Physiologic abnormalities and outcome from acute disease. Evidence for a predictable relationship.

Initial physiologic data from 1625 nonoperative patients with 18 acute life-threatening diseases treated in an intensive care unit suggest a uniform predictable relationship between acute changes in normal physiologic balance and a patient's risk of death. We found that incremental deviations from normal physiologic balance represent equivalent and predictable incremental risks to survival, regardless of the disease initiating the physiologic disturbance. The relative impact of these physiologic abnormalities on outcome may depend on our understanding of the disease's mechanism of action. Diseases for which there is good understanding of underlying pathophysiology and precise treatment appear to have lower death rates throughout the range of physiologic imbalance compared with those for which pathophysiologic knowledge is limited or unknown. These results document the importance of pathophysiologic understanding to improving survival from acute disease. More importantly, they suggest a predictable relationship between risk of death and physiologic abnormalities for a wide range of diseases. The existence of such a relationship could greatly expand our prognostic ability and permit improved evaluation of new therapeutic discoveries.

Reliability of a measure of severity of illness: acute physiology of chronic health evaluation--II.

This study examines how reliably the components of the APACHE II score (Acute Physiology Score (APS), age and chronic health) are abstracted from the medical record in terms of inter-rater reproducibility (Intraclass Correlation Coefficient [ICC], kappa). In the sample studied, assignment of the APS is highly reproducible (ICC = 0.90). Reproducibility of the age variable (ICC = 0.998) suggests that age is accurately abstracted. Chronic health data does not fare as well as the APS and age (kappa = 0.66). This study suggests that the components of the APACHE II score can be collected reliably.

Identification of low-risk monitor admissions to medical-surgical ICUs.

A total of 5,790 intensive care unit (ICU) admissions from 13 tertiary care institutions were studied to identify patients who were at such low risk of receiving unique ICU therapies that admission might have been avoided or the length of ICU stay reduced. We used acute severity of disease on admission to the ICU along with the type of disease or surgery to risk stratify individual ICU patients. Among 1,941 patients who only received monitoring services on admission to the ICU, 1,358 (70 percent) were predicted to have less than a 10 percent risk of requiring subsequent active ICU treatment. Only 58 (4.3 percent) of these low-risk patients actually received active treatment. The identification of low-risk patients was equally accurate in estimation and validation data sets. Our methods should allow physicians and hospitals to assess their current ICU utilization and, if appropriate, guide reductions in use.

Predicting the duration of mechanical ventilation. The importance of disease and patient characteristics.

STUDY OBJECTIVE: To analyze the determinants of an individual patient's duration of mechanical ventilation and assess interhospital variations for average durations of ventilation. DESIGN: Prospective, multicenter, inception, cohort study. SETTING: Forty-two ICUs at 40 US hospitals. PATIENTS: A total of 5,915 patients undergoing mechanical ventilation on ICU day 1 selected from the acute physiology and chronic health evaluation (APACHE) III database of 17,440 admissions. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: Utilizing APACHE III data collected on the 5,915 patients, multivariate regression analysis was performed on selected patients and disease characteristics to determine which variables were significantly associated with the duration of mechanical ventilation. An equation predicting duration of ventilation was then developed using the significant predictor variables and its accuracy was evaluated. Variables significantly associated with duration of ventilation included primary reason for ICU admission, day 1 acute physiology score (APS) of APACHE III, age, prior patient location and hospital length of stay, activity limits due to respiratory disease, serum albumin, respiratory rate, and PaO2/FIo2 measurements. Using an equation derived from these variables, predicted durations of ventilation were then calculated and compared with actual observed durations for each of the 42 ICUs. Average duration of ventilation for the 42 ICUs ranged from 2.6 to 7.9 days, but 60% of this variation was accounted for by differences in patient characteristics. CONCLUSIONS: For patients admitted to the ICU and ventilated on day 1, total duration of ventilation is primarily determined by admitting diagnosis and degree of physiologic derangement as measured by APS. An equation developed using multivariate regression techniques can accurately predict average duration of ventilation for groups of ICU patients, and we believe this equation will be useful for comparing ventilator practices between ICUs, controlling for patient differences in clinical trials of new therapies or weaning techniques, and as a quality improvement mechanism.

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.

The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults.

The objective of this study was to refine the APACHE (Acute Physiology, Age, Chronic Health Evaluation) methodology in order to more accurately predict hospital mortality risk for critically ill hospitalized adults. We prospectively collected data on 17,440 unselected adult medical/surgical intensive care unit (ICU) admissions at 40 US hospitals (14 volunteer tertiary-care institutions and 26 hospitals randomly chosen to represent intensive care services nationwide). We analyzed the relationship between the patient's likelihood of surviving to hospital discharge and the following predictive variables: major medical and surgical disease categories, acute physiologic abnormalities, age, preexisting functional limitations, major comorbidities, and treatment location immediately prior to ICU admission. The APACHE III prognostic system consists of two options: (1) an APACHE III score, which can provide initial risk stratification for severely ill hospitalized patients within independently defined patient groups; and (2) an APACHE III predictive equation, which uses APACHE III score and reference data on major disease categories and treatment location immediately prior to ICU admission to provide risk estimates for hospital mortality for individual ICU patients. A five-point increase in APACHE III score (range, 0 to 299) is independently associated with a statistically significant increase in the relative risk of hospital death (odds ratio, 1.10 to 1.78) within each of 78 major medical and surgical disease categories. The overall predictive accuracy of the first-day APACHE III equation was such that, within 24 h of ICU admission, 95 percent of ICU admissions could be given a risk estimate for hospital death that was within 3 percent of that actually observed (r2 = 0.41; receiver operating characteristic = 0.90). Recording changes in the APACHE III score on each subsequent day of ICU therapy provided daily updates in these risk estimates. When applied across the individual ICUs, the first-day APACHE III equation accounted for the majority of variation in observed death rates (r2 = 0.90, p less than 0.0001).

Neurological intensive care admissions: identifying candidates for intermediate care and the services they receive.

OBJECTIVE: The high cost and scarcity of intensive care unit (ICU) beds has resulted in a need for improved utilization. This study describes the characteristics of patients who are admitted to the ICU for neurosurgical and neurological care, identifies patients who might receive all or most of their care in an intermediate care unit, and describes the services the patients would receive in an intermediate care unit. METHODS: We describe patients who received neurological care and who were part of a prospective study of 17,440 patients admitted to 42 ICUs at 40 United States hospitals. We identified patients who received only monitoring during ICU Day 1 and then used a previously validated equation to distinguish which patients were at low risk (< 10%) for subsequent active life-supporting therapy. We also describe the services these patients received during their ICU stay. RESULTS: Among 3000 patients admitted to the ICU for neurological care, 1350 received active therapy and 1650 (55%) underwent monitoring and received concentrated nursing care on ICU Day 1. After excluding those patients who received active therapy at admission, 1288 (78%) of the 1650 patients who underwent monitoring at admission were at low risk (< 10%) for subsequent active therapy; 95.8% received no active therapy. These patients who were at low risk for subsequent active therapy were significantly (P < 0.001) more often admitted postoperatively, were younger and less severely ill, and had lower ICU and hospital mortality rates (0.9 and 3.9%, respectively) than patients who received active treatment at admission. CONCLUSIONS: Patients receiving neurological care at an ICU who receive only monitoring during their 1st ICU day and have a less than 10% predicted risk of active treatment can be safely transferred to an intermediate care unit. Some of these patients may not require ICU admission. We suggest guidelines for equipping and staffing neurological intermediate care units based on the type and amount of therapy received by these patients.

Acute physiology and chronic health evaluation (APACHE II) and Medicare reimbursement.

This article describes the potential for the acute physiology score (APS) of acute physiology and chronic health evaluation (APACHE) II, to be used as a severity adjustment to diagnosis-related groups (DRG's) or other diagnostic classifications. The APS is defined by a relative value scale applied to 12 objective physiologic variables routinely measured on most hospitalized patients shortly after hospital admission. For intensive care patients, APS at admission is strongly related to subsequent resource costs of intensive care for 5,790 consecutive admissions to 13 large hospitals, across and within diagnoses. The APS could also be used to evaluate quality of care, medical technology, and the response to changing financial incentives.

The use of intensive care: a comparison of a university and community hospital.

We compared 223 consecutive intensive care unit (ICU) admissions to a community hospital (CH) with 613 such admissions at a university hospital (UH) using a new clinical scale aimed at quantifying severity of illness. Both ICU's had similar technical resources and treatment capabilities. At the CH, however, patients were more often admitted for monitoring rather than for treatment of UH admissions had a substantially greater acute severity of illness (p less than .001) than CH patients in most diagnostic categories. These findings suggest that use of the ICU was substantially different in the two hospitals, with the CH admitting many more stable patients. This study also suggests that evaluation of ICU use is improved by quantitative measurement of severity of illness.

Initial international use of APACHE. An acute severity of disease measure.

We need objective and reliable ways of measuring the severity of disease of hospitalized patients. This paper demonstrates the international predictive accuracy of a severity of disease measure on 1504 consecutive, unscheduled intensive care admissions to 14 hospitals in the United States, France, Spain, and Finland. Using laboratory data gathered within 24 hours of ICU admission, the Acute Physiology Score of APACHE (Acute Physiology and Chronic Health Evaluation) was a strong and stable predictor of hospital survival and concurrent therapeutic effort. In ordinary least squares and logistic multiple regression analysis, the impact of the Acute Physiology Score (APS) was highly significant (p less than 0.001) and of virtually identical magnitude in the United States and European hospitals. The use of this severity of disease measure should help researchers gain insights concerning the efficacy of medical services and the characteristics of physician decision making by permitting more precise prognostic stratification of severely ill patients.

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.

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.

Relationship between acute physiologic derangement and risk of death.

Initial evidence from 481 acutely ill patients with 12 major life-threatening diseases suggests a consistent relationship between the magnitude of physiologic derangement and the patient's risk of death. These results were found in postoperative and nonoperative diseases, including gastrointestinal bleeding, intracranial bleeding, pneumonia, congestive heart failure, trauma and hemorrhagic shock. There appear to be substantial differences in the inherent risk of these diseases, but within each diagnosis, the impact of incremental increases in physiologic derangement on mortality appears to be similar. The existence of a uniform relationship in a variety of diagnoses could have important implications for the researcher and clinician wishing to evaluate outcome from intense medical care. It would allow more reproducible and precise stratification of patients by risk of death prior to therapy, thereby improving our understanding of the efficacy of new and existing treatments.

Statistical validation of a severity of illness measure.

This paper provides statistical detail on the predictive power of a new severity of illness scale--APACHE (Acute Physiology and Chronic Health Evaluation)--on 833 consecutive medical admissions to an intensive care unit (ICU). In a multivariate logistic regression analysis of routine physiologic and other data obtained within 24 hours of ICU admission, severity of illness and age were significantly (p less than .0001) related to survival. Using the estimated equation to forecast death rates for independent data, APACHE allowed accurate estimates of death rates for groups of patients whose mortality at hospital discharge varied from 3 to 80 per cent. The Acute Physiology Score of APACHE is also strongly and significantly associated with outcome within a number of specific cardiovascular, neurologic, respiratory, and gastrointestinal diagnoses. After multi-institutional validation studies, APACHE could prove useful in a wide range of studies involving acutely ill patients.

The value of measuring severity of disease in clinical research on acutely ill patients.

There are five major factors that determine outcome from disease: (1) disease type, (2) the severity of the disease, (3) the patient's age, (4) his prior health status, and (5) the therapy available. Evaluation of new treatments for various diseases is often done with little information on individual patients' severity. The most widely used method of controlling for acute severity fails to account for interaction among major organ systems and for important threshold effects found within physiologic measurements. To illustrate, we simulated a clinical trial comparing severity and outcome for two groups randomly chosen from 50 consecutive respiratory failure patients. Mean values for a variety of clinical, demographic, and physiologic measures were similar. A severity of disease classification, however, predicted differential mortality (25% vs 37%) that matched actual death rates. Uniform and accurate measurement of acute severity of disease in individual patients could improve the precision of clinical research.

Utilizing findings from the APACHE III research to develop operational information system for the ICU--the APACHE III ICU Management System.

The APACHE III data base reflects the disease, physiologic status, and outcome data from 17,400 ICU patients at 40 hospitals, 26 of which were randomly selected from representative geographic regions, bed size, and teaching status. This provides a nationally representative standard for measuring several important aspects of ICU performance. Results from the study have now been used to develop an automated information system to provide real time information about expected ICU patient outcome, length of stay, production cost, and ICU performance. The information system provides several new capabilities to ICU clinicians, clinic, and hospital administrators. Among the system's capabilities are: the ability to compare local ICU performance against predetermined criteria; the ability to forecast nursing requirements; and, the ability to make both individual and group patient outcome predictions. The system also provides improved administrative support by tracking ICU charges at the point of origin and reduces staff workload eliminating the requirement for several manually maintained logs and patient lists. APACHE III has the capability to electronically interface with and utilize data already captured in existing hospital information systems, automated laboratory information systems, and patient monitoring systems. APACHE III will also be completely integrated with several CIS vendors' products.

Identification of low-risk monitor patients within a medical-surgical intensive care unit.

We studied the hospital course of 1148 consecutive intensive care unit (ICU) admissions to test the feasibility of identifying patients suitable for early transfer. Based on the type of treatment each admission received during the initial 16 hours in ICU, we divided the patients into active treatment or monitored categories. Which of the 513 monitored admissions received active treatment before discharge was analyzed with a multivariate logistic regression analysis, using variables such as age, sex, indication for admission, and a new severity-of-illness scale. The most important variable in identifying low-risk monitored patients was the severity of illness measure, which performed well in both estimation and validation data sets. Within the 513 monitored admissions, 154 had predicted risks of requiring active intensive therapy of less than 5 per cent. Only five persons actually received such treatment. This approach might assist in reducing the ever-increasing demand for intensive care.