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

This study examined the efficacy of predicting power for hospital mortality of three different scoring systems in a neurosurgical intensive care unit (NICU). An eight-bed NICU in a 1,270-bed medical centre (Taichung Veterans General Hospital). Two hundred patients with head injury, brain tumour, hypertensive intracerebral haemorrhage, rupture of aneurysm or arteriovenous malformation, or other categories were included in our study in a consecutive period of 14 months. Patients less than 14 years old were not included. On the first day of admission, data were collected from each patient to compute the Acute Physiology and Chronic Health (APACHE) II and III, and Glasgow Coma Scale (GCS) scores. Hospital mortality was defined as when death occurred before discharge from hospital. none. Sensitivity, specificity and correct prediction outcomes were measured by logistic regression in three scoring systems. The Youden index was also obtained. The best cutoff point in each scoring system was determined by logistic regression or by the Youden index. Data obtained by logistic regression were compared by McNemar's test. The differences in Youden index were calculated by the Student's t-test. The area under the Receiver Operating Characteristic (ROC) curve was computed and the area of each scoring system was then compared by the Wilcoxon Mann-Whitney test. The correct prediction of outcome was 85.5% in APACHE III, 77.5% in APACHE II and 75.0% in GCS. The area under the Receiver Operating Characteristic (ROC) curve was 0.892 in APACHE III, 0.826 in APACHE II and 0.868 in GCS. For the prediction of dead patients at the best cutoff point, APACHE III and GCS were better than APACHE II, (both p < 0.01 respectively). For the prediction of alive patients at the best cutoff point, APACHE III was better than GCS and APACHE II (p < 0.01 respectively). The APACHE III system seems to be the most reliable. The results reveal that the APACHE III system is better in predicting power for hospital mortality than either the GCS or APACHE II systems in our NICU patients.

The current prognostic models for mortality and functional outcome after intracerebral hemorrhage (ICH) are not simple enough. To predict the outcome of ICH, a new simple model, ICH index (ICHI), was established and evaluated in this study. Medical records of all cases with ICH in our hospital from January 2008 to August 2009 were reviewed. Multiple linear regression analyses were used to assess the contributions of independent variables to hospital mortality after ICH. Age, serum glucose, white blood cell counts (WBC), and Glasgow Coma Scale (GCS) score were found to be greatly associated with mortality. A formula of ICH index [ICHI = age (years)/10 + glucose (mmol/L) + WBC (10(9) /L) - GCS score] was established. Furthermore, the receiver operating characteristic (ROC) analyses were performed to estimate the predictive value of the ICHI. The model showed an area under the ROC curve (AURC) of 0.923 (95% CI: 0.883-0.963, P < 0.001). The best cut-off value of ICHI for mortality was 18, which gave sensitivity, specificity, and Youden's index of 0.65, 0.95, and 0.60, respectively. The hospital mortality was extremely increased when 18 < ICHI < 28 (mortality 72.0%) and when ICHI ≥ 28 (mortality 100%), in contrast with overall mortality (21.6%). The ICHI can be a simple predictive model and complementary to other prognostic models.

There are numerous prehospital descriptive scoring systems, and it is uncertain whether they are efficient in assessing of the severity of illness and whether they have a prognostic role in the estimation of the illness outcome (in comparison with that of the prognostic scoring system Acute Physiology and Chronic Health Evaluation [APACHE] II). The purpose of the present study was to assess the value of the various scoring systems in predicting outcome in nontraumatic coma patients and to evaluate the importance of mental status measurement in relation to outcome. In a prehospital setting, postintervention values of the Mainz Emergency Evaluation System (MEES) and Glasgow Coma Scale (GCS) were measured for each patient. The APACHE II score was recorded on the day of admission to the hospital. This study was undertaken over a 2-year period (from January 1996 to October 1998), and included 286 consecutive patients (168 men, 118 women) who were hospitalized for nontraumatic coma. Patients younger than 16 years were not included. Their age varied from 16 to 87 years, with mean +/- standard deviation of 51.8 +/- 16.9 years. Sensitivity, specificity and correct prediction of outcome were measured using the chi2 method, with four severity scores. The best cutoff point in each scoring system was determined using the Youden index. The difference in Youden index was calculated using the Z score. For each score, the receiver operating characteristic (ROC) curve was obtained. The difference in ROC was calculated using the Z score. P < 0.05 was considered statistically significant. For prediction of mortality, the best cutoff points were 19 for APACHE II, 18 for MEES and 5 for GCS. The best cutoffs for the Youden index were 0.63 for APACHE II, 0.61 for MEES and 0.65 for GCS. The correct prediction of outcome was achieved in 79.9% for APACHE II, 78.3% for MEES and 81.9% for GCS. The area under the ROC curve (mean +/- standard error) was 0.86 +/- 0.02 for APACHE II, 0.84 +/- 0.06 for MEES and 0.88 +/- 0.03 for GCS. There were no statistically significant differences among APACHE II, MEES and GCS scores in terms of correct prediction of outcome, Youden index or area under ROC curve. APACHE II is not much better than prehospital descriptive scoring systems (MEES and GCS). APACHE II and MEES should not replace GCS in assessment of illness severity or in prediction of mortality in nontraumatic coma. For the assessment of mortality, the GCS score provides the best indicator for these patients (simplicity, less time-consuming and effective in an emergency situation.

P-37 The evaluation of European system for cardiac operative risk evaluation and cardiac surgery scoring system to predict morbidity and mortality in open heart surgery

Comparing logistic models based on modified GCS motor component with other prognostic tools in prediction of mortality: Results of study in 7226 trauma patients

Introduction: Non Invasive Ventilation (NIV) is an effective ventilatory measure in the exacerbation of Chronic Obstructive Pulmonary Disease (COPD). However, the response among some patients is not favourable, leading to a requirement for invasive ventilation. This not only increases hospital stays but is also reported to elevate mortality rates. Therefore, there is a need for a scoring system that predicts NIV failures. The Heart rate, Acidosis, Consciousness, Oxygenation and Respiratory rate (HACOR) score is a promising tool that has demonstrated excellent predictive power in detecting early NIV failure. Aim: To predict NIV failure based on the HACOR score at two hours after the initiation of NIV in patients admitted for acute exacerbation of COPD with type 2 respiratory failure. Materials and Methods: The present hospital-based crosssectional study was conducted in the Department of Pulmonary Medicine, Government Hospital for Chest and Communicable Diseases, Andhra Medical College, Visakhapatnam, Andhra Pradesh, India, from November 2023 to April 2024. A total of 70 patients admitted with acute exacerbation of COPD and type 2 respiratory failure were included. All five variables of the HACOR score were measured two hours after the initiation of NIV to predict early NIV failure. These variables include heart rate, arterial blood pH, Glasgow Coma Scale (GCS) score (consciousness), oxygenation and respiratory rate. After the initiation of NIV, the HACOR score was evaluated at various intervals. The diagnostic accuracy of the HACOR score in predicting NIV failure was recorded, which was the main outcome of the study. Receiver Operating Characteristic (ROC) curve and Youden index were applied. The dependent variables were analysed using the Youden index to obtain the best cut-off value of the HACOR score. Results: Out of 70 patients, 66 (94.3%) were males and 4 (5.7%) were females, with a mean±Standard Deviation (SD) age of 68.5±6.1 years. The study reported a 27.1% (19 patients) NIV failure rate based on the HACOR score measured after two hours of NIV. The total study population was divided into two groups based on the measured HACOR score cut-off value of 5. A total of 33 patients had a score ≥5, while 37 patients had a score &lt;5. Among the group with a score ≥5, 17 (51.5%) patients experienced NIV failure. In the group with a score &lt;5, 2 (5.4%) patients had NIV failure. The area under the ROC curve (AUROC) for the entire population was 0.899 for a score ≥5, with a sensitivity of 89.47% and a specificity of 86.27% in determining the outcome of NIV. Conclusion: The HACOR score is a simple bedside test that can effectively predict early NIV failure with good predictive power. A score ≥5 at two hours after the initiation of NIV therapy can be considered a cut-off value for predicting NIV failure.

Recent Developments in Acute Pancreatitis

Introduction: The commonly used Glasgow Coma Scale (GCS) score for assessing consciousness has several shortcomings, especially in intubated patients. Recently, the Full Outline UnResponsive Score (FOUR) has been validated as an alternative to the GCS due to its additional benefits in evaluating brainstem reflexes and respiratory patterns. The use of the FOUR score can overcome the shortcomings of the GCS and aid in prognosticating patients with altered sensorium. Aim: To compare the FOUR score with GCS score to find a better scoring system for predicting outcomes among children aged 3-12 years with non-traumatic causes of Altered Level Of Consciousness (ALOC) in the hospital. Materials and Methods: A prospective cohort study was conducted on a total of 100 children with ALOC in the Paediatric Intensive Care Unit (PICU) of Gandhi Medical College and Hospital, Telangana, from December 2020 to November 2021. FOUR and GCS assessments were performed simultaneously within two hours of admission. For statistical analysis, continuous variables are expressed as mean±standard deviations. The predictive values of the GCS score and FOUR score were established using the Receiver Operating Characteristic (ROC) curve, by calculating the Area Under the Curve (AUC) with a 95% Confidence Interval (CI). Results: The mean age of the study population was 7.3±5.2 years, and the mean duration of hospital stay was 7.5±6.74 days. In-hospital mortality was 34%, and the survival rate was 66%. The mean FOUR score for in-hospital mortality and survival was 8.47±3.01 and 12.24±1.46, respectively (p-value &lt;0.001). The mean GCS scores were 11.35±1.64 in survivors and 7.45 ±2.63 in non-survivors (p-value &lt;0.001). A FOUR score of &lt;10 was associated with higher mortality than a FOUR score of &gt;10 (p&lt;00.05). The Area Under Curve (AUC) for the FOUR score was 0.862 with a 95% CI (0.774 to 0.95) in the ROC curve (p-value &lt;0.001), and for the GCS score, the AUC was 0.822 with a 95% CI (0.723 to 0.92) and p-value &lt;0.001. The FOUR Score has a higher AUC than the GCS in the ROC curve, indicating that the FOUR Score has better discrimination than the GCS in outcome assessment. Conclusion: FOUR score and GCS score were comparable for predicting outcomes in children with ALOC. However, the FOUR score showed better discrimination than the GCS; hence, the FOUR score can be used as an alternative tool to the GCS for prognosis.

Background: The Rapid Acute Physiology Score (RAPS) and Rapid Emergency Medicine Score (REMS) are risk adjustment methods for emergency medical admissions developed for use in audit, research, and clinical practice....

BackgroundOutcome prediction in critically ill patients under invasive ventilation remains extremely challenging. The driving pressure (ΔP) and the mechanical power of ventilation (MP) are associated with patient-centered outcomes like mortality and duration of ventilation. The objective of this study was to assess the predictive validity for mortality of the ΔP and the MP at 24 h after start of invasive ventilation.MethodsThis is a post hoc analysis of an observational study in intensive care unit patients, restricted to critically ill patients receiving invasive ventilation for at least 24 h. The two exposures of interest were the modified ΔP and the MP at 24 h after start of invasive ventilation. The primary outcome was 90-day mortality; secondary outcomes were ICU and hospital mortality. The predictive validity was measured as incremental 90-day mortality beyond that predicted by the Acute Physiology, Age and Chronic Health Evaluation (APACHE) IV score and the Simplified Acute Physiology Score (SAPS) II.ResultsThe analysis included 839 patients with a 90-day mortality of 42%. The median modified ΔP at 24 h was 15 [interquartile range 12 to 19] cm H2O; the median MP at 24 h was 206 [interquartile range 145 to 298] 10−3 J/min/kg predicted body weight (PBW). Both parameters were associated with 90-day mortality (odds ratio (OR) for 1 cm H2O increase in the modified ΔP, 1.05 [95% confidence interval (CI) 1.03 to 1.08]; P < 0.001; OR for 100 10−3 J/min/kg PBW increase in the MP, 1.20 [95% CI 1.09 to 1.33]; P < 0.001). Area under the ROC for 90-day mortality of the modified ΔP and the MP were 0.70 [95% CI 0.66 to 0.74] and 0.69 [95% CI 0.65 to 0.73], which was neither different from that of the APACHE IV score nor that of the SAPS II.ConclusionsIn adult patients under invasive ventilation, the modified ΔP and the MP at 24 h are associated with 90 day mortality. Neither the modified ΔP nor the MP at 24 h has predictive validity beyond the APACHE IV score and the SAPS II.

The Conduct and Reporting of Meta-Analyses of Studies of Diagnostic Tests, and a Consideration of ROC Curves: Answers to the January 2010 Journal Club Questions

BACKGROUND:An acute ST-elevation myocardial infarction (STEMI) is a medical event characterized by transmural myocardial ischemia that leads to myocardial injury or necrosis. This study was undertaken to develop, evaluate, and compare models for assessing the risk of hospital mortality in patients with acute myocardial infarction. METHODS:The study made use of data from the Yazd Cardiovascular Diseases Registry (YCDR), which is a cohort study of inpatient records of ischemic heart disease in Yazd province, Iran. A total of 1,861 patients who had experienced a STEMI were included in the analysis. Decision tree analysis was conducted using R software with the rpart package. Additionally, to analyze the data and adjust for any confounding variables, logistic regression was performed using the glm2 package. To compare the effectiveness of the two models, accuracy measures were used, and the Receiver Operating Characteristic (ROC) curve was applied.RESULTS:In this study, three clinical, laboratory, and clinical-laboratory models were created. In a clinical-laboratory model, variables such as blood sugar (BS), triglycerides, HDL cholesterol, peak myocardial band (MBpick), CVA history, and low ejection fraction (EF) were found to increase the risk of in-hospital mortality in patients with ST-elevation myocardial infarction (STEMI). Conversely, higher levels of hemoglobin, low HDL-C, and previous myocardial infarction (MI) were associated with a protective effect against the risk of in-hospital mortality from acute myocardial infarction.The performance of the models in terms of Receiver Operating Characteristic (ROC) curve was 86.5%, 79.5%, and 90.2% for logistic regression model in three different models: clinical, laboratory, and combined clinical-laboratory. The accuracy of these models was calculated to be 88.3%, 81.3%, and 93%, respectively. Important variables influencing the prediction of in-hospital mortality in STEMI patients included Killip class, triglycerides, blood sugar, creatinine levels, the need for treatment due to ventricular fibrillation or ventricular tachycardia (VF/VT), age, and hemoglobin (HB). In the ROC curve analysis of the decision tree algorithm across the clinical, laboratory, and combined clinical-laboratory models, the performance levels were 74.6%, 69.8%, and 81.7%, respectively. The accuracy of the decision tree was 93.0%, 92.5%, and 95.8%.CONCLUSION:The findings of this study indicated that the decision tree algorithm had higher accuracy across all three models: clinical, laboratory, and combined clinical-laboratory compared to logistic regression. However, logistic regression showed higher sensitivity and better ROC curve performance than the decision tree algorithm.

A new method to predict hospital mortality in severe community acquired pneumonia

Objective To study the sensitivity and specificity of the Chinese eating assessment tool (EAT-10) in screening acute stroke patients for oropharyngeal dysphagia (OD). Methods A total of 130 inpatients with acute stroke were screened using the Chinese EAT-10. On the same day they were also screened using the gold stan-dard technique for diagnosing dysphasia—videofluoroscopy. A receiver operating characteristics (ROC) curve was developed to study EAT-10′s sensitivity and specificity. A Youden index, positive predictive value (PPV), negative predictive value (NPV), and positive and negative likelihood ratios (LHR+ and LHR-) were quantified. Results According to the ROC curve, a cut-off point of 1 (EAT-10 score≥1) gave the best sensitivity (77.9%), the highest NPV (73.2%), with 66.1% specificity, 71.6% PPV, 2.30 LHR+ and 0.33 LHR- in screening for OD. The test-retest reliability was above 0.7. An investigator consistency reliability test showed good repeatability, and the consistency between each item and the mean total score was high. Conclusion The Chinese EAT-10 has good test-retest reliability and investigator consistency. The optimal cut-off point is 1, with good sensitivity and NPV at scores≥1. The test can be recommended as a screening tool for OD in acute stroke patients. Key words: Chinese eating assessment tool; Stroke; Oropharyngeal dysphagia; Dysphagia

Objective To explore the discriminability of acute physiology and chronic health evaluation (APACHE) Ⅳ and model for end-stage liver disease (MELD) scores on hospital mortality after liver transplantation (LT). Methods Clinical data of 195 patients [171 males, 24 females, mean age of (48±11) years old] who underwent orthotopic LT from February 2006 to July 2009 in Liver Transplantation Center, the Third Affiliated Hospital of Sun Yat-sun University were studied retrospectively. The informed consents of all patients were obtained and the ethical committee approval was received. The required parameters for APACHE IV and MELD scores and hospital mortality were collected, and the APACHE IV and MELD scores were calculated. The receiver operating characteristic (ROC) curves of discriminating patients′ mortality by two scores were drawn. The discriminability of two scores on hospital mortality were judged from the area under ROC curves (A). The discriminability was invalid when A value was 0.9. The difference of A value between two scores were compared by Wilcoxon rank sum test. Results The hospital mortality of 195 patients in this study was 13.8% (27/195). The mean APACHE Ⅳ and MELD scores were (42±22), (18±11) respectively for all the patients. The mean APACHE Ⅳ and MELD scores were (36±16), (17±10) respectively for the survivals, while were (75±25), (26±13) respectively for the deaths. The A value of APACHE Ⅳ score in discriminating hospital mortality was 0.937 with a high discrimination. The A value of MELD score in discriminating hospital mortality was 0.694 with a low discrimination. The discriminability of APACHE Ⅳ score was higher than that of MELD score (Z=3,493, P<0.05). The predictive cutoff point of APACHE Ⅳ score was 56 with the sensitivity 0.85, specificity 0.91 and Youden index 0.76. The predictive cutoff point of MELD score was 20 with the sensitivity 0.70, specificity 0.72 and Youden index 0.43. Conclusions Compared with MELD score, the discriminability of APACHE Ⅳ score on hospital mortality after liver transplantation is higher, and the sensitivity and specificity are also higher. Key words: Liver transplantation; Acute physiology and chronic health evaluation; Hospital mortality; Intensive care; Diagnosis, differential