Abstract:
Context: Determining which patients are ready for discharge from an Intensive Care Unit (ICU) presents a huge
challenge, as ICU readmissions are associated with several negative outcomes such as increased mortality,
length of stay, and cost compared to those patients who are not readmitted during their hospital stay. For
these reasons, enhancing risk stratification in order to identify patients at high risk of clinical deterioration
might benefit and improve the outcomes of critically ill hospitalized patients. Existing work on predicting ICU
readmissions relies on information available at the time of discharge, however, in order to be more useful and
to prevent complications, predictions need to be made earlier.
Goals: In this work, we investigate the hypothesis that the basal characteristics and information collected at
the time of the patient’s admission can enable accurate predictions of ICU readmission.
Materials and Methods: We analyzed an anonymized dataset of 11,805 adult patients from three ICUs in a
Brazilian university hospital. After excluding 1879 patients who died during their first ICU admission, our final
dataset contained 9,926 patients. Of these, 658 patients (6.6%) had been readmitted to the ICU. The original
dataset had 185 attributes, including demographics, length of stay prior to ICU admission, comorbidities,
severity indexes, interventions, organ support care during ICU stay and laboratory results. The problem
of predicting ICU readmissions was modeled as a binary classification task. We tested eight classification
algorithms (including Bayesian algorithms, decision trees, rule-based, and ensemble methods) over different
sets of attributes and evaluated their results based on six metrics.
Results: Predictions made solely based on the attributes collected at the admission are highly accurate. Their
quality in terms of prediction is no different from predictions made using the complete set of attributes for our
dataset and for a subset of attributes selected by a feature selection method. Furthermore, our AUROC score
of 0.91 (95% CI [0.89,0.92]) is higher than existing results published in the literature for other datasets.
Discussion and Conclusion: The results confirm our hypothesis. Our findings suggest that early markers can
be used to anticipate patients at high risk of clinical deterioration after ICU discharge.
