Comparison of the performance of two comorbidity measures, with and without information from prior hospitalizations.

OBJECTIVES: This study compares the performance of two comorbidity risk adjustment methods (the Deyo et al adaptation of the Charlson index and the Elixhauser et al method) in five groups of California hospital patients with common reasons for hospitalization, and assesses the contribution to model performance made by information drawn from prior hospital admissions. METHODS: California hospital discharge abstract data for the calendar years 1994 through 1997 were used to create a longitudinal data set for patients in the five disease groups. Eleven logistic regression models were estimated to predict the risk of in-hospital death for patients in each group, with both comorbidity risk adjustment methods applied to patient information available from only the index hospitalization, and to information available from both the index and prior hospitalizations. RESULTS: For every comparison made, the level of statistical performance (area under the receiver operating characteristics curve) demonstrated by models using the Elixhauser et al method was superior to that of models using the Deyo et al adaptation method. Although most patients have information available from prior hospital admissions, this additional information yields only small improvements in the performance of models using either comorbidity risk adjustment method. CONCLUSIONS: Better discrimination is achieved with the Elixhauser et al method using only information from the index hospitalization than is achieved with the Deyo et al adaptation using information from all identified hospital admissions. Both comorbidity risk adjustment methods achieve their best performance when information from the index hospitalization and prior admissions is separated into independent indicators of comorbid illness.

Initial development of a system-wide maternal-fetal outcomes assessment program.

OBJECTIVE: This study was undertaken to develop a comprehensive risk-assessment approach capable of evaluating maternal and fetal outcomes. STUDY DESIGN: Data from 10,984 women and 11,066 infants delivered at 79 military treatment facilities in the United States from 1995 to 1997 were used to develop two individual but complementary risk-adjustment models for maternal and, separately, fetal outcomes. A range of maternal and delivery-related risk variables and clinically important outcomes were identified by expert opinion and selected and weighted with ordinal logistic regression analysis. Receiver operating characteristic curves for the maternal and fetal models were determined. Variation across the facilities in risk-adjusted performance was also evaluated. RESULTS: Risk factors and poor outcomes were rare for both mothers and infants, with 96.9% of infants and 97.7% of mothers having good or excellent outcomes (0.7% mortality and 0.01% mortality, respectively). Despite the low frequency of poor outcomes both models performed well, with receiver operating characteristic curves of 0.75 for maternal outcomes and 0.78 for infant outcomes. When the models were applied to the military treatment facilities, there were significant differences among facilities in risk-adjusted outcomes. Twenty-four of the facilities in the study (30%) had outcomes odds ratios that were significantly >1 or significantly <1 (P <.05). There did not appear to be any relationship between the performance of a military treatment facility for maternal outcome and that for infant outcome. CONCLUSION: Complementary risk models for maternal and infant outcomes were developed that had satisfactory discriminatory power across a variety of facilities within a large health system. With further development and refinement this approach holds promise of being able to detect variations in risk-adjusted performance that could be used to identify best practices. The results might also be used to help coordinate and improve the quality of care for the entire conception-to-delivery process.

A predictive risk model for outcomes of ischemic stroke.

BACKGROUND AND PURPOSE: The great variability of outcome seen in stroke patients has led to an interest in identifying predictors of outcome. The combination of clinical and imaging variables as predictors of stroke outcome in a multivariable risk adjustment model may be more powerful than either alone. The purpose of this study was to determine the multivariable relationship between infarct volume, 6 clinical variables, and 3-month outcomes in ischemic stroke patients. METHODS: Included in the study were 256 eligible patients from the Randomized Trial of Tirilazad Mesylate in Acute Stroke (RANTTAS). Six clinical variables and 1-week infarct volume were the prespecified predictor variables. The National Institutes of Health Stroke Scale, Barthel Index, and Glasgow Outcome Scale were the outcomes. Multivariable logistic regression techniques were used to develop the model equations, and bootstrap techniques were used for internal validation. Predictive performance of the models was assessed for discrimination with receiver operator characteristic (ROC) curves and for calibration with calibration curves. RESULTS: The predictive models had areas under the ROC curve of 0.79 to 0.88 and demonstrated nearly ideal calibration curves. The areas under the ROC curves were statistically greater (P<0.001) with both clinical and imaging information combined than with either alone for predicting excellent recovery and death or severe disability. CONCLUSIONS: Combined clinical and imaging variables are predictive of 3-month outcome in ischemic stroke patients. Demonstration of this relationship with acute clinical variables and 1-week infarct information supports future attempts to predict 3-month outcome with all acute variables.

Evaluation of acute physiology and chronic health evaluation III predictions of hospital mortality in an independent database.

OBJECTIVE: To 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. DESIGN: Nonrandomized, observational, cohort study. SETTING: Two hundred eighty-five ICUs in 161 U.S. hospitals, including 65 members of the Council of Teaching Hospitals and 64 nonteaching hospitals. PATIENTS: A 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. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We 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 III's 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. CONCLUSIONS: APACHE 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.

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.

Evaluating laboratory usage in the intensive care unit: patient and institutional characteristics that influence frequency of blood sampling.

OBJECTIVES: To develop a predictive equation to estimate the frequency of blood drawing for intensive care unit (ICU) laboratory tests and to evaluate variations in ICU blood sampling practices after adjusting for patient and institutional factors. DESIGN: Prospective, inception, cohort study. SETTING: Forty-two ICUs in 40 hospitals, including 20 teaching and 17 nonteaching ICUs. PATIENTS: A consecutive sample of 17,440 ICU admissions, in which 14,043 blood samples were drawn for laboratory testing on ICU days 2 to 7. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Patient demographic, physiologic, and treatment data were obtained on ICU day 1; the type and number of blood samples for laboratory testing were recorded on ICU days 1 to 7. In the 42 ICUs, a mean of 16.2 blood samples were drawn for tests on ICU days 2 to 7, but varied between 23 samples in the teaching ICUs and 9.9 samples in nonteaching ICUs. Using only ICU day 1 patient data, we predicted the subsequent number of samples drawn on ICU day 2 (R2 = .26 across individual patients) and on ICU days 2 to 7 (R2 = .26 across individual patients). The most important determinants of the number of blood samples drawn on ICU days 2 to 7 were the ICU day 1 Acute Physiology Score and admission diagnosis. After controlling for patient variables, hospital teaching status, number of beds, and location in the East and South were significantly (p < .05) associated with increased blood sampling on ICU day 2 and on ICU days 2 to 7. More frequent use of an arterial cannula and mechanical ventilation were also associated with increased blood sampling on subsequent days. CONCLUSIONS: The ability to adjust for patient and institutional variables and to predict the number of blood samples drawn for laboratory tests can allow ICUs to compare their practices with those of other units. When integrated into a continuous quality improvement process, this information can be used to identify and focus on opportunities for improving blood conservation and reducing excessive diagnostic testing.

Long-term follow-up of patients with meningiomas involving the cavernous sinus: recurrence, progression, and quality of life.

OBJECTIVE: In this report, we evaluate the recurrence- and progression-free survival of patients with meningiomas involving the cavernous sinus. METHODS: The rates of tumor recurrence and progression in 119 patients with meningiomas involving the cavernous sinus treated between 1983 and 1993 were analyzed. RESULTS: The mean follow-up period was 33.8 months. There were seven recurrences of completely resected tumors (7 of 73, 10%) and seven progressions of incompletely resected tumors (7 of 46 15%). Life table analysis of recurrence- and progression-free survival was performed. The recurrence-free survival rate was 94% at 3 years and 81% at 5 years. In contrast, the progression-free survival rate was 87% at 3 years and 62% at 5 years (P = 0.0456). There were two patterns of recurrence or progression. The first group of tumors had an aggressive biological behavior with growth at multiple areas of resection; the second group showed growth at or near the margins of resection. Functional status continued to improve slowly during the follow-up period; however, there was no statistically significant difference between the Karnofsky scores obtained 3 to 12 months postoperatively and the follow-up Karnofsky scores (obtained > 1 yr postoperatively). CONCLUSION: Surgery for meningiomas involving the cavernous sinus seems to provide excellent tumor control. Our experience suggests that for the majority of these tumors, complete tumor resection can be performed with acceptable risks of morbidity and mortality and seems to increase the duration of recurrence-free survival. The long-term quality of life of surviving patients is satisfactory, with the great majority of the patients being independent.

Variations in the use of do-not-resuscitate orders in ICUS. Findings from a national study.

STUDY OBJECTIVES: To describe the variation in frequency of do-not-resuscitate (DNR) orders in 42 US ICUs and to examine the relationship between published guidelines and qualitative observations about terminal care in 9 ICUs. DESIGN: Prospective inception cohort. SETTING: Forty-two ICUs in 40 US hospitals with more than 200 beds: 26 randomly selected and 14 large tertiary-care volunteers. PARTICIPANTS: A consecutive sample of 17,440 ICU admissions during 1988 to 1990. MEASUREMENTS AND RESULTS: We used age, race, comorbid conditions, disease, functional status, and acute physiology score on ICU day 1 to predict the likelihood of a DNR order for each patient. A cross-validated model was then used to predict variations in the risk of an ICU DNR order from 0 to 45% (area under receiver operating characteristic curve = 0.9). The model was then used to compare aggregate observed with predicted frequency of ICU DNR orders. Finally, we compared observations of DNR practices by a team of clinical and organizational researchers at 9 of the 42 ICUs with published guidelines and risk-adjusted DNR frequency: 1,577 admissions (9%) had DNR orders written in the ICU (range, 1.5 to 22%). The ICU site was a significant (p < 0.0001) predictor of variance in the patient level model. DNR orders were written significantly (p < 0.05) less frequently than predicted in 5 and more frequently than predicted in 3 of 42 ICUs. Nonwhite patients had significantly (p = 0.0001) fewer DNR orders after adjustment. The research team's implicit judgments following on-site analysis failed to distinguish ICUs with more or less DNR orders than predicted. Site-visited ICUs exhibited practices to emulate and practice to avoid. CONCLUSIONS: The frequency of ICU DNR orders can be predicted based on individual risk factors for groups of ICU patients. After adjusting for differences in patient characteristics, there is significant variation in the frequency of DNR orders in a national sample of ICUs. These variations may be due to unmeasured differences in patient characteristics such as treatment preferences, religious affiliation, educational level, or physician practices. We found no relationship between risk-adjusted DNR order frequency and adherence to published guidelines.

Planning patient services for intermediate care units: insights based on care for intensive care unit low-risk monitor admissions.

OBJECTIVE: To describe the technology and nursing services that would be required to care for intensive care unit (ICU) low-risk monitor admissions in an intermediate unit. DESIGN: Prospective, multicenter, inception cohort analysis. SETTING: Forty U.S. hospitals with > 200 beds, including 26 hospitals that were randomly selected and 14 that volunteered for the study. PATIENTS: A sample of 8,040 ICU patients admitted to the ICU for monitoring, who received no active life-support treatment on ICU day 1. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Demographic, physiologic, and treatment information were obtained during ICU days 1 to 7. A previously validated multivariate equation was used to identify 6,180 monitor admissions at low (< 10%) risk for receiving active treatment during their entire ICU stay. We used daily Therapeutic intervention Scoring System (TISS) data to identify the equipment, type and amount of nursing care, and the types of active treatment that would have been used had these ICU patients been admitted to an intermediate care unit. Mean day-1 ICU TISS scores were as follows: 16.4 for all patients; 18.3 for surgical patients; and 13.5 for medical admissions. Concentrated nursing care accounted for 89% and technologic monitoring for 11% of day-1 TISS points. Surgical admissions had a 2.8-day mean ICU length of stay and received an average of 16.5 TISS points per patient per day. Medical admissions had a 2.7-day mean ICU length of stay and received an average of 12.3 TISS points per patient per day. Subsequent active life-support therapy was received by 4.4% of these ICU low-risk monitor admissions. CONCLUSIONS: The services received by ICU low-risk monitor admissions provide insight regarding the equipment and nursing care that might be required, and the kinds of emergencies that might occur, if these patients were cared for in medical and surgical intermediate care units. Our data suggest that if ICU low-risk monitor patients were admitted to an intermediate care unit, they would mainly require concentrated nursing care (nurse/patient ratio of 1:3 to 1:4) and limited technologic monitoring.

A comparison of risks and outcomes for patients with organ system failure: 1982-1990.

OBJECTIVES: To compare the outcomes for patients with one or more organ system failures treated in 1988 to 1990 with those outcomes from 1979 to 1982; to document risk factors for developing organ system failure; and investigate the relationship of these factors to hospital survival. DESIGN: Prospective, multicenter, inception cohort analysis. SETTING: Sixty intensive care units (ICUs) at 53 U.S. hospitals. PATIENTS: A total of 17,440 ICU admissions treated in 1988 to 1990 and 5,677 ICU admissions treated in 1979 to 1982. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: At the time of organ system failure, patients were classified by demographic, physiologic, and diagnostic information. The type and number of organ system failures and physiologic responses were recorded for < or = 7 days of ICU treatment, and all patients were followed for status at hospital discharge. Hospital survival and the prognostic value of assessing the number of organ system failures were compared with risk assessment, based on use of a prognostic scoring system that estimated the patient's probability of hospital mortality. The incidence of organ system failure (48%) among patients treated in 1988 to 1990 was similar (44%) to the occurrence rate in patients in 1979 to 1982; and an identical proportion (14%) developed multiple organ system failure. There was a significant (p < .0003) improvement in hospital mortality for patients with three or more organ system failures on day 4 or later of organ system failure. However, overall hospital mortality rates from multiple organ system failure were not different over this 8-yr period. The most important predictor of hospital mortality was the severity of physiologic disturbance on the initial day of failure. Discrimination of patients by risk of hospital mortality was better using the prognostic scoring system on day 1 of organ system failure (receiver operating characteristic curve = 0.88) than using a model based on the number of organ system failures (receiver operating characteristic curve = 0.68). CONCLUSIONS: Organ system failure remains a major contributor to death in patients in ICUs. The incidence and overall outcome have not significantly changed over the past 8 yrs, but there has been significant improvement in survival for patients with persistent severe organ system failure. A continuous measure of individual patient severity of illness is a more sensitive and accurate method for describing patients and estimating outcome than counting the number of organ system failures.

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 importance of technology for achieving superior outcomes from intensive care. Brazil APACHE III Study Group.

OBJECTIVE: To test the hypothesis that technology availability, staffing, and diagnostic diversity in an intensive care unit (ICU) are associated with the ability to decrease hospital mortality. DESIGN: Prospective multicenter descriptive cohort study. SETTING: Ten Brazilian medical-surgical ICUs. PATIENTS: 1734 consecutive adult ICU admissions. MEASUREMENTS AND RESULTS: We recorded the amount of technology, number of diagnoses, and availability of nurses at each ICU. We also used demographic, clinical and physiologic information for an average of 173 admissions to each ICU to calculate standardized mortality ratios (SMRs) for each ICU. The mean SMR for the ten ICUs was 1.67 (range 1.01-2.30). A greater availability of ICU equipment and services was significantly (p < 0.001) associated with a lower SMR. CONCLUSION: The ability of Brazilian ICUs to reduce hospital mortality is associated with the amount of technology available in these units.

Intensive care unit admissions with cirrhosis: risk-stratifying patient groups and predicting individual survival.

Prognosis for acutely ill patients with cirrhosis is influenced by the severity of hepatic abnormalities and by dysfunction of other organ systems. The purpose of this study was to examine the usefulness of the Acute Physiology, Age, and Chronic Health Evaluation (APACHE III) prognostic system for risk-stratifying groups of intensive care unit (ICU) patients with cirrhosis and in predicting individual survival. We used data for 17,440 ICU admissions at 40 American hospitals to select 117 of the 537 patients with a history of cirrhosis who were ventilated on ICU day 1, a group known to have a high mortality rate. We then calculated each patient's probability of hospital death on ICU days 1 through 7, using seven previously validated multivariate equations. Hospital mortality was 63% for the 117 study patients. The most important determinants of risk for hospital death on ICU day 1 were the acute physiology score of APACHE III, ICU admission diagnosis, and operative status. Daily changes in the acute physiology score caused a rise or fall in the probability of hospital mortality and was useful in assessing individual response to therapy. APACHE III accurately risk stratifies critically ill patients with cirrhosis because it accounts for many of the factors known to influence prognosis. This capability can be used to assess severity of illness and risk-stratify patients with cirrhosis during clinical trials. Daily prognostic estimates based on physiological changes over time reflect patient response and can help physicians to assess the incremental benefit of therapy.

Application of the APACHE III prognostic system in Brazilian intensive care units: a prospective multicenter study.

OBJECTIVE: To compare patients and their outcomes at ten Brazilian intensive care units (ICUs) with those reported from the United States. DESIGN: Prospective multicenter inception cohort study. SETTING: Ten Brazilian adult medical-surgical ICUs. PATIENTS: 1734 consecutive adult ICU admissions. MEASUREMENTS AND RESULTS: We used demographic, clinical and physiologic information and the APACHE III prognostic system to predict risk of hospital death for 1734 ICU admissions. We then divided the observed by the predicted hospital death rate to calculate standardized mortality ratios (SMRs) for patient groups and each ICU. Hospital mortality for Brazilian patients (34%) was double that found in the United States (17%, p < 0.01). Discrimination of survivors from non-survivors using APACHE III was good (area under a receiver operating characteristic curve = 0.82), but the predicted risk of death was significantly (p < 0.0001) lower than observed outcome (SMR = 1.67). Three of the ten Brazilian ICUs, however, had SMRs of 1.01 to 1.1 and no significant difference between observed and predicted outcomes; the remaining seven ICUs had significantly higher SMRs, ranging from 1.50 to 2.30. CONCLUSION: The APACHE III prognostic system was a good discriminator of hospital mortality for ICU admissions at 10 Brazilian ICUs. There was substantial and significant variation, however, in SMRs among the Brazilian ICUs, which suggests that further evaluations of international differences in intensive care using a common risk assessment system should be performed and factors associated with variations in risk-adjusted mortality scrutinized.

Severity stratification and outcome prediction for multisystem organ failure and dysfunction.

Multiple organ system failure or dysfunction (MOSF/MODS) remains a major cause of morbidity and mortality in hospitalized adults. Among intensive care unit (ICU) patients the extent of physiologic derangement, the type of associated disease or injury, increasing age, and life-threatening comorbid conditions are the major determinants of risk for developing MOSF and for survival during the 1980s. Hospital mortality for patients with a single organ system failure (OSF) lasting more than 1 day approached 40%; and for those with two OSFs hospital mortality increased to 60%. These outcomes did not change over the decade. For patients with three or more OSFs persisting after 3 days of OSF, however, data suggest that between 1982 and 1990 the mortality has been reduced from 98% to 84% (p = 0.0003). Because of variations in the types and combinations of OSFs, associated disease, and extent of physiologic derangement, it is difficult to interpret variations in mortality among patients with one or more OSFs defined using categorical criteria. For this and other reasons, outcome prediction based on a comprehensive assessment of patient risk factors is a more sensitive, specific, useful approach to quantifying MODS than a simple count of the number and duration of OSFs. Because repeated assessment of risk factors during subsequent ICU days reflects complications and response to therapy, daily outcome predictions are even more precise than estimates at ICU admission. The ability to more accurately predict survival from MODS/MOSF can improve our ability to test new therapies, evaluate how outcome has changed over time, and assess the efficacy of supportive therapy for individuals.

Does selective decontamination of the digestive tract reduce mortality for severely ill patients?

OBJECTIVE: To investigate the relationship between baseline risk of death and reduced mortality after selective decontamination of the digestive tract in intensive care unit patients. DESIGN: Analysis of data from a meta-analysis of 23 randomized, controlled trials. PATIENTS: A total of 4,142 adult intensive care unit patients from the 23 trials. MEASUREMENTS AND MAIN RESULTS: Mortality for patients receiving selective decontamination of the digestive tract treatment was analyzed as a function of baseline risk of death at study entry, using weighted least squares regression across all 23 trials. In testing whether the slope of the regression is different than 1.0, the observed t value is 3.32 (p < .004), suggesting that the efficacy of selective decontamination of the digestive tract in reducing mortality is significantly better in populations at high mortality risk at study entry. CONCLUSIONS: Mortality reduction from selective decontamination of the digestive tract appears related to the mortality risk of patients at the time of study entry. Future trials should consider using baseline risk assessment as part of trial design and outcome analysis.

Use of predicted risk of mortality to evaluate the efficacy of anticytokine therapy in sepsis. The rhIL-1ra Phase III Sepsis Syndrome Study Group.

OBJECTIVES: To investigate a novel anticytokine therapy in patients with sepsis syndrome, and the relationship between a patient's baseline mortality risk and survival benefit. DESIGN: Data from a recent phase III, double-blind, placebo-controlled, multicenter clinical trial with patients randomized to three treatment arms: an intravenous loading dose of recombinant human interleukin-1-receptor antagonist (rhIL-1ra) or placebo, followed by a continuous infusion of rhIL-1ra (1.0 mg/kg/hr, or 2.0 mg/kg/hr), or placebo for 72 hrs. SETTING: Sixty-three investigative centers in eight countries. PATIENTS: The study population consisted of 893 patients: 302 placebo patients; 298 patients treated with 1.0 mg/kg/hr of rhIL-1ra; and 293 patients treated with 2.0 mg/kg/hr of rhIL-1ra. MEASUREMENTS AND MAIN RESULTS: An independent, sepsis-specific, log-normal regression model that predicts the risk of mortality over 28 days was applied to all patients enrolled into the rhIL-1ra sepsis study. The ability of the Predicted Risk of Mortality model to predict 28-day mortality in the placebo patients was determined and the relationship between mortality risk and efficacy of rhIL-1ra was investigated. The trial data were also analyzed using two other risk-assessment models for comparison with Predicted Risk of Mortality. A significant increase in survival time was demonstrated for all patients treated with rhIL-1ra (n = 893, p < .02 Predicted Risk of Mortality log-normal), but patients with a Predicted Risk of Mortality of < 24% derived little benefit. Retrospective examination of time-to-death data demonstrated that rhIL-1ra reduced risk of death in the first 2 days for patients with > or = 24% Predicted Risk of Mortality (n = 580, p < .005 Predicted Risk of Mortality log-normal). This same effect was not present in patients with a Predicted Risk of Mortality of < 24% on entry into the study. The Predicted Risk of Mortality model predicted a 28-day mortality rate of 35% for placebo patients compared with 34% observed and accurately stratified patients along the full range of risks. There was a wide distribution of individual patient risks for 28-day mortality for all patients, as well as within categorical subgroups, such as shock and organ system dysfunction. Two alternate risk models were assessed and the Acute Physiology Score of Acute Physiology and Chronic Health Evaluation III also demonstrated a statistically significant survival benefit for rhIL-1ra (p = .04 Predicted Risk of Mortality log-normal) for all patients treated. CONCLUSIONS: Using an appropriate analytic model, a statistically significant increase in survival time from rhIL-1ra was measured. A direct relationship was found between a patient's Predicted Risk of Mortality at study entry to efficacy of rhIL-1ra. Individual risk or severity assessment may be a useful tool for evaluating the clinical benefit of new therapeutic approaches to sepsis and for monitoring outcomes at the bedside.

Hospital and 1-year survival of patients admitted to intensive care units with acute exacerbation of chronic obstructive pulmonary disease.

OBJECTIVE: To describe outcomes and identify variables associated with hospital and 1-year survival for patients admitted to an intensive care unit (ICU) with an acute exacerbation of chronic obstructive pulmonary disease (COPD). DESIGN: Prospective, multicenter, inception cohort study. SETTING: Forty-two ICUs at 40 US hospitals. PATIENTS: A total of 362 admissions for COPD exacerbation selected from the Acute Physiology and Chronic Health Evaluation (APACHE) III database of 17,440 ICU admissions. MEASUREMENTS AND RESULTS: Hospital mortality for the 362 admissions was 24%. For the 167 patients aged 65 years or older, mortality was 30% at hospital discharge, 41% at 90 days, 47% at 180 days, and 59% at 1 year. Median survival for all patients was 224 days, and median survival for the patients who died within 1 year was 30.5 days. On multiple regression analysis, variables associated with hospital mortality included age, severity of respiratory and nonrespiratory organ system dysfunction, and hospital length of stay before ICU admission. Development of nonrespiratory organ system dysfunction was the major predictor of hospital mortality (60% of total explanatory power) and 180-day outcomes (54% of explanatory power). Respiratory physiological variables (respiratory rate, serum pH, PaCO2, PaO2, and alveolar-arterial difference in partial pressure of oxygen [PAO2-PaO2]) indicative of advanced dysfunction were more strongly associated with 180-day mortality rates (22% of explanatory power) than hospital death rates (4% of explanatory power). After controlling for severity of illness, mechanical ventilation at ICU admission was not associated with either hospital mortality or subsequent survival. CONCLUSIONS: Patients with COPD admitted to an ICU for an acute exacerbation have a substantial hospital mortality (24%). For patients aged 65 years or older, mortality doubles in 1 year from 30% to 59%. Hospital and longer-term mortality is closely associated with development of nonrespiratory organ system dysfunction; severity of the underlying respiratory function substantially influences mortality following hospital discharge. The need for mechanical ventilation at ICU admission did not influence either short- or long-term outcomes. Physicians should be aware of these relationships when making treatment decisions or evaluating new therapies.