Comparison of Predisposition, Insult/Infection, Response, and Organ dysfunction, Acute Physiology And Chronic Health Evaluation II, and Mortality in Emergency Department Sepsis in patients meeting criteria for early goal-directed therapy and the severe sepsis resuscitation bundle

Purpose

The aim of the study was to examine the performance of the Predisposition, Insult/Infection, Response, and Organ dysfunction (PIRO) model compared with the Acute Physiology and Chronic Health Evaluation (APACHE) II and Mortality in Emergency Department Sepsis (MEDS) scoring systems in predicting in-hospital mortality for patients presenting to the emergency department (ED) with severe sepsis or septic shock.

Materials and Methods

This study was an analysis of a prospectively maintained registry including adult patients with severe sepsis or septic shock meeting criteria for early goal-directed therapy and the severe sepsis resuscitation bundle over a 6-year period. The registry contains data on patient demographics, sepsis category, vital signs, laboratory values, ED length of stay, hospital length of stay, physiologic scores, and outcome status. The discrimination and calibration characteristics of PIRO, APACHE II, and MEDS were analyzed.

Results

Five-hundred forty-one patients with age 63.5 ± 18.5 years were enrolled, 61.9% in septic shock, 46.9% blood-culture positive, and 31.8% in-hospital mortality. Median (25th and 75th percentile) PIRO, APACHE II, and MEDS scores were 6 (5 and 8), 28 (22 and 34), and 12 (9 and 15), with predicted mortalities of 48.5% (40.1 and 63.9), 66.0% (42.0 and 83.0), and 16.0% (9.0 and 39.0), respectively. The area under the receiver operating characteristic curves for PIRO was 0.71 (95% confidence interval, 0.66-0.75); APACHE II, 0.71 (0.66-0.76); and MEDS, 0.63 (0.60-0.70). The standardized mortality ratio was 0.70 (0.08-1.41), 0.70 (−0.46 to 1.80), and 4.00 (−8.53 to 16.62), respectively. Actual mortality significantly increased with increasing PIRO score in patients with APACHE II 25 or more (P < .01).

Conclusions

The PIRO, APACHE II, and MEDS have variable abilities to early discriminate and estimate in-hospital mortality of patients presenting to the ED meeting criteria for early goal-directed therapy and the severe sepsis resuscitation bundle. The PIRO may provide additional risk stratification in patients with APACHE II 25 or more. More studies are required to evaluate the clinical applicability of PIRO in high-risk patients with severe sepsis and septic shock.     

A comparison of severity of illness scoring systems for elderly patients with severe pneumonia

OBJECTIVE: To evaluate the predictive ability of three severity of illness scoring systems in elderly patients with severe pneumonia requiring mechanical ventilation compared to a younger age group. DESIGN: Prospective cohort study. SETTING: Two university-affiliated tertiary care hospitals. PATIENTS AND PARTICIPANTS: One hundred four patients 75 years of age and older and 253 patients younger than 75 years of age enrolled from medical intensive care units. MEASUREMENTS AND RESULTS: Probabilities of hospital death for patients were estimated by the Acute Physiology and Chronic Health Evaluation (APACHE) II, the Mortality Probability Model (MPM) II and the Simplified Acute Physiology Score (SAPS) II. Predicted risks of hospital death were compared with observed outcomes using three methods of assessing the overall goodness of fit. The actual mortality of the elderly group was 54.87 % (95 % confidence interval [CI]: 45.2-64.4 %) compared to 28.9 % (95 % CI, 23.3-34.4 %) in the younger age group. There was a significant difference in the predictive accuracy of the scoring systems as assessed by the c-index, which is equivalent to the area under the receiver operator characteristics (ROC) curve, between the two groups, but not within individual groups. Calibration was insufficient for APACHE II and SAPS II in the elderly cohort as in-hospital mortality was lower than the predicted mortality for both models. CONCLUSIONS: Although the three severity of illness scoring systems (APACHE II, MPM II and SAPS II) demonstrated average discrimination when applied to estimate hospital mortality in the elderly patients with severe pneumonia, MPM II had the closest fit to our database. Alternative modeling approaches might be needed to customize the model coefficients to the elderly population for more accurate probabilities or to develop specialized models targeted to the designed population.     

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.     

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.     

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.     

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.     

The use of intensive care information systems alters outcome prediction

Objective: To study the effect of using an Intensive Care Information System (ICIS) on severity scores and prognostic indices: Acute Physiology and Chronic Health Evaluation II (APACHE II), Simplified Acute Physiology Score II (SAPS II), and Mortality Probability Models II (MPM II). Design: Prospective pilot study. Setting: A 20-bed medical-surgical intensive care unit (ICU) in a teaching hospital. Patients: 50 consecutive adult patients admitted to the ICU on a bed equipped with an ICIS. Interventions: None. Measurements and results: In each patient all the physiologic variables, as required by the severity scores, were both manually charted and recorded by ICIS. ICIS registration resulted in the extraction of more abnormal values for all physiologic variables (except temperature): p < 0.05. Higher severity scores and mortality prediction were achieved by using ICIS charting: predicted mortality increased by 15 % for APACHE II compared to manual charting, 25 % for SAPS II, and 24 % for MPM₀. ICIS charting resulted in higher severity scores and mortality prediction for 29 of the 50 patients using APACHE II with a mean increase in mortality prediction in this subgroup of 27 %. In the case of SAPS II, ICIS charting resulted in higher scores in 23 of the 50 patients and in the case of MPM₀ in 13 patients, the mean increase in mortality in these subgroups being 64 and 148 %, respectively. Conclusions: The use of ICIS charting to acquire the most abnormal physiologic values for severity scores and the derived prognostic indices results in a higher mortality prediction. Comparison of groups of patients and/or ICUs based on severity scores is impossible without standardization of data collection. The mortality prediction models have to be revalidated for the use of ICIS charting. While awaiting this, we suggest that every patient record in local regional, national, or international ICU databases should be marked as being recorded by manual or by ICIS charting. Received: 16 December 1997 Accepted: 11 June 1998     

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.     

Recalibration of risk prediction models in a large multicenter cohort of admissions to adult, general critical care units in the United Kingdom

OBJECTIVE: To assess the performance of published risk prediction models in common use in adult critical care in the United Kingdom and to recalibrate these models in a large representative database of critical care admissions. DESIGN: Prospective cohort study. SETTING: A total of 163 adult general critical care units in England, Wales, and Northern Ireland, during the period of December 1995 to August 2003. PATIENTS: A total of 231,930 admissions, of which 141,106 met inclusion criteria and had sufficient data recorded for all risk prediction models. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The published versions of the Acute Physiology and Chronic Health Evaluation (APACHE) II, APACHE II UK, APACHE III, Simplified Acute Physiology Score (SAPS) II, and Mortality Probability Models (MPM) II were evaluated for discrimination and calibration by means of a combination of appropriate statistical measures recommended by an expert steering committee. All models showed good discrimination (the c index varied from 0.803 to 0.832) but imperfect calibration. Recalibration of the models, which was performed by both the Cox method and re-estimating coefficients, led to improved discrimination and calibration, although all models still showed significant departures from perfect calibration. CONCLUSIONS: Risk prediction models developed in another country require validation and recalibration before being used to provide risk-adjusted outcomes within a new country setting. Periodic reassessment is beneficial to ensure calibration is maintained.     

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.     

Diagnostic value and prognostic evaluation of Presepsin for sepsis in an emergency department

Introduction

Presepsin levels are known to be increased in sepsis. The aim of this study was to evaluate the early diagnostic and prognostic value of Presepsin compared with procalcitonin (PCT), Mortality in Emergency Department Sepsis (MEDS) score and Acute Physiology and Chronic Health Evaluation II (APACHE II) score in septic patients in an emergency department (ED) and to investigate Presepsin as a new biomarker of sepsis.

Methods

This study enrolled 859 consecutive patients with at least two diagnostic criteria for systemic inflammatory response syndrome (SIRS) who were admitted to Beijing Chao-yang Hospital ED from December 2011 to October 2012, and 100 age-matched healthy controls. Patients were stratified into four groups: SIRS, sepsis, severe sepsis, and septic shock. Plasma Presepsin and serum PCT were measured, and MEDS score and APACHE II score were calculated at enrollment. Comparisons were analyzed using the Kruskal-Wallis and Mann–Whitney U tests.

Results

On admission, the median levels of plasma Presepsin increased with sepsis severity. The areas under the receiver operating characteristic (AUC) curves of Presepsin were greater than those of PCT in diagnosing sepsis, and predicting severe sepsis and septic shock. The AUC of Presepsin for predicting 28-day mortality in septic patients was slightly lower than that of PCT, MEDS score and APACHE II score. The AUC of a combination of Presepsin and MEDS score or APACHE II score was significantly higher than that of MEDS score or APACHE II score alone in predicting severe sepsis, and was markedly higher than that of Presepsin alone in predicting septic shock and 28-day mortality in septic patients, respectively. Plasma Presepsin levels in septic patients were significantly higher in non-survivors than in survivors at 28 days’ follow-up. Presepsin, MEDS score and APACHE II score were found to be independent predictors of severe sepsis, septic shock and 28-day mortality in septic patients. The levels of plasma Presepsin were positively correlated with PCT, MEDS score and APACHE II score in every septic group.

Conclusion

Presepsin is a valuable biomarker for early diagnosis of sepsis, risk stratification, and evaluation of prognosis in septic patients in the ED.     

Prognostic factors for primary septicemia and wound infection caused by Vibrio vulnificus

Objectives

The purpose of this study was to explore the predictive factors for mortality in primary septicemia or wound infections caused by Vibrio vulnificus.

Methods

A retrospective review of 90 patients 18 years and older who were hospitalized due to V vulnificus infection between January 2000 and December 2006 was performed. Clinical characteristics, laboratory studies, treatments, and outcomes retrieved from medical records were analyzed. Multiple logistic regression and receiver operating characteristic curve analyses were performed.

Results

Of 90 patients identified as V vulnificus infections, 39 had primary septicemia and 51 had wound infection. The mean age was 63.0 ± 11.9 years. The mean Acute Physiology and Chronic Health Evaluation (APACHE II) and Mortality in Emergency Department Sepsis (MEDS) scores on admission were 11.1 ± 4.9 and 5.5 ± 3.8, respectively. Fifteen patients died, yielding an in-hospital mortality rate of 17%. Multivariate analysis revealed that higher APACHE II (odds ratio, 1.5; 95% confidence interval [CI], 1.2-1.8; P< .0001) and MEDS (odds ratio, 1.3; 95% CI, 1.1-1.6; P = .0201) scores on admission were significantly associated with mortality. The area under the receiver operating characteristic curves values for APACHE II and MEDS in predicting in-hospital mortality were 0.928 (95% CI, 0.854-0.972) and 0.830 (95% CI, 0.736-0.901), respectively.

Conclusions

The APACHE II and MEDS scores on admission are significant prognostic indicators in primary septicemia or wound infections caused by V vulnificus. A further prospective study to strengthen this point is required.     

Mortality discrimination in acute myocardial infarction: comparison between APACHE III and SAPS II prognosis systems

Objective: To compare the Acute Physiology, Age and Chronic Health Evaluation (APACHE) III with the Simplified Acute Physiology Score (SAPS II) in discriminating in-hospital mortality for intensive care unit (ICU) patients with acute myocardial infarction (AMI). Design: Prospective, observational, multicenter study. Setting: 70 Spanish ICUs. Patients and participants: 1711 patients with AMI and representative of Spanish ICUs. Measurements and results: APACHE III score, APACHE III system probability of death (APACHE III probability), SAPS II score and in-hospital mortality were noted for each patient. Two hundred and twenty three (13.0 %) patients died in the hospital. The sensitivity (± SE), specificity (± SE), and accuracy (± SE) for the APACHE III score were, respectively, 75.8 ± 2.9, 75.9 ± 1.1, and 75.9 ± 1.0. The corresponding figures for APACHE III probability were 75.3 ± 2.9, 79.2 ± 1.1, and 78.7 ± 1.0, and for SAPS II 72.2 ± 3.0, 75.9 ± 1.1, and 75.4 ± 1.0. Conclusions: The results indicate good discrimination by the three tests. APACHE III probability shows a statistically significant improvement in accuracy and specificity when compared with the two scores. Received: 4 July 1996 Accepted: 27 November 1996     

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.     

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.     

Risk stratification of severe sepsis patients in the emergency department

Objective

To determine the efficacy of the Mortality in Emergency Department Sepsis (MEDS) score in the stratification of patients who presented to the emergency department (ED) with severe sepsis.

Methods

Adults who presented to the ED with severe sepsis were retrospectively recruited and divided into group A (MEDS score <12) and group B (MEDS score ⩾12). Their outcomes were evaluated with 28 day hospital mortality rate, length of hospital stay, Kaplan‐Meier survival analysis, and receiver operating characteristic (ROC) analysis. Discriminatory power of the MEDS score in mortality prediction was further compared with the Acute Physiology and Chronic Health Evaluation (APACHE) II model.

Results

In total, 276 patients (44.6% men and 55.4% women) were analysed, with 143 patients placed in group A and 133 patients in group B. Patients with MEDS score ⩾12 had a significantly higher mortality rate (48.9% v 17.5%, p<0.01) and higher median APACHE II score (25 v 20 points, p<0.01). Significant difference in mortality risk was also demonstrated with Kaplan‐Meier survival analysis (log rank test, p<0.01). No difference in the length of hospital stay was found between the groups. ROC analysis indicated a better performance in mortality prediction by the MEDS score compared with the APACHE II score (ROC 0.75 v 0.62, p<0.01).

Conclusion

Our results showed that mortality risk stratification of severe sepsis patients in the ED with MEDS score is effective. The MEDS score also discriminated better than the APACHE II model in mortality prediction.     

Comparison of two simplified severity scores (SAPS and APACHE II) for patients with acute myocardial infarction

The Simplified Acute Physiology Score (SAPS), the Acute Physiology and Chronic Health Evaluation II (APACHE II), the Acute Physiology Score (APS), and the Coronary Prognostic Index (CPI), calculated within the first 24 h of ICU admission, were compared in 76 patients with acute myocardial infarction (AMI). Sixteen (21%) patients subsequently died in the ICU. The nonsurvivors had significantly higher SAPS, APACHE II, and CPI scores than the survivors. ROC curves drawn for each severity index were in a discriminating position. There were no significant differences either between the areas under the ROC curves drawn for SAPS, APACHE II, and CPI, or between the overall accuracies of these indices. APS provided less homogeneous information. We conclude that SAPS and APACHE II, two severity indices which are easy to use, assess accurately the short-term prognosis, i.e., the ICU outcome, of patients with AMI.     

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     

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.     

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.