Dataset Information

An Application of Bayesian Approach in Modeling Risk of Death in an Intensive Care Unit.

ABSTRACT:

Background and objectives

There are not many studies that attempt to model intensive care unit (ICU) risk of death in developing countries, especially in South East Asia. The aim of this study was to propose and describe application of a Bayesian approach in modeling in-ICU deaths in a Malaysian ICU.

Methods

This was a prospective study in a mixed medical-surgery ICU in a multidisciplinary tertiary referral hospital in Malaysia. Data collection included variables that were defined in Acute Physiology and Chronic Health Evaluation IV (APACHE IV) model. Bayesian Markov Chain Monte Carlo (MCMC) simulation approach was applied in the development of four multivariate logistic regression predictive models for the ICU, where the main outcome measure was in-ICU mortality risk. The performance of the models were assessed through overall model fit, discrimination and calibration measures. Results from the Bayesian models were also compared against results obtained using frequentist maximum likelihood method.

Results

The study involved 1,286 consecutive ICU admissions between January 1, 2009 and June 30, 2010, of which 1,111 met the inclusion criteria. Patients who were admitted to the ICU were generally younger, predominantly male, with low co-morbidity load and mostly under mechanical ventilation. The overall in-ICU mortality rate was 18.5% and the overall mean Acute Physiology Score (APS) was 68.5. All four models exhibited good discrimination, with area under receiver operating characteristic curve (AUC) values approximately 0.8. Calibration was acceptable (Hosmer-Lemeshow p-values > 0.05) for all models, except for model M3. Model M1 was identified as the model with the best overall performance in this study.

Conclusion

Four prediction models were proposed, where the best model was chosen based on its overall performance in this study. This study has also demonstrated the promising potential of the Bayesian MCMC approach as an alternative in the analysis and modeling of in-ICU mortality outcomes.

SUBMITTER: Wong RS

PROVIDER: S-EPMC4805172 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

An Application of Bayesian Approach in Modeling Risk of Death in an Intensive Care Unit.

Wong Rowena Syn Yin RS Ismail Noor Azina NA

PloS one 20160323 3

<h4>Background and objectives</h4>There are not many studies that attempt to model intensive care unit (ICU) risk of death in developing countries, especially in South East Asia. The aim of this study was to propose and describe application of a Bayesian approach in modeling in-ICU deaths in a Malaysian ICU.<h4>Methods</h4>This was a prospective study in a mixed medical-surgery ICU in a multidisciplinary tertiary referral hospital in Malaysia. Data collection included variables that were defined ...[more]

PMID: 27007413

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Project description:Critically ill intensive care unit (ICU) patients commonly develop severe muscle wasting and impaired muscle function, leading to delayed recovery, with subsequent increased morbidity and financial costs, and decrease quality of life of survivors. Acute Quadriplegic Myopathy (AQM) is one of the most common neuromuscular disorders associated with ICU-acquired muscle weakness. Although there are no available treatments for the ICU-acquired muscle weakness, it has been demonstrated that early mobilization can improve its prognosis and functional outcomes. This study aims at improving our understanding of the effects of passive mechanical loading on skeletal muscle structure and function by using a unique experimental rat ICU model allowing analyses of the temporal sequence of changes in mechanically ventilated and pharmacologically paralyzed animals at durations varying from 6 h to 14 days. Results show that passive mechanical loading alleviated the muscle wasting and the loss of force-generation associated with the ICU intervention, resulting in a doubling of the functional capacity of the loaded vs. unloaded muscles after a 2-week ICU intervention. We demonstrated that the improved maintenance of muscle structure and function is likely a consequence of a reduced oxidative stress, and a reduced loss of the molecular motor protein myosin. A complex temporal gene expression pattern, delineated by microarray analysis, was observed with loading-induced changes in transcript levels of sarcomeric proteins, muscle developmental processes, stress response, ECM/cell adhesion proteins and metabolism. Thus, the results from this study show that passive mechanical loading alleviates the severe negative consequences on muscle structure and function associated with mechanical silencing in ICU patients, strongly supporting early and intense physical therapy in immobilized ICU patients. This study aims to unravel the effects of passive mechanical loading on skeletal muscle structure and function in an experimental rat ICU model at duration varying between 6h and 14 days. A total of 23 experimental female Sprague-Dawley rats were included in this study. The experimental rats were anaesthetized, treated with the neuromuscular blocking agent (NMBA) M-NM-1-cobrotoxin, mechanically ventilated and monitored for durations varying from 6h to 4 days (n=13), from 5 to 8 days (n=4), and from 9 to 14 days (n=6). The left leg of the animal was activated for 6 hours at the shortest duration and 12 hours per day at durations 12 hours and longer throughout the experiment, using a mechanical lever arm that produced a continuous passive maximal ankle joint flexions-extensions at a speed of 13.3 cycles per minute. Muscle biopsies were obtained from gastrocnemius muscle (proximal part) immediately after euthanasia, were quickly frozen in liquid propane cooled by liquid nitrogen, and stored at -80M-BM-0C. RNA was extracted.

Dataset Information

An Application of Bayesian Approach in Modeling Risk of Death in an Intensive Care Unit.

Background and objectives

Methods

Results

Conclusion

Publications

An Application of Bayesian Approach in Modeling Risk of Death in an Intensive Care Unit.

Similar Datasets

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