Proach in developing ICU prognostic models. The Bayesian approach considers data

Proach in developing ICU prognostic models. The Bayesian approach considers data to be fixed and assumes Necrostatin-1 biological activity parameters as random variables, where the uncertainty of unknown parameters is taken into consideration via probability theory. This provides a degree of uncertainty in the model, yielding predictions that are more realistic and safeguards against overfitting of models more than frequentist approaches. Moreover, the Bayesian approach relies on exact inference, instead of large sample asymptotic approximations. This facilitates an easier and more intuitive interpretation of the credible intervals of the estimated parameters of a predictive model. The flexibility of the Bayesian approach to update prior information about the underlying parameters with information from cumulative or past experience is also considered one of its advantages [10]. Despite these advantages, the Bayesian approach is not so popular because it is often computationally intensive, especially for models that involve many variables. Its application requires the use of specialized software and the buy L-660711 sodium salt knowledge to perform MCMC analyses. As such, the Bayesian approach is underutilized in areas of prognostic modeling for general ICU mortality outcomes. Although there were several studies that applied Bayesian MCMC for prediction of in-hospital risk of death, their areas were limited to specific subgroups of patients with diseases such as trauma [11], cancer and AIDS [12], acute myocardial infarction [13] and malaria [14]. These studies were mostly focused on application of Bayesian MCMC approach in variable selection and model choice. In this study, the Bayesian MCMC approach was used for identification of significant risk predictors and estimation of model parameters.Materials and Methods Data collectionThis prospective study involved a cohort of 1,286 critically ill patients who were admitted to the Hospital Sultanah Aminah Johor Bahru (HSA) ICU between January 1, 2009 and June 30, 2010. The single multidisciplinary ICU in HSA is equipped with sixteen beds and provides services to general medical, surgical and trauma patients. Post-coronary artery bypass graft (CABG) patients were excluded from the study because these patients receive treatment in a separate unit in the hospital. Patients who were below 16 years of age, those who were transferred from another ICU/hospital, with less than 4 hours of ICU stay, as well as, patients who were seeking jir.2012.0140 treatment for burns and transplant procedures were excluded from analysis. Data from the first admission was used for patients with multiple admissions. A total of 1,111 patients met the inclusion criteria and were considered for the study. This study was approved by the Medical Research and Ethics Committee, Ministry of Health, Malaysia. The requirement for informed consent from all participants was waived because data collection was based on existing medical and laboratory records and there was no clinical intervention in this study. Patient records or information were anonymized and deidentified prior to analysis. Routine data collection was manually performed by HSA ICU nurses, and then manually transferred to an online database by the medical officers. Individual user accounts were created for each of the data entry personnel in order to preserve SART.S23503 data integrity and traceability. Data collected at the time of ICU admission included socio-demographic (age, gender and ethnicity), admission (date and time of admission, source.Proach in developing ICU prognostic models. The Bayesian approach considers data to be fixed and assumes parameters as random variables, where the uncertainty of unknown parameters is taken into consideration via probability theory. This provides a degree of uncertainty in the model, yielding predictions that are more realistic and safeguards against overfitting of models more than frequentist approaches. Moreover, the Bayesian approach relies on exact inference, instead of large sample asymptotic approximations. This facilitates an easier and more intuitive interpretation of the credible intervals of the estimated parameters of a predictive model. The flexibility of the Bayesian approach to update prior information about the underlying parameters with information from cumulative or past experience is also considered one of its advantages [10]. Despite these advantages, the Bayesian approach is not so popular because it is often computationally intensive, especially for models that involve many variables. Its application requires the use of specialized software and the knowledge to perform MCMC analyses. As such, the Bayesian approach is underutilized in areas of prognostic modeling for general ICU mortality outcomes. Although there were several studies that applied Bayesian MCMC for prediction of in-hospital risk of death, their areas were limited to specific subgroups of patients with diseases such as trauma [11], cancer and AIDS [12], acute myocardial infarction [13] and malaria [14]. These studies were mostly focused on application of Bayesian MCMC approach in variable selection and model choice. In this study, the Bayesian MCMC approach was used for identification of significant risk predictors and estimation of model parameters.Materials and Methods Data collectionThis prospective study involved a cohort of 1,286 critically ill patients who were admitted to the Hospital Sultanah Aminah Johor Bahru (HSA) ICU between January 1, 2009 and June 30, 2010. The single multidisciplinary ICU in HSA is equipped with sixteen beds and provides services to general medical, surgical and trauma patients. Post-coronary artery bypass graft (CABG) patients were excluded from the study because these patients receive treatment in a separate unit in the hospital. Patients who were below 16 years of age, those who were transferred from another ICU/hospital, with less than 4 hours of ICU stay, as well as, patients who were seeking jir.2012.0140 treatment for burns and transplant procedures were excluded from analysis. Data from the first admission was used for patients with multiple admissions. A total of 1,111 patients met the inclusion criteria and were considered for the study. This study was approved by the Medical Research and Ethics Committee, Ministry of Health, Malaysia. The requirement for informed consent from all participants was waived because data collection was based on existing medical and laboratory records and there was no clinical intervention in this study. Patient records or information were anonymized and deidentified prior to analysis. Routine data collection was manually performed by HSA ICU nurses, and then manually transferred to an online database by the medical officers. Individual user accounts were created for each of the data entry personnel in order to preserve SART.S23503 data integrity and traceability. Data collected at the time of ICU admission included socio-demographic (age, gender and ethnicity), admission (date and time of admission, source.

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