scholarly journals Impact of Operator Expertise on Collection of the APACHE II Score and on the Derived Risk of Death and Standardized Mortality Ratio

2005 ◽  
Vol 33 (5) ◽  
pp. 585-590 ◽  
Author(s):  
D. Ledoux ◽  
S. Finfer ◽  
S. Mckinley
Keyword(s):  
Goodness Of Fit ◽  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Clinical Staff ◽  
Apache Ii ◽  
Apache Ii Score ◽  
Standardized Mortality Ratio ◽  
Risk Of Death ◽  
Mortality Ratio ◽  
The Impact

We assessed the impact of operator expertise on collection of the APACHE II score, the derived risk of death and standardized mortality ratio in 465 consecutive patients admitted to a multi-disciplinary tertiary hospital ICU. Research coordinators and junior clinical staff independently collected the APACHE II variables; experts (senior clinical staff) rescored 20 % of the records. Agreement was moderate between junior clinical staff and research coordinators or senior clinical staff for most variables of the acute physiology score (weighted κ<0.6); agreement between research coordinators and senior clinical staff data collectors was good (weighted κ >0.75). The APACHE II score and its derived risk of death (ROD) were significantly lower using the junior clinical staff dataset compared to research coordinators and senior clinical staff (APACHE II score: 13.4±9.2 vs 16.8±8.5 vs 17.1±7.7, P<0.001; ROD: 14.7%±22.4% vs 21.6%±22.6% vs 20.8%±22.4%, P<0.01 respectively). The discriminative capacity was not altered by the lack of agreement (area under Receiver Operator Characteristic curve >0.8) but calibration of ROD from the junior clinical staff dataset was poor (Goodness-of-fit: P=0.001). The standardized mortality ratio (SMR) was higher with the junior clinical staff dataset (SMR: 1.22, 95% CI: 0.96-1.52 vs 0.87, 95% CI: 0.70-1.06 vs 0.76, 95% CI: 0.40-1.3 calculated from junior clinical staff, research coordinators and senior clinical staff data-sets respectively). We conclude that the expertise of data collectors significantly influences the APACHE II score, the derived risk of death and the standardized mortality ratio. Given the importance of such scores, ICUs should be provided with sufficient resources to train and employ dedicated data collectors.

scholarly journals The APACHE II measured on patients' discharge from the Intensive Care Unit in the prediction of mortality

2013 ◽  
Vol 21 (3) ◽  
pp. 811-819 ◽  
Author(s):  
Luciana Gonzaga dos Santos Cardoso ◽  
Paulo Antonio Chiavone
Keyword(s):  
Linear Regression Analysis ◽  
Prognostic Index ◽  
Apache Ii ◽  
Standardized Mortality Ratio ◽  
Good Discrimination ◽  
Risk Of Death ◽  
Prediction Of Mortality ◽  
Significant Area ◽  
Mortality Ratio ◽  
Good Calibration

OBJECTIVE: to analyze the performance of the Acute Physiology and Chronic Health Evaluation (APACHE II), measured based on the data from the last 24 hours of hospitalization in ICU, for patients transferred to the wards. METHOD: an observational, prospective and quantitative study using the data from 355 patients admitted to the ICU between January and July 2010, who were transferred to the wards. RESULTS: the discriminatory power of the AII-OUT prognostic index showed a statistically significant area beneath the ROC curve. The mortality observed in the sample was slightly greater than that predicted by the AII-OUT, with a Standardized Mortality Ratio of 1.12. In the calibration curve the linear regression analysis showed the R2 value to be statistically significant. CONCLUSION: the AII-OUT could predict mortality after discharge from ICU, with the observed mortality being slightly greater than that predicted, which shows good discrimination and good calibration. This system was shown to be useful for stratifying the patients at greater risk of death after discharge from ICU. This fact deserves special attention from health professionals, particularly nurses, in managing human and technological resources for this group of patients.

Validation of the ‘paediatric extracorporeal membrane oxygenation prediction’ model in a UK extracorporeal membrane oxygenation centre

Perfusion ◽  
2020 ◽  
Vol 35 (8) ◽  
pp. 802-805
Author(s):  
Hari Krishnan Kanthimathinathan ◽  
Sarah Webb ◽  
David Ellis ◽  
Margaret Farley ◽  
Timothy J Jones
Keyword(s):  
Extracorporeal Membrane Oxygenation ◽  
Prediction Model ◽  
Risk Adjustment ◽  
Goodness Of Fit ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Standardized Mortality Ratio ◽  
Membrane Oxygenation ◽  
Mortality Ratio ◽  
Operating Characteristic Curve

Introduction: There is a need for a universal risk-adjustment model that may be used regardless of the indication and nature of neonatal or paediatric extracorporeal membrane oxygenation support. The ‘paediatric extracorporeal membrane oxygenation prediction’ model appeared to be a promising candidate but required external validation. Methods: We performed a validation study using institutional database of extracorporeal membrane oxygenation patients (2008-2019). We used the published paediatric extracorporeal membrane oxygenation prediction score calculator to derive estimated mortality based on the model in this cohort of patients in our institutional database. We used standardized mortality ratio, area under the receiver operating characteristic curve and Hosmer-Lemeshow goodness-of-fit test in 10 deciles to assess model performance. Results: We analysed 154 extracorporeal membrane oxygenation episodes in 150 patients. About 53% of the patients were full term (age ⩽30 days and gestation at birth ⩾37 weeks) neonates. The commonest category of extracorporeal membrane oxygenation support was cardiac (42%). The overall in-paediatric intensive care unit mortality was 37% (57/154) and the in-hospital mortality was 42% (64/154). Distribution of estimated mortality risk was similar to the derivation study. The calculated standardized mortality ratio was 0.81 based on the paediatric extracorporeal membrane oxygenation prediction model of risk-adjustment. The area under the receiver operating characteristic curve was 0.55 (0.45-0.64) and Hosmer-Lemeshow-test p value <0.001 was unable to support goodness-of-fit. Conclusion: This small single-centre study with a small number of events was unable to validate the paediatric extracorporeal membrane oxygenation prediction-model of risk-adjustment. Although this remains the most promising of all the available models, further validation in larger data sets and/or refinement may be required before widespread use.

scholarly journals PERFORMANCE OF PROGNOSTIC SCORES IN AKI POPULATION IN ICU

2020 ◽  
pp. 1-4
Author(s):  
Jasmin das
Keyword(s):  
Hospital Mortality ◽  
Goodness Of Fit ◽  
Characteristic Curve ◽  
Prognostic Score ◽  
Apache Ii ◽  
Prognostic Scores ◽  
Apache Ii Score ◽  
P Value ◽  
Goodness Of Fit Test ◽  
Good For

Acute kidney injury in hospitalized patients is associated with high mortality rates and increased length of hospital stay. Prognostication of patients with AKI is of immense value in making decisions regarding the optimal type and intensity of treatment, patient selection, and clinical discussions on prognosis and in assessment of the quality of an ICU. Prognostic scores are comprised of relevant clinical and laboratory variables of patients associated to the clinical endpoint. There are limited studies that have evaluated which prognostic score may be used in patients with AKI. Studies have shown that APACHE II underestimates hospital mortality whereas AKI specific Liano score has better statistical correlation with mortality. Materials and methods: All patients admitted to the ICU fulfilling the inclusion criteria during the study period were recruited and evaluated for AKI by both RIFLE and AKI criteria. Prognostic scores, APACHE II and Liano were used in predicting hospital mortality. Assessment of score performance was made through analysis of the discrimination and calibration using area under a receiver operating characteristic curve (AUROC) and Hosmer and Lemeshow goodness of fit test. Results: Mean APACHE II score was higher in AKI subjects compared to non AKI and was statistically significant and it increased with the severity of AKI. The AUROC for APACHE II score was 0.739 and 0.706 for AKIN and RIFLE respectively and signifies APACHE II score increases with AKI. An AUROC curve of prognostic scores for predicting mortality was 0.677 and 0.639 for Liano and APACHE II respectively and on comparison showed insignificant p value (0.6331). Assessment of calibration showed that the calibration was good for specific score. Conclusion:Assessment of performance of both the prognostic scores APACHE II and Liano had poor discrimination but calibration was good for Liano model

Cohort study of the APACHE II score and mortality for different types of intensive care unit patients

2021 ◽  
pp. postgradmedj-2021-140376
Author(s):  
Veli Sungono ◽  
Hori Hariyanto ◽  
Tri Edhi Budhi Soesilo ◽  
Asri C Adisasmita ◽  
Syahrizal Syarif ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Goodness Of Fit ◽  
Characteristic Curve ◽  
Secondary Data ◽  
Apache Ii ◽  
Surgical Patients ◽  
Medical Emergency ◽  
Apache Ii Score ◽  
Icu Patients

ObjectivesFind the discriminant and calibration of APACHE II (Acute Physiology And Chronic Health Evaluation) score to predict mortality for different type of intensive care unit (ICU) patients.MethodsThis is a cohort retrospective study using secondary data of ICU patients admitted to Siloam Hospital of Lippo Village from 2014 to 2018 with minimum age ≥17 years. The analysis uses the receiver operating characteristic curve, student t-test and logistic regression to find significant variables needed to predict mortality.ResultsA total of 2181 ICU patients: men (55.52%) and women (44.48%) with an average age of 53.8 years old and length of stay 3.92 days were included in this study. Patients were admitted from medical emergency (30.5%), neurosurgical (52.1%) and surgical (17.4%) departments, with 10% of mortality proportion. Patients admitted from the medical emergency had the highest average APACHE score, 23.14±8.5, compared with patients admitted from neurosurgery 15.3±6.6 and surgical 15.8±6.8. The mortality rate of patients from medical emergency (24.5%) was higher than patients from neurosurgery (3.5%) or surgical (5.3%) departments. Area under curve of APACHE II score showed 0.8536 (95% CI 0.827 to 0.879). The goodness of fit Hosmer-Lemeshow show p=0.000 with all ICU patients’ mortality; p=0.641 with medical emergency, p=0.0001 with neurosurgical and p=0.000 with surgical patients.ConclusionAPACHE II has a good discriminant for predicting mortality among ICU patients in Siloam Hospital but poor calibration score. However, it demonstrates poor calibration in neurosurgical and surgical patients while demonstrating adequate calibration in medical emergency patients.

scholarly journals Improvement of APACHE II score system for disease severity based on XGBoost algorithm

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yan Luo ◽  
Zhiyu Wang ◽  
Cong Wang
Keyword(s):  
Intensive Care ◽  
Hospital Mortality ◽  
Patient Outcomes ◽  
External Validation ◽  
Apache Ii ◽  
Validation Dataset ◽  
Apache Ii Score ◽  
Severity Scores ◽  
The Impact ◽  
Improve Patient

Abstract Background Prognostication is an essential tool for risk adjustment and decision making in the intensive care units (ICUs). In order to improve patient outcomes, we have been trying to develop a more effective model than Acute Physiology and Chronic Health Evaluation (APACHE) II to measure the severity of the patients in ICUs. The aim of the present study was to provide a mortality prediction model for ICUs patients, and to assess its performance relative to prediction based on the APACHE II scoring system. Methods We used the Medical Information Mart for Intensive Care version III (MIMIC-III) database to build our model. After comparing the APACHE II with 6 typical machine learning (ML) methods, the best performing model was screened for external validation on anther independent dataset. Performance measures were calculated using cross-validation to avoid making biased assessments. The primary outcome was hospital mortality. Finally, we used TreeSHAP algorithm to explain the variable relationships in the extreme gradient boosting algorithm (XGBoost) model. Results We picked out 14 variables with 24,777 cases to form our basic data set. When the variables were the same as those contained in the APACHE II, the accuracy of XGBoost (accuracy: 0.858) was higher than that of APACHE II (accuracy: 0.742) and other algorithms. In addition, it exhibited better calibration properties than other methods, the result in the area under the ROC curve (AUC: 0.76). we then expand the variable set by adding five new variables to improve the performance of our model. The accuracy, precision, recall, F1, and AUC of the XGBoost model increased, and were still higher than other models (0.866, 0.853, 0.870, 0.845, and 0.81, respectively). On the external validation dataset, the AUC was 0.79 and calibration properties were good. Conclusions As compared to conventional severity scores APACHE II, our XGBoost proposal offers improved performance for predicting hospital mortality in ICUs patients. Furthermore, the TreeSHAP can help to enhance the understanding of our model by providing detailed insights into the impact of different features on the disease risk. In sum, our model could help clinicians determine prognosis and improve patient outcomes.

scholarly journals Influencing Factors and Prevention of Sepsis or Acute Kidney Injury in 85 Patients with Severe Trauma

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Hong Zhang ◽  
Dan Chen ◽  
Lihua Wang ◽  
Bing Li
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Influencing Factors ◽  
Severe Trauma ◽  
Apache Ii ◽  
Apache Ii Score ◽  
Trauma Patients ◽  
Risk Of Death ◽  
Disturbance Of Consciousness ◽  
Prevention And Treatment

Severe trauma can cause systemic reactions, leading to massive bleeding, shock, asphyxia, and disturbance of consciousness. At the same time, patients with severe trauma are at high risk of sepsis and acute renal injury. The occurrence of complications will increase the difficulty of clinical treatment, improve the mortality rate, and bring heavy physical and mental burdens and economic pressure to patients and their families. It is of great clinical significance to understand the high risk factors of sepsis and AKI and actively formulate prevention and treatment measures. In this study, the clinical data of 85 patients with severe trauma were analyzed by univariate and multivariate logistic regression to identify the risk factors leading to sepsis or AKI and analyze the prevention and treatment strategies. The results showed that multiple injuries, APACHE II score on admission, SOFA score on admission, and mechanical ventilation were independent influencing factors of sepsis in patients with severe trauma, while hemorrhagic shock, APACHE II score on admission, CRRT, and sepsis were independent influencing factors of AKI in patients with severe trauma. Severe trauma patients complicated with sepsis or AKI will increase the risk of death. In the course of treatment, prevention and intervention should be given as far as possible to reduce the incidence of complications.

Mortality risk scoring in emergency general surgery: Are we using the best tool?

2020 ◽  
pp. 175045892092013
Author(s):  
Azeem Thahir ◽  
Rui Pinto-Lopes ◽  
Stavroula Madenlidou ◽  
Laura Daby ◽  
Chandima Halahakoon
Keyword(s):  
Mortality Rate ◽  
Goodness Of Fit ◽  
Characteristic Curve ◽  
Emergency Laparotomy ◽  
Receiver Operator Characteristic ◽  
Characteristic Analysis ◽  
Goodness Of Fit Test ◽  
Risk Of Death ◽  
Possum Score ◽  
Operative Severity Score

Background It is imperative that an accurate assessment of risk of death is undertaken preoperatively on all patients undergoing an emergency laparotomy. Portsmouth-Physiological and Operative Severity Score for the enumeration of Mortality and Morbidity (P-POSSUM) is one of the most widely used scores. National Emergency Laparotomy Audit (NELA) presents a novel, validated score, but no direct comparison with P-POSSUM exists. We aimed to determine which would be the best predictor of mortality. Methods We analysed all the entries on the online NELA database over a four-and-a-half-year period. The Hosmer–Lemeshow goodness of fit test was performed to assess model calibration. For the outcome of death and for each scoring system, a non-parametric receiver operator characteristic analysis was done. The sensitivity, specificity, area under receiver operator characteristic curve and their standard errors were calculated. Results Data pertaining to 650 patients were included. There were 59 deaths, giving an overall observed mortality rate of 9.1%. Predicted mortality rate for the P-POSSUM score and NELA score were 15.2% and 7.8%, respectively. The discriminative power for mortality was highest for the NELA score (C-index = 0.818, CI: 0.769–0.867, p < 0.001), when compared to P-POSSUM (C-index = 0.769, CI: 0.712–0.827, p < 0.001). Conclusions The NELA score showed good discrimination in predicting mortality in the entire cohort. The P-POSSUM over-predicted observed mortality and the NELA score under-predicted observed mortality.

scholarly journals The Diagnostic Value of Mir-133a in ST Elevation and Non-ST Elevation Myocardial Infarction: A Meta-Analysis

Cells ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 793
Author(s):  
Yehuda Wexler ◽  
Udi Nussinovitch
Keyword(s):  
Myocardial Infarction ◽  
Cardiovascular Disease ◽  
Diagnostic Performance ◽  
Characteristic Curve ◽  
Diagnostic Value ◽  
Receiver Operator Characteristic Curve ◽  
St Elevation Myocardial Infarction ◽  
Diagnostic Potential ◽  
St Elevation ◽  
The Impact

Numerous studies have reported correlations between plasma microRNA signatures and cardiovascular disease. MicroRNA-133a (Mir-133a) has been researched extensively for its diagnostic value in acute myocardial infarction (AMI). While initial results seemed promising, more recent studies cast doubt on the diagnostic utility of Mir-133a, calling its clinical prospects into question. Here, the diagnostic potential of Mir-133a was analyzed using data from multiple papers. Medline, Embase, and Web of Science were systematically searched for publications containing “Cardiovascular Disease”, “MicroRNA”, “Mir-133a” and their synonyms. Diagnostic performance was assessed using area under the summary receiver operator characteristic curve (AUC), while examining the impact of age, sex, final diagnosis, and time. Of the 753 identified publications, 9 were included in the quantitative analysis. The pooled AUC for Mir-133a was 0.73. Analyses performed separately on studies using healthy vs. symptomatic controls yielded pooled AUCs of 0.89 and 0.68, respectively. Age and sex were not found to significantly affect diagnostic performance. Our findings indicate that control characteristics and methodological inconsistencies are likely the causes of incongruent reports, and that Mir-133a may have limited use in distinguishing symptomatic patients from those suffering AMI. Lastly, we hypothesized that Mir-133a may find a new use as a risk stratification biomarker in patients with specific subsets of non-ST elevation myocardial infarction (NSTEMI).

scholarly journals Geografias da Diabetes Mellitus em Portugal: Como as Condições do Contexto Influenciam o Risco de Morrer

2014 ◽  
Vol 27 (3) ◽  
pp. 309 ◽  
Author(s):  
Paula Santana ◽  
Cláudia Costa ◽  
Adriana Loureiro ◽  
João Raposo ◽  
José Manuel Boavida
Keyword(s):  
Diabetes Mellitus ◽  
Relative Risk ◽  
Rural Areas ◽  
Public Health Problem ◽  
Standardized Mortality Ratio ◽  
Geographic Pattern ◽  
Bayesian Hierarchical ◽  
Risk Of Death ◽  
Mortality Ratio ◽  
Relative Risk Of Death

<strong>Introduction:</strong> Diabetes Mellitus is a public health problem that is on the increase throughout the world, including in Portugal. This paper aims to identify the changing geographic pattern of this cause of death in Portugal and its association with sociomaterial deprivation.<br /><strong>Material and Methods:</strong> This is a transversal ecological study of the deaths by Diabetes Mellitus in Portuguese municipalities in three periods (1989-1993, 1999-2003 and 2006-2010). It uses a Bayesian hierarchical model in order to obtain a smooth standardized mortality ratio and the relative risk of death by Diabetes Mellitus associated to sociomaterial deprivation.<br /><strong>Results:</strong> In 1989-1993, the highest smooth standardized mortality ratio values were found in coastal urban municipalities (80% of municipalities with smooth standardized mortality ratio ≥ 161, of which 60% are urban); in 2006-2010, the opposite was found, with the highest smooth standardized mortality ratio values occurring in rural areas in southern inland regions (76.9% of municipalities with smooth standardized mortality ratio ≥ 161, of which 69.2% are rural), particularly the Alentejo. The relative risk of death by Diabetes Mellitus increases with vulnerability associated to social and economic conditions in the area of residence, and is significant in the last two periods (relative risk: 1.00; IC95%: 0.98-1.02).<br /><strong>Discussion:</strong> Diabetes Mellitus presents a geographic pattern marked by coastal-inland and urban-rural asymmetry. However, this has been altering over the last twenty years. 48% of the population reside in municipalities where the smooth standardized mortality ratio has increased in the last twenty years, particularly in the rural areas of inland Portugal.<br /><strong>Conclusion: </strong>The highest smooth standardized mortality ratio are currently found in rural municipalities with the highest index of sociomaterial deprivation.<br /><strong>Keywords:</strong> Demography; Diabetes Mellitus/epidemiology; Diabetes Mellitus/mortality; Portugal; Socioeconomic Factors.

scholarly journals Relationship between Volume and Survival in Closed Intensive Care Units Is Weak and Apparent Only in Mechanically Ventilated Patients

2013 ◽  
Vol 119 (4) ◽  
pp. 871-879 ◽  
Author(s):  
Rafael Fernández ◽  
Susana Altaba ◽  
Lluis Cabre ◽  
Victoria Lacueva ◽  
Antonio Santos ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Standardized Mortality Ratio ◽  
Hospital Level ◽  
Mechanically Ventilated ◽  
Mechanically Ventilated Patients ◽  
Risk Of Death ◽  
Mortality Ratio ◽  
Low Volume ◽  
Predicted Risk ◽  
Ventilated Patients

Abstract Background: Recent studies have found an association between increased volume and increased intensive care unit (ICU) survival; however, this association might not hold true in ICUs with permanent intensivist coverage. Our objective was to determine whether ICU volume correlates with survival in the Spanish healthcare system. Methods: Post hoc analysis of a prospective study of all patients admitted to 29 ICUs during 3 months. At ICU discharge, the authors recorded demographic variables, severity score, and specific ICU treatments. Follow-up variables included ICU readmission and hospital mortality. Statistics include logistic multivariate analyses for hospital mortality according to quartiles of volume of patients. Results: The authors studied 4,001 patients with a mean predicted risk of death of 23% (range at hospital level: 14–46%). Observed hospital mortality was 19% (range at hospital level: 11–35%), resulting in a standardized mortality ratio of 0.81 (range: 0.5–1.3). Among the 1,923 patients needing mechanical ventilation, the predicted risk of death was 32% (14–60%) and observed hospital mortality was 30% (12–61%), resulting in a standardized mortality ratio of 0.96 (0.5–1.7). The authors found no correlation between standardized mortality ratio and ICU volume in the entire population or in mechanically ventilated patients. Only mechanically ventilated patients in very low-volume ICUs had slightly worse outcome. Conclusion: In the currently studied healthcare system characterized by 24/7 intensivist coverage, the authors found wide variability in outcome among ICUs even after adjusting for severity of illness but no relationship between ICU volume and outcome. Only mechanically ventilated patients in very low-volume centers had slightly worse outcomes.

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