To develop a regional ICU mortality prediction model during the first 24 h of ICU admission utilizing MODS and NEMS with six other independent variables from the Critical Care Information System (CCIS) Ontario, Canada (original) (raw)
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Development and validation of the critical care outcome prediction equation, version 4
Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine, 2013
To revise and validate the accuracy of the critical care outcome prediction equation (COPE) model, version 4. Observational cohort analysis of 214 616 adult consecutive intensive care unit admissions recorded from 23 ICUs over 12 years. Data derived from the Victorian Admitted Episode Database (VAED) were used to identify treatment-independent risk factors consistently associated with hospital mortality. A revised version of the COPE-4 model using a random intercept hierarchical logistic regression model was developed in a sample of 35 878 (16.7%) consecutive ICU separations. Accuracy was tested by comparing observed and predicted mortality in the remaining 178 741 (83.3%) records and in 23 institutional cohorts. Stability was assessed using the standardised mortality ratio, Hosmer-Lemeshow H10 statistic, calibration plot and Brier score. The COPE-4 model had satisfactory overall discrimination with an area under receiver operating characteristic curve of 0.82 for both datasets. The...
BioMed Research International, 2020
Background. Increasing attention has been paid to the predictive power of different prognostic scoring systems for decades. In this study, we compared the abilities of three commonly used scoring systems to predict short-term and long-term mortalities, with the intention of building a better prediction model for critically ill patients. We used the data from the National Health Insurance Research Database (NHIRD) in Taiwan, which included information on patient age, comorbidities, and presence of organ failure to build a new prediction model for short-term and long-term mortalities. Methods. We retrospectively collected the medical records of patients in the intensive care unit of a regional hospital in 2012 and linked them to the claims data from the NHIRD. The Acute Physiology and Chronic Health Evaluation II (APACHE II) score, Elixhauser Comorbidity Index (ECI), and Charlson Comorbidity Index (CCI) were compared for their predictive abilities. Multiple logistic regression tests were performed, and the results were presented as receiver operating characteristic curves and C-statistic. Results. The APACHE II score has the best predictive power for inhospital mortality (0.79; C − statistic = 0:77 − 0:83) and 1-year mortality (0.77; C − statistic = 0:74 − 0:79). The ECI and CCI alone have poorer predictive power and need to be combined with other variables to be comparable to the APACHE II score, as predictive tools. Using CCI together with age, sex, and whether or not the patient required mechanical ventilation is estimated to have a C-statistic of 0.773 (95% CI 0.744-0.803) for inhospital mortality, 0.782 (95% CI 0.76-0.81) for 30-day mortality, and 0.78 (95% CI 0.75-0.80) for 1-year mortality. Conclusions. We present a new prognostic model that combines CCI with age, sex, and mechanical ventilation status and can predict mortality, comparable to the APACHE II score.
BioMed Research International
Background.A variety of scoring systems have been introduced for use in both the emergency department (ED) such as WPS, REMS, and MEWS and the intensive care unit (ICU) such as APACHE II, SAPS II, and SOFA for risk stratification and mortality prediction. However, the performance of these models in the ICU remains unclear and we aimed to evaluate and compare their performance in the ICU. Methods. This multicenter retrospective cohort study was conducted on severely ill patients admitted to the ICU directly from the ED in seven tertiary hospitals in Iran from August 2018 to August 2020. We evaluated all models in terms of discrimination (AUROC), the balance between positive predictive value and sensitivity (AUPRC), calibration (Hosmer-Lemeshow test and calibration plots), and overall performance using the Brier score (BS). The endpoint was considered inhospital mortality. Results. Among the 3,455 patients included in the study, 54.4% of individuals were male ( N = 1,879 ) and 26.5% d...
Evaluation of Basic Parameters for Prediction of ICU Mortality
Journal of Critical and Intensive Care
Aim: The performance of common mortality prediction models in the intensive care units (ICU) are extensively validated, predominantly in high-income countries. Simple and fast models with region specific features are needed. Study design: Retrospective case-control study Methods: We reviewed the medical records of 1057 ICU-admitted patients within three years. Patient survival was defined as discharge before 28 days. Multivariate logistic regression modeling was applied, basic parameters were selected, and a simple model was tried using four of them (age, albumin, platelet, C-reactive protein); as Quick Prediction of Mortality (Qpm) score, and then tested. The Qpm score predictions were compared to calculated APACHE II predicted mortality (APM) score predictions. Both scores were then weighted by calculated standardized mortality ratios (SMR). Results: 933 patients were included into the analyses. The patients' overall observed mortality rate was 47%. APACHEII score prediction was 49% (p< 0.001, AUC= 0.810, r: 0.518). Qpm score prediction was 57% (p< 0.001, AUC= 0.699, r: 0.338). The SMR for Qpm was 0.82 in comparison to APM score SMR = 0.96. Conclusion: This simple prediction model has showed an acceptable performance in our ICU sample and needs to be prospectively evaluated for feasibility. In addition, further studies could be planned for external evaluations and validations in different settings.
Acta Anaesthesiologica Scandinavica, 2017
Background: Intensive care unit (ICU) mortality prediction scores deteriorate over time, and their complexity decreases clinical applicability and commonly causes problems with missing data. We aimed to develop and internally validate a new and simple score that predicts 90-day mortality in adults upon acute admission to the ICU: the Simplified Mortality Score for the Intensive Care Unit (SMS-ICU). Methods: We used data from an international cohort of 2139 patients acutely admitted to the ICU and 1947 ICU patients with severe sepsis/septic shock from 2009 to 2016. We performed multiple imputations for missing data and used binary logistic regression analysis with variable selection by backward elimination, followed by conversion to a simple point-based score. We assessed the apparent performance and validated the score internally using bootstrapping to present optimism-corrected performance estimates. Results: The SMS-ICU comprises seven variables available in 99.5% of the patients: two numeric variables: age and lowest systolic blood pressure, and five dichotomous variables: haematologic malignancy/metastatic cancer, acute surgical admission and use of vasopressors/inotropes, respiratory support and renal replacement therapy. Discrimination (area under the receiver operating characteristic curve) was 0.72 (95% CI: 0.71-0.74), overall performance (Nagelkerke's R 2) was 0.19 and calibration (intercept and slope) was 0.00 and 0.99, respectively. Optimism-corrected performance was similar to apparent performance. Conclusions: The SMS-ICU predicted 90-day mortality with reasonable and stable performance. If performance remains adequate after external validation, the SMS-ICU could prove a valuable tool for ICU clinicians and researchers because of its simplicity and expected very low number of missing values. Editorial comment Predicting mortality or survival for critically ill patients is challenging. In this paper, a relatively simple and new model using seven variables is presented based on analysis of a large international cohort. This model next needs to be tested and validated on a different large and reliable database before its predictive value can be appreciated.
Critical Care
Background Multiple factors contribute to mortality after ICU, but it is unclear how the predictive value of these factors changes during ICU admission. We aimed to compare the changing performance over time of the acute illness component, antecedent patient characteristics, and ICU length of stay (LOS) in predicting 1-year mortality. Methods In this retrospective observational cohort study, the discriminative value of four generalized mixed-effects models was compared for 1-year and hospital mortality. Among patients with increasing ICU LOS, the models included (a) acute illness factors and antecedent patient characteristics combined, (b) acute component only, (c) antecedent patient characteristics only, and (d) ICU LOS. For each analysis, discrimination was measured by area under the receiver operating characteristics curve (AUC), calculated using the bootstrap method. Statistical significance between the models was assessed using the DeLong method (p value
Critical Care Medicine, 2007
The Australian and New Zealand Risk of Death (ANZROD) model currently used for benchmarking intensive care units (ICUs) in Australia and New Zealand utilises physiological data collected up to 24 hours after ICU admission to estimate the risk of hospital mortality. This study aimed to develop the Australian and New Zealand Risk of Death admission (ANZROD 0) model to predict hospital mortality using data available at presentation to ICU and compare its performance with the ANZROD in Australian and New Zealand hospitals. Data pertaining to all ICU admissions between 1 January 2006 and 31 December 2015 were extracted from the Australian and New Zealand Intensive Care Society Adult Patient Database. Hospital mortality was modelled using logistic regression with development (two-thirds) and validation (one-third) datasets. All predictor variables available at ICU admission were considered for inclusion in the ANZROD 0 model. Model performance was assessed using Brier score, standardised mortality ratio and area under the receiver operating characteristic curve. The relationship between ANZROD 0 and ANZROD predicted risk of death was assessed using linear regression. After standard exclusions, 1,097,416 patients were available for model development and validation. Observed mortality was 9.5%. Model performance measures (Brier score, standardised mortality ratio and area under the receiver operating characteristic curve) for the ANZROD 0 and ANZROD in the validation dataset were 0.069, 1.0 and 0.853; 0.057, 1.0 and 0.909, respectively. There was a strong positive correlation between the mortality predictions with an overall R 2 of 0.73. We found that the ANZROD 0 model had acceptable calibration and discrimination. Predictions from the models had high correlations in all major diagnostic groups, with the exception of cardiac surgery and possibly trauma and sepsis.
BMC Health Services Research, 2009
Background Risk adjustment and mortality prediction in studies of critical care are usually performed using acuity of illness scores, such as Acute Physiology and Chronic Health Evaluation II (APACHE II), which emphasize physiological derangement. Common risk adjustment systems used in administrative datasets, like the Charlson index, are entirely based on the presence of co-morbid illnesses. The purpose of this study was to compare the discriminative ability of the Charlson index to the APACHE II in predicting hospital mortality in adult multisystem ICU patients. Methods This was a population-based cohort design. The study sample consisted of adult (>17 years of age) residents of the Calgary Health Region admitted to a multisystem ICU between April 2002 and March 2004. Clinical data were collected prospectively and linked to hospital outcome data. Multiple regression analyses were used to compare the performance of APACHE II and the Charlson index. Results The Charlson index was...
Predicting the risk of death in patients in intensive care unit
Archives of Iranian medicine, 2007
The ability to identify critically ill patients who will not survive until hospital discharge may yield substantial cost savings. The aim of this study was to validate the mortality prediction model II (MPM II) in in-hospital mortality of critically ill patients for quality management and risk-adjusted monitoring. The data were collected prospectively from consecutive admissions to the Intensive Care Unit of Imam Hossein Medical Center in Tehran. A total of 274 admissions were analyzed using tests of discrimination and calibration of the logistic regression equation for mortality prediction model II at admission (MPM0 II) and at 24th hour (MPM24 II). The mortality prediction model II exhibited excellent discrimination (receiver operating characteristic curve area). Calibration curves and Hosmer-Lemeshow statistics demonstrated good calibration of both models on outcome. We recommend using mortality prediction model II in Iranian ICUs for routine audit requirements. Mortality predict...
Resuscitation
The National Early Warning System (NEWS) is based on vital signs; the Laboratory Decision Tree Early Warning Score (LDT-EWS) on laboratory test results. We aimed to develop and validate a new EWS (the LDTEWS:NEWS risk index) by combining the two and evaluating the discrimination of the primary outcome of unanticipated intensive care unit (ICU) admission or in-hospital mortality, within 24 h. Methods: We studied emergency medical admissions, aged 16 years or over, admitted to Oxford University Hospitals (OUH) and Portsmouth Hospitals (PH). Each admission had vital signs and laboratory tests measured within their hospital stay. We combined LDT-EWS and NEWS values using a linear time-decay weighting function imposed on the most recent blood tests. The LDTEWS:NEWS risk index was developed using data from 5 years of admissions to PH, and validated on a year of data from both PH and OUH. We tested the risk index's ability to discriminate the primary outcome using the c-statistic. Results: The development cohort contained 97,933 admissions (median age = 73 years) of which 4723 (4.8%) resulted inhospital death and 1078 (1.1%) in unanticipated ICU admission. We validated the risk index using data from PH (n = 21,028) and OUH (n = 16,383). The risk index showed a higher discrimination in the validation sets (c-statistic value (95% CI)) (PH, 0.901 (0.898-0.905); OUH, 0.916 (0.911-0.921)), than NEWS alone (PH, 0.877 (0.873-0.882); OUH, 0.898 (0.893-0.904)). Conclusions: The LDTEWS:NEWS risk index increases the ability to identify patients at risk of deterioration, compared to NEWS alone.