SAPS 3, APACHE IV or GRACE: which score to choose for acute coronary syndrome patients in intensive care units?

Severity indexes of blunt trauma victims in intensive therapy: prediction capacity for mortality

OBJECTIVE

To identify the predictive capacity for mortality of the indexes Revised Trauma Score, Rapid Emergency Medicine Score, modified Rapid Emergency Medicine Score, and Simplified Acute Physiology Score III in blunt trauma victims hospitalized in an intensive care unit and compare their performance.

METHOD

Retrospective cohort of patients with blunt trauma in an intensive care unit from medical records. Receiver Operating Characteristic and a 95% confidence interval of the area under the curve were analyzed to compare results.

RESULTS

Out of 165 analyzed patients, 66.7% have received surgical treatment. The mortality in the intensive care unit and in the hospital was 17.6% and 20.6%, respectively. For the mortality in the intensive care unit, the area under the curve varied from 0.672 to 0.738; however, better results have been observed in surgical patients (0.747 to 0.811). Similar results have been observed for in-hospital mortality. In all analyses, the areas under the curve of the indexes presented no significant difference.

CONCLUSION

The accuracy of the severity indexes was moderate, with an improved performance when applied to surgical patients. The four indexes presented a similar prediction for the analyzed outcomes.

Stressors in intensive cardiac care units: Patients' perceptions

BACKGROUND

Patients with acute coronary syndrome (ACS) are often admitted to intensive cardiac care units (ICCUs) to have their vital signs and ventricular function monitored. In most cases, they are conscious and bedbound, causing high stress and anxiety levels.

AIMS AND OBJECTIVES

To assess the stressors affecting patients admitted to ICCUs.

DESIGN

This is a cross-sectional study performed in a public hospital in São Paulo, Brazil.

METHODS

A sample of 100 patients with ACS admitted to an ICCU, after 24 hours of hospitalization, was recruited. Sociodemographic and clinical data were collected, and the Intensive Care Unit Stressor Assessment Scale (ICESS) was applied to investigate stressors. The ICESS' internal consistency was estimated by Cronbach's alpha, and the stressors were analysed by descriptive statistics, association tests, and correlation tests.

RESULTS

Most patients were male, married, and aged 51 to 60 years. The ICESS showed an excellent internal consistency. The mean score identified in patients pointed to a non-stressed to moderately stressed condition. The indicators considered to be more stressful were as follows: "Not knowing ICU length of stay," "Being unable to fulfil family roles," "Missing husband or wife," and "Loss of self-control."

CONCLUSIONS

Stress levels were considered to be non-stressful to moderately stressful. The main stressors were related to psychological distress and physical discomfort dimensions.

RELEVANCE TO CLINICAL PRACTICE

Mapping the main stressors of patients with coronary artery disease will allow health professionals to implement strategies to reduce them and consequently reduce their anxiety levels.

APACHE IV score in postoperative kidney transplantation

Objectives

To evaluate the calibration and discrimination of APACHE IV in the postoperative period after kidney transplantation.

Methods

This clinical cohort study included 986 hospitalized adult patients in the immediate postoperative period after kidney transplantation, in a single center in southern Brazil.

Results

Kidney transplant patients who died in hospital had significantly higher APACHE IV values and higher predicted mortality. The APACHE IV score showed adequate calibration (H-L 11.24 p = 0.188) and a good discrimination ROC curve of 0.738 (95%CI 0.643 - 0.833, p < 0.001), although SMR overestimated mortality (SMR = 0.73; 95%CI: 0.24 - 1.42, p = 0.664).

Conclusions

The APACHE IV score showed adequate performance for predicting hospital outcomes in the postoperative period for kidney transplant recipients.

Validation of the Sepsis Severity Score Compared with Updated Severity Scores in Predicting Hospital Mortality in Sepsis Patients

INTRODUCTION

Recently, the Sepsis Severity Score (SSS) was constructed to predict mortality in sepsis patients. The aim of this study was to compare performance of the SSS with the Acute Physiology and Chronic Health Evaluation (APACHE) II-IV, Simplified Acute Physiology Score (SAPS) II, and SAPS 3 scores in predicting hospital outcome in sepsis patients.

METHODS

A retroprospective analysis was conducted in the medical intensive care unit of a tertiary university hospital.

RESULTS

A total of 913 patients were enrolled; 476 of these patients (52.1%) had septic shock. The median SSS was 80 (range 20-137). The SSS presented good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.892. However, the AUC of the SSS did not differ significantly from that of APACHE II (P = 0.07), SAPS II (P = 0.06), and SAPS 3 (P = 0.11). The APACHE IV score showed the best discrimination with an AUC of 0.948 and the overall performance by a Brier score of 0.096. The AUC of the APACHE IV score was statistically greater than the SSS, APACHE II, SAPS II, and SAPS 3 (P <0.0001 for all) and APACHE III (P = 0.0002). The calibration of all scores was poor with the Hosmer-Lemeshow goodness-of-fit H test <0.05.

CONCLUSIONS

The SSS provided as good discrimination as the APACHE II, SAPS II, and SAPS 3 scores. However, the APACHE IV score had the best discrimination and overall performance in our sepsis patients. The SSS needs to be adapted and modified with new parameters to improve its performance.

Clinical Features of Kidney Transplant Recipients Admitted to the Intensive Care Unit

INTRODUCTION

There is a paucity of data regarding the complications in kidney transplant patients who may require intensive care unit (ICU) management, despite being the most common solid organ transplant worldwide.

OBJECTIVE

To identify the main reasons for ICU admission and to determine the factors associated with hospital mortality in kidney transplant recipients.

DESIGN

This single-center retrospective cohort study was conducted between September 2013 and June 2014, including all consecutive kidney transplant patients requiring ICU admission. We collected data on patient demographics, transplant characteristics, clinical data, and prognostic scores. The independent determinants of hospital mortality were identified by multiple logistic regression analysis. We also assessed the performance of Simplified Acute Physiology Score 3 (SAPS 3) and Acute Physiology and Chronic Health Evaluation II (APACHE II) scores.

RESULTS

We analyzed data from 413 patients, the majority of whom were admitted late after renal transplantation (1169 days; 63-3003 days). The main reason for admission was sepsis (33.2%), followed by cardiovascular disease (16%). Age (odds ratio [OR] 1.05, confidence interval [CI], 1.01-1.09), SAPS 3 score (OR 1.04, CI, 1.01-1.08), the need for mechanical ventilation (OR 26.47, CI, 10.30-68.08), and vasopressor use (OR 3.34, CI, 1.37-8.13) were independently associated with hospital mortality. The performance of SAPS 3 and APACHE II scores was poor in this population and overestimated the mortality rates.

CONCLUSION

Sepsis was the main reason for ICU admission in kidney transplant recipients, followed by cardiovascular disease. Age and disease severity were associated with hospital mortality.

The Ability of the Acute Physiology and Chronic Health Evaluation (APACHE) IV Score to Predict Mortality in a Single Tertiary Hospital

Background

The Acute Physiology and Chronic Health Evaluation (APACHE) II model has been widely used in Korea. However, there have been few studies on the APACHE IV model in Korean intensive care units (ICUs). The aim of this study was to compare the ability of APACHE IV and APACHE II in predicting hospital mortality, and to investigate the ability of APACHE IV as a critical care triage criterion.

Methods

The study was designed as a prospective cohort study. Measurements of discrimination and calibration were performed using the area under the receiver operating characteristic curve (AUROC) and the Hosmer-Lemeshow goodness-of-fit test respectively. We also calculated the standardized mortality ratio (SMR).

Results

The APACHE IV score, the Charlson Comorbidity index (CCI) score, acute respiratory distress syndrome, and unplanned ICU admissions were independently associated with hospital mortality. The calibration, discrimination, and SMR of APACHE IV were good (H = 7.67, P = 0.465; C = 3.42, P = 0.905; AUROC = 0.759; SMR = 1.00). However, the explanatory power of an APACHE IV score >93 alone on hospital mortality was low at 44.1%. The explanatory power was increased to 53.8% when the hospital mortality was predicted using a model that considers APACHE IV >93 scores, medical admission, and risk factors for CCI >3 coincidentally. However, the discriminative ability of the prediction model was unsatisfactory (C index <0.70).

Conclusions

The APACHE IV presented good discrimination, calibration, and SMR for hospital mortality.

External validation of SAPS 3 and MPM0-III scores in 48,816 patients from 72 Brazilian ICUs

BACKGROUND

The performance of severity-of-illness scores varies in different scenarios and must be validated prior of being used in a specific settings and geographic regions. Moreover, models' calibration may deteriorate overtime and performance of such instruments should be reassessed regularly. Therefore, we aimed at to validate the SAPS 3 in a large contemporary cohort of patients admitted to Brazilian ICUs. In addition, we also compared the performance of the SAPS 3 with the MPM₀-III.

METHODS

This is a retrospective cohort study in which 48,816 (medical admissions = 67.9%) adult patients are admitted to 72 Brazilian ICUs during 2013. We evaluated models' discrimination using the area under the receiver operating characteristic curve (AUROC). We applied the calibration belt to evaluate the agreement between observed and expected mortality rates (calibration).

RESULTS

Mean SAPS 3 score was 44.3 ± 15.4 points. ICU and hospital mortality rates were 11.0 and 16.5%. We estimated predicted mortality using both standard (SE) and Central and South American (CSA) customized equations. Predicted mortality rates were 16.4 ± 19.3% (SAPS 3-SE), 21.7 ± 23.2% (SAPS 3-CSA) and 14.3 ± 14.0% (MPM₀-III). Standardized mortality ratios (SMR) obtained for each model were: 1.00 (95% CI, 0.98-0.102) for the SAPS 3-SE, 0.75 (0.74-0.77) for the SAPS 3-CSA and 1.15 (1.13-1.18) for the MPM₀-III. Discrimination was better for SAPS 3 models (AUROC = 0.85) than for MPM₀-III (AUROC = 0.80) (p < 0.001). We applied the calibration belt to evaluate the agreement between observed and expected mortality rates (calibration): the SAPS 3-CSA overestimated mortality throughout all risk classes while the MPM₀-III underestimated it uniformly. The SAPS 3-SE did not show relevant deviations from ideal calibration.

CONCLUSIONS

In a large contemporary database, the SAPS 3-SE was accurate in predicting outcomes, supporting its use for performance evaluation and benchmarking in Brazilian ICUs.

Validation of the APACHE IV model and its comparison with the APACHE II, SAPS 3, and Korean SAPS 3 models for the prediction of hospital mortality in a Korean surgical intensive care unit

Background

The Acute Physiology and Chronic Health Evaluation (APACHE) IV model has not yet been validated in Korea. The aim of this study was to compare the ability of the APACHE IV with those of APACHE II, Simplified Acute Physiology Score (SAPS) 3, and Korean SAPS 3 in predicting hospital mortality in a surgical intensive care unit (SICU) population.

Methods

We retrospectively reviewed electronic medical records for patients admitted to the SICU from March 2011 to February 2012 in a university hospital. Measurements of discrimination and calibration were performed using the area under the receiver operating characteristic curve (AUC) and the Hosmer-Lemeshow test, respectively. We calculated the standardized mortality ratio (SMR, actual mortality predicted mortality) for the four models.

Results

The study included 1,314 patients. The hospital mortality rate was 3.3%. The discriminative powers of all models were similar and very reliable. The AUCs were 0.80 for APACHE IV, 0.85 for APACHE II, 0.86 for SAPS 3, and 0.86 for Korean SAPS 3. Hosmer and Lemeshow C and H statistics showed poor calibration for all of the models (P < 0.05). The SMRs of APACHE IV, APACHE II, SAPS 3, and Korean SAPS 3 were 0.21, 0.11 0.23, 0.34, and 0.25, respectively.

Conclusions

The APACHE IV revealed good discrimination but poor calibration. The overall discrimination and calibration of APACHE IV were similar to those of APACHE II, SAPS 3, and Korean SAPS 3 in this study. A high level of customization is required to improve calibration in this study setting.

Evaluation of Simplified Acute Physiology Score 3 performance: a systematic review of external validation studies

Introduction

Simplified Acute Physiology Score 3 (SAPS 3) was the first critical care prognostic model developed from worldwide data. We aimed to systematically review studies that assessed the prognostic performance of SAPS 3 general and customized models for predicting hospital mortality in adult patients admitted to the ICU.

Methods

Medline, Lilacs, Scielo and Google Scholar were searched to identify studies which assessed calibration and discrimination of general and customized SAPS 3 equations. Additionally, we decided to evaluate the correlation between trial size (number of included patients) and the Hosmer-Lemeshow (H-L) statistics value of the SAPS 3 models.

Results

A total of 28 studies were included. Of these, 11 studies (42.8%) did not find statistically significant mis-calibration for the SAPS 3 general equation. There was a positive correlation between number of included patients and higher H-L statistics, that is, a statistically significant mis-calibration of the model (r = 0.747, P <0.001). Customized equations for major geographic regions did not have statistically significant departures from perfect calibration in 9 of 19 studies. Five studies (17.9%) developed a regional customization and in all of them this new model was not statistically different from a perfect calibration for their populations. Discrimination was at least very good in 24 studies (85.7%).

Conclusions

Statistically significant departure from perfect calibration for the SAPS 3 general equation was common in validation studies and was correlated with larger studies, as should be expected, since H-L statistics (both C and H) are strongly dependent on sample size This finding was also present when major geographic customized equations were evaluated. Local customizations, on the other hand, improved SAPS 3 calibration. Discrimination was almost always very good or excellent, which gives excellent perspectives for local customization when a precise local estimate is needed.