Assessment of the performance of five intensive care scoring models within a large Scottish database

OBJECTIVE: To assess and compare the performance of five severity of illness scoring systems used commonly for intensive care unit (ICU) patients in the United Kingdom. The five models analyzed were versions II and III of the Acute Physiology and Chronic Health Evaluation (APACHE) system, a version of APACHE II using United Kingdom (UK)-derived coefficients (UK APACHE II), version II of the Simplified Acute Physiology Score (SAPS), and version II of the Mortality Probability Model, computed at admission (MPM0) and after 24 hrs in the ICU (MPM24). DESIGN: A 2-yr prospective cohort study of consecutive admissions to intensive care units. SETTING: A total of 22 general ICUs in Scotland PATIENTS: A total of 13,291 admissions to the study, which after prospectively agreed exclusions left a total of 10,393 patients for the analysis. OUTCOME MEASURES: Death or survival at hospital discharge. MEASUREMENTS AND MAIN RESULTS: All the models showed reasonable discrimination using the area under the receiver operating characteristic curve (APACHE III, 0.845; APACHE II, 0.805; UKAPACHE II, 0.809; SAPS II, 0.843; MPM0, 0.785; MPM24, 0.799). The levels of observed mortality were significantly different than that predicted by all models, using the Hosmer-Lemeshow goodness-of-fit C test (p < .001), with the results of the test being confirmed by calibration curves. When excluding patients discharged in the first 24 hrs to allow for comparisons using the same patient group, APACHE III, MPM24, and SAPS II (APACHE III, 0.795; MPM24, 0.791; SAPS II, 0.784) showed significantly better discrimination than APACHE II, UK APACHE II, and MPM0 (APACHE II, 0.763; UK APACHE II, 0.756; MPM0, 0.741). However, calibration changed little for all models with observed mortality still significantly different from that predicted by the scoring systems (p < .001). For equivalent data sets, APACHE II demonstrated superior calibration to all the models using the chi-squared value from the Hosmer-Lemeshow test for both populations (APACHE III, 366; APACHE II, 67; UKAPACHE II, 237; SAPS II, 142; MPM0, 452; MPM24, 101). CONCLUSIONS: SAPS II demonstrated the best overall performance, but the superior calibration of APACHE II makes it the most appropriate model for comparisons of mortality rates in different ICUs. The significance of the Hosmer-Lemeshow C test in all the models suggest that new logistic regression coefficients should be generated and the systems retested before they could be used with confidence in Scottish ICUs.     

Mortality predictions using current physiologic scoring systems in patients meeting criteria for early goal-directed therapy and the severe sepsis resuscitation bundle

Physiologic scoring systems are often used to prognosticate mortality in critically ill patients. This study examined the performance of Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) II, Mortality in Emergency Department Sepsis (MEDS), and Mortality Probability Models (MPM) II0 in predicting in-hospital mortality of patients in the emergency department meeting criteria for early goal-directed therapy and the severe sepsis resuscitation bundle. The discrimination and calibration characteristics of APACHE II, SAPS II, MEDS, and MPM II0 were evaluated. Data are presented as median and quartiles (25th, 75th). Two-hundred forty-six patients aged 68 (52, 81) years were analyzed from a prospectively maintained sepsis registry, with 76.0% of patients in septic shock, 45.5% blood culture positive, and 35.0% in-hospital mortality. Acute Physiology and Chronic Health Evaluation II, SAPS II, and MEDS scores were 29 (21, 37), 54 (40, 70), and 13 (11, 16), with predicted mortalities of 64% (40%, 85%), 58% (25%, 84%), and 16% (9%, 39%), respectively. Mortality Probability Models II0 showed a predicted mortality of 60% (27%, 80%). The area under the receiver operating characteristic curves was 0.73 for APACHE II, 0.71 for SAPS II, 0.60 for MEDS, and 0.72 for MPM II0. The standardized mortality ratios were 0.59, 0.63, 1.68, and 0.64, respectively. Thus, APACHE II, SAPS II, MEDS, and MPM II0 have variable abilities to discriminate early and estimate in-hospital mortality of patients presenting to the emergency department requiring the severe sepsis resuscitation bundle. Adoption of these prognostication tools in this setting may influence therapy and resource use for these patients.     

Outcome prediction models on admission in a medical intensive care unit: do they predict individual outcome?

Prospectively acquired data from 941 patients staying greater than 24 h in a medical ICU were analyzed to determine the relevance of scoring on ICU admission by the following methods of outcome prediction: Acute Physiology and Chronic Health Evaluation (APACHE II), Simplified Acute Physiology Score (SAPS), and Mortality Prediction Model (MPM). Analysis was performed separately for all patients (group A) and for a subsample (group B), obtained by excluding coronary care patients. Calculation of risk and classification of patients were carried out as recommended in the literature for MPM, APACHE II, and SAPS. In group A, sensitivities (correct prediction of hospital mortality) were 44.7%, 51.1%, and 21.2% and specificities (correct prediction of survival) were 84.5%, 85.4%, and 96.8%, respectively; overall correct classification rates were 73.3%, 75.8%, and 75.6%. In group B, sensitivities were slightly higher, but total correct classification rates did not reach group A levels. Goodness-of-fit testing showed low levels of fit for all methods in both groups. Application of APACHE II to diagnostic subgroups, using disease-adapted risk calculations, revealed marked inconsistencies between the estimated risk and the observed mortality. We conclude that the estimation of risk on admission by the three methods investigated might be helpful for global comparisons of ICU populations, although the lack of disease specificity reduces their applicability for severity grading of a given illness. The inaccuracy of these methods makes them ineffective for predicting individual outcome; thus, they provide little advantage in clinical decision-making.     

Is a specific oncological scoring system better at predicting the prognosis of cancer patients admitted for an acute medical complication in an intensive care unit than general gravity scores?

Objective To evaluate the effectiveness of a specific oncologic scoring system—the ICU Cancer Mortality model (ICM)—in predicting hospital mortality in comparison to two general severity scores—the Acute Physiology and Chronic Health Evaluation (APACHE II) and the Simplified Acute Physiology Score (SAPS II).Patients and methods All 247 patients admitted for a medical acute complication over an 18-month period in an oncological medical intensive care unit were prospectively registered. Their data, including type of complication, vital status at discharge and cancer characteristics as well as other variables necessary to calculate the three scoring systems were retrospectively assessed.Results Observed in-hospital mortality was 34%. The predicted in-hospital mortality rate for APACHE II was 32%; SAPS II, 24%; and ICM, 28%. The goodness of fit was inadequate except for the ICM score. Comparison of the area under the ROC curves revealed a better fit for ICM (area 0.79). The maximum correct classification rate was 72% for APACHE II, 74% for SAPS II and 77% for ICM. APACHE II and SAPS II were better at predicting outcome for survivors to hospital discharge, although ICM was better for non-survivors. Two variables were independently predicting the risk of death during hospitalisation: ICM (OR=2.31) and SAPS II (OR=1.05).Conclusions Gravity scores were the single independent predictors for hospital mortality, and ICM was equivalent to APACHE II and SAPS II.     

Comparison of acute physiology and chronic health evaluations II and III and simplified acute physiology score II: a prospective cohort study evaluating these methods to predict outcome in a German interdisciplinary intensive care unit

OBJECTIVE: To evaluate the ability of three scoring systems to predict hospital mortality in adult patients of an interdisciplinary intensive care unit in Germany. DESIGN: A prospective cohort study. SETTING: A mixed medical and surgical intensive care unit at a teaching hospital in Germany. PATIENTS: From a total of 3,108 patients, 2,795 patients (89.9%) for Acute Physiology and Chronic Health Evaluation (APACHE) II and 2,661 patients (85.6%) for APACHE III and Simplified Acute Physiology Score (SAPS) II could be enrolled to the study because of defined exclusion criteria. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Probabilities of hospital death for patients were estimated by applying APACHE II and III and SAPS II and compared with observed outcomes. The overall goodness-of-fit of the three models was assessed. Hospital death rates were equivalent to those predicted by APACHE II but higher than those predicted by APACHE III and SAPS II. Calibration was good for APACHE II. For the other systems, it was insufficient, but better for SAPS II than for APACHE III. The overall correct classification rate, applying a decision criterion of 50%, was 84% for APACHE II and 85% for APACHE III and SAPS II. The areas under the receiver operating characteristic curve were 0.832 for APACHE II and 0.846 for APACHE III and SAPS II. Risk estimates for surgical and medical admissions differed between the three systems. For all systems, risk predictions for diagnostic categories did not fit uniformly across the spectrum of disease categories. CONCLUSIONS: Our data more closely resemble those of the APACHE II database, demonstrating a higher degree of overall goodness-of-fit of APACHE II than APACHE III and SAPS II. Although discrimination was slightly better for the two new systems, calibration was good with a close fit for APACHE II only. Hospital mortality was higher than predicted for both new models but was underestimated to a greater degree by APACHE III. Both score systems demonstrated a considerable variation across the spectrum of diagnostic categories, which also differed between the two models.     

Application of mortality prediction systems to individual intensive care units

Objective: To evaluate the predictive accuracy of the severity of illness scoring systems in a single institution. Design: A prospective study conducted by collecting data on consecutive patients admitted to the medical intensive care unit over 20 months. Surgical and coronary care admissions were excluded. Setting: Veterans Affairs Medical Center at Buffalo, New York. Patients and participants: Data collected on 302 unique, consecutive patients admitted to the medical intensive care unit. Interventions: None. Measurements and results: Data required to calculate the patients' predicted mortality by the Mortality Probability Model (MPM) II, Acute Physiology and Chronic Health Evaluation (APACHE) II and Simplified Acute Physiology Score (SAPS) II scoring systems were collected. The probability of mortality for the cohort of patients was analyzed using confidence interval analyses, receiver operator characteristic (ROC) curves, two by two contingency tables and the Lemeshow–Hosmer chi-square statistic. Predicted mortality for all three scoring systems lay within the 95 % confidence interval for actual mortality. For the MPM II, SAPS II and APACHE II, the c-index (equivalent to the area under the ROC curve) was 0.695 ± 0.0307 SE, 0.702 ± 0.063 SE and 0.672 ± 0.0306 SE, respectively, which were not statistically different from each other but were lower than values obtained in previous studies. Conclusion: Although the overall mortality was consistent with the predicted mortality, the poor fit of the data to the model impairs the validity of the result. The observed outcoume could be due to erratic quality of care, or differences between the study population and the patient population in the original studies. The data cannot be used to distinguish between these possibilities. To increase predictive accuracy when studying individual intensive care units and enhance quality of care assessments it may be necessary to adapt the model to the patient population. Received: 21 December 1998 Final revision received: 26 May 1999 Accepted: 1 June 1999     

Comparison of three severity scores for critically ill cancer patients

Objective To compare three scoring systems, the Acute Physiology and Chronic Health Evaluation (APACHE) II, the Simplified Acute Physiology Score (SAPS) II and a modified Mortality Probability Model II (ICU cancer mortality model, ICMM) for their prognostic value for mortality during hospital stay in a group of cancer patients admitted to a medical ICU.Design Prospective cohort study.Setting Medical ICU of a tertiary care hospital.Patients Two hundred forty-two consecutive cancer patients admitted to the ICU.Measurements and results Variables included in APACHE II, SAPS II and the ICMM scores as well as demographic data were assessed during the first 24 h of stay in the ICU. Hospital mortality was measured; it was 44%. Calibration for all three scoring systems was acceptable, SAPS II yielded a significantly superior discrimination between survivors and non-survivors. The areas under the receiver operating characteristic curves were 0.776 for APACHE II, 0.825 for SAPS II and 0.698 for the ICMM.Conclusion The SAPS II was superior to APACHE II and ICMM. The newly developed ICMM does not improve mortality prediction in critically ill cancer patients.     

Comparison of Acute Physiology and Chronic Health Evaluation II (APACHE II) and Simplified Acute Physiology Score II (SAPS II) scoring systems in a single Greek intensive care unit

OBJECTIVE: To evaluate Acute Physiology and Chronic Health Evaluation (APACHE) II and Simplified Acute Physiology Score (SAPS) II scoring systems in a single intensive care unit (ICU), independent from the ICUs of the developmental sample; and to compare the performance of APACHE II and SAPS II by means of statistical analyses in such a clinical setting. DESIGN: Prospective, cohort study. SETTING: A single ICU in a Greek university hospital. PATIENTS: In a time interval of 5 yrs, data for 681 patients admitted to our ICU were collected. The original exclusion criteria of both systems were employed. Patients <17 yrs of age were dropped from the study to keep compatibility with both systems. Eventually, a total of 661 patients were included in the analysis. INTERVENTIONS: Demographics, clinical parameters essential for the calculation of APACHE II and SAPS II scores, and risk of hospital death were recorded. Patient vital status was followed up to hospital discharge. MEASUREMENTS AND MAIN RESULTS: Both systems showed poor calibration and underestimated mortality but had good discriminative power, with SAPS II performing better than APACHE II. The evaluation of uniformity of fit in various subgroups for both systems confirmed the pattern of underprediction of mortality from both models and the better performance of APACHE II over our data sample. CONCLUSIONS: APACHE II and SAPS II failed to predict mortality in a population sample other than the one used for their development. APACHE II performed better than SAPS II. Validation in such a population is essential. Because there is a great variation in clinical and other patient characteristics among ICUs, it is doubtful that one system can be validated in all types of populations to be used for comparisons among different ICUs.     

Validation of severity scoring systems SAPS II and APACHE II in a single-center population

OBJECTIVE: To validate two severity scoring systems, the Simplified Acute Physiology Score (SAPS II) and Acute Physiology and Chronic Health Evaluation (APACHE II), in a single-center ICU population. DESIGN AND SETTING: Prospective data collection in a two four-bed multidisciplinary ICUs of a teaching hospital. PATIENTS AND METHODS: Data were collected in ICU over 4 years on 1,721 consecutively admitted patients (aged 18 years or older, no transferrals, ICU stay at least 24 h) regarding SAPS II, APACHE II, predicted hospital mortality, and survival upon hospital discharge. RESULTS: At the predicted risk of 0.5, sensitivity was 39.4 % for SAPS II and 31.6 % for APACHE II, specificity 95.6 % and 97.2 %, and correct classification rate 85.6 % and 85.5 %, respectively. The area under the ROC curve was higher than 0.8 for both models. The goodness-of-fit statistic showed no significant difference between observed and predicted hospital mortality (H = 7.62 for SAPS II, H = 3.87 for APACHE II; and C = 9.32 and C = 5.05, respectively). Observed hospital mortality of patients with risk of death higher than 60 % was overpredicted by SAPS II and underpredicted by APACHE II. The observed hospital mortality was significantly higher than that predicted by the models in medical patients and in those admitted from the ward. CONCLUSIONS: This study validates both SAPS II and APACHE II scores in an ICU population comprised mainly of surgical patients. The type of ICU admission and the location in the hospital before ICU admission influence the predictive ability of the models.     

Severity scores in respiratory intensive care: APACHE II predicted mortality better than SAPS II

BACKGROUND: In recent years several scoring systems have been developed to describe the severity of illness, to establish the individual prognosis, and to group adult ICU patients by predicted risk of mortality. In addition, these scores can be used to measure and/or compare the quality of care in different ICUs. We compared the mortality predictions of the Acute Physiology and Chronic Health Evaluation (APACHE II) score and a new Simplified Acute Physiology Score (SAPS II) in patients with respiratory disease who require intensive care. PATIENTS & METHODS: We prospectively studied all 306 admissions from January 1, 1992 through December 31, 1994. McNemar and Hosmer-Lemeshow tests, and receiver operating characteristic (ROC) curves were used to describe and analyze our data. RESULTS: The average APACHE II score was 17.5 (SD 6.0), corresponding to a mean predicted death rate of 24.9% (SD 17.2%) as compared to an observed overall RICU mortality rate of 21.6%. The average SAPS II score was 39.1 (SD 11.1) corresponding to a mean predicted death rate of 26.0% (SD 18.4%). The ratio between the actual and predicted hospital mortality was 86% for APACHE II and 83% for SAPS II. Survivors had a significantly lower predicted risk of death than nonsurvivors (p < 0.0001) with both indices, and a higher Glasgow coma scale score (p < 0.0001). The ROC-curve analysis suggested the superior predictive ability of APACHE II in our patients. Area under the APACHE II ROC curve was 80.88% (standard error [SE] 2.89%), significantly larger (p < 0.01) than that found for SAPS II (73.52%, SE 3.61%). CONCLUSIONS: The APACHE II score was a good predictor of hospital outcome and better than SAPS II in our population.     

Performance assessment of the Simplified Acute Physiology Score II,the Acute Physiology and Chronic Health Evaluation II score,and the Sequential Organ Failure Assessment score in predicting the outcomes of adult patients with hepatic portal venous gas in the ED

ObjectiveThis study aims to evaluate the performance of Simplified Acute Physiology Score II (SAPS II), the Acute Physiology and Chronic Health Evaluation II (APACHE II) score, and the Sequential Organ Failure Assessment (SOFA) score for predicting illness severity and the mortality of adult hepatic portal venous gas (HPVG) patients presenting to the emergency department (ED). This will assist emergency physicians in risk stratification.MethodsData for 48 adult HPVG patients who visited our ED between December 2009 and December 2013 were analyzed. The SAPS II, APACHE II score, and SOFA score were calculated based on the worst laboratory values in the ED. The probability of death was calculated for each patient based on these scores. The ability of the SAPS II, APACHE II score, and SOFA score to predict group mortality was assessed by using receiver operating characteristic curve analysis and calibration analysis.ResultsThe sensitivity, specificity, and accuracy were 92.6%,71.4%, and 83.3%, respectively, for the SAPS II method; 77.8%, 81%, and 79.2%, respectively, for the APACHE II scoring system, and 77.8%, 76.2%, and 79.2%, respectively, for the SOFA score. In the receiver operating characteristic curve analysis, the areas under the curve for the SAPS II, APACHE II scoring system, and SOFA score were 0.910, 0.878, and 0.809, respectively.ConclusionThis is one of the largest series performed in a population of adult HPVG patients in the ED. The results from the present study showed that SAPS II is easier and more quickly calculated than the APACHE II and more superior in predicting the mortality of ED adult HPVG patients than the SOFA. We recommend that the SAPS II be used for outcome prediction and risk stratification in adult HPVG patients in the ED.     

Scoring systems in cancer patients admitted for an acute complication in a medical intensive care unit

OBJECTIVE: To validate and compare two severity scoring systems, the Acute Physiology and Chronic Health Evaluation (APACHE) II and Simplified Acute Physiology Score (SAPS) II and to determine their prognostic value for mortality during the hospital stay and after discharge in a specific group of cancer patients admitted to intensive care unit (ICU) for an acute medical complication. DESIGN: Prospective cohort study. SETTING: The medical ICU of a European cancer hospital. SUBJECTS: A total of 261 consecutive cancer patients admitted to ICU for an acute medical complication. MEASUREMENTS: Variables included into the APACHE II and SAPS II scores, as well as characteristics of the cancer, were collected during the first 24 hrs of the ICU stay. Hospital and in-ICU mortalities, overall survival, and survival after day 30 were measured. RESULTS: Observed hospital and ICU mortalities were 33% and 23%. Median survival time was 94 days and 1-yr survival rate was 23%. The mean predicted risk of death was 26.5% with APACHE II and 26.1% with SAPS II. Correlation between both systems was excellent. Calibration for mortality prediction ability of both scoring systems was similar. Discrimination between survivors and nonsurvivors was superior with SAPS II according to the area under the receiver operating characteristic curve but was better with APACHE II for survivors using thresholds minimizing the overall misclassification rates. Multivariate prognostic analysis showed that the scoring systems were the only significant factors for hospital and in-ICU mortalities, whereas characteristics related to the cancer (extent, phase) were the factors predicting survival after discharge. CONCLUSION: The prognosis of cancer patients admitted to ICU for a medical problem is first determined by the acute physiologic changes induced by the complication, as evaluated by the severity scores. There is no major difference between the two assessed scoring systems. They are, however, not accurate enough to be used in the routine management of these patients. After recovery from complications, characteristics related to the neoplastic disease, however, retrieve their independent influence on the further survival.     

Simple translational equations to compare illness severity scores in intensive care trials

Purpose

Comparison of illness severity for intensive care unit populations assessed according to different scoring systems should increase our ability to compare and meta-analyze past and future trials but is currently not possible. Accordingly, we aimed to establish a methodology to translate illness severity scores obtained from one system into another.

Materials and methods

Using the Australian and New-Zealand intensive care adult patient database, we obtained simultaneous admission Acute Physiology and Chronic Health Evaluation (APACHE) II and APACHE III scores and Simplified Acute Physiology Score (SAPS) II in 634 428 patients admitted to 153 units between 2001 and 2010. We applied linear regression analyses to create models enabling translation of one score into another. Sensitivity analyses were performed after removal of diagnostic categories excluded from the original APACHE database, after matching for similar risk of death, after splitting data according to country of origin (Australia or New Zealand) and after splitting admissions occurring before or after 2006.

Results

The translational models were APACHE III = 3.08 × APACHE II + 5.75; APACHE III = 1.47 × SAPS II + 8.6; and APACHE II = 0.36 × SAPS II + 4.4. The area under the receiver operating curve for mortality prediction was 0.853 (95% confidence interval, 0.851-0.855) for the “APACHE II derived APACHE III” score and 0.854 (0.852-0.855) for the “SAPS II derived APACHE III” vs 0.854 (0.852-0.855) for the original APACHE III score. Similarly, it was 0.841 (0.839-0.843) for the “SAPS II derived APACHE II score” vs 0.842 (0.840-0.843) for the original APACHE II score. Correlation coefficients as well as intercepts remained very similar in all subgroups analyses.

Conclusions

Simple and robust translational formulas can be developed to allow clinicians to compare illness severity between studies involving critically ill patients. Further studies in other countries and health care systems are needed to confirm the generalizability of these results.     

Multicenter study of obstetric admissions to 14 intensive care units in southern England

OBJECTIVES: To identify pregnant and postpartum patients admitted to intensive care units (ICUs), the cause for their admission, and the proportion that might be appropriately managed in a high-dependency environment (HDU) by using an existing database. To estimate the goodness-of-fit for the Simplified Acute Physiology Score II, the Acute Physiology and Chronic Health Evaluation (APACHE) II, and the APACHE III scoring systems in the obstetrical population. DESIGN: Retrospective analysis of demographic, diagnostic, treatment, and severity of illness data. SETTING: Fourteen ICUs in Southern England. PATIENTS: Pregnant or postpartum (<42 days) admissions between January 1, 1994, and December 31, 1996. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We identified 210 patients, constituting 1.84% (210 of 11,385) of all ICU admissions and 0.17% (210 of 122,850) of all deliveries. Most admissions followed postpartum complications (hypertensive disease of pregnancy [39.5%] and major hemorrhage [33.3%]). Seven women were transferred to specialist ICUs. There was considerable variation between ICUs with respect to the number and type of interventions required by patients. Some 35.7% of patients stayed in ICU for <2 days and received no specific ICU interventions; these patients might have been safely managed in an HDU. There were seven maternal deaths (3.3%); fetal mortality rate was 20%. The area under the receiver operator characteristic curve and the standardized mortality ratio were 0.92 (confidence interval [CI], 0.85-0.99) and 0.43 for the Simplified Acute Physiology Score II, 0.94 (CI, 0.86-1.0) and 0.24 for APACHE II, and 0.98 (CI, 0.96-1.0) and 0.43 for APACHE III, respectively. CONCLUSIONS: Existing databases can both identify critically ill obstetrical patients and provide important information about them. Obstetrical ICU admissions often require minimal intervention and are associated with low mortality rates. Many might be more appropriately managed in an HDU. The commonly used severity of illness scoring systems are good discriminators of outcome from intensive care admission in this group but may overestimate mortality rates. Severity of illness scoring systems may require modification in obstetrical patients to adjust for the normal physiologic responses to pregnancy.     

Outcomes of critically ill elderly patients: is high-dependency care for geriatric patients worthwhile?

OBJECTIVES: To study the outcomes of elderly patients in a high-dependency care unit and to evaluate the costs and benefits of a geriatric high-dependency unit (GHDU). DESIGN: Prospective data collection and analysis. SETTING: Geriatric high-dependency unit. PATIENTS: One hundred fifty patients > or =70 yrs of age who had been admitted to the GHDU over a 10-month period were investigated during their treatment and rehabilitation. MEASUREMENT AND MAIN RESULTS: The patients' Acute Physiology and Chronic Health Evaluation (APACHE) II scores and Simplified Acute Physiology Scores (SAPS) were recorded. The APACHE II scores and SAPSs provided a close correlation with the patients' mortality (correlation coefficients were 0.97 and 0.92, respectively). The SAPS proved to have a better linear relationship with the elderly patients' mortality in comparison with APACHE II scores. Most of the elderly patients included in the study were suffering from multiple premorbid medical problems. Overall, the mortality rate up to 1 month after discharge from the hospital was 48%. For patients ranging in age from 70 to 84 yrs, the 1-month mortality was 39.6%; however, for patients > or =85 yrs of age, the 1-month mortality was 68.1%. The mortality ratio was 0.96 (for all patients), 0.88 (for those ages 70-84 yrs), and 1.05 (for those age 85 yrs and above). For patients with nil organ system failure, the mortality rate was 32%. For patients with one organ system failure, the mortality increased to 48%. For patients with two organ system failures, the mortality rate was 86%. Survival for patients with three or more organ system failures was unprecedented. Survivors and nonsurvivors were compared. Three poor-prognosis groups were identified: group 1, patients who had received preadmission cardiopulmonary resuscitation; group 2, patients with a recent history of malignant diseases; and group 3, patients who had been mechanically ventilated. All three groups had a significantly higher mortality than those without these factors (p<.05). Patients in the 85 yrs and above group had a significantly higher mortality rate than those in the 70- to 84-yr age group (p<.05). Patients with SAPS and APACHE II scores >20 and >30, respectively, had a poor prognosis. The geriatric outcome scoring system (GOSS) was used as the functional outcome test for the survivors. The GOSS has three components: activities of daily living, mobility status, and social condition. At 1 month after discharge, 66.7% of the survivors returned to their premorbid activities of daily living abilities, 79.5% maintained their mobility status, and 91.7% remained at the same social environment. No survivors deteriorated more than one grade in any of the three components measured by the GOSS. The severity-of-illness scores, percentage of mechanical ventilation utilization, mortality rate, length of GHDU stay, and total hospital stay were comparable with those of other intensive care units (ICUs). The cost of 1 GHDU bed-day was equivalent to 24% of 1 ICU bed-day. CONCLUSION: The prognostic information that we gathered from an unselected group of critically ill elderly patients is useful. The GHDU achieved treatment results similar to those achieved by an ICU and is therefore seen as an innovative way of treating critically ill elderly patients. High-dependency care for the elderly patient is worthwhile.     

Quantifying risk and benchmarking performance in the adult intensive care unit

Morbidity, mortality, and length-of-stay outcomes in patients receiving critical care are difficult to interpret unless they are risk-stratified for diagnosis, presenting severity of illness, and other patient characteristics. Acuity adjustment systems for adults include the Acute Physiology And Chronic Health Evaluation (APACHE), the Mortality Probability Model (MPM), and the Simplified Acute Physiology Score (SAPS). All have recently been updated and recalibrated to reflect contemporary results. Specialized scores are also available for patient subpopulations where general acuity scores have drawbacks. Demand for outcomes data is likely to grow with pay-for-performance initiatives as well as for routine clinical, prognostic, administrative, and research applications. It is important for clinicians to understand how these scores are derived and how they are properly applied to quantify patient severity of illness and benchmark intensive care unit performance.     

Recent innovations in intensive care unit risk-prediction models

During the past 20 years, ICU risk-prediction models have undergone significant development, validation, and refinement. Among the general ICU severity of illness scoring systems, the Acute Physiology and Chronic Health Evaluation (APACHE), Mortality Prediction Model (MPM), and the Simplified Acute Physiology Score (SAPS) have become the most accepted and used. To risk-adjust patients with longer, more severe illnesses like sepsis and acute respiratory distress syndrome, several models of organ dysfunction or failure have become available, including the Multiple Organ Dysfunction Score (MODS), the Sequential Organ Failure Assessment (SOFA), and the Logistic Organ Dysfunction Score (LODS). Recent innovations in risk adjustment include automatic physiology and diagnostic variable retrieval and the use of artificial intelligence. These innovations have the potential of extending the uses of case-mix and severity-of-illness adjustment in the areas of clinical research, patient care, and administration. The challenges facing intensivists in the next few years are to further develop these models so that they can be used throughout the IUC stay to assess quality of care and to extend them to more specific patient groups such as the elderly and patients with chronic ICU courses.     

Operational performance of validated physiologic scoring systems for predicting in-hospital mortality among critically ill emergency department patients

OBJECTIVES: New Simplified Acute Physiology Score (SAPS) II, Morbidity Probability Model at admission (MPM0 II), and Logistic Organ Dysfunction System (LODS) have all demonstrated high accuracy for predicting mortality in intensive care unit populations. We tested the prognostic accuracy of these instruments for predicting mortality among a cohort of critically ill emergency department patients. DESIGN: Secondary analysis of a randomized controlled trial. SETTING: Urban, tertiary emergency department, census >100,000. PATIENTS: Nontrauma emergency department patients admitted to an intensive care unit, aged >17 yrs, with initial emergency department vital signs consistent with shock (systolic blood pressure <100 mm Hg or shock index >1.0), and with agreement of two independent observers for at least one sign and symptom of inadequate tissue perfusion. INTERVENTIONS: Emergency department variables needed for calculation of each scoring system were prospectively collected, and published formulas were used to calculate the probability of in-hospital death for each scoring system. The main outcome was actual in-hospital mortality. The area under the receiver operating characteristic curve was used to evaluate the predictive ability of each scoring system. MEASUREMENTS AND MAIN RESULTS: Ninety-one of 202 patients (45%) were included. The mean age was 56 +/- 16 yrs, 42% were female, the mean initial systolic blood pressure was 84 +/- 13 mm Hg, and the average length of stay in the emergency department was 4.2 +/- 2.0 hrs. The in-hospital mortality rate was 21%. The area under the receiver operating characteristic curve for calculated probability of in-hospital mortality for SAPS II was 0.72 (95% confidence interval, 0.57-0.87), for MPM0 II 0.69 (95% confidence interval, 0.54-0.84), and for LODS 0.60 (95% confidence interval, 0.45-0.76). CONCLUSIONS: Using variables available in the emergency department, three previously validated intensive care unit scoring systems demonstrated moderate accuracy for predicting in-hospital mortality.     

Impact of exclusion criteria on case mix, outcome, and length of stay for the severity of disease scoring methods in common use in critical care

OBJECTIVE: To identify the exclusion criteria for the major severity of disease scoring methods in critical care and to investigate the impact of the exclusion criteria on the case mix, outcomes and length of stay for admissions to intensive care units (ICUs) in England, Wales and Northern Ireland. DESIGN: Cohort study-analysis of prospectively collected data. SETTING: 127 adult, general (mixed medical/surgical) ICUs in England, Wales and Northern Ireland. PATIENTS: 120,503 admissions between 1995 and 2001. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Thirteen different exclusion criteria were identified from the original methodological/validation papers and data collection manuals for APACHE II, APACHE III, SAPS II and MPM II. Application of the original exclusion criteria for the four, major severity of disease scoring methods resulted in exclusion of between 11.5% and 14.6% of admissions. Hospital mortality for the overall cohort was 29.0% but ranged from 4.7% to 50.1% among those groups excluded. After application of the exclusion criteria for each scoring method, there was little difference in overall hospital mortality or median ICU and hospital length of stay for the included admissions when compared with the original cohort. At the level of individual ICUs, there were differences in hospital mortality before and after exclusions-minimum -3.1% to maximum 9.5% (APACHE II), minimum -2.8% to maximum 9.4% (APACHE III), minimum -3.1% to maximum 16.1% (SAPS II) and minimum -3.1% to maximum 16.5% (MPM II). The mean difference across individual ICUs was -0.5 % (95% CI -0.7% to -0.2%) for APACHE II, -0.2% (95% CI -0.2% to 0.1%) for APACHE III, 2.0% (95% CI 1.7% to 2.4%) for SAPS II and 2.1% (95% CI 1.7% to 2.5%) for MPM II. SAPS II and MPM II showed systematic variation. A survey of the literature found wide variation in the exclusion criteria reported in subsequent, published research using a single severity of disease scoring method (APACHE II). CONCLUSIONS: Exclusion criteria used in critical care research are often ill-defined and poorly reported. More attention to the choice of exclusion criteria and their effect on the reported results is essential. We hope this study will raise the need for both better reporting of exclusion criteria applied in studies and promote the need for a common set of explicit exclusion criteria for these methods.     

A comparison of methods to predict mortality of intensive care unit patients

This paper presents results of the first study explicitly designed to compare three methods for predicting hospital mortality of ICU patients: the Acute Physiology Score (APS), the Simplified Acute Physiology Score (SAPS), and the Mortality Prediction Model (MPM). With respect to sensitivity, specificity, and total correct classification rates, these methods performed comparably on a cohort of 1,997 consecutive ICU admissions. In these patients from a single hospital, the APS overestimated and the SAPS underestimated the probability of hospital mortality. The MPM probabilities most closely matched the observed outcomes. Each method holds considerable promise for assessing the severity of illness of critically ill patients. The MPM should be particularly useful for comparing ICU performance, since it is independent of ICU treatment and can be calculated at the time a patient is admitted.