Modelling risks of hospital mortality for critically ill patients / Rowena Wong Syn Yin

Rowena Wong, Syn Yin (2017) Modelling risks of hospital mortality for critically ill patients / Rowena Wong Syn Yin. PhD thesis, University of Malaya.

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Abstract

Intensive care unit (ICU) prognostic models can be used to predict mortality outcomes for critically ill patients who require intensive treatment due to the severity of their illness. These physiological and statistical-based models stratify patients according to their severity of illness and provide an objective approach in predicting hospital mortality risks. These models are useful tools in assisting clinicians in decision making, interpretation of diagnosis and prescription of appropriate treatment options to patients. They can also be effectively used for benchmarking purposes to evaluate and compare the clinical performances of different ICUs and assist hospital administration in making informed changes in resource allocations. Although these models are predominantly used in developed countries, they are not that popular in developing countries due to costs, facilities and resources considerations. In this study, the advantages, limitations and evolutions of three selected well-established ICU prognostic systems were reviewed and discussed. The Acute Physiology and Chronic Health Evaluation (APACHE IV) model was chosen as the reference model in this study due to its promising potential as a suitable benchmarking tool. The first objective of this study is to investigate the validity of APACHE IV model in predicting mortality risk in a Malaysian ICU. A prospective independent observational study was conducted at a single-centre multidisciplinary ICU in Hospital Sultanah Aminah Johor Bahru (HSA ICU). External validation of APACHE IV involved a cohort of 916 admissions to HSA ICU in the year 2009. APACHE IV was found to be not suitable for application in HSA ICU. Although the model exhibited good discrimination, calibration was observed to be poor. The model overestimated risk of death in HSA ICU, especially for mid- to high- risk patient groups. The model's lack of fit was mainly attributed to differences in case mix and patient management between APACHE IV and HSA ICU. The second objective of this research involves investigation of the significant factors that affect mortality risk in HSA ICU and development of a prognostic model that is suitable for application in HSA ICU. Bayesian Markov Chain Monte Carlo and decision tree approaches were explored as alternative methods in the modelling of ICU risk of death, where five different types of Bayesian models and a decision tree model were proposed in this research. Although the performance of the decision tree model was comparable to the Bayesian models, it was not as informative as the Bayesian models, especially in predicting individual patient mortality risk. One of the Bayesian models was chosen as the best model to be used as the future reference model in HSA ICU. This model comprises seven variables (age, gender, Acute Physiological Score (APS), absence of Glasgow Coma Scale score, mechanical ventilation, presence of chronic health and ICU admission diagnoses) that are readily available in any intensive care unit setting. This research has shown the promising potential of the Bayesian approach as an alternative in the analysis and modelling of ICU mortality risks.

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