Abstract
BackgroundThe Sequential Organ Failure Assessment (SOFA) score is commonly used in ICUs around the world, designed to assess the severity of the patient's clinical state based on function/dysfunction of six major organ systems. The goal of this work is to build a computational model to predict mortality based on a series of SOFA scores. In addition, we examined the possibility of improving the prediction by incorporating a new component designed to measure the performance of the gastrointestinal system, added to the other six components.MethodsIn this retrospective study, we used patients’ three latest SOFA scores recorded during an individual ICU stay as input to different machine learning models and ensemble learning models. We added three validated parameters representing gastrointestinal failure. Among others, we used classification models such as Support Vector Machines (SVMs), Neural Networks, Logistic Regression and a penalty function used to increase model robustness in regard to certain extreme cases, which may be found in ICU population. We used the Area under Curve (AUC) performance metric to examine performance.ResultsWe found an ensemble model of linear and logistic regression achieves a higher AUC compared related works in past years. After incorporating the gastrointestinal failure score along with the penalty function, our best performing ensemble model resulted in an additional improvement in terms of AUC metrics. We implemented and compared 36 different models that were built using both the information from the SOFA score as well as that of the gastrointestinal system. All compared models have approximately similar and relatively large AUC (between 0.8645 and 0.9146) with the best results are achieved by incorporating the gastrointestinal parameters into the prediction models.ConclusionsOur findings indicate that gastrointestinal parameters carry significant information as a mortality predictor in addition to the conventional SOFA score. This information improves the predictive power of machine learning models by extending the SOFA to include information related to gastrointestinal organ system. The described method improves mortality prediction by considering the dynamics of the extended SOFA score. Although tested on a limited data set, the results' stability across different models suggests robustness in real-time use.
Highlights
Patient outcome prediction is invaluable in the intensive care unit (ICU) setting
Our findings indicate that gastrointestinal parameters carry significant information as a mortality predictor in addition to the conventional Sequential Organ Failure Assessment (SOFA) score
This information improves the predictive power of machine learning models by extending the SOFA to include information related to gastrointestinal organ system
Summary
The sequential organ failure assessment (SOFA) score is a common scoring tool in ICUs around the world. Since its devise in the 1990’s [1], originally coined as Sepsis-related Organ Failure Assessment, the correlation between mortality and the SOFA score has been well established [2,3] and the score is considered a major tool for assessing mortality risk in the ICU, alongside other commonly used scoring systems, such as the Acute Physiology and Chronic Health Evaluation and the Simplified Acute Physiology Score (APACHE and SAPS, respectively). The Sequential Organ Failure Assessment (SOFA) score is commonly used in ICUs around the world, designed to assess the severity of the patient’s clinical state based on function/ dysfunction of six major organ systems. We examined the possibility of improving the prediction by incorporating a new component designed to measure the performance of the gastrointestinal system, added to the other six components.
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