Context:Acute Physiology and Chronic Health Evaluation II (APACHE II) and sequential organ failure assessment (SOFA) are of the most validated and prevalent general scoring systems over the world.Aims:The aim of the current study was to evaluate APACHE II and SOFA ability in predicting the outcomes (survivors, nonsurvivors) in surgical and medical Intensive Care Unit (ICU).Setting and Design:This was an observational and prospective study of 300 consecutive patients admitted in surgical and medical ICU during a 6-month period.Materials and Methods:APACHE II and SOFA scores and demographic characteristics were recorded for each patient separately in the first admission 24 h.Statistical Analysis Used:Receiver operator characteristic (ROC) curves, Hosmer–Lemeshow test, and logistic regression were used in the statistical analysis (95% confidence interval).Results:Data analysis showed a significant statistical difference in APACHE II and SOFA scores between survivor and nonsurvivor patients (P < 0.0001, P = 0.001; respectively). The discrimination power was acceptable for APACHE II and poor for SOFA (area under ROC [AUC] curve: 73.7% (standard error [SE]: 3.2%), 63.4% [SE: 3.6%]; respectively). The acceptable calibration was seen just for SOFA (χ2 = 11.018, P = 0.051).Conclusions:Both APACHE II and SOFA showed good predictive accuracy for results in surgical and medical ICUs; however, the SOFA is the choice to select, because of being simpler and easier to record data.

Context: The Glasgow Coma Scale (GCS) is the most commonly used scale, and Full Outline of Unresponsiveness (FOUR) score is new validated coma scale as an alternative to GCS in the evaluation of the level of consciousness. Aim: The aim of the current study was to evaluate FOUR score and GCS ability in predicting the outcomes (Survivors, nonsurvivors) in Medical Intensive Care Unit (MICU). Setting and Design: This was an observational and prospective study of 300 consecutive patients admitted to the MICU during a 14 months’ period. Materials and Methods: FOUR score, GCS score, and demographic characteristics of all patients were recorded in the first admission 24 h. Statistical Analysis Used: A receiver operator characteristic (ROC) curve, Hosmer–Lemeshow test, and Logistic regression were used in the statistical analysis (95% confidence interval). Results: Data analysis showed a significant statistical difference in FOUR score and GCS score between survivors and nonsurvivors ( P < 0.0001, P < 0.0001; respectively). The discrimination power was good for both FOUR score and GCS (area under ROC curve: 87.3% (standard error [SE]: 2.1%), 82.6% [SE: 2.3%]; respectively). The acceptable calibration was seen just for FOUR score (χ 2 = 8.059, P = 0.428). Conclusions: Both FOUR score and GCS are valuable scales for predicting outcomes in patients are admitted to the MICU; however, the FOUR score showed better discrimination and calibration than GCS, so it is superior to GCS in predicting outcomes in this patients population.

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Background:The pediatric risk of mortality (PRISM III), pediatric index of mortality (PIM3), and pediatric logistic organ dysfunction (PELOD-2) are of the most used predictive models in predicting the risk of mortality in the pediatric intensive care unit (PICU). Objectives: The current study aimed at comparing the predictive ability of these three modes in medical/surgical ICUs (MICU/SICU). Methods:A total of 90 consecutive patients, aged ≤ 18 years, admitted to MICUs or SICUs were enrolled in the current observational, prospective study. The PRISM III, PIM3, and PELOD-2 as well as demographic characteristics of the subjects were recorded on admission. A receive operator characteristic (ROC) curve, logistic regression, and the Hosmer-Lemeshow goodness-of-fit test were used for statistical analyses [95% confidence interval (CI)]. Results: Data analysis showed a significant difference in PRISM III, PIM3, and PELOD-2 scores between survivors and nonsurvivors (P < 0.001, P < 0.001, P < 0.001, respectively). The discrimination power was moderate for PRISM III (area under ROC curve (AUC): 77.3%; standard error (SE): 6.0%), and good for PIM3 and PELOD-2 (AUC: 82.4%, SE: 5.5% and AUC: 80.3%, SE: 4.9%, respectively). All the three models were well calibrated (χ 2 = 4.73, P = 0.79; χ 2 = 3.09, P = 0.93; and χ 2 = 5.01, P = 0.66, respectively).Conclusions: PRISM III, PIM3, and PELOD-2 had good performance in predicting outcomes in children admitted to MICUs or SICUs. Further studies on different ICUs may provide more conclusive results with greater generalization of the validity of these predictive models.

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.

A bstract Background Advanced age is one of the key risk factors for mortality and morbidity in intensive care units. The full outline of unresponsiveness (FOUR) score has been developed and introduced to address the limitations of the Glasgow Coma Scale (GCS). The current study aimed to evaluate the ability of the FOUR score in predicting the outcomes (survivors, nonsurvivors). Materials and methods This observational study of 168 consecutive elderly patients admitted to medical intensive care during the 14 months carried out prospectively. FOUR score in the 24, 48, and 72 hours of admission, and demographic characteristics of all elderly patients were calculated, then recorded. The receiver operating characteristic (ROC) curve, logistic regression, and Hosmer-Lemeshow test were used (95% confidence interval) for statistical analysis. Results FOUR scores in 24, 48, and 72 hours between survivors and nonsurvivors ( p <0.0001, p <0.0001, and p <0.0001, respectively) were statistically different. The discrimination power of FOUR score 24 hours of admission was excellent [area under ROC (AUC): 85.7% [standard error (SE)]: 2.8%]; it was acceptable for 48 and 72 hours of admission [AUC: 76.3% (SE: 3.6%), AUC: 75/0% (SE: 3.8%), respectively]. The FOUR score of 24 and 48 hours (x 2 = 10.06, p = 0.261, x 2 = 6.82, p = 0.448, respectively) showed acceptable calibration. Conclusions The FOUR score is a suitable scoring system for prognostication of outcomes in critically ill elderly patients. The FOUR score 24 hours of admission was superior in terms of discrimination power than 48 and 72 hours, but better calibration power belonged to FOUR score 48 hours. How to cite this article Ramazani J, Hosseini M. Prediction of Mortality in the Medical Intensive Care Unit with Serial Full Outline of Unresponsiveness Score in Elderly Patients. Indian J Crit Care Med 2022;26(1):94–99.

Background: Recent data have shown that the proportion of older adult patients admitted to intensive care units is increased and the severity of illness is an independent risk factor associated with mortality. The aim of the current study was to compare the prognostic value of the Glasgow Coma Scale (GCS) and GCS-Age Prognosis (GAP) scores in older adult patients (aged ≥65 years) admitted to Medical Intensive Care Unit (MICU). Methods: This was a prospective study of 168 consecutive older adult patients admitted to medical ICU during a 14-month period. For each patient, the GCS and GAP score in the first 24hours of admission and demographic characteristics were calculated and recorded. For statistical analysis, the logistic regression, Receiver operator characteristic (ROC) curve, and Hosmer-Lemeshow test were used (95% confidence interval). Results: Survivors had a significantly higher GCS and GAP scores in the first 24h of MICU admission compared with nonsurvivors (p<0.001, p<0.001, respectively). The discrimination power of both models was good ((area under curve [AUC]:83.8% (standard error [SE]:3%), AUC: 85.4% (SE: 2.9%), respectively). Based on the Hosmer-Lemeshow goodness of fit test, just GCS had an acceptable calibration (x2=13.18, p=0.068). Conclusions: For older adult patients admitted to the MICU, GCS and GAP scores reliably predict outcomes. Based on AUCs the discrimination power of models was good, but the calibration was acceptable just for GCS, thus the GCS is the better predictive model than GAP and useful in determining the prognosis of older adult patients in MICU.

Background & Objectives: Assessment and predictor systems of outcome have been presented in order to estimate the risk of anesthesia in cardiac surgery. Regarding to the importance of outcome prediction we aimed to determine how CARE-Score could predict the outcome (morbidity and mortality) of patients in cardiac surgery Material &Methods: This is a descriptive-cohort study that accomplished on 130 cardiac patients in cardiac surgery department in Imam Reza Hospital, Mashhad. The sampling was done in a period 3 months. In order to collection the information, we used the sample selection sheet, demographic sheet, informative sheet, CARE-SCORE and morbidity form. This study aimed to determine the relationship between tested quantitative variables using the Pearson correlation coefficient. 95% confidence (α = 0/05) were considered. Data analysis was performed using spss software version 12. Results: The results showed a significant relationship between CARE-SCORE and morbidity (r = 0.86, p<0.001), CARE-SCORE and hospital stay (r =0.72, p<0.001). The patients with the higher score had more morbidity and lengthen hospital stay. Conclusion: As regards, CARE-SCORE can predict the morbidity after the cardiac surgery simply, we advise the Anesthesiologist to use this predictive model in cardiac surgery units preoperatively.