Comparison of the APACHE III, APACHE II and Glasgow Coma Scale in acute head injury for prediction of mortality and functional outcome

Comparative Evaluation of Mortality Predictors in Trauma Patients: A Prospective Single-center Observational Study Assessing Injury Severity Score Revised Trauma Score Trauma and Injury Severity Score and Acute Physiology and Chronic Health Evaluation II Scores

Aim

This prospective cohort study aimed to compare the predictive accuracy of outcome (survival/death) among trauma patients using various prognostic scores.

Methods

Over 3 months, 240 trauma patients in a tertiary care hospital were assessed for demographic details, trauma characteristics, vital signs, Glasgow coma scale, arterial blood gas values, and lab markers. Injury severity score (ISS), revised trauma score (RTS), trauma and injury severity score (TRISS), and acute physiology and chronic health evaluation II (APACHE II) were applied at admission, 24 hours, and 48 hours post-admission.

Results

Road traffic accidents (55.83%) were the primary cause of trauma, followed by falls (33.75%) and violence (10.41%). The all-cause mortality rate was 23.33%, with 34.16% requiring ICU admission. Head injuries (65.83%) were both the most frequent injury site and cause of mortality.

Conclusion

Analysis indicated that APACHE II outperformed other scores in predicting outcomes, with ISS following closely. The study concludes that trauma severity correlates with ICU admission and mortality, emphasizing APACHE II as a superior predictor, particularly for traumatic brain injuries leading to ICU admission and mortality.

Clinical significance

This study contributes to the existing body of knowledge by addressing the gap in comparing prognostic abilities among scoring systems for trauma patients. The unexpected superiority of APACHE II suggests its potential as a valuable tool in predicting outcomes in this specific patient population.

How to cite this article

Gupta J, Kshirsagar S, Naik S, Pande A. Comparative Evaluation of Mortality Predictors in Trauma Patients: A Prospective Single-center Observational Study Assessing Injury Severity Score Revised Trauma Score Trauma and Injury Severity Score and Acute Physiology and Chronic Health Evaluation II Scores. Indian J Crit Care Med 2024;28(5):475-482.

Interfacial Polymerization Produced Magnetic Particles with Nano-Filopodia for Highly Accurate Liquid Biopsy in the PSA Gray Zone

Magnetic particles are leading separation materials for biological purification and detection. Existing magnetic particles, which almost rely on molecule-level interactions, however, often encounter bottlenecks in highly efficient cell-level separation due to the underestimate of surface structure effects. Here, immune cell-inspired magnetic particles with nano-filopodia (NFMPs) produced by interfacial polymerization for highly efficient capture of circulating tumor cells (CTCs) and further accurate clinical diagnosis of prostate cancer are reported . The unprecedented construction of nano-filopodia on polymer-based magnetic particles is achieved by introducing electrostatic interactions in emulsion interfacial polymerization. Due to the unique nano-filopodia, the NFMPs allow remarkably enhanced CTCs capture efficiency (86.5% ± 2.8%) compared with smooth magnetic particles (SMPs, 35.7% ± 5.7%). Under the assistance of machine learning by combining with prostate-specific antigen (PSA) and free to total PSA (F/T-PSA), the NFMPs strategy demonstrates high sensitivity (100%), high specificity (93.3%), and a high area under the curve (AUC) value (98.1%) for clinical diagnosis of prostate cancer in the PSA gray zone. The NFMPs are anticipated as an efficient platform for CTCs-based liquid biopsy toward early cancer diagnosis and prognosis evaluation.

Treatment of pediatric patients with traumatic brain injury by Dutch Helicopter Emergency Medical Services (HEMS)

BACKGROUND

Sparse data are available on prehospital care by Helicopter Emergency Medical Service (HEMS) for pediatric patients with traumatic brain injury (TBI). This study focusses on prehospital interventions, neurosurgical interventions and mortality in this group.

METHODS

We performed a retrospective analysis of pediatric (0-18 years of age) patients with TBI treated by Rotterdam HEMS.

RESULTS

From January 2012 to December 2017 415 pediatric (<18 years of age) patients with TBI were included. Intubation was required in in 92 of 111 patients with GCS ≤ 8, 92 (82.9%), compared to 12 of 77 (15.6%) with GCS 9-12, and 7 of 199 (3.5%) with GCS 13-15. Hyperosmolar therapy (HSS) was started in 73 patients, 10 with a GCS ≤8. Decompressive surgery was required in 16 (5.8%), nine patients (56.3%) of these received HSS from HEMS. Follow-up data was available in 277 patients. A total of 107 (38.6%) patients were admitted to a (P)ICU. Overall mortality rate was 6.3%(n = 25) all with GCS ≤8, 15 (60.0%) died within 24 hours and 24 (96.0%) within a week. Patients with neurosurgical interventions (N = 16) showed a higher mortality rate (18.0%).

CONCLUSIONS

The Dutch HEMS provides essential emergency care for pediatric TBI patients, by performing medical procedures outside of regular EMS protocol. Mortality was highest in patients with severe TBI (n = 111) (GCS≤8) and in those who required neurosurgical interventions. Despite a relatively good initial GCS (>8) score, there were patients who required prehospital intubation and HSS. This group will require further investigation to optimize care in the future.

Identification of key predictors of hospital mortality in critically ill patients with embolic stroke using machine learning

Embolic stroke (ES) is characterized by high morbidity and mortality. Its mortality predictors remain unclear. The present study aimed to use machine learning (ML) to identify the key predictors of mortality for ES patients in the intensive care unit (ICU). Data were extracted from two large ICU databases: Medical Information Mart for Intensive Care (MIMIC)-IV for training and internal validation, and eICU Collaborative Research Database (eICU-CRD) for external validation. We developed predictive models of ES mortality based on 15 ML algorithms. We relied on the synthetic minority oversampling technique (SMOTE) to address class imbalance. Our main performance metric was area under the receiver operating characteristic (AUROC). We adopted recursive feature elimination (RFE) for feature selection. We assessed model performance using three disease-severity scoring systems as benchmarks. Of the 1566 and 207 ES patients enrolled in the two databases, there were 173 (15.70%), 73 (15.57%), and 36 (17.39%) hospital mortality in the training, internal validation, and external validation cohort, respectively. The random forest (RF) model had the largest AUROC (0.806) in the internal validation phase and was chosen as the best model. The AUROC of the RF compact (RF-COM) model containing the top six features identified by RFE was 0.795. In the external validation phase, the AUROC of the RF model was 0.838, and the RF-COM model was 0.830, outperforming other models. Our findings suggest that the RF model was the best model and the top six predictors of ES hospital mortality were Glasgow Coma Scale, white blood cell, blood urea nitrogen, bicarbonate, age, and mechanical ventilation.

Quantitative EEG parameters can improve the predictive value of the non-traumatic neurological ICU patient prognosis through the machine learning method

Background

Better outcome prediction could assist in reliable classification of the illnesses in neurological intensive care unit (ICU) severity to support clinical decision-making. We developed a multifactorial model including quantitative electroencephalography (QEEG) parameters for outcome prediction of patients in neurological ICU.

Methods

We retrospectively analyzed neurological ICU patients from November 2018 to November 2021. We used 3-month mortality as the outcome. Prediction models were created using a linear discriminant analysis (LDA) based on QEEG parameters, APACHEII score, and clinically relevant features. Additionally, we compared our best models with APACHEII score and Glasgow Coma Scale (GCS). The DeLong test was carried out to compare the ROC curves in different models.

Results

A total of 110 patients were included and divided into a training set (n=80) and a validation set (n = 30). The best performing model had an AUC of 0.85 in the training set and an AUC of 0.82 in the validation set, which were better than that of GCS (training set 0.64, validation set 0.61). Models in which we selected only the 4 best QEEG parameters had an AUC of 0.77 in the training set and an AUC of 0.71 in the validation set, which were similar to that of APACHEII (training set 0.75, validation set 0.73). The models also identified the relative importance of each feature.

Conclusion

Multifactorial machine learning models using QEEG parameters, clinical data, and APACHEII score have a better potential to predict 3-month mortality in non-traumatic patients in neurological ICU.

Machine learning decision tree algorithm role for predicting mortality in critically ill adult COVID-19 patients admitted to the ICU

Background

Coronavirus disease-19 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and is currently a major cause of intensive care unit (ICU) admissions globally. The role of machine learning in the ICU is evolving but currently limited to diagnostic and prognostic values. A decision tree (DT) algorithm is a simple and intuitive machine learning method that provides sequential nonlinear analysis of variables. It is simple and might be a valuable tool for bedside physicians during COVID-19 to predict ICU outcomes and help in critical decision-making like end-of-life decisions and bed allocation in the event of limited ICU bed capacities. Herein, we utilized a machine learning DT algorithm to describe the association of a predefined set of variables and 28-day ICU outcome in adult COVID-19 patients admitted to the ICU. We highlight the value of utilizing a machine learning DT algorithm in the ICU at the time of a COVID-19 pandemic.

Methods

This was a prospective and multicenter cohort study involving 14 hospitals in Saudi Arabia. We included critically ill COVID-19 patients admitted to the ICU between March 1, 2020, and October 31, 2020. The predictors of 28-day ICU mortality were identified using two predictive models: conventional logistic regression and DT analyses.

Results

There were 1468 critically ill COVID-19 patients included in the study. The 28-day ICU mortality was 540 (36.8 %), and the 90-day mortality was 600 (40.9 %). The DT algorithm identified five variables that were integrated into the algorithm to predict 28-day ICU outcomes: need for intubation, need for vasopressors, age, gender, and PaO2/FiO2 ratio.

Conclusion

DT is a simple tool that might be utilized in the ICU to identify critically ill COVID-19 patients who are at high risk of 28-day ICU mortality. However, further studies and external validation are still required.

Establishment of ICU Mortality Risk Prediction Models with Machine Learning Algorithm Using MIMIC-IV Database

Objective: The mortality rate of critically ill patients in ICUs is relatively high. In order to evaluate patients’ mortality risk, different scoring systems are used to help clinicians assess prognosis in ICUs, such as the Acute Physiology and Chronic Health Evaluation III (APACHE III) and the Logistic Organ Dysfunction Score (LODS). In this research, we aimed to establish and compare multiple machine learning models with physiology subscores of APACHE III—namely, the Acute Physiology Score III (APS III)—and LODS scoring systems in order to obtain better performance for ICU mortality prediction. Methods: A total number of 67,748 patients from the Medical Information Database for Intensive Care (MIMIC-IV) were enrolled, including 7055 deceased patients, and the same number of surviving patients were selected by the random downsampling technique, for a total of 14,110 patients included in the study. The enrolled patients were randomly divided into a training dataset (n = 9877) and a validation dataset (n = 4233). Fivefold cross-validation and grid search procedures were used to find and evaluate the best hyperparameters in different machine learning models. Taking the subscores of LODS and the physiology subscores that are part of the APACHE III scoring systems as input variables, four machine learning methods of XGBoost, logistic regression, support vector machine, and decision tree were used to establish ICU mortality prediction models, with AUCs as metrics. AUCs, specificity, sensitivity, positive predictive value, negative predictive value, and calibration curves were used to find the best model. Results: For the prediction of mortality risk in ICU patients, the AUC of the XGBoost model was 0.918 (95%CI, 0.915–0.922), and the AUCs of logistic regression, SVM, and decision tree were 0.872 (95%CI, 0.867–0.877), 0.872 (95%CI, 0.867–0.877), and 0.852 (95%CI, 0.847–0.857), respectively. The calibration curves of logistic regression and support vector machine performed better than the other two models in the ranges 0–40% and 70%–100%, respectively, while XGBoost performed better in the range of 40–70%. Conclusions: The mortality risk of ICU patients can be better predicted by the characteristics of the Acute Physiology Score III and the Logistic Organ Dysfunction Score with XGBoost in terms of ROC curve, sensitivity, and specificity. The XGBoost model could assist clinicians in judging in-hospital outcome of critically ill patients, especially in patients with a more uncertain survival outcome.

Development and Validation of a Nomogram Incorporating Colloid Osmotic Pressure for Predicting Mortality in Critically Ill Neurological Patients

Backgrounds: The plasma colloid osmotic pressure (COP) values for predicting mortality are not well-estimated. A user-friendly nomogram could predict mortality by incorporating clinical factors and scoring systems to facilitate physicians modify decision-making when caring for patients with serious neurological conditions.

Methods: Patients were prospectively recruited from March 2017 to September 2018 from a tertiary hospital to establish the development cohort for the internal test of the nomogram, while patients recruited from October 2018 to June 2019 from another tertiary hospital prospectively constituted the validation cohort for the external validation of the nomogram. A multivariate logistic regression analysis was performed in the development cohort using a backward stepwise method to determine the best-fit model for the nomogram. The nomogram was subsequently validated in an independent external validation cohort for discrimination and calibration. A decision-curve analysis was also performed to evaluate the net benefit of the insertion decision using the nomogram.

Results: A total of 280 patients were enrolled in the development cohort, of whom 42 (15.0%) died, whereas 237 patients were enrolled in the validation cohort, of which 43 (18.1%) died. COP, neurological pathogenesis and Acute Physiology and Chronic Health Evaluation II (APACHE II) score were predictors in the prediction nomogram. The derived cohort demonstrated good discriminative ability, and the area under the receiver operating characteristic curve (AUC) was 0.895 [95% confidence interval (CI), 0.840–0.951], showing good correction ability. The application of this nomogram to the validation cohort also provided good discrimination, with an AUC of 0.934 (95% CI, 0.892–0.976) and good calibration. The decision-curve analysis of this nomogram showed a better net benefit.

Conclusions : A prediction nomogram incorporating COP, neurological pathogenesis and APACHE II score could be convenient in predicting mortality for critically ill neurological patients.

The influence of hydrogen ions on coagulation in traumatic brain injury, explored by molecular dynamics

Background: Patients in intensive care units with traumatic brain injuries (TBI) frequently present acid-base abnormalities and coagulability disorders, which complicate their condition.Objective: To identify protonation through in silico simulations of molecules involved in the process of coagulation in standard laboratory tests.Materials and methods: Ten patients with TBI were selected from the intensive care unit in addition to ten "healthy control subjects", and another nine patients as "disease control subjects"; the latter being a comparative group, corresponding to subjects with diabetes mellitus 2 (DM2). Fibrinogen, FVII, FVIII, FIX, FX, and D-dimer in the presence of acidification were evaluated in 20 healthy subjects in order to compare clinical results with molecular dynamics (MD), and to explain proton interactions and coagulation molecules.Results: The TBI group presented a slight, non-significant increase in D-dimer; but this was not present in "disease control subjects". Levels of fibrinogen, FVII, FIX, FX, and D-dimer were affected in the presence of acidification. We observed that various specific residues of coagulation factors "trap" ions.Conclusion: Protonation of tissue factor and factor VIIa may favor anticoagulant mechanisms, and protonation does not affect ligand binding sites of GPIIb/IIIa (PAC1) suggesting other causes for the low affinity to PAC1.

A Prospective Study of Comparing the Application of Two Generation Scoring Systems in Patients with Acute Cerebral Infarction

INTRODUCTION

This study aims to compare the value of acute physiologic and chronic health evaluation scoring systems (APACHE II and APACHE III) among patients with acute cerebral infarction.

METHODS

The APACHE II and APACHE III scores were determined in 399 patients with acute cerebral infarction within 24 h of admission in order to investigate their predictive value for prognosis in acute cerebral infarction. The area under the ROC curve was used to measure the ability of two scoring systems in predicting the prognosis of patients, and the area under the curve of the two scoring systems was compared.

RESULTS

The APACHE II and APACHE III scoring systems demonstrated good predictive value for prognosis in acute cerebral infarction, and the areas under the receiver operating characteristic were 0.808 and 0.818, respectively. There was no significant difference in the area under the curve between these two scoring systems.

CONCLUSION

Both the APACHE II and APACHE III scoring systems had good predictive value for prognosis in acute cerebral infarction, and there was no obvious difference between these two systems. Preference was suggested for APACHE II.

Evaluation of the Application of APACHE II Combined With NIHSS Score in the Short-Term Prognosis of Acute Cerebral Hemorrhage Patient

Objective: This study aims to evaluate the effects of combining Acute Physiology and Chronic Health Disease Classification System II (APACHE II) scores and the NIHSS score for short-term prognosis of cerebral hemorrhage patients.

Methods: APACHE II and NIHSS scores were respectively carried out for 189 acute cerebral hemorrhage patients who were admitted to the hospital for 24 h, and the area under ROC curve was used to measure the ability of these score systems to forecast the prognosis, in order to find the best dividing value. The discriminant analysis method should be used to carry out a comprehensive analysis of these two score methods and establish the mathematical model to provide a reasonable basis for accurately mastering these illness conditions, and its prognosis.

Results: The areas under the ROC curve of APACHE II and NIHSS scores in forecasting cerebral hemorrhage prognosis was 0.853 and 0.845, respectively, the dividing value was 15 and 17, respectively, and the forecasting accuracy was 77.2 and 79.9%, respectively; The forecasting accuracy of the combined discrimination model was 85.96%.

Conclusion: APACHE II and NIHSS scores have good forecasting value to the short-term prognosis of acute cerebral hemorrhage patients, and the combination of these two can provide a higher forecasting value.

An automated computerized critical illness severity scoring system derived from APACHE III: modified APACHE

PURPOSE

To evaluate the performance of an automated computerized ICU severity scoring derived from the APACHE III.

MATERIALS AND METHODS

Within a retrospective cohort of patients admitted to Veterans Health Administration ICUs between 2009 and 2015, we created an automated illness severity score(modified APACHE or mAPACHE), that we extracted from the electronic health records, using the same scoring as the APACHE III excluding the Glasgow Coma Scale, urine output, arterial blood gas components of APACHE III. We assessed the mAPACHE discrimination by using the area under the curve(AUC), and calibration by using the Hosmer-Lemeshow test and calculating the difference between observed and expected mortality across equal-sized risk deciles for death.

RESULTS

The ICU and 30-day mortality was 5.07% of 7.82%, respectively (n = 490,955 patients). The AUC of mAPACHE for ICU and 30-day mortality was 0.771 and 0.786, respectively. The Hosmer-Lemeshow test was significant for both ICU and 30-day mortality (p < .001). The absolute difference between observed and expected mortality did not exceed ±1.53% across equal-sized deciles of risk for death. The AUC for ICU mortality was >0.7 in all admission diagnosis categories except in endocrine, respiratory, and sepsis. The AUC for 30-day mortality was >0.7 in every category.

CONCLUSION

mAPACHE has adequate performance to predict mortality.

Assessment of postoperative complications using E-PASS and APACHE II in patients undergoing oral and maxillofacial surgery

Background

The prediction of postoperative complications is important for oral and maxillofacial surgeons. We herein aimed to evaluate the efficacy of the Estimation of Physiologic Ability and Surgical Stress (E-PASS) and Acute Physiology, Age, and Chronic Health Evaluation (APACHE) II scoring systems to predict postoperative complications in patients undergoing oral and maxillofacial surgery.

Methods

Thirty patients (22 males, 8 females; mean age: 65.1 ± 12.9 years) who underwent major oral surgeries and stayed in the intensive care unit for postoperative management were enrolled in this study. Postoperative complications were discriminated according to the necessity of the therapeutic intervention by the Medical Department, i.e. according to the Clavien–Dingo classification. E-PASS and APACHE II scores as well as laboratory test values were compared between patients with/without postoperative complications.

Results

Postoperative complications were developed in seven patients. The comprehensive risk score (CRS: 1.13 ± 0.24) and APACHE II score (13.0 ± 2.58) were significantly higher in patients with postoperative complications than in those without ones (p < 0.01, p < 0.05, respectively). The CRS showed an appropriate discriminatory power for predicting postoperative complications (area under the curve: 0.814). Furthermore, a correlation was detected between APACHE II scores and postoperative data until C-reactive protein levels decreased to < 1.0 mg/L (r = 0.43, p < 0.05).

Conclusion

The E-PASS and APACHE II scoring systems were both shown to be useful to predict postoperative complications after oral and maxillofacial surgery.

Predicting Mortality of Patients With Sepsis: A Comparison of APACHE II and APACHE III Scoring Systems

Background

Acute Physiology, Age and Chronic Health Evaluation (APACHE) II and III scores were developed in 1985 and 1991, respectively, and are used mainly for critically ill patients of all disease categories admitted to the intensive care unit (ICU). They differ in how chronic health status is assessed, in the number of physiologic variables included (12 vs. 17), and in the total score. These two scoring systems have not been compared in predicting hospital mortality in patients with sepsis.

Methods

We retrospectively identified all septic patients admitted to our 54-bed medical-surgical ICU between June 2009 and February 2014 using the APACHE outcomes database. We calculated correlation coefficients for APACHE II and APACHE III scores in predicting hospital mortality. Receiver-operating characteristic (ROC) curves were also used to assess the mortality predictions.

Results

We identified a total of 2,054 septic patients. Average APACHE II score was 19 ± 7, and average APACHE III score was 68 ± 28. ICU mortality was 11.8% and hospital mortality was 18.3%. Both APACHE II (r = 0.41) and APACHE III scores (r = 0.44) had good correlations with hospital mortality. There was no statistically significant difference between the two correlations (P = 0.1). ROC area under the curve (AUC) was 0.80 (95% confidence interval (CI): 0.78 - 0.82) for APACHE II, and 0.83 (95% CI: 0.81 - 0.85) for APACHE III, suggesting that both scores have very good discriminative powers for predicting hospital mortality.

Conclusions

This study shows that both APACHE II and APACHE III scores in septic patients were very strong predictors of hospital mortality. APACHE II was as good as APACHE III in predicting hospital mortality in septic patients.

Predictive Value of Brain Arrest Neurological Outcome Scale (BrANOS) on Mortality and Morbidity After Cardiac Arrest

OBJECTIVE

There are several prediction scales and parameters for prognosis after a cardiac arrest. One of these scales is the brain arrest neurological outcome scale (BrANOS), which consists of duration of cardiac arrest, Glasgow Coma Scale score and Hounsfield unit measured on cranial computed tomography (CT) scan. The objective of this study is to investigate the effectiveness of BrANOS on predicting the mortality and disability after a cardiac arrest.

METHODS

We retrospectively investigated cardiac arrest patients who were hospitalized in our intensive care unit (ICU) within a 3-year period. Inclusion criteria were age over 18 years old, survival of more than 24 hours after cardiac arrest and availability of cranial CT. We recorded the age, sex, diagnosis, duration of cardiac arrest and hospital stay, mortality, Glasgow Outcome Score (GOS) and BrANOS score. The primary endpoint of the study was to establish the relationship between mortality and BrANOS score in patients who survived for more than 24 hours after a cardiac arrest. The secondary endpoint of the study was to determine the 2-year life expectancy and GOS after cardiac arrest.

RESULTS

The mean age of the patients was 57±17 years (33 females, 67 males). ICU mortality rate was 57%. The BrANOS mean score was 10.3±3.2. There was a significant difference between survivors and non-survivors in terms of the BrANOS score (8.8±3.2 vs. 11.6±2.7; p<0.001). BrANOS reliably predicted the survival with a ROC area under the curve of 0.733. The scale of >14 predicted death with 100% accuracy. All the patients without disability had a BrANOS score of <10. The BrANOS score also correlated well with GOS (p<0.001). The 2-year life expectancy rate was 31% in patients who survived more than 24 hours after a cardiac arrest.

CONCLUSION

In this study, we demonstrated that BrANOS provided reliable data for prognostic evaluation after a cardiac arrest.

Comparison of Glasgow Coma Scale, Full Outline of Unresponsiveness and Acute Physiology and Chronic Health Evaluation in Prediction of Mortality Rate Among Patients With Traumatic Brain Injury Admitted to Intensive Care Unit

Background

Traumatic brain injury (TBI) is a common cause of mortality and disability worldwide. Choosing an appropriate diagnostic tool is critical in early stage for appropriate decision about primary diagnosis, medical care and prognosis.

Objectives

This study aimed to compare the Glasgow coma scale (GCS), full outline of unresponsiveness (FOUR) and acute physiology and chronic health evaluation (APACHE II) with respect to prediction of the mortality rate of patients with TBI admitted to intensive care unit.

Patients and Methods

This diagnostic study was conducted on 80 patients with TBI in educational hospitals. The scores of APACHE II, GCS and FOUR were recorded during the first 24 hours of admission of patients. In this study, early mortality means the patient death before 14 days and delayed mortality means the patient death 15 days after admitting to hospital. The collected data were analyzed using descriptive and inductive statistics.

Results

The results showed that the mean age of the patients was 33.80 ± 12.60. From a total of 80 patients with TBI, 16 (20%) were females and 64 (80%) males. The mortality rate was 15 (18.7%). The results showed no significant difference among three tools. In prediction of early mortality, the areas under the curve (AUCs) were 0.92 (CI = 0.95. 0.81 - 0.97), 0.90 (CI = 0.95. 0.74 - 0.94), and 0.96 (CI = 0.95. 0.87 - 0.9) for FOUR, APACHE II and GCS, respectively. In delayed mortality, the AUCs were 0.89 (CI = 0.95. 0.81-0.94), 0.94 (CI = 0.95. 0.74 - 0.97) and 0.90 (CI = 0.95. 0.87 - 0.95) for FOUR, APACHE II and GCS, respectively.

Conclusions

Considering that GCS is easy to use and the FOUR can diagnose a locking syndrome along same values of subscales. These two subscales are superior to APACHI II in prediction of early mortality. Conversation APACHE II is more punctual in the prediction of delayed mortality.

Application of the APACHE II score to assess the condition of patients with critical neurological diseases

The Acute Physiology And Chronic Health Evaluation II (APACHE II) scoring system has been commonly used to assess the severity of patients' diseases in general intensive care units (ICUs). However, few studies have investigated the application of this scoring system in patients in neurologic ICUs. In this study, the APACHE II scores of 102 patients in the neurologic ICU were calculated within the first 24 h. The actual mortality and predicted mortality were obtained based on these scores and analyzed statistically. The data indicated that cerebral hemorrhage, cerebral infarction and intracranial infection accounted for the top three causes for admission to the neurologic ICU, and these conditions were associated with high APACHE II scores and high predicted mortality. Additionally, the actual mortality rate was lower than the predicted rate after effective treatment. All patients were divided into groups according to their APACHE II scores, and we found that higher APACHE II scores were associated with higher actual mortality, especially for patients whose APACHE II scores were greater than 10. The APACHE II scores of the deceased patient group were higher than those of the surviving group, and this difference was statistically significant. In conclusion, our study found that the APACHE II scoring system may provide valuable information for predicting patient's condition and prognosis in neurologic ICUs.

Using population-based critical care data to evaluate trauma outcomes

BACKGROUND

The analysis of mortality is an integral part of the evaluation of trauma care. When specific data are not available, general prediction models can be used to adjust for case mix. The aim of this study was to evaluate the feasibility of conducting a population-based analysis of trends in trauma mortality, using critical care audit data, and to investigate whether such data could provide a benchmark for the assessment of service reconfiguration.

METHODS

Retrospective cohort study of adult trauma patients, requiring admission to a critical care unit in Scotland, 2002-2011, using nationally collected data. Results are presented as standardised mortality ratios of observed mortality divided by APACHE II predicted mortality. Tests for trends in numbers and ratios over time were performed using linear regression.

FINDINGS

4503 patients were identified. There was a significant increase in the number of trauma patients admitted per year (p = 0.011). The median predicted probability of in-hospital death was 7% (interquartile range 1-13%), against an actual mortality was 11.6%. There was no significant change in the standardised mortality ratios of trauma patients (p = 0.1224).

CONCLUSIONS

This study demonstrated the feasibility of utilising critical care unit audit data for analysing outcomes from trauma care. It also showed the potential of such an approach to establish a baseline against which to compare the impact of future service reconfiguration. In contrast to healthcare systems with regionalised trauma care, there appears to have been little change in the mortality of trauma patients requiring critical care unit admission in Scotland.

Comparison of acute physiology and chronic health evaluation II and Glasgow Coma Score in predicting the outcomes of Post Anesthesia Care Unit's patients

Context:

Acute physiology and chronic health evaluation II (APACHE II) is one of the most general classification systems of disease severity in Intensive Care Units and Glasgow Coma Score (GCS) is one of the most specific ones.

Aims:

The aim of the current study was to assess APACHE II and GCS ability in predicting the outcomes (survivors, non-survivors) in the Post Anesthesia Care Unit's (PACU).

Settings and Design:

This was an observational and prospective study of 150 consecutive patients admitted in the PACU during 6-month period.

Materials and Methods:

Demographic information recorded on a checklist, also information about severity of disease calculated based on APACHE II scoring system in the first admission 24 h and GCS scale.

Statistical Analysis Used:

Logistic regression, Hosmer-Lemeshow test and receiver operator characteristic (ROC) curves were used in statistical analysis (95% confidence interval).

Results:

Data analysis showed a significant statistical difference between outcomes and both APACHE II and Glasgow Coma Score (GCS) (P < 0.0001). The ROC-curve analysis suggested that the predictive ability of GCS is slightly better than APACHE II in this study. For GCS the area under the ROC curve was 86.1% (standard error [SE]: 3.8%), and for APACHE II it was 85.7% (SE: 3.5%), also the Hosmer-Lemeshow statistic revealed better calibration for GCS (χ² = 5.177, P = 0.521), than APACHE II (χ² = 10.203, P = 0.251).

Conclusions:

The survivors had significantly lower APACHE II and higher GCS compared with non-survivors, also GCS showed more predictive accuracy than APACHE II in prognosticating the outcomes in PACU.

The ability of two scoring systems to predict in-hospital mortality of patients with moderate and severe traumatic brain injuries in a Moroccan intensive care unit

Aim of Study:

We aim to assess and to compare the predicting power for in-hospital mortality (IHM) of the Acute Physiology and Chronic Health Evaluation-II (APACHE-II) and the Simplified Acute Physiology Score-II (SAPS-II) for traumatic brain injury (TBI).

Patients and Methods:

This retrospective cohort study was conducted during a period of 2 years and 9 months in a Moroccan intensive care unit. Data were collected during the first 24 h of each admission. The clinical and laboratory parameters were analyzed and used as per each scoring system to calculate the scores. Univariate and multivariate analyses through regression logistic models were performed, to predict IHM after moderate and severe TBIs. Areas under the receiver operating characteristic curves (AUROC), specificities and sensitivities were determined and also compared.

Results:

A total of 225 patients were enrolled. The observed IHM was 51.5%. The univariate analysis showed that the initial Glasgow coma scale (GCS) was lower in nonsurviving patients (mean GCS = 6) than the survivors (mean GCS = 9) with a statistically significant difference (P = 0.0024). The APACHE-II and the SAPS-II of the nonsurviving patients were higher than those of the survivors (respectively 20.4 ± 6.8 and 31.2 ± 13.6 for nonsurvivors vs. 15.7 ± 5.4 and 22.7 ± 10.3 for survivors) with a statistically significant difference (P = 0.0032 for APACHE-II and P = 0.0045 for SAPS-II). Multivariate analysis: APACHE-II was superior for predicting IHM (AUROC = 0.92).

Conclusion:

The APACHE-II is an interesting tool to predict IHM of head injury patients. This is particularly relevant in Morocco, where TBI is a greater public health problem than in many other countries.

Comparison of the predictive power of the LODS and APACHE II scoring systems in a neurological intensive care unit

OBJECTIVE

A prospective study to compare the power of the Logistic Organ Dysfunction System (LODS) and the Acute Physiology and Chronic Health Evaluation II (APACHE II) scoring systems to predict survival, in patients admitted to the neurological intensive care unit (NICU).

METHODS

Clinical data from 521 consecutive NICU patients were collected during the first 24 h of admission and were used to compare the predictive power of both scoring systems.

RESULTS

The observed mortality rate was 10.0% compared with predicted mortality rates of 7.2% and 4.8% according to LODS and APACHE II, respectively. Both scoring systems had excellent discrimination but LODS had superior calibration.

CONCLUSION

The LODS scoring system was more stable than the APACHE II scoring system in the NICU setting.

Prediction of prognosis in acute paraquat poisoning using severity scoring system in emergency department

OBJECTIVE

The aim of this study was to validate and compare the performance of serum paraquat level, severity index of paraquat poisoning (SIPP), Acute Physiology And Chronic Health Evaluation II (APACHE II), modified Simplified Acute Physiology Score II (MSAPS II), and modified Expanded Simplified Acute Physiology Score II (MSAPS IIe) calculated immediately after arrival on emergency department (ED) for assessing the mortality of acute paraquat poisoning.

METHODS

A retrospective study design was employed with the main outcome measure being mortality from year 2001 to 2010. MSAPS II and MSAPS IIe were employed in that assessment of the 24-hour urine output were not included. The performance of APACHE II, MSAPS II, MSAPS IIe, serum paraquat level and SIPP for prediction of mortality in acute paraquat poisoning were compared.

RESULTS

A total of 102 patients were enrolled in the study. The area under the ROC curve for APACHE II (0.800) was statistically lower than those for MSAPS II, MSAPS IIe, SIPP and serum paraquat (0.879, 0.893, 0.924,and 0.951, respectively). The Hosmer-Lemeshow goodness-of-fit test C statistic revealed that APACHE II, MSAPS II, MSAPS IIe and serum paraquat level showed good calibrations (chi-square 8.477 and p = 0.388, chi-square 4.614 and p = 0.798, chi-squared 5.301 and p = 0.725, chi-squared 1.009 and p = 0.985 respectively), but poor calibration for SIPP (chi-square 21.293 and p = 0.006).

CONCLUSION

Serum paraquat level is still the most reliable prognosis factor in acute paraquat poisoning. But MSAPS II or MSAPS IIe calculated immediately after arrival on ED may be helpful to predict mortality in acute paraquat poisoning especially when hospital has no facility to measure serum paraquat level.

The motor component does not convey all the mortality prediction capacity of the Glasgow Coma Scale in trauma patients

PURPOSE

We tested the hypothesis that the motor component of the Glasgow Coma Scale (GCS) conveys most of the predictive information of triage scores (Triage Revised Trauma Score [T-RTS] and the Mechanism, GCS, Age, arterial Pressure score [MGAP]) in trauma patients.

METHOD

We conducted a multicenter prospective observational study and evaluated 1690 trauma patients in 14 centers. We compared the GCS, T-RTS, MGAP, and Trauma Related Injury Severity Score (reference standard) using the full GCS or its motor component only using logistic regression model, area under the receiver operating characteristic curve, and reclassification technique.

RESULTS

Although some changes were noted for the GCS itself and the Trauma Related Injury Severity Score, no significant change was observed using the motor component only for T-RTS and MGAP when considering (1) the odds ratio of variables included in the logistic model as well as their discrimination and calibration characteristics, (2) the area under the receiver operating characteristic curve (0.827 ± 0.014 vs 0.831 ± 0.014, P = .31 and 0.863 ± 0.011 vs 0.859 ± 0.012, P = .23, respectively), and (3) the reclassification technique. Although the mortality rate remained less than the predetermined threshold of 5% in the low-risk stratum, it slightly increased for MGAP (from 1.9% to 3.9%, P = .048).

CONCLUSION

The use of the motor component only of the GCS did not change the global performance of triage scores in trauma patients. However, because a subtle increase in mortality rate was observed in the low-risk stratum for MGAP, replacing the GCS by its motor component may not be recommended in every situation.

150 years of treating severe traumatic brain injury: a systematic review of progress in mortality

Considerable effort and resources have been devoted to preserving life in patients with severe closed traumatic brain injury (TBI). We sought to identify temporal trends in mortality rates of these patients from the late 1800s to the present. We searched the literature for articles on severe TBI, abstracting numbers of patients studied, numbers of deaths, and years of patient entry. Mortality rates were calculated for each study, and meta-regression was used to pool data and to test for significant temporal trends. We reviewed 207 case series comprising more than 140,000 cases of severe closed TBI admitted to hospital over a span of almost 150 years. Since the late 1800s mortality has fallen by almost 50%. However, the rate has varied considerably among the four epochs chosen. Between 1885 and 1930, mortality decreased at a rate of 3% per decade. From 1970 to 1990, mortality declined at a rate of 9% per decade. Both changes are significant. There was no observed improvement in mortality between 1930 and 1970, nor is progress evident since 1990. The authors discuss possible reasons for the apparently intermittent progress in TBI survival over time.

Combining coma score and serum biomarker levels to predict unfavorable outcome following childhood brain trauma

This study aims to determine if pairing the Glasgow Coma Scale (GCS) with serum biomarker levels may achieve higher outcome predictive values than using either the GCS or biomarker levels alone in childhood brain trauma. Twenty-eight critically ill children with isolated accidental brain trauma were studied in a prospective observational study. The GCS was recorded at various time points post injury. Enzyme-linked immunosorbent assay (ELISA) was used to quantify eight different serum biomarker levels (S100b, NSE, IL-6, IL-8, IL-10, L-selectin, SICAM, and endothelin) on day 1 post injury. The Glasgow Outcome Score (GOS) was used to assess global outcome at 6 months post injury. Outcome predictive values of the GCS, individual biomarker levels, and paired combinations of the GCS and biomarkers were compared using receiver operator characteristic (ROC) curve analysis and its multivariate extension, multivariate ROC curve (MultiROC). When using either the GCS or individual biomarker levels alone to predict unfavorable outcome, only the PICU discharge summated GCS achieved an area under the ROC curve (AUC) of more than 0.95. This high degree of outcome predictability was also achieved by pairing the GCS with a single biomarker level. The most pronounced improvement in outcome prediction was observed by pairing the post-resuscitation summated GCS with the day-1 serum IL-8 level, which increased the AUC from 0.78 to 0.98 and the sensitivity and specificity from 75% to 100% and 96% respectively. Paired combinations of the GCS and serum biomarker levels greatly enhanced the accuracy of post-traumatic unfavorable outcome prediction than may be achieved using either the GCS or individual biomarker levels alone.

Relationship of aggressive monitoring and treatment to improved outcomes in severe traumatic brain injury

OBJECT

Despite being common practice for decades and being recommended by national guidelines, aggressive monitoring and treatment of patients with severe traumatic brain injury (TBI) have not been supported by convincing evidence.

METHODS

The authors reviewed trials and case series reported after 1970 in which patients were treated for severe closed TBI, and mortality rates and favorable outcomes at 6 months after injury were analyzed. The patient groups were divided into those with and without intracranial pressure (ICP) monitoring and intensive therapy, and the authors performed a meta-analysis to assess the effects of treatment intensity on outcome.

RESULTS

Although the mortality rate fell during the years reviewed, it was consistently approximately 12% lower among patients in the intense treatment group (p < 0.001). Favorable outcomes did not change significantly over time, and were 6% higher among the aggressively treated patients (p = 0.0105).

CONCLUSIONS

Aggressive ICP monitoring and treatment of patients with severe TBI is associated with a statistically significant improvement in outcome. This improvement occurs independently of temporal effects.

Good mortality prediction by Glasgow Coma Scale for neurosurgical patients

BACKGROUND

How to effectively use the finite resources of an intensive care unit (ICU) for neurosurgical patients is a critical decision-making process. Mortality prediction models are effective tools for allocating facilities. This study intended to distinguish the prediction power of the Acute Physiology and Chronic Health Evaluation II (APACHE II), the Simplified Acute Physiology Score II (SAPS II), and the Glasgow Coma Scale (GCS) for neurosurgical patients.

METHODS

According to the definitions of the APACHE II, this study recorded both APACHE II and SAPS II scores of 154 neurosurgical patients in the ICU of a 600-bed general hospital. Linear regression models of GCS (GCS-mr) were constructed. The t test, receiver operating characteristic (ROC) curve and Wilcoxon signed rank test were used as the statistical evaluation methods.

RESULTS

There were 50 (32.5%) females and 104 (67.5%) males in this study. Among them, 108 patients survived and 46 patients died. The areas under the ROC curves (AUC) of SAPS II and APACHE II were 0.872 and 0.846, respectively. The AUC of GCS-mr was 0.866, and the R(2) was 0.389. The evaluation powers of SAPS II, GCS-mr and APACHE II were the same (p > 0.05). Patients with GCS <or= 5 or motor component of GCS (GCS-M) <or= 3 had a higher probability of mortality than patients with GCS > 5 or GCS-M > 3 (p < 0.01).

CONCLUSION

The predictive powers of SAPS II, APACHE II and GCS-mr were the same. The GCS-mr is more convenient for predicting mortality in neurosurgical patients. Both GCS <or= 5 and GCS-M <or= 3 are good indicators of mortality in these patients.

Acute Physiology and Chronic Health Evaluation II and Simplified Acute Physiology Score II in predicting hospital mortality of neurosurgical intensive care unit patients

We study the predictive power of Acute Physiology and Chronic Health Evaluation II (APACHE II) and Simplified Acute Physiology Score II (SAPS II) in neurosurgical intensive care unit (ICU) patients. Retrospective investigation was conducted on 672 consecutive ICU patients during the last 2 yr. Data were collected during the first 24 hours of admission and analyzed to calculate predicted mortality. Mortality predicted by two systems was compared and, multivariate analyses were then performed for subarachnoid hemorrhage (SAH) and traumatic brain injury (TBI) patients. Observed mortality was 24.8% whereas predicted mortalities were 37.7% and 38.4%, according to APACHE II and SAPS II. Calibration curve was close to the line of perfect prediction. SAPS II was not statistically significant according to a Lemeshow-Hosmer test, but slightly favored by area under the curve (AUC). In SAH patients, SAPS II was an independent predictor for mortality. In TBI patients, both systems had independent prognostic implications. Scoring systems are useful in predicting mortality and measuring performance in neurosurgical ICU setting. TBI patients are more affected by systemic insults than SAH patients, and this discrepancy of predicting mortality in each neurosurgical disease prompts us to develop a more specific scoring system targeted to cerebral dysfunction.

Clinical survey of sedation and analgesia procedures in intensive care units

BACKGROUND

The proper use of sedation and analgesia in the intensive care unit (ICU) minimizes its physical and psychological impact. Otherwise, patients can suffer from recall, nightmares, and depression after discharge. We investigated the sedatives, analgesics, and muscle relaxants used in the ICU.

METHODS

We visited 79 ICUs in 52 training hospitals and noted the use of sedatives, analgesics, and muscle relaxants from July, 2007, to December, 2007, using a 5-item questionnaire with 57 sub-questions. The survey evaluated the ICU system administration of analgesics and muscle relaxants.

RESULTS

Most ICU management is done by the anesthesiology department (55%). Most have resident doctors (63.3%) and an ICU committee (60.8%) in charge of the ICU, as well as a special ICU chart (88.6%) and scoring system (65.8%). Most hospitals have a consulting system (94.9%). The standard ICU analgesics are fentanyl (65.8%), NSAIDs (53.2%), and morphine (48.1%).

CONCLUSIONS

Adequate sedation is difficult to achieve in the ICU, but is important for patient comfort and to reduce ICU stay duration. Awareness of patient status and appropriate drug/protocol use are therefore important.

Predicting outcome after traumatic brain injury: development and international validation of prognostic scores based on admission characteristics

BACKGROUND

Traumatic brain injury (TBI) is a leading cause of death and disability. A reliable prediction of outcome on admission is of great clinical relevance. We aimed to develop prognostic models with readily available traditional and novel predictors.

METHODS AND FINDINGS

Prospectively collected individual patient data were analyzed from 11 studies. We considered predictors available at admission in logistic regression models to predict mortality and unfavorable outcome according to the Glasgow Outcome Scale at 6 mo after injury. Prognostic models were developed in 8,509 patients with severe or moderate TBI, with cross-validation by omission of each of the 11 studies in turn. External validation was on 6,681 patients from the recent Medical Research Council Corticosteroid Randomisation after Significant Head Injury (MRC CRASH) trial. We found that the strongest predictors of outcome were age, motor score, pupillary reactivity, and CT characteristics, including the presence of traumatic subarachnoid hemorrhage. A prognostic model that combined age, motor score, and pupillary reactivity had an area under the receiver operating characteristic curve (AUC) between 0.66 and 0.84 at cross-validation. This performance could be improved (AUC increased by approximately 0.05) by considering CT characteristics, secondary insults (hypotension and hypoxia), and laboratory parameters (glucose and hemoglobin). External validation confirmed that the discriminative ability of the model was adequate (AUC 0.80). Outcomes were systematically worse than predicted, but less so in 1,588 patients who were from high-income countries in the CRASH trial.

CONCLUSIONS

Prognostic models using baseline characteristics provide adequate discrimination between patients with good and poor 6 mo outcomes after TBI, especially if CT and laboratory findings are considered in addition to traditional predictors. The model predictions may support clinical practice and research, including the design and analysis of randomized controlled trials.

Pediatric illness severity measures predict delirium in a pediatric intensive care unit

CONTEXT

Delirium in children is a serious but understudied neuropsychiatric disorder. So there is little to guide the clinician in terms of identifying those at risk.

OBJECTIVE

To study, in a pediatric intensive care unit (PICU), the predictive power of widely used generic pediatric mortality scoring systems in relation to the occurrence of pediatric delirium (PD).

DESIGN AND METHODS

Four-year prospective observational study, 2002-2005. Predictors used were the Pediatric Index of Mortality (PIM) and Pediatric Risk of Mortality (PRISM II).

SETTING

A tertiary 8-bed PICU in The Netherlands.

PATIENTS

877 critically ill children who were acutely, nonelectively, and consecutively admitted.

MAIN OUTCOME MEASURE

Pediatric delirium.

MAIN RESULTS

Out of 877 children with mean age 4.4 yrs, 40 were diagnosed with PD (Cumulative incidence: 4.5%), 85% of whom (versus 40% with nondelirium) were mechanically ventilated. The area under the curve was 0.74 for PRISM II and 0.71 for the PIM, with optimal cut-off points at the 60th centile (PRISM: sensitivity: 76%; specificity: 62%; PIM: sensitivity: 82%; specificity: 62%). A PRISM II or PIM score above the 60th centile was strongly associated with later PD in terms of relative risk (PRISM II: risk ratio = 4.9; 95% confidence interval: 2.3-10.1; PIM: RR = 6.7; 95% confidence interval: 3.0-15.0). Given the low incidence of PD, values for positive predictive value were lower (PRISM II: 8.3%; PIM: 8.9%, rising to, respectively, 10.1% and 10.6% in mechanically ventilated patients) and values for negative predictive value were higher (PRISM II: 98.3%; PIM: 98.7%).

LIMITATIONS

Given the relatively low incidence of delirium, a low detection rate biased toward the most severe cases cannot be excluded.

CONCLUSIONS

Given the fact that PIM and PRISM II are widely used mortality scoring instruments, prospective associations with PD suggest additional value for ruling in, or out, patients at risk of PD.

Role of Plasma DNA as a Predictive Marker of Fatal Outcome following Severe Head Injury in Males

The prediction of outcome is one of the major problems associated with traumatic brain injury. Recently, investigations have been performed on the potential use of circulating cell-free DNA in plasma for clinical diagnosis and prognosis of a variety of conditions. In this study, we investigated DNA plasma concentrations after severe traumatic brain injury (TBI) and its correlation with primary outcome. We studied 41 male victims of TBI, with isolated severe TBI or severe TBI with associated exracranial injuries. Control samples were obtained from 13 healthy male volunteers. Plasma DNA was measured by a real-time PCR assay for the beta-globin gene. The mean time for first sampling (study entry) was 11.7 +/- 5.2 h after injury; subsequent DNA determinations were performed 24 h after study entry. Mean plasma DNA concentrations were significantly increased in TBI patients (366,485 and 131,708 kilogenomes-equivalents/L, at study entry and 24 h later, respectively) compared with the control group (3031 kilogenomes-equivalents/L). Additionally, a significant correlation between higher plasma DNA concentrations, determined 24 h after study entry, and fatal outcome was observed. However, at second sampling, there was no significant correlation between plasma DNA concentrations and the presence of associated extracranial injuries. High plasma DNA concentrations at second sampling time predicted fatal outcome with a sensitivity of 67% and specificity of 76%, considering a cut-off value of 77,883 kilogenomes-equivalents/L. Thus, this study showed that severe TBI is associated with elevated DNA plasma levels and suggests that persistent DNA elevations correlate with mortality.

Identifying traumatic brain injury in patients with isolated head trauma: are arterial lactate and base deficit as helpful as in polytrauma?

BACKGROUND

Increase in lactate (LAC) within the central nervous system after head trauma is an established marker of traumatic brain injury (TBI).

OBJECTIVE

To investigate the utility of arterial base deficit (BD) and LAC in identifying TBI in patients with isolated head injury (IHI).

MATERIALS AND METHODS

TBI was defined as Glasgow Coma Scale < or =8, head Abbreviated Injury Severity Score >2 or brain haematoma on CT scan. Patients were divided into two groups: IHI with and without TBI. Data were reported as means (SDs). 131 patients with IHI were studied (mean (SD) age 39 (19) years, 78% male).

RESULTS

17% of the patients sustained TBI. The mean differences for arterial BD (0.65 mmol/l, 95% CI -0.8 to 2.1) and LAC (0.34 mmol/l, 95% CI -0.7 to 1.4) in patients with and without TBI were not significant. Analysis of receiver operating characteristic curves confirmed that arterial BD and LAC were unable to detect TBI in patients with IHI.

CONCLUSION

Arterial BD and LAC are poor predictors of TBI in isolated head trauma.

Trampoline injuries

OBJECTIVE

To describe the mechanism, location and types of injury for all patients treated for trampoline-associated injuries at St Olav's University Hospital, Trondheim, Norway, from March 2001to October 2004.

MATERIALS AND METHODS

Patients were identified from a National Injury Surveillance System. All patients were asked to complete a standard questionnaire at their first visit at the hospital. Most data were recorded prospectively, but data on the mechanism of injury, the number of participants on the trampoline at the time of injury, adult supervision and whether the activity occurred at school or in another organised setting were collected retrospectively.

RESULTS

A total of 556 patients, 56% male and 44% female, were included. The mean age of patients was 11 (range 1-62) years. 77% of the injuries occurred on the body of the trampoline, including falls on to the mat, collisions with another jumper, falls on to the frame or the springs, and performing a somersault, whereas 22% of the people fell off the trampoline. In 74% of the cases, more than two people were on the trampoline, with as many as nine trampolinists noted at the time of injury. For children <11 years, 22% had adult supervision when the injury occurred. The most common types of injuries were fractures (36%) and injury to ligaments (36%). Injuries to the extremities predominated (79%), and the lower extremities were the most commonly injured part of the body (44%). A ligament injury in the ankle was the most often reported diagnosis (20%), followed by an overstretching of ligaments in the neck (8%) and a fracture of the elbow (7%). Regarding cervical injuries, two patients had cervical fractures and one patient had an atlantoaxial subluxation. Three patients with fractures in the elbow region reported an ulnar nerve neuropathy. 13% of the patients were hospitalised for a mean of 2.2 days.

CONCLUSION

Trampolining can cause serious injuries, especially in the neck and elbow areas of young children. The use of a trampoline is a high-risk activity. However, a ban is not supported. The importance of having safety guidelines for the use of trampolines is emphasised.

Neurologic and non-neurologic predictors of mortality in ischemic stroke patients admitted to the intensive care unit

BACKGROUND/PURPOSE

Patients with severe strokes may have different associated medical comorbidities from those with mild strokes. This study evaluated the neurologic and non-neurologic medical predictors of mortality in patients with severe cerebral infarction in the acute stage.

METHODS

Patients admitted to a neurologic intensive care unit (ICU) due to cerebral infarction were included. Neurologic and non-neurologic predictors for in-unit mortality were determined by logistic regression analyses. Two models using (A) neurologic factors and (B) combined neurologic and non-neurologic factors as mortality predictors were developed. The performance of the models in predicting overall, neurologic and non-neurologic mortalities was compared by areas under the receiver-operating characteristic curves (AUC) of the derived regressive equations.

RESULTS

Of 231 patients with cerebral infarction admitted to the ICU, 34 (14.7%) died during ICU stay. Conscious state and acute physiologic abnormalities were significant predictors of mortality. The length of ICU stay in patients with non-neurologic mortality was longer than in those with neurologic mortality (p = 0.044). The AUC of Model B was larger than that of Model A in predicting overall (0.768 +/- 0.045 vs. 0.863 +/- 0.033, p = 0.005) and non-neurologic mortalities (0.570 +/- 0.073 vs. 0.707 +/- 0.074, p = 0.009), while there was no difference in predicting death from neurologic causes (0.858 +/- 0.044 vs. 0.880 +/- 0.032, p = 0.217).

CONCLUSION

Impaired consciousness and acute physiologic abnormalities are independent predictors of mortality for severe ischemic stroke during the acute stage. Neurologic factors predict early mortality from intrinsic cerebral dysfunction, while non-neurologic factors, especially the associated physiologic abnormalities, predict late mortality from medical complications.

Hypernatremia in the neurologic intensive care unit: how high is too high?

Hypernatremia is associated with increased mortality in hospitalized patients and in medical/surgical intensive care units. This relationship has not been studied in neurologic/neurosurgical intensive care units (NNICUs), where hypernatremia is often a component of treatment of cerebral edema. We performed a retrospective analysis of prospectively collected data in patients admitted to the NNICU over a 6.5-year period. Hypernatremia (serum sodium >150 mEq/L) was seen in 339 patients (7.9%) and was more common (24.3%) in patients who were treated with mannitol. Hypernatremic patients had a lower median admission Glasgow Coma Scale score (8 vs 14, P < .001), higher initial Acute Physiology and Chronic Health Evaluation II probability of death (34.9% vs 19.1%, P < .001), higher incidence of mechanical ventilation (80.5% vs 41.1.5%, P < .001), higher mortality (30.1% vs 10.2%, P < .001), and higher incidence of renal failure (10.3% vs 0.9%, P < .001). Mortality increased with increasing hypernatremia; however, only severe hypernatremia (serum sodium >160 mEq/L) was independently associated with increased mortality. Other factors independently associated with mortality were age, mechanical ventilation, initial Acute Physiology and Chronic Health Evaluation II probability of death or low admission Glasgow Coma Scale score, and a diagnosis of cerebrovascular disease. In conclusion, hypernatremia is common in the NNICU, more so in patients treated with mannitol. In this population, severe (but not mild or moderate) hypernatremia is independently associated with increased mortality.

Predictors of Mortality and Management of Patients with Traumatic Inferior Vena Cava Injuries

The aim of this study was to determine factors that predict mortality in patients with traumatic inferior vena cava (IVC) injuries and to review the current management of this lethal injury. A 7-year retrospective review of all trauma patients with IVC injuries was performed. Factors associated with mortality were assessed by univariate analysis. Significant variables were included in a multivariate regression analysis model to determine independent predictors of mortality. Statistical significance was determined at P ≤ 0.05. A literature review of traumatic IVC injuries was performed and compared with our institutional experience. Thirty-six IVC injuries were identified (mortality, 56%; mechanisms of injury, 28% blunt and 72% penetrating). There was no difference in mortality based on mechanism of injury. Injuries with closer proximity to the heart were associated with increased mortality (P < 0.001). Univariate analysis demonstrated that non-survivors had a higher injury severity scale, a lower systolic blood pressure in the emergency department, a lower Glasgow coma score (GCS), and were more likely to have thoracotomies performed in the emergency department or operating room. Multivariate analysis revealed that only GCS (P = 0.03) was an independent predictor of mortality. Typical factors predicting mortality were identified in our cohort of patients, including GCS. The mechanism of injury is not associated with survival outcome, although mortality is higher with injuries more proximal to the heart. The form of management by IVC level is reviewed in our patient population and compared with the literature.

Use of multiple antimicrobial drugs by clinical patients: a prognostic index of hospital mortality?

PURPOSE

To quantify the use of multiple and prolonged antibiotics and anti-infective drug therapy in clinical patients in a 144-bed hospital.

METHODS

Adult patients (2,790 patients with 3,706 admissions over a period of 19 months) were investigated prospectively regarding treatment with anti-infective agents. The mean age was 57.4 (range, 18.8-97 years), and 54.3% were females (2012).

RESULTS

Hospital stay was 5.5 (6.7 days (range, 2-226 days), with duration up to 10 days for 91.9% of the subjects. Antibiotics or other agents were administered to 1,166 subjects (31.5%), 325 (8.8%) required assistance in the ICU, and a total of 141 (3.8%) died. The association between anti-infective drug therapy and hospital mortality was statistically significant (P < .01) with a strong linear correlation (r = 0.902, P = .014). The quantity of prescribed antimicrobial drugs, age, and need for ICU assistance were independent variables for death by logistic regression analysis. The odds ratio for anti-infective drug therapy was 1.341 (1.043 to 1.725); for age, 1.042 ( 1.026 to 1.058); and for stay in the ICU, 11.226 ( 6.648 to 18.957).

CONCLUSIONS

1) The use of large amounts of anti-infective drug therapy was associated with higher hospital mortality according to both univariate and logistic regression analysis; 2) The adverse influence was less marked than that of hospitalization in ICU but of a similar order of magnitude as age; 3) Further studies should elucidate whether infectious foci, noninfectious morbidity, or drug effects underlie this undesirable concurrence.

Case mix, outcomes and comparison of risk prediction models for admissions to adult, general and specialist critical care units for head injury: a secondary analysis of the ICNARC Case Mix Programme Database

INTRODUCTION

This report describes the case mix and outcome (mortality, intensive care unit (ICU) and hospital length of stay) for admissions to ICU for head injury and evaluates the predictive ability of five risk adjustment models.

METHODS

A secondary analysis was conducted of data from the Intensive Care National Audit and Research Centre (ICNARC) Case Mix Programme, a high quality clinical database, of 374,594 admissions to 171 adult critical care units across England, Wales and Northern Ireland from 1995 to 2005. The discrimination and calibration of five risk prediction models, SAPS II, MPM II, APACHE II and III and the ICNARC model plus raw Glasgow Coma Score (GCS) were compared.

RESULTS

There were 11,021 admissions following traumatic brain injury identified (3% of all database admissions). Mortality in ICU was 23.5% and in-hospital was 33.5%. Median ICU and hospital lengths of stay were 3.2 and 24 days, respectively, for survivors and 1.6 and 3 days, respectively, for non-survivors. The ICNARC model, SAPS II and MPM II discriminated best between survivors and non-survivors and were better calibrated than raw GCS, APACHE II and III in 5,393 patients eligible for all models.

CONCLUSION

Traumatic brain injury requiring intensive care has a high mortality rate. Non-survivors have a short length of ICU and hospital stay. APACHE II and III have poorer calibration and discrimination than SAPS II, MPM II and the ICNARC model in traumatic brain injury; however, no model had perfect calibration.