire ICU stay. MEASUREMENTS: Demographic, physiologic, and treatment information obtained during the first 24 h in the ICU and over the first 7 ICU days. RESULTS: The most important determinants of subsequent risk for life-supporting (active) treatment were diagnosis, the acute physiology score of APACHE III, age, operative status, and the patient's location and hospital length of stay before ICU admission. Among 8,040 ICU monitoring admissions, 6,180 (76.8%) had a low (< 10%) risk for receiving active treatment during the ICU stay; 95.6% received no subsequent active treatment. Review of outcomes and the type and amount of therapy received suggest that most low-risk ICU monitor admissions could be safely cared for in an intermediate care setting. CONCLUSION: Objective predictions can accurately identify groups of ICU admissions who are at a low risk for receiving life support. This capability can be used to assess ICU resource use and develop strategies for providing graded critical care services at a reduced cost.
APACHE↗
The use of APACHE III to evaluate ICU length of stay, resource use, and mortality after coronary artery by-pass surgery.
OBJECTIVE: To identify patient characteristics that are associated with increased ICU length of stay, resource use, and hospital mortality after coronary artery bypass surgery. DESIGN: Prospective, multicenter study. SETTING: Six tertiary care hospitals. PARTICIPANTS: A consecutive sample of 2,435 unselected ICU admissions following coronary artery by-pass surgery. MATERIALS AND METHODS: Demographic, operative characteristics and APACHE III score were collected during the first postoperative day; and APACHE III scores and therapeutic interventions during the first three postoperative days. Hospital survival and ICU length of stay were also recorded. Multivariate equations were derived and cross-validated to predict hospital mortality, ICU length of stay, and ICU resource use. RESULTS: Unadjusted hospital mortality rate was 3.9% (range 1.0% to 6.0%), mean ICU length of stay was 3.7 days (range 3.2 to 4.7 days), and first 3-day ICU resource use (TISS points) was 99 (range 68 to 116). The range of actual to predicted ICU length of stay varied from 0.86 to 1.26; and resource use from 0.71 to 1.16. CONCLUSIONS: A limited number of operative characteristics, the post-operative acute physiology score (APS) of APACHE III and patient demographic data can predict hospital death rate, ICU length of stay, and resource use immediately following coronary by-pass surgery. These estimates may compliment assessments based on pre-operative risk factors in order to more precisely evaluate and improve the efficacy and efficiency of cardiovascular surgery.
APACHE↗
Daily prognostic estimates for critically ill adults in intensive care units: results from a prospective, multicenter, inception cohort analysis.
OBJECTIVE: To develop daily prognostic estimates for individual patients treated in adult intensive care units (ICU). DESIGN: Prospective, multicenter, inception cohort analysis. SETTING: Forty-two ICUs at 40 U.S. hospitals with > 200 beds including 20 ICUs in tertiary care centers with major teaching activities. PATIENTS: A consecutive sample of 17,440 ICU admissions. MEASUREMENTS AND MAIN RESULTS: A series of multivariate equations were developed using the patient's primary reason for ICU admission, age, chronic health status, treatment before ICU admission, admission Acute Physiology Score, current day Acute Physiology Score, and change between the current and previous day's Acute Physiology Score. The equations were used to create daily risk predictions and cross-validated within the 17,440-patient sample. The single most important factor determining daily risk of hospital death during each of the initial 7 days of ICU care was the current day's Acute Physiology Score of the Acute Physiology and Chronic Health Evaluation (APACHE) III score. The admission Acute Physiology Score and change from previous to current day's Acute Physiology Score were also important, as were ICU admission diagnosis, age, chronic health status, and treatment before ICU admission. Equations incorporating these risk factors had receiver operating characteristics areas ranging from 0.9 on the first ICU day to 0.84 for patients remaining in the ICU for 7 days. The percent of cases with cross-validated predicted risks over 90% increased from 2.3% (n = 406) of cases on day 1 to 9% of all patients remaining in the ICU on ICU day 7 (n = 218). The 1,033 patients who had a daily risk estimate of > 90% during any of their initial 7 ICU days had a 90% mortality rate and represented 47% of all ICU deaths and 31% of the total number of hospital deaths. CONCLUSIONS: Equations using initial and repeated physiologic measurements provide a high degree of explanatory power for subsequent hospital mortality rate. These daily prognostic estimates deserve evaluation for their potential role in improving the process and outcome from clinical decision-making.
Adult↗
The performance of intensive care units: does good management make a difference?
A significant portion of health care resources are spent in intensive care units with, historically, up to two-fold variation in risk-adjusted mortality. Technological, demographic, and social forces are likely to lead to an increased volume of intensive care in the future. Thus, it is important to identify ways of more efficiently managing intensive care units and reducing the variation in patient outcomes. Based on data collected from 17,440 patients across 42 ICUs, the present study examines the factors associated with risk-adjusted mortality, risk-adjusted average length of stay, nurse turnover, evaluated technical quality of care, and evaluated ability to meet family member needs. Using the Apache III methodology for risk-adjustment, findings reveal that: 1) technological availability is significantly associated with lower risk-adjusted mortality (beta = -.42); 2) diagnostic diversity is significantly associated with greater risk-adjusted mortality (beta = .46); and 3) caregiver interaction comprising the culture, leadership, coordination, communication, and conflict management abilities of the unit is significantly associated with lower risk-adjusted length of stay (beta = .34), lower nurse turnover (beta = -.36), higher evaluated technical quality of care (beta = .81), and greater evaluated ability to meet family member needs (beta = .74). Furthermore, units with greater technological availability are significantly more likely to be associated with hospitals that are more profitable, involved in teaching activities, and have unit leaders actively participating in hospital-wide quality improvement activities. The findings hold a number of important managerial and policy implications regarding technological adoption, specialization, and the quality of interaction among ICU team members. They suggest intervention "leverage points" for care givers, managers, and external policy makers in efforts to continuously improve the outcomes of intensive care.
Caregivers↗
Evaluation of definitions for adult respiratory distress syndrome.
We conducted a cohort study of 423 intensive care unit (ICU) admissions with a primary clinical diagnosis of acute respiratory failure, a PaO2/FIO2 on ICU admission of < 300 mm Hg, and an ICD-9 discharge diagnosis of adult respiratory distress syndrome (ARDS) (518.5 or 518.82) drawn from a nationally representative database of 17,440 ICU admissions to evaluate current and proposed revisions for definitions of ARDS. A variety of nonpulmonary physiologic risk factors, from shock to elevated serum bilirubin measurements, were significant (p < 0.01) for hospital mortality. Multivariable analysis using the admission APACHE III score, primary ICU admission diagnosis, and treatment location before ICU admission provided greater accuracy in prediction (ROC = 0.80) than the individual PaO2/FIO2 (ROC = 0.68). Patients were given an individual risk of hospital mortality based on their admission APACHE III score, treatment location before ICU admission, and ICU admitting diagnosis. Dividing the patient population into groups using a PaO2/FIO2 < or = 150 resulted in a wide range of individual risk for hospital mortality, from < 10 to > 90% in both groups. We conclude that ARDS is a complex clinical entity with a variety of pulmonary and nonpulmonary risk factors for both its development and its prognosis. Current and proposed categorical definitions based on the severity of hypoxemia result in a wide distribution of individual patient risks. Use of these findings in the design and conduct of future clinical trials would improve the evaluation of new therapies.
Adolescent↗
Do formal advance directives affect resuscitation decisions and the use of resources for seriously ill patients? SUPPORT Investigators. Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments.
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Adult↗
Improving intensive care unit discharge decisions: supplementing physician judgment with predictions of next day risk for life support.
OBJECTIVE: To develop predictive equations, estimating the probability that an individual intensive care unit (ICU) patient will receive life support within the next 24 hrs. DESIGN: Prospective, multicenter, inception cohort study. SETTING: Forty-two ICUs in 40 U.S. hospitals, including 26 that were randomly selected and 14 volunteer hospitals, primarily university or large tertiary care centers. PATIENTS: A consecutive sample of 17,440 ICU admissions. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A series of multivariate equations were developed to create daily estimates of probability of life support in the next 24 hrs. These equations used demographic, physiologic, and treatment information obtained at the time of ICU admission and during the first 7 ICU days. The most important determinants of next day risk for life support were the current day's therapy and Acute Physiology Score of the Acute Physiology and Chronic Health Evaluation (APACHE) III score. Other predictor variables included diagnosis, age, chronic health status, emergency surgery, previous day Acute Physiology Score, and hospital stay and location before ICU admission. The cross-validated ICU day 1, 2, and 3 predictive equations had receiver operating characteristic areas of 0.90. Survival, ICU readmission rate, and the number and type of therapies received by patients predicted at < 10% risk for active treatment suggest that discharge of patients meeting these criteria to an intermediate care unit or hospital ward could reduce ICU bed demand without compromising patient safety. CONCLUSIONS: Accurate, objective predictions of next day risk for life support can be developed, using readily available patient information. Supplementing physician judgment with these objective risk assessments deserves evaluation for the role of these assessments in enhancing patient safety and improving ICU resource utilization.
Adolescent↗
The value of commercial funding in health services research: the case of the APACHE III methodology.
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Capital Financing↗
The case for using objective scoring systems to predict intensive care unit outcome.
The acceptance and more widespread use of objective probability estimates will depend on addressing several practical issues. Physicians generally are unfamiliar with these estimates, their origin, and how they should be used. Physicians need to understand the conceptual origins of prognostic systems, their strengths and limitations, and their usefulness in helping to resolve uncertainty and improve critical decision making. Better communication and understanding of patient and family preferences and values will be necessary to ensure that the use of numerical predictions will not be the dominant or exclusive consideration. No matter how refined and elaborate these systems become, they represent simplification of even more complex biologic decision-making systems. The predictions must be interpreted carefully and used by trained intensivists, with the constant opportunity to exercise human discretion. The challenge to the developers of these systems will be to improve their accuracy, refine their use with specific disease entities that are important in determining outcome in critical care (e.g. multi-organ system failure, sepsis, adult respiratory distress syndrome), improve display of data and ease of data capture with automated systems, and construct data-bases and predictive equations that are fluid, constantly growing, and adjusting to capture developments in medical care.
Decision Making, Organizational↗
Intensive care at two teaching hospitals: an organizational case study.
OBJECTIVE: To examine structural and organizational characteristics at two ICUs with marked differences in risk-adjusted survival. METHODS: We performed on-site organizational analysis in two ICUs at two major teaching hospitals. Our main outcome measures were interviews and direct observations by a team of clinical and organizational researchers; demographic, clinical, and survival data for 888 ICU admissions; and questionnaire responses from 70 nurses and 42 physicians on ICU structure and organization. ICU performance was measured using risk-adjusted survival and the ratios of actual to predicted ICU length of stay and resource use. RESULTS: Structural and organizational questionnaires, self-evaluation by staff members, and the research team's implicit judgments following detailed on-site analysis failed to distinguish units with higher and lower risk-adjusted survival. Both units exhibited practices to emulate and practices to avoid. CONCLUSIONS: The methods used in this study can identify organizational problems and potential means for improvement. The best practices and suggestions for improvement at these units provide examples of methods for improving ICU management.
Adult↗
Do-not-resuscitate orders in intensive care units. Current practices and recent changes.
OBJECTIVES: To describe the characteristics of patients with do-not-resuscitate (DNR) orders and the frequency and timing of these orders in a representative sample of intensive care units (ICUs) and to compare practices from 1980 to 1990. DESIGN: Prospective inception cohort. SETTING: A total of 42 ICUs in 40 US hospitals with 200 or more beds; 26 randomly selected hospitals and 14 large, tertiary care hospitals that volunteered to be studied. PARTICIPANTS: A consecutive sample of 17,440 ICU admissions from 1988 to 1990. MEASUREMENTS: Patient demographic characteristics, comorbid conditions, disease, and physiological abnormalities. MAIN OUTCOME MEASURES: Frequency and timing of DNR orders; ICU resource use before and after DNR orders; and patients' hospital and ICU discharge status. RESULTS: Physicians wrote DNR orders for 1577 ICU admissions (9%) (hospital range, 1.5% to 22%). Patients with ICU DNR orders were older, more functionally impaired, had more comorbid illness, a higher severity of illness, and required the use of more ICU resources compared with patients without DNR orders. Compared with data from a similar survey from 1979 to 1982, ICU DNR orders were more frequent in 1988 to 1990 (9% vs 5.4%; P < .001) and preceded 60% of all in-unit deaths compared with only 39% in 1979 to 1982 (P < .001). Do-not-resuscitate orders were written sooner (for 3.6% vs 2.0% of patients on day 1 in the ICU) and patients with DNR orders remained in the ICU longer in 1988 to 1990 (2.8 vs 1.4 days) than in 1979 to 1982, and had lower ICU and hospital mortality rates (64% vs 74%, P < .001; and 85% vs 94%, P < .001). CONCLUSIONS: Over the last decade physicians and patients' families set limits earlier and more frequently in cases likely to have poor outcomes. We attribute this change to a greater dialogue about setting limits to care and a greater knowledge of treatment outcomes among physicians and families. These changes in practice preceded implementation of the Patient Self-determination Act, designed to ensure patient autonomy for decisions about life-sustaining therapy.
Adult↗
The clinical evaluation of new drugs for sepsis. A prospective study design based on survival analysis.
OBJECTIVE: To develop a survival model and severity assessment method to estimate the 28-day mortality risk for patients with sepsis syndrome entering phase 2 and 3 drug evaluations. DESIGN: Retrospective analysis of intensive care unit admissions with sepsis syndrome by means of log-normal regression to identify risk factors for 28-day mortality. Prospective application of the model to patients with gram-negative infection meeting sepsis syndrome criteria from separate data collection (validation group). PATIENTS: A total of 58,737 intensive care unit admissions at 107 hospitals in the United States and Western Europe screened to yield 1195 patients meeting entry criteria for the sepsis syndrome study for the original model; 295 hospitalized patients with gram-negative infection meeting criteria for sepsis syndrome for validation. MAIN OUTCOME MEASURES: Survival time and mortality at 28 days after fulfillment of the sepsis syndrome criteria. RESULTS: Acute physiologic abnormalities were the most important prognostic factors influencing outcome (82% of total chi 2). Specific disease resulting in intensive care unit admission and the time the patient was in the hospital and intensive care unit before qualification were also independent risks, as were age and a clinical history of cirrhosis. The model's overall classification accuracy was a Somers' Dyx of .52 (rank correlation between predicted risk and 28-day mortality) (receiver operating characteristic area, 0.76), with equal accuracy (Dyx = .59; receiver operating characteristic area, 0.80) in the independent group of patients. CONCLUSIONS: We created an accurate independent estimate for 28-day mortality risk for patients with sepsis syndrome (severe sepsis). This estimate could improve the evaluation of new drugs by investigating whether the drug's benefit varies by patient risk and then determining the amount of benefit for individual patients.
Adolescent↗
Variations in mortality and length of stay in intensive care units.
OBJECTIVE: To evaluate the amount of variation in in-hospital mortality and length of intensive care unit (ICU) stay that can be accounted for by clinical data available at ICU admission. DESIGN: Inception cohort study. SETTING: Forty-two ICUs in 40 hospitals, including 26 hospitals that were randomly selected and 14 large tertiary care hospitals that volunteered for the study. PARTICIPANTS: A consecutive sample of 16,622 patients and 17,440 ICU admissions. MEASUREMENTS AND MAIN OUTCOMES: Data on selected demographic characteristics, comorbidity, and specific physiologic variables were recorded during the first ICU day for an average of 415 admissions at each ICU; hospital discharge status (dead or alive) and length of ICU stay were recorded for individual patients; and the ratio of actual to predicted in-hospital mortality, standardized mortality ratios, and the ratio of actual to predicted length of ICU stay were recorded for individual ICUs. RESULTS: Unadjusted in-hospital mortality rates for the 42 units varied from 6.4% to 40%, and 90% (R2 = 0.90) of this variation was attributable to patient characteristics at admission. The standard mortality ratio varied from 0.67 to 1.25. The mean unadjusted length of ICU stay varied from 3.3 to 7.3 days, and 78% of the variation (R2 = 0.78) was attributed to patient and selected institutional characteristics. The best performing unit had a length of stay ratio of 0.88, whereas the poorest performing unit had a ratio of 1.21. CONCLUSIONS: Clinicians can use readily available admission data to adjust for considerable variations in patient severity and type in different ICUs. Such data should permit precise evaluation and comparison of ICU effectiveness and efficiency, which varied substantially in this study, and result in improved methods of risk prediction and evaluation of new medical practices.
Aged↗
Organ system dysfunction and risk prediction.
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Humans↗
Value and cost of teaching hospitals: a prospective, multicenter, inception cohort study.
OBJECTIVE: To examine variations in case-mix, structure, resource use, and outcome performance among teaching and nonteaching intensive care units (ICU). DESIGN: Prospective inception cohort study. PATIENTS: A consecutive sample of 15,297 patients at 35 hospitals, which compared 8,269 patients admitted to 20 teaching ICUs at 18 hospitals vs. 7,028 patients admitted to 17 non-teaching ICUs at 17 hospitals. INTERVENTIONS: None. MEASUREMENTS: We selected demographic, physiologic, and treatment information for an average of 415 patients at each ICU, and collected data on hospital and ICU structure. Outcomes were compared using ratios of observed to risk-adjusted predicted hospital death rates, ICU length of stay, and resource use. MAIN RESULTS: When compared to nonteaching ICUs, teaching ICUs had twice the number of physicians who regularly provided services and cared for significantly younger and more severely ill (p < .001) patients. Risk-adjusted ICU length of stay was similar, but resource use was significantly (p < .001) greater in teaching ICUs, with $3,000 (10.5%) of estimated total costs for an average ICU admission related to increased use of diagnostic testing and invasive procedures in teaching ICUs. Risk-adjusted hospital death rates were not significantly different (p = .1) between all teaching and nonteaching ICUs, but were significantly (p < .05) better in four teaching ICUs, but in only one nonteaching ICU. The 14 hospitals that were members of the Council of Teaching Hospitals had significantly better risk-adjusted outcome in their 16 ICUs than all others (odds ratio = 1.21, confidence interval 1.06 to 1.38, p = .004). CONCLUSIONS: Teaching ICUs care for more complex patients in a substantially more complicated organizational setting. The best risk-adjusted survival rates occur at teaching ICUs, but production cost is higher in teaching units, secondary to increased testing and therapy. Teaching ICUs are also successfully transferring knowledge to trainees who, after their training, are achieving equivalent results at slightly lower cost in nonteaching ICUs.
Adolescent↗