Variation in ICU risk-adjusted mortality: impact of methods of assessment and potential confounders

BACKGROUND: Federal and state agencies are considering ICU performance assessment and public reporting; however, an accurate method for measuring performance must be selected. In this study, we determine whether a substantial variation in ICU mortality performance still exists in modern ICUs, and compare the predictive accuracy, reliability, and data burden of existing ICU risk-adjustment models. METHODS: A retrospective chart review of 11,300 ICU patients from 35 California hospitals from 2001 to 2004 was performed. We calculated standardized mortality ratios (SMRs) for each hospital using the mortality probability model III (MPM(0) III), the simplified acute physiology score (SAPS) II, and the acute physiology and chronic health evaluation (APACHE) IV risk-adjustment models. We compared discrimination, calibration, data reliability, and abstraction time for the models. RESULTS: Regardless of the model used, there was a large variation in SMRs among the ICUs studied. The discrimination and calibration were adequate for all risk-adjustment models. APACHE IV had the best discrimination (area under the receiver operating characteristic curve [AUC], 0.892) compared to MPM(0) III (AUC, 0.809), and SAPS II (AUC, 0.873; p < 0.001). The models differed substantially in data abstraction times, as follows: MPM(0)III, 11.1 min (95% confidence interval [CI], 8.7 to 13.4); SAPS II, 19.6 min (95% CI, 17.0 to 22.2); and APACHE IV, 37.3 min (95% CI, 28.0 to 46.6). CONCLUSIONS: We found substantial variation in the ICU risk-adjusted mortality rates that persisted regardless of the risk-adjustment model. With unlimited resources, the APACHE IV model offers the best predictive accuracy. If constrained by cost and manual data collection, the MPM(0) III model offers a viable alternative without a substantial loss in accuracy.     

Validation of the LOD score compared with APACHE II score in prediction of the hospital outcome in critically ill patients

The Logistic Organ Dysfunction score (LOD) is an organ dysfunction score that can predict hospital mortality. The aim of this study was to validate the performance of the LOD score compared with the Acute Physiology and Chronic Health Evaluation II (APACHE II) score in a mixed intensive care unit (ICU) at a tertiary referral university hospital in Thailand. The data were collected prospectively on consecutive ICU admissions over a 24 month period from July1, 2004 until June 30, 2006. Discrimination was evaluated by the area under the receiver operating characteristic curve (AUROC). The calibration was assessed by the Hosmer-Lemeshow goodness-of-fit H statistic. The overall fit of the model was evaluated by the Brier's score. Overall, 1,429 patients were enrolled during the study period. The mortality in the ICU was 20.9% and in the hospital was 27.9%. The median ICU and hospital lengths of stay were 3 and 18 days, respectively, for all patients. Both models showed excellent discrimination. The AUROC for the LOD and APACHE II were 0.860 [95% confidence interval (CI) = 0.838-0.882] and 0.898 (95% Cl = 0.879-0.917), respectively. The LOD score had perfect calibration with the Hosmer-Lemeshow goodness-of-fit H chi-2 = 10 (p = 0.44). However, the APACHE II had poor calibration with the Hosmer-Lemeshow goodness-of-fit H chi-2 = 75.69 (p < 0.001). Brier's score showed the overall fit for both models were 0.123 (95%Cl = 0.107-0.141) and 0.114 (0.098-0.132) for the LOD and APACHE II, respectively. Thus, the LOD score was found to be accurate for predicting hospital mortality for general critically ill patients in Thailand.     

Prediction of risk of death using 30-day outcome: a practical end point for quality auditing in intensive care

STUDY OBJECTIVE: To validate the APACHE (acute physiology and chronic health evaluation) III unadjusted and similar hospital mortality estimate models on 30-day mortality, and to propose a simple approach to modeling local 30-day in-hospital mortality of critically ill hospitalized adults for quality management and risk-adjusted monitoring. DESIGN: Noninterventional, observational study. PATIENTS: A total of 5,278 consecutive eligible hospital admissions between January 1, 1995, and December 31, 1999. MEASUREMENTS: Prospective collection of demographic, diagnostic, physiologic, laboratory, and hospital admission and discharge data. RESULTS: The APACHE III mortality predictions exhibited excellent discrimination (receiver operating characteristic [ROC] curve area) for 30-day outcome (ROC area, 0.89) and hospital outcome (ROC area, 0.89). Calibration curves and Hosmer-Lemeshow statistics demonstrated good calibration of all models on 30-day outcome, except for the unadjusted APACHE III model. New, simplified risk adjustment models showed good discrimination and calibration on development and test data. ROC areas were 0.88 (developmental data) and 0.87 (test data), and the new model calibration was equivalent to the APACHE III model. CONCLUSION: For quality audit, 30-day in-hospital mortality can be used as an alternative outcome to survival to hospital discharge. New logistic regression models provide evidence that local models, possessing good calibration and discrimination, may be built from a few explanatory variables.     

Intensivmedizin bei älteren Patienten: Wie nützlich sind die Scoresysteme APACHE II und III?

OBJECTIVE: Scores like APACHE (Acute Physiology And Chronic Health Evaluation) were evaluated for unselected intensive care unit (ICU) admissions. Can they also be used for risk stratification and quality assurance in selected subgroups like elderly patients? METHODS: Over a 3-year period data of all admissions of a 12 bed interdisciplinary ICU were collected. APACHE II and III scores and probabilities of hospital deaths were compared with observed outcomes. The discriminatory power was evaluated by calculating the areas under the receiver operating characteristic (ROC) curves. Calibration was analyzed with standardized mortality ratios (SMR) and the Hosmer-Lemeshow goodness-of-fit statistic. RESULTS: Of 3382 admissions due to exclusion criteria, 2795 patients were analyzed, 1396 (49.9%) of these were > or = 65 years, mean age 75 (65-99) years. 62.5% were non-operative, 37.5% postoperative admissions, 35% after emergency operations. ICU mortality was 11.7%, hospital mortality 25.1%. The areas under the ROC curves were 0.77 for APACHE II and 0.79 for APACHE III (whole collective 0.83 and 0.85, respectively). The SMR was 1.17 for APACHE II and 1.23 for APACHE III compared with 1.06 and 1.22 for all patients, respectively. Calibration for elderly patients was insufficient for APACHE II (Hosmer-Lemeshow chi-square = 19, p < 0.025) as well as for APACHE III (chi-square = 41, p < 0.001), while it was good for all patients for APACHE II (chi-square = 12, p > 0.1) but not so for APACHE III (chi-square = 48, p < 0.001). CONCLUSIONS: APACHE II and III both show good discrimination for elderly patients although a little inferior than for all patients. Both scores can be used for risk stratification of elderly ICU patients. Mortality prognosis is not sufficient for geriatric patients although APACHE II calibrates well for all. Application of these scores for quality assurance in selected subgroups like elderly patients cannot be recommended based on these data.     

End-stage renal failure patients requiring renal replacement therapy in the intensive care unit: incidence,clinical features,and outcome

AIMS: To study incidence, clinical features, and outcome of critically ill patients with end-stage renal failure (ESRF) requiring renal replacement therapy (RRT) in the intensive care unit (ICU) and to test the validity of severity scoring systems for these patients. METHODS: Data for ESRF patients treated with RRT were collected from 81 Australian adult ICUs providing RRT. They were compared with matched controls with acute renal failure. RESULTS: Thirty-eight ESRF patients received RRT in the ICU over 3 months. The mean APACHE II score was 21.8 (predicted mortality: 37%) and the SAPS II score 44.7 (predicted mortality: 37%). The hospital mortality was 34%. Receiver operating characteristic curves showed good discrimination ability for hospital mortality for these two scores (AUC: 0.81 for APACHE II and 0.84 for SAPS II). Using admission diagnosis and SAPS II scores, 32 ESRF patients treated with continuous RRT (CRRT) were matched to 32 acute renal failure patients also treated with CRRT. ICU mortality (22 vs. 38%) and hospital mortality (38 vs. 38%) were comparable between the two groups. CONCLUSIONS: ESRF patients requiring RRT in the ICU were relatively frequent. Severity scores could be used to predict the hospital outcome for these patients. Their mortality, when treated with CRRT, was similar to that of diagnosis- and severity-score-matched patients with acute renal failure.     

A cross-cultural comparison of critical care delivery: Japan and the United States

OBJECTIVE: To compare the utilization and outcomes of critical care services in a cohort of hospitals in the United States and Japan. DESIGN: Prospective data collection on 5,107 patients and detailed organizational characteristics from each of the participating Japanese study hospitals between 1993 and 1995, with comparisons made to prospectively collected data on the 17,440 patients included in the US APACHE (acute physiology and chronic health evaluation) III database. SETTING: Twenty-two Japanese and 40 US hospitals. PATIENTS: Consecutive, unselected patients from medical, surgical, and mixed medical/surgical ICUs. MEASUREMENTS: Severity of illness, predicted risk of in-hospital death, and ICU and hospital length of stay (LOS) were assessed using APACHE III. Japanese ICU directors completed a detailed survey describing their units. MAIN RESULTS: US and Japanese ICUs have a similar array of modalities available for care. Only 1.0% (range, 0.56 to 2.7%) of beds in Japanese hospitals were designated as ICUs. The organization of the Japanese and US ICUs varied by hospital, but Japanese ICUs were more likely to be organized to care for heterogeneous diagnostic populations. Sample case-mix differences reflect different disease prevalence. ICU utilization for women is significantly lower (35.5% vs 44.8% of patients) and there were relatively fewer patients > or = 85 years old in the Japanese ICU cohort (1.2% vs 4.6%), despite a higher per capita rate of individuals > or = 85 years old in Japan. The utilization of ICUs for patients at low risk of death significantly less in Japan (10.2%) than in the United States (12.9%). The APACHE III score stratified patient risk. Overall mortality was similar in both national samples after accounting for differences in hospital LOS, utilizing a model that was highly discriminating (receiver operating characteristic, 0.87) when applied to the Japanese sample. The application of a US-based mortality model to a Japanese sample overestimated mortality across all but the highest (> 90%) deciles of risk. Significant variation in expected performance was noted between hospitals. Risk-adjusted ICU LOS was not significantly longer in Japan; however, total hospital stay was nearly twice that found in the US hospitals, reflecting differences in hospital utilization philosophies. CONCLUSIONS: Similar high-technology critical care is available in both countries. Variations in ICU utilization reflect differences in case-mix and bed availability. Japanese ICU utilization by gender reflects differences in disease prevalence, whereas differences in utilization by age may reflect differences in cultural norms regarding the limits of care. Such differences provide context from which to assess the delivery of care across international borders. Miscalibration of predictive models applied to international data samples highlight the impact that differences in resource use and local practice cultures have on outcomes. Models may require modification in order to account for these differences. Nevertheless, with large databases, it is possible to assess critical care delivery systems between countries accounting for differences in case-mix, severity of illness, and cultural normative standards facilitating the design and management such systems.     

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).     

Identifying potentially ineffective care in the sickest critically ill patients on the third ICU day

OBJECTIVE: To determine if an increase in the third-ICU-day acute physiology score (APS) of the APACHE (acute physiology and chronic health evaluation) III prognostic system can identify potentially ineffective care. DESIGN: Retrospective cohort study. SETTING: Academic medical center. PATIENTS: Adult patients with first-ICU-day predicted mortality rate > or = 80%. MEASUREMENTS: Demographics, ICU admission source, admission type, ICU admission diagnosis, first- and third-ICU-day APSs, APACHE III score, APACHE III-predicted hospital mortality, hospital discharge status, 100-day survival, and ICU and hospital length of stay. RESULTS: A total of 302 patients (age [mean +/- SD], 64.7 +/- 15.8 years; 54.3% male gender) were included in the study. Respiratory failure was the most common reason for ICU admission. Nonoperative admissions accounted for 94.7%. The first- and third-ICU-day APSs were 106.8 +/- 19.8 and 70.5 +/- 29.9, respectively. The first- and third-ICU-day predicted hospital mortality rates were 87.8 +/- 5.3% and 86.5 +/- 14.8%, respectively. The hospital mortality rate was 61.3%, and the 100-day survival rate 28.5%. The third-ICU-day APS was higher than the first-ICU-day APS in 34 patients (11.3%). Only 2 of these 34 patients (6%) survived to hospital discharge, compared to 115 of 268 patients (43%) without an increase in APS (p < 0.0001). Of the two hospital survivors with increased APS, only one patient survived 100 days after hospital discharge. In predicting 100-day mortality, the sensitivity of an increase in the third-ICU-day APS was 15.3% (95% confidence interval, 11.1 to 20.7%), specificity was 98.8% (95% confidence interval, 93.7 to 99.8%), positive predictive value was 97.1% (95% confidence interval, 85.1 to 99.5%), and negative predictive value was 31.7% (95% confidence interval, 26.4 to 37.5%). CONCLUSIONS: A higher APS on the third ICU day, compared to the first ICU day, identifies potentially ineffective care in patients with the first-day predicted hospital mortality rate > or = 80%.     

RIFLE classification as predictive factor of mortality in patients with cirrhosis admitted to intensive care unit

Background and Aim:  To evaluate the association of the Risk, Injury, Failure, Loss and End-stage renal failure (RIFLE) score on mortality in patients with decompensated cirrhosis admitted to intensive care unit (ICU).
Methods:  A cohort of 412 patients with cirrhosis consecutively admitted to ICU was classified according to the RIFLE score. Multivariable logistic regression analysis was used to evaluate the factors associated with mortality. Liver-specific, Acute Physiology and Chronic Health Evaluation (APACHE) II, Sequential Organ Failure Assessment (SOFA) and RIFLE scores on admission, were compared by receiver–operator characteristic curves.
Results:  The overall mortality during ICU stay or within 6 weeks after discharge from ICU was 61.2%, but decreased over time (76% during first interval, 1989–1992 vs 50% during the last, 2005–2006, P  < 0.001). Multivariate analysis showed that RIFLE score (odds ratio: 2.1, P  < 0.001) was an independent factor significantly associated with mortality. Although SOFA had the best discrimination (area under receiver–operator characteristic curve = 0.84), and the APACHE II had the best calibration, the RIFLE score had the best sensitivity (90%) to predict death in patients during follow up.
Conclusions:  RIFLE score was significantly associated with mortality, confirming the importance of renal failure in this large cohort of patients with cirrhosis admitted to ICU, but it is less useful than other scores.     

Severity scoring in the critically ill: part 1--interpretation and accuracy of outcome prediction scoring systems

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

Assessment of factors predicting outcome of acute respiratory distress syndrome in North India

OBJECTIVES: Information on the spectrum and outcome of acute respiratory distress syndrome (ARDS) in tropical countries is scanty. This study was designed to assess the factors predicting the outcome of ARDS in North India. METHODOLOGY: Consecutive patients requiring mechanical ventilation for ARDS over a 2 year period at the Respiratory Intensive Care Unit (RICU) of a tertiary referral hospital were studied. Hospital survival was correlated with age, aetiology, disease severity scores (APACHE III, SAPS II, lung injury score) and organ failure using univariate analysis. Factors significantly influencing mortality were examined by multivariate analysis to identify factors independently affecting outcome. RESULTS: Sepsis (28.6%), followed by malaria (21.4%), were the commonest risk factors. Seven out of eight patients (87.5%) with sepsis died. The presence of sepsis, more than three organ failure prior to admission, APACHE III score > 57 and SAPS II score > 39 were significantly associated with mortality. Only APACHE III score > 57 or SAPS II score > 39 were, however, independently predictive of a poor outcome following multivariate analysis. CONCLUSIONS: Sepsis, associated with a very poor outcome, and malaria were important risk factors for the development of ARDS. APACHE III score > 57 or SAPS II score > 39 were associated with increased risk of mortality.     

Acute respiratory failure due to pneumocystis pneumonia in patients without human immunodeficiency virus infection: outcome and associated features

OBJECTIVE: To examine outcome and associated factors of acute respiratory failure (ARF) in non-HIV-related Pneumocystis pneumonia (PCP) in patients admitted to a medical ICU between 1995 and 2002. DESIGN: A retrospective review of medical records and an APACHE (acute physiology and chronic health evaluation) III database. SETTING: Academic tertiary medical center. RESULTS: We identified 30 patients with non-HIV-related PCP and ARF. In-hospital, 6-month, and 1-year mortality rates were 67%, 77%, and 80%, respectively. Median age was 63.5 years. Median APACHE III score on day 1 was 65.5. Median ICU and hospital lengths of stay were 13 days and 21 days, respectively. All seven patients having a pneumothorax died. All but one patient had an elevated lactate dehydrogenase level (median, 563 U/L). The diagnosis was made using BAL in 28 patients and by transbronchial biopsy in the remaining 2 patients. All patients were immunosuppressed (eight were receiving corticosteroids, seven were receiving chemotherapy, and the remainder received both). Median immunosuppressive prednisone-equivalent dose was 40 mg (median length of treatment, 4.5 months). Not a single patient received PCP prophylaxis. All but one patient required intubation and invasive positive pressure ventilation (PPV). Hospital mortality was associated with high APACHE III scores on day 1 (p = 0.05), intubation delay (p = 0.03), length of PPV (p = 0.003), and development of pneumothorax (p = 0.033). Logistic regression analysis demonstrated that association of intubation delay with hospital mortality persisted after adjusting for severity of illness (p = 0.03). CONCLUSIONS: Among patients with ARF secondary to non-HIV-related PCP, poor prognostic factors include high APACHE III scores, intubation delay, longer duration of PPV, and development of pneumothorax. None of the patients in this series received PCP prophylaxis prior to the development of pneumonia.     

Predicting outcome from intensive care for patients with rheumatologic diseases.

OBJECTIVE. To evaluate 2 prognostic scoring systems in patients with an underlying rheumatologic diagnosis admitted to an intensive care unit (ICU). METHODS. A retrospective case series review, carried out at a medical ICU in a military referral hospital. All adult ICU admissions with a known rheumatologic diagnosis were evaluated during 28 consecutive months. There were 48 ICU admissions available for review in 36 patients (1.33 ICU admissions/patient) during the study period. All patients were assigned an APACHE II and TISS score based on the first 24 h of ICU admission. RESULTS. Eleven ICU admissions resulted in patient death (22.9%) and the remaining 37 admissions (77.1%) in patient survival and hospital discharge. Overall patient mortality was 30.6% for the study population. The APACHE II and TISS scores were each significantly different for survivor and nonsurvivor subgroups (APACHE II p less than 0.0001; TISS p less than 0.0001). CONCLUSIONS. In this group of patients evaluated at a single institution both the APACHE II and TISS scoring systems allowed subgroup separation between survivors and nonsurvivors of ICU admission. However, these scoring methods demonstrated limitations in terms of outcome prediction when applied to the individual patient.     

Hospital volume-outcome relationships among medical admissions to ICUs

BACKGROUND: Positive relationships between hospital volume and outcomes have been demonstrated for several surgeries and medical conditions. However, little is known about the volume-outcome relationship in patients admitted to medical ICUs. OBJECTIVE: To determine the relationship between hospital volume and risk-adjusted in-hospital mortality for patients admitted to ICUs with respiratory, neurologic, and GI disorders. DESIGN: Retrospective cohort study. SETTING: Twenty-nine hospitals in a single metropolitan area. PATIENTS: Adult ICU admissions from 1991 through 1997. METHODS: Using Cox proportional hazards models, we compared in-hospital mortality between tertiles of hospital volume (high, medium, and low) for respiratory (n = 16,949), neurologic (n = 13,805), and GI (n = 12,881) diseases after adjusting for age, gender, admission severity of illness, admitting diagnosis, and source. Severity of illness was measured using the APACHE (acute physiology and chronic health evaluation) III methodology. RESULTS: Among respiratory and neurologic ICU admissions, hazard ratios were similar (p > or = 0.05) in patients in low-, medium-, and high-volume hospitals. However, among GI diagnoses, risk of mortality was lower in high-volume hospitals, relative to low-volume hospitals (hazard ratio, 0.68; 95% confidence interval [CI], 0.54 to 0.85; p < 0.001), and was somewhat lower in medium-volume hospitals (hazard ratio, 0.83; 95% CI, 0.68 to 1.01; p = 0.06). Among subgroups based on severity of illness, high-volume hospitals had lower mortality, relative to low-volume hospitals, among sicker patients (APACHE III score > 57) in the respiratory cohort (hazard ratio, 0.77; 95% CI, 0.59 to 0.99) and the GI cohort (hazard ratio, 0.67; 95% CI, 0.53 to 0.85). CONCLUSIONS: Associations between ICU volume and risk-adjusted mortality were significant for patients with GI diagnoses and for sicker patients with respiratory diagnoses. However, associations were not significant for patients with neurologic diagnoses. The lack of a consistent volume-outcome relationship may reflect unmeasured patient complexity in higher-volume hospitals, relative standardization of care across ICUs, or lack of efficacy of some accepted ICU processes of care.     

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. (Hepatology 1996 Jun;23(6):1393-401)     

Performance of standard severity scoring systems for outcome prediction in patients admitted to a respiratory intensive care unit in North India

OBJECTIVE: There are little data on the value of using severity scoring systems developed in western countries to assess critically ill patients in India. The authors evaluated the performance of Acute Physiology and Chronic Health Evaluation version II (APACHE II), Simplified Acute Physiology Score version II (SAPS II) and Mortality Probability Models version II at admission and at 24 h (MPM(0) and MPM(24), respectively) in predicting patient outcomes in their Respiratory Intensive Care Unit. METHODS: Data from 459 consecutive adult admissions were collected prospectively. Standardized mortality ratios were computed as an index of the overall model performance. Model calibration was assessed using Lemeshow-Hosmer goodness-of-fit tests and through calibration curves. Model discrimination was assessed through receiver operating curve analysis and by drawing 2 x 2 classification matrices. RESULTS: Overall standardized mortality ratio exceeded 1.5 for all models. All models had modest discrimination (area under receiver-operating-characteristic curves 0.66-0.78) and poor calibration (high Lemeshow-Hosmer C and H statistic values). All models had a tendency to underpredict hospital death in patients with lower mortality probability estimates. There were no major differences between the models with regard to either discrimination or calibration performance. CONCLUSIONS: Standard severity scoring systems developed in western countries are poor at predicting patient outcome in critically ill patients admitted to a respiratory intensive care unit in Northern India. Caution must be exercised in using such models in their present form on Indian patients until either they are customized for local use or fresh models are developed from Indian cohorts.     

Results of report cards for patients with congestive heart failure depend on the method used to adjust for severity

BACKGROUND: The validity of outcome report cards may depend on the ways in which they are adjusted for risk. OBJECTIVES: To compare the predictive ability of generic and disease-specific survival prediction models appropriate for use in patients with heart failure, to simulate outcome report cards by comparing survival across hospitals and adjusting for severity of illness using these models, and to assess the ways in which the results of these comparisons depend on the adjustment method. DESIGN: Analysis of data from a prospective cohort study. SETTING: A university hospital, a Veterans Affairs (VA) medical center, and a community hospital. PATIENTS: Sequential patients presenting in the emergency department with acute congestive heart failure. MEASUREMENTS: Unadjusted 30-day and 1-year mortality across hospitals and 30-day and 1-year mortality adjusted by using disease-specific survival prediction models (two sickness-at-admission models, the Cleveland Health Quality Choice model, the Congestive Heart Failure Mortality Time-Independent Predictive Instrument) and generic models (Acute Physiology and Chronic Health Evaluation [APACHE] II, APACHE III, the mortality prediction model, and the Chadson comorbidity index). RESULTS: The community hospital's unadjusted 30-day survival rate (85.0%) and the VA medical center's unadjusted 1-year survival rate (60.9%) were significantly lower than corresponding rates at the university hospital (92.7% and 67.5%, respectively). No severity model had excellent ability to discriminate patients by survival rates (all areas under the receiver-operating characteristic curve < 0.73). Whether the VA medical center, the community hospital, both, or neither had worse survival rates on simulated report cards than the university hospital depended on the prediction model used for adjustment. CONCLUSIONS: Results of simulated outcome report cards for survival in patients with congestive heart failure depend on the method used to adjust for severity.     

Outcome of patients with small-vessel vasculitis admitted to a medical ICU

PURPOSES: This study aims to describe the clinical course and prognostic factors of patients with small-vessel vasculitis admitted to a medical ICU. METHODS: We reviewed the clinical records of 38 patients with small-vessel vasculitis admitted consecutively to the ICU between January 1997 and May 2004. The APACHE (acute physiology and chronic health evaluation) III prognostic system was used to determine the severity of illness on the first ICU day; the sequential organ failure assessment (SOFA) score was used to measure organ dysfunction, and the Birmingham vasculitis activity score for Wegener granulomatosis (BVAS/WG) was used to assess vasculitis activity. Outcome measures were the 28-day mortality and ICU length of stay. RESULTS: Nineteen patients (50%) had Wegener granulomatosis, 16 patients (42%) had microscopic polyangiitis, 2 patients had CNS vasculitis, and 1 patient had Churg-Strauss syndrome. Reasons for ICU admission included alveolar hemorrhage in 14 patients (37%), sepsis in 5 patients (13%), seizures in 3 patients (8%), and pneumonia in 2 patients (5%). The median ICU length of stay was 4.0 days (interquartile range, 2.0 to 6.0 days). The APACHE III score was lower in survivors than nonsurvivors (p = 0.010). The predicted hospital mortality was 54% for nonsurvivors and 21% for survivors (p = 0.0038). The mean SOFA score was 11.6 (SD, 2.6) in nonsurvivors, compared to 6.9 (SD, 2.4) in survivors (p = 0.0004). Mean BVAS/WG scores were 8.6 (SD, 3.6) in nonsurvivors and 4.7 (SD, 4.6) in survivors (p = 0.0889). Twenty-six percent of the patients received invasive mechanical ventilation, and 33% underwent dialysis. The 28-day and 1-year mortality rates were 11% and 29%, respectively. CONCLUSIONS: The mortality of patients with small-vessel vasculitis admitted to the ICU is lower than predicted, and alveolar hemorrhage is the most common reason for ICU admission.     

Intensivist-to-bed ratio: association with outcomes in the medical ICU

OBJECTIVE: With an increasing number of critical care beds, a shortage of critical care physicians, and pressure from purchasers, there is a need to define the optimal intensivist-to-ICU bed ratio. The objective of this study was to determine if there are any associations between the intensivist-to-ICU bed ratio and the outcome of patients admitted to the medical ICU. DESIGN: Retrospective cohort study. SETTING: A tertiary care medical center. PATIENTS: All critically ill patients admitted to a medical ICU between December 8, 2001, and July 14, 2003. INTERVENTIONS: None. MEASUREMENTS: Demographics, APACHE (acute physiology and chronic health evaluation) III-predicted mortality, ICU length of stay (LOS), hospital LOS, and ICU and hospital mortality rates. Four time periods based on intensivist-to-ICU bed ratios of 1:7.5, 1:9.5, 1:12, and 1:15 were identified. Regression analyses were performed to develop customized models to predict ICU and hospital LOS and mortality. The ICU LOS ratio, defined as the ratio of the observed to predicted LOS, and standardized mortality ratio (SMR) were calculated for each of the four periods. RESULTS: A total of 2,492 patients were included in the study. There was no difference in the severity of illness at the time of ICU admission among the four periods. The mean ICU LOS ratio was longer for an intensivist-to-ICU bed ratio of 1:15 compared to the other periods. The ICU and hospital SMR did not differ significantly among the four periods. CONCLUSION: Differences in intensivist-to-ICU bed ratios, ranging from 1:7.5 to 1:15, were not associated with differences in ICU or hospital mortality. However, a ratio of 1:15 was associated with increased ICU LOS.     

Predictors of long-term mortality in patients with cirrhosis of the liver admitted to a medical ICU

CONTEXT: The long-term survival of patients with cirrhosis of the liver admitted to the ICU has not been described. OBJECTIVE: The main objectives were to determine the 1-year and 5-year mortality rates of a cohort of patients admitted to a medical ICU (MICU), and to identify the risk factors that may predict long-term outcomes. DESIGN: This is a cohort study. We used a model-building (MB) and model validation (MV) procedure that has previously been described to determine the risk factors for overall mortality.Settings: An MICU in a major referral medical center. PATIENTS: Four hundred twenty consecutive patients admitted to the ICU from January 1, 1993, through October 31, 1998, met the inclusion criteria of diagnosis of liver failure, cirrhosis, chronic liver disease, variceal bleeding, hepatic encephalopathy, or hepatorenal syndrome. Patients with acute liver failure who had undergone liver transplantation, or candidates for orthotopic liver transplantation were excluded. INTERVENTION: None. RESULTS: The 1-year mortality rate was 69%, and the 5-year mortality rate was 77%. The median survival time was 1 month. The independent predictors of mortality in patients in the MB group who retained their significance in the MV group were as follows: an acute physiology, age, and chronic health evaluation (APACHE) III score of >/= 90 (hazard ratio [HR], 2.2; 95% confidence interval [CI], 1.6 to 2.8; p < 0.0001), the use of pressors (HR, 2.5; 95% CI, 1.9 to 3.2; p < 0.0001), and jaundice (HR, 1.7; 95% CI, 1.4 to 2.2; p < 0.0001). Patients with all three risk factors (ie, APACHE III score >/= 90, use of pressors, and jaundice) had a 92% 1-month mortality rate compared to 11.2% for patients with no risk factors. CONCLUSIONS: Patients admitted to an MICU with underlying cirrhosis who are not eligible for liver transplantation have a poor long-term prognosis, even if they survive the ICU admission, particularly as the number of risk factors increases.