scholarly journals Dynamic APACHE II Score to Predict Outcome Among Intensive Care Unit Patients

2021 ◽  
Author(s):  
Yao Tian ◽  
Yang YAO ◽  
Jing Zhou ◽  
Xin Diao ◽  
Hui Chen ◽  
...  
Keyword(s):  
Intensive Care ◽  
Hospital Mortality ◽  
Goodness Of Fit ◽  
Apache Ii ◽  
Secondary Outcome ◽  
Apache Ii Score ◽  
Optimal Timing ◽  
Goodness Of Fit Test ◽  
Icu Patients ◽  
Mimic Iii

Abstract Purpose: The Acute Physiology and Chronic Health Evaluation II (APACHE II) score is used to determine disease severity and predict outcomes in critically ill patients. However, there is no dynamic APACHE II score for predicting outcomes among ICU patients.The aim of this study is to explore the optimal timing to predict the outcomes of ICU patients by dynamically evaluating APACHE II score.Methods: Study data of demographics and comorbidities from the first 24 h after ICU admission were retrospectively extracted from MIMIC-III, a multiparameter intensive care database. The primary outcome was hospital mortality. 90-day mortality was a secondary outcome. APACHE II scores on days 1, 2, 3, 5, 7, 14 and 28 were compared using area under the receiver operating characteristic (AUROC) analysis. Hospital survival was visualised using Kaplan-Meier Curves.Results:A total of 6374 eligible subjects were extracted from the MIMIC-III. Mean APACHE II score on day 1 were 18.4±6.3, hospital and 90-day mortality was 19.1% and 25.8%, respectively.The optimal timing where predicted hospital mortality was on day 3 with an area under the cure of 0.666 (0.607-0.726)(P＜0.0001). The best tradeoff for preciction was found at 17 score, more than 17 score predicted mortality of non-survivors with a sensitivity of 92.8% and PPV of 23.1%. Hosmer-lemeshow goodness of fit test showed that APACHE II 3 has a good predictive calibration ability (X2 =6.198, P=0.625) and consistency of predicted death and actual death was 79.4%. The calibration of APACHE II 1 was poor (X2=294.898, P<0.001).Conclusions: APACHE II on 3 dayis the optimal prognostic marker and 17 score provided the best dignostic accuracy to predict outcomes for ICU patients. These finding will help medical make clinical judgment.

The Utility of Acute Physiology and Chronic Health Evaluation II Scores for Prediction of Mortality among Intensive Care Unit (ICU) and Non-ICU Patients with Methicillin-Resistant Staphylococcus aureus Bacteremia

2012 ◽  
Vol 33 (6) ◽  
pp. 558-564 ◽  
Author(s):  
Vanessa Stevens ◽  
Thomas P. Lodise ◽  
Brian Tsuji ◽  
Meagan Stringham ◽  
Jill Butterfield ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Staphylococcus Aureus ◽  
Intensive Care ◽  
Hospital Mortality ◽  
Chronic Health Evaluation ◽  
Apache Ii ◽  
Apache Ii Score ◽  
Health Evaluation ◽  
Icu Patients ◽  
Time Points

Objective.Bloodstream infections due to methicillin-resistant Staphylococcus aureus (MRSA) have been associated with significant risk of in-hospital mortality. The acute physiology and chronic health evaluation (APACHE) II score was developed and validated for use among intensive care unit (ICU) patients, but its utility among non-ICU patients is unknown. The aim of this study was to determine the ability of APACHE II to predict death at multiple time points among ICU and non-ICU patients with MRSA bacteremia.Design.Retrospective cohort study.Participants.Secondary analysis of data from 200 patients with MRSA bacteremia at 2 hospitals.Methods.Logistic regression models were constructed to predict overall in-hospital mortality and mortality at 48 hours, 7 days, 14 days, and 30 days using APACHE II scores separately in ICU and non-ICU patients. The performance of APACHE II scores was compared with age adjustment alone among all patients. Discriminatory ability was assessed using the c-statistic and was compared at each time point using X2 tests. Model calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test.Results.APACHE II was a significant predictor of death at all time points in both ICU and non-ICU patients. Discrimination was high in all models, with c-statistics ranging from 0.72 to 0.84, and was similar between ICU and non-ICU patients at all time points. APACHE II scores significantly improved the prediction of overall and 48-hour mortality compared with age adjustment alone.Conclusions.The APACHE II score may be a valid tool to control for confounding or for the prediction of death among ICU and non-ICU patients with MRSA bacteremia.

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 Feasibility of transitioning from APACHE II to SAPS III as prognostic model in a Brazilian general intensive care unit. A retrospective study

2014 ◽  
Vol 133 (3) ◽  
pp. 199-205 ◽  
Author(s):  
Ary Serpa Neto ◽  
Murillo Santucci Cesar de Assunção ◽  
Andréia Pardini ◽  
Eliézer Silva
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Retrospective Study ◽  
Hospital Mortality ◽  
Critically Ill ◽  
Predictive Models ◽  
Critically Ill Patients ◽  
Goodness Of Fit ◽  
Apache Ii ◽  
Goodness Of Fit Test

CONTEXT AND OBJECTIVE: Prognostic models reflect the population characteristics of the countries from which they originate. Predictive models should be customized to fit the general population where they will be used. The aim here was to perform external validation on two predictive models and compare their performance in a mixed population of critically ill patients in Brazil.DESIGN AND SETTING: Retrospective study in a Brazilian general intensive care unit (ICU).METHODS: This was a retrospective review of all patients admitted to a 41-bed mixed ICU from August 2011 to September 2012. Calibration (assessed using the Hosmer-Lemeshow goodness-of-fit test) and discrimination (assessed using area under the curve) of APACHE II and SAPS III were compared. The standardized mortality ratio (SMR) was calculated by dividing the number of observed deaths by the number of expected deaths.RESULTS: A total of 3,333 ICU patients were enrolled. The Hosmer-Lemeshow goodness-of-fit test showed good calibration for all models in relation to hospital mortality. For in-hospital mortality there was a worse fit for APACHE II in clinical patients. Discrimination was better for SAPS III for in-ICU and in-hospital mortality (P = 0.042). The SMRs for the whole population were 0.27 (confidence interval [CI]: 0.23 - 0.33) for APACHE II and 0.28 (CI: 0.22 - 0.36) for SAPS III.CONCLUSIONS: In this group of critically ill patients, SAPS III was a better prognostic score, with higher discrimination and calibration power.

The Relationship of the ACCP/SCCM Consensus Conference Classification of Sepsis to Mortality and Multiorgan Dysfunction among Medical ICU Patients

1996 ◽  
Vol 11 (6) ◽  
pp. 326-334 ◽  
Author(s):  
Marin H. Kollef ◽  
Paul R. Eisenberg
Keyword(s):  
Septic Shock ◽  
Severe Sepsis ◽  
Hospital Mortality ◽  
Consensus Conference ◽  
Organ System ◽  
Apache Ii ◽  
Apache Ii Score ◽  
Icu Patients ◽  
Multiorgan Dysfunction

To determine the relation between the proposed ACCP/SCCM Consensus Conference classification of sepsis and hospital outcomes, we conducted a single-center, prospective observational study at Barnes Hospital, St. Louis, MO, an academic tertiary care hospital. A total of 324 consecutive patients admitted to the medical intensive care unit (ICU) were studied for prospective patient surveillance and data collection. The main outcome measures were the number of acquired organ system derangements and hospital mortality. Fifty-seven (17.6%) patients died during the study period. The proposed classifications of sepsis (e.g., systemic inflammatory response syndrome [SIRS], sepsis, severe sepsis, septic shock) correlated with hospital mortality ( r = 0.330; p < 0.001) and development of an Organ System Failure Index (OSFI) of 3 or greater ( r = 0.426; p < 0.001). Independent determinants of hospital mortality for this patient cohort ( p < 0.05) were development of an OSFI of 3 or greater (adjusted odds ratio [AOR], 13.9; 95% confidence interval [CI], 6.4–30.2; p < 0.001); presence of severe sepsis or septic shock (AOR, 2.6; 95% CI, 1.2–5.6; p = 0.002), and an APACHE II score ≥ of 18 or greater (AOR, 2.4; 95% CI, 1.0–5.8; p = 0.045). Intra-abdominal infection (AOR, 19.1; 95% CI, 1.6–230.1; p = 0.011), an APACHE II score ≥ of 18 or greater (AOR, 8.9; 95% CI, 4.2–18.6; p < 0.001), and presence of severe sepsis or septic shock (AOR, 2.9; 95% CI, 1.5–5.4; p = 0.001) were independently associated with development of an OSFI of 3 or greater. These data confirm that acquired multiorgan dysfunction is the most important predictor of mortality among medical ICU patients. In addition, they identify the proposed ACCP/SCCM Consensus Conference classification of sepsis as an additional independent determinant of both hospital mortality and multiorgan dysfunction.

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.

Outcomes of Critically Ill Gynecological Cancer Patients Admitted to Intensive Care Unit

2012 ◽  
Vol 30 (1) ◽  
pp. 7-11 ◽  
Author(s):  
Silvio A. Ñamendys-Silva ◽  
María O. González-Herrera ◽  
Julia Texcocano-Becerra ◽  
Angel Herrera-Gómez
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Prognostic Factors ◽  
Mortality Rate ◽  
Hospital Mortality ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Gynecological Cancer ◽  
Apache Ii ◽  
Apache Ii Score

Purpose: To assess the characteristics of critically ill patients with gynecological cancer, and to evaluate their prognosis. Methods: Fifty-two critically ill patients with gynecological cancer admitted to intensive care unit (ICU) were included. Univariate and multivariate logistic regressions were used to identify factors associated with hospital mortality. Results: Thirty-five patients (67.3%) had carcinoma of the cervix uteri and 11 (21.2%) had ovarian cancer. The mortality rate in the ICU was 17.3% (9 of 52) and hospital mortality rate were 23%(12 of 52). In the multivariate analysis, independent prognostic factors for hospital mortality were vasopressor use (odds ratio [OR] = 8.60, 95% confidence interval [CI] 2.05-36; P = .03) and the Acute Physiology and Chronic Health Evaluation (APACHE) II score (OR = 1.43, 95% CI 1.01-2.09; P = .048). Conclusions: The independent prognostic factors for hospital mortality were the need for vasopressors and the APACHE II score.

scholarly journals Influence of time elapsed from end of emergency surgery until admission to intensive care unit, on Acute Physiology and Chronic Health Evaluation II (APACHE II) prediction and patient mortality rate

2005 ◽  
Vol 123 (4) ◽  
pp. 167-174 ◽  
Author(s):  
Paulo Antonio Chiavone ◽  
Samir Rasslan
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Mortality Rate ◽  
Length Of Stay ◽  
Hospital Mortality ◽  
Emergency Surgery ◽  
Apache Ii ◽  
Apache Ii Score ◽  
Death Risk ◽  
Expected Mortality

CONTEXT AND OBJECTIVE: Patients are often admitted to intensive care units with delay in relation to when this service was indicated. The objective was to verify whether this delay influences hospital mortality, length of stay in the unit and hospital, and APACHE II prediction. DESIGN AND SETTING: Prospective and accuracy study, in intensive care unit of Santa Casa de São Paulo, a tertiary university hospital. METHODS: We evaluated all 94 patients admitted following emergency surgery, from August 2002 to July 2003. The variables studied were APACHE II, death risk, length of stay in the unit and hospital, and hospital mortality rate. The patients were divided into two groups according to the time elapsed between end of surgery and admission to the unit: up to 12 hours and over 12 hours. RESULTS: The groups were similar regarding gender, age, diagnosis, APACHE II score and hospital stay. The death risk factors were age, APACHE II and elapsed time (p < 0.02). The mortality rate for the over 12-hour group was higher (54% versus 26.1%; p = 0.018). For the over 12-hour group, observed mortality was higher than expected mortality (p = 0.015). For the up to 12-hour group, observed and expected mortality were similar (p = 0.288). CONCLUSION: APACHE II foresaw the mortality rate among patients that arrived faster to the intensive care unit, while the mortality rate was higher among those patients whose admission to the intensive care unit took longer.

Prognostic factors for patients with lymphoma requiring intensive care

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 17519-17519
Author(s):  
A. Scheliga ◽  
F. M. Vieira ◽  
N. Spector ◽  
S. Romano ◽  
J. I. Salluh ◽  
...  
Keyword(s):  
Intensive Care ◽  
Hospital Mortality ◽  
Hodgkin’S Lymphoma ◽  
Male Gender ◽  
Apache Ii ◽  
Hodgkin's Lymphoma ◽  
Apache Ii Score ◽  
Non Hodgkin’S Lymphoma ◽  
Non Hodgkin's Lymphoma ◽  
Organ Failures

17519 Background: Prognosis of patients (pts) with hematological malignancies (HM) in the intensive care unit (ICU) seems to be improving, despite different biological behaviors and outcomes. The study of homogenous groups of pts might provide useful clinical insights. The aim of this study was to evaluate the outcomes of critically ill patients with lymphomas (CIPL). Methods: During 66 months, all consecutive CIPL admitted to an oncologic ICU were studied. Variables collected were: age, gender, performance status, type and status of lymphoma, neutropenia, infection at admission, use of mechanical ventilation (MV), the Acute Physiology and Chronic Health Evaluation (APACHE) II score, comorbidities and number of acute organ failures (AOF). Variables selected in the univariate analysis (p < 0.25) and those clinically relevant were entered in a multivariable logistic regression analysis [results expressed as odds-ratios (OR), 95% confidence interval (CI)]. The end-point was hospital mortality. Results: A total of 120 CIPL were studied. Mean age was 51 ± 20 years and 54% were males. APACHE II was 19 ± 7 points. Diagnoses were High Grade Non-Hodgkin’s Lymphoma (77.5%), Hodgkin’s disease (17.5%) and Low Grade Non-Hodgkin’s Lymphoma (5%). Reasons for ICU admission were severe sepsis (62%) and acute respiratory failure (22%). During ICU stay 90% pts received MV, 71% vasopressors and 27.5% dialysis. Twenty-three (19%) pts had neutropenia. End-of-life decisions were implemented in 31% pts and all of them died at the ICU. The ICU and hospital mortality rates were 53% and 67% respectively, with no difference among the groups of lymphomas (p = 0.877). Variables identified in the multivariate analysis were: age [OR = 1.03 (95% CI = 1.01–1.06)], male gender [3.72 (1.27–10.90)], uncontrolled disease [OR = 6.28 (1.80–21.95), for pts with newly diagnosed disease and OR = 5.33 (1.45–19.47), for those with recurrence/progression, sepsis [OR = 5.31 (1.62–17.37)] and AOF [OR = 2.35 (1.53–3.61)]. Conclusions: Higher age, male gender, the severity of organ failures, sepsis and disease status were the main adverse factors. Type of lymphoma and neutropenia had no impact in the outcome. The appropriate use of such easily available clinical characteristics may avoid forgoing intensive care for lymphoma pts with a chance of survival. No significant financial relationships to disclose.

Sign in / Sign up

Export Citation Format

Share Document