William A. Knaus

APACHE II: a severity of disease classification system.

This paper presents the form and validation results of APACHE II, a severity of disease classification system. APACHE II uses a point score based upon initial values of 12 routine physiologic measurements, age, and previous health status to provide a general measure of severity of disease. An increa

Variations in Mortality and Length of Stay in Intensive Care Units

Intensive care units (ICUs), first introduced in the 1960s, now account for approximately 7% of total U.S. hospital beds, 20% to 30% of hospital costs, and 1% of the U.S. gross domestic product [1-3]. These economic and institutional consequences have increased the need for outcome evaluation and guidance regarding efficient utilization. Mortality rates, an insensitive measure for an entire hospital [4-6], are high enough in ICUs to serve as one reliable performance indicator. Substantial progress has also been made in identifying clinical risk factors for death and resource utilization for patients in ICUs [7-11]. The objective of this study was to explore the ability to evaluate ICU performance using risk-adjusted in-hospital mortality rates and length of ICU stay. In this report, we focus on the amount of variation that can be accounted for after adjusting for patient characteristics present at admission. We describe the nature and relative importance of these factors and the extent of the remaining variation in outcome performance after such adjustment. Methods Hospital and Intensive Care Unit Selection We used a stratified random process based on geographic region, size, and teaching status (defined by the presence of resident housestaff or of one or more accredited graduate medical training programs) to select 26 hospitals from 1691 nonfederal U.S. hospitals with more than 200 beds. Twenty-three of the 26 hospitals agreed to participate. The reasons for nonparticipation of the three hospitals were the sale of the hospital, a severe nursing shortage (making data collection assistance unlikely), and a poor fiscal condition (making closure imminent). We chose three alternate hospitals from the same strata using an identical process. Fourteen other hospitals, primarily large tertiary care or university teaching hospitals with an interest in the project, also volunteered to participate in the study, giving a total of 40 hospitals. In hospitals with more than one ICU, data collection took place in the unit with the highest annual admission rate. Data were also collected in two ICUs at two volunteer institutions, giving a total of 42 ICUs for analysis. We excluded burn, pediatric, neonatal, and coronary care units from consideration. Thus, data collection took place in adult general medical, general surgical, and combined medical-surgical units. Patient Selection and Data Collection Data collection began in May 1988 and was completed in February 1990; the study period at each ICU averaged 9.7 months (range, 3 to 17 months). In most ICUs, data were collected concurrently for consecutive ICU admissions. A systematic scheme (for example, every second or third patient) was used in 20% of the units when patient volume precluded data collection on consecutive admissions. Patients were excluded from the study if they were admitted for chest pain, rule-out myocardial infarction, coronary artery bypass surgery, or burn injury; were younger than 16 years; or had a length of ICU stay of less than 4 hours. Information collected for each patient included age, an extensive listing of coexisting illnesses, location before ICU admission (emergency, recovery, hospital, or operating room; ICU readmission; or transfer from another ICU or hospital), and surgery status (elective or emergency, which was defined as surgery for an immediately life-threatening condition). We also recorded the primary reason for ICU admission using 78 mutually exclusive disease categories [12]. During the first ICU day, each patients clinical record was reviewed for APACHE III and Therapeutic Intervention Scoring System scoring [12, 13]. The APACHE III score consists of an acute physiology score obtained by applying weights to 17 potential physiologic variables, a weight applied to age, and additional weight to one of seven comorbid conditions that influence the risk for short-term death by reducing immune response. A higher APACHE III score (0 to 299) is associated with a higher risk for in-hospital death. The Therapeutic Intervention Scoring System is also a weighted (1 to 4) scoring system derived from 80 interventions and specific nursing tasks representing the intensity of care provided. During the subsequent 6 ICU days, we recorded all changes in the APACHE III acute physiology score and, using the Therapeutic Intervention Scoring System, in the type and amount of monitoring and treatment received. We also recorded length of stay in the ICU and in the hospital and followed all patients for survival at ICU and hospital discharge and at 30 days after discharge for all Medicare patients and for a 15% random sample of all other patients. Patient data were entered into on-site microcomputers by trained data collectors using specially designed software, and data underwent continuous quality monitoring and review. A formal interobserver reliability study was conducted at 11 of the hospitals [14]. Further details on data collection procedures are available elsewhere [12, 15]. Equations for Predicting In-Hospital Death and Length of ICU Stay For each patient, we estimated the probability of in-hospital death using a multiple logistic regression equation. The variables used in this analysis were preselected based on previous research [15]. The primary determinants of short-term outcome were defined as acute physiologic abnormalities within 24 hours of ICU admission (APACHE III acute physiologic score); the patients physiologic reserve as measured by age and the presence of specific comorbid conditions (as represented in the APACHE III score); the underlying disease prompting ICU admission; the location and duration of treatment immediately before ICU admission; and one institutional characteristic: the nature of hospital discharge practices as measured by the excess mean duration of hospital stay for survivors. This variable was determined for each unit based on a statistical analysis of length of hospital stay for all hospital survivors compared with average stay for all ICUs based on disease and APACHE III score (see footnote in Appendix Table 1). This variable was used to account for variations in triage pressure or practice style, both of which affect hospital discharge patterns: Hospitals that discharge patients later are likely to report more deaths because of the longer time during which their patients could die in the hospital [16]. Appendix Table 1. Relation of Prognostic Factors to In-Hospital Mortality and Length of Intensive Care Unit Stay Table 1. Characteristics of Patients, Hospitals, and Intensive Care Units* Each patients first admission to the ICU within the study period was included in the analysis. Second and subsequent readmissions to the ICU were excluded from the mortality analysis to avoid counting two outcomes for the same individual. The mortality equation was cross-validated using a grouped jackknife approach [17]: All patient data were divided into 10 independent groups using a random-number generator, and 10 different regression models were estimated, with each model excluding one group. Each model was used to calculate predictions for the excluded group. We then compared the predicted risks for individual patients from the excluded groups with the predictions based on the equation estimated on the entire sample. For both the grouped jackknife approach and the equation estimated on all patients, the equation was estimated each time with the same fixed set of predictor variables, without using stepwise variable selection or other search techniques. We developed a separate multiple regression equation to estimate length of ICU stay based on the same patient and institutional characteristics as described above, with the following exceptions. The excess mean adjusted length of hospital stay and the patients length of hospital stay before ICU admission were deleted, and traditional hospital descriptors, such as geographic region, bed size, and teaching status, were added. This analysis excluded patients discharged to another ICU for which there was incomplete data on total length of ICU stay but included the fact that a patient was readmitted to the unit. In cases in which length of ICU stay exceeded 40 days, such stays were rounded down to 40 days and then included in the analysis. To allow for nonlinearities in the relation between continuous variables and length of ICU stay, the method of restricted cubic splines was used [18]. This technique permits the weight attributable to a variable to vary continuously throughout its possible range. The model for length of ICU stay was also cross-validated using the same grouped jackknife approach as has been described for mortality. To measure how much of the variation in mortality and length of stay were accounted for by the equations, we used the area under a receiver operating characteristic curve [19] for the dichotomous outcome, mortality, and the R2 for the continuous variable, length of stay. Except for the comparisons of the full equation with the cross-validated predictions, all these results report associations with the cross-validated predicted risks. Risk-adjusted (Standardized) Ratios for In-Hospital Mortality and Length of ICU Stay To calculate a risk-adjusted mortality rate for each ICU, we added individual patient predictions using the cross-validated equations and then calculated a standardized mortality ratio by dividing actual by mean predicted group death rate at hospital discharge. The units were then ranked by relative performance according to their standardized mortality ratio. We used a chi-square test to determine the significance of differences between actual and predicted survival rates for each unit, and a P value 0.05 at the unit level was considered to be significant. To determine the amount of variation across ICUs that was accounted for by predictions, we estimated a univariate least-squares regression equation across all 42 ICUs, using the observed deat

Evaluation of Acute Physiology and Chronic Health Evaluation III predictions of hospital mortality in an independent database

OBJECTIVEnTo assess the accuracy and validity of Acute Physiology and Chronic Health Evaluation (APACHE) III hospital mortality predictions in an independent sample of U.S. intensive care unit (ICU) admissions.nnnDESIGNnNonrandomized, observational, cohort study.nnnSETTINGnTwo hundred eighty-five ICUs in 161 U.S. hospitals, including 65 members of the Council of Teaching Hospitals and 64 nonteaching hospitals.nnnPATIENTSnA consecutive sample of 37,668 ICU admissions during 1993 to 1996; including 25,448 admissions at hospitals with >400 beds and 1,074 admissions at hospitals with <200 beds.nnnINTERVENTIONSnNone.nnnMEASUREMENTS AND MAIN RESULTSnWe used demographic, clinical, and physiologic information recorded during ICU day 1 and the APACHE III equation to predict the probability of hospital mortality for each patient. We compared observed and predicted mortality for all admissions and across patient subgroups and assessed predictive accuracy using tests of discrimination and calibration. Aggregate hospital death rate was 12.35% and predicted hospital death rate was 12.27% (p =.541). The model discriminated between survivors and nonsurvivors well (area under receiver operating curve = 0.89). A calibration curve showed that the observed number of hospital deaths was close to the number of deaths predicted by the model, but when tested across deciles of risk, goodness-of-fit (Hosmer-Lemeshow statistic, chi-square = 48.71, 8 degrees of freedom, p< .0001) was not perfect. Observed and predicted hospital mortality rates were not significantly (p < .01) different for 55 (84.6%) of APACHE IIIs 65 specific ICU admission diagnoses and for 11 (84.6%) of the 13 residual organ system-related categories. The most frequent diagnoses with significant (p < .01) differences between observed and predicted hospital mortality rates included acute myocardial infarction, drug overdose, nonoperative head trauma, and nonoperative multiple trauma.nnnCONCLUSIONSnAPACHE III accurately predicted aggregate hospital mortality in an independent sample of U.S. ICU admissions. Further improvements in calibration can be achieved by more precise disease labeling, improved acquisition and weighting of neurologic abnormalities, adjustments that reflect changes in treatment outcomes over time, and a larger national database.

Daily prognostic estimates for critically ill adults in intensive care units: Results from a prospective, multicenter, inception cohort analysis

ObjectiveTo develop daily prognostic estimates for individual patients treated in adult intensive care units (ICU). DesignProspective, multicenter, inception cohort analysis. SettingForty-two ICUs at 40 U.S. hospitals with >200 beds including 20 ICUs in tertiary care centers with major teaching activities. PatientsA consecutive sample of 17,440 ICU admissions. Measurements and Main ResultsA series of multivariate equations were developed using the patients 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 days 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 days 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 days 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. ConclusionsEquations 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. (Crit Care Med 1994; 22:1359–1372)

Value and cost of teaching hospitals: a prospective, multicenter, inception cohort study.

ObjectiveTo 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: Aconsecutive 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 regulary 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

Proposed definitions for diagnosis, severity scoring, stratification, and outcome for trials on intraabdominal infection

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. (Crit Care Med 1993; 21:1432–1442)

PREDICTING OUTCOME IN CRITICAL CARE : THE CURRENT STATUS OF THE APACHE PROGNOSTIC SCORING SYSTEM

Analysis of the experience with scientific studies on patients with secondary intraabdominal infection has revealed that problems of interpretation and comparability between studies exist as they relate to variable diagnostic criteria, unmeasured severity of disease, and unclear outcome measures. A consistent system of definitions has been developed to address these deficiencies. Intraabdominal infection is defined as clinical peritonitis requiring both operative and microbiological confirmation for proof of infection. The APACHE II system is proposed for grading the severity of the infection and for stratification of patient risk of mortality. Mortality and time until death, on one hand, and recovery and time until recovery, on the other, are proposed as the main outcome measures, both being independently and positively defined. It is anticipated that this system of minimum rules will produce studies that can be compared, hence, accelerating knowledge and understanding about intraabdominal infection and its best treatment.RésuméLanalyse des différentes études scientifiques sur les infections intra-abdominales a montré que les problèmes dinterprétation et de comparabilité relèvent de critiques diagnostiques variables, et de labsence dévaluation de la sévérité de la maladie, et de son évolution. Un système de définitions a été développé pour pallier ces déficiences. Linfection intra-abdominale se définit comme péritonite clinique exigeant la confirmation opératoire et microbiologique de linfection. Le score APACHE II est proposé pour classer linfection selon sa sévérité et prévoir le risque de mortalité. Mortalité et délai avant la mort, guérison st délai avant la guérison sont proposés comme critères dévolution. On espére que le fait dadopter ces règles permettra de comparer les études et fera progresser notre compréhension de linfection intra-abdominale et son traitement.ResumenEl análisis de los resultados de investigaciones científicas en pacientes con infección intraabdominal secundaria ha revelado que los problemas de interpretación y comparabilidad entre los diversos estudios se relacionan con criterios diagnósticos variables, con gravedad no definida de la enfermedad, y con parámetras indeterminados de desenlace. Se ha establecido un Sistema consistente de definiciones con el objeto de eliminar tales deficiencias. La infección intraabdominal es definida como una peritonitis clfnica que requiere confirmación, tanto operatoria como microbiológica como prueba de la infección. Se propone el sistema APACHE II para determinar el grado de gravedad de la infección y para la estratificación del riesgo de mortalidad. La mortalidad y el período hasta la muerte del paciente por un lado, y la recuperación y el perfodo hasta la recuperación por otro, son propuestos como los parámetras principales de desenlace, ambos definidos en forma independiente y positiva. Se espera que este sistema de reglas mínimas habrá de producir estudios que sean comparables, lo cual acelere la adquisición de nuevos conocimientos y la mejor comprensión de la infección intraabdominal y de su tratamiento.

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

The APACHE (Acute Physiology and Chronic Health Evaluation) prognostic scoring system was developed in 1981 at the George Washington University Medical Center as a way to measure disease severity. APACHE II, introduced in 1985, was a simplified modification of the original APACHE. The APACHE II score consisted of three parts: 12 acute physiological variables, age and chronic health status. Probability of death can be derived by using the disease category and the APACHE II score. The uses of APACHE II include risk stratification to account for case mix in clinical studies, comparison of the quality of care among ICUs, and assessment of group and individual prognoses. APACHE III, a refinement of APACHE II, will be introduced in late 1990. The APACHE III data base includes 17,457 patients from a representative sample of 40 American hospitals. Additional potential uses of APACHE III include the identification of factors in the ICU which contribute to outcome and assistance in individual patient decision-making. This article reviews the development, current uses and potential applications of the APACHE system.RésuméL’APACHE (acute physiology and chronic health evaluation) est un système de gradation pronostique qui s’est développé en 1981 à George Washington University Medical Center afin de mesurer la sévérité de la maladie. APACHE II, introduit en 1985, fut une modification qui a simplifié l’APACHE original. LAPACHE II consiste en trois parties : 12 variables physiolo-giques aiguës, âge et état de santé chronique. La probabilité de la mortalité peut être déduite en ulilisant la catégorie de maladie et le système d’évaluation APACHE II. Les utilisations de l’APACHE II incluent la stratification du risque afin de tenir compte de l’identification des cas dans des études cliniques mixtes, la comparaison de la qualité des soins entre les unités de soins intensifs et l’évaluation des pronostics individuel et de groupe. APACHE III, un rafinement de l’APACHE II, sera introduit vers la fin des années 1990. Les données de l’APACHE III incluent 17,457 patients d’une population représentative de 40 hôpitaux américains. Les utilisations potentielles addition-nelles de l’APACHE III incluent l’identification des facteurs aux soins intensifs qui contribuent à l’issue et a l’assistance concernant les décisions sur certains patients. Cet article revoit le développement des utilisations courantes, des applications potentielles du système APACHE.

Predicting Patient Outcome from Intensive Care: A Guide to APACHE, MPM, SAPS, PRISM, and Other Prognostic Scoring Systems

There are five major factors that determine outcome from disease: (1) disease type, (2) the severity of the disease, (3) the patients 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.

Severity Stratification and Outcome Prediction for Multisystem Organ Failure and Dysfunction

Accurate prognosis is critical to the practice and im provement of intensive care. Recently, a number of gen eral prognostic scoring systems have been developed and their primary goal is to predict patient outcomes. We describe the principles underlying these systems and the methods they use to create predictions. We also explain how predictions of patient outcomes can be used to improve the precision of clinical trials, to evalu ate hospital and intensive care unit use and outcome, and eventually to assist in clinical decision-making.