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Observational Study
, 15 (7), 846-853

Predicting Intensive Care Unit Readmission With Machine Learning Using Electronic Health Record Data

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Observational Study

Predicting Intensive Care Unit Readmission With Machine Learning Using Electronic Health Record Data

Juan C Rojas et al. Ann Am Thorac Soc.

Abstract

Rationale: Patients transferred from the intensive care unit to the wards who are later readmitted to the intensive care unit have increased length of stay, healthcare expenditure, and mortality compared with those who are never readmitted. Improving risk stratification for patients transferred to the wards could have important benefits for critically ill hospitalized patients.

Objectives: We aimed to use a machine-learning technique to derive and validate an intensive care unit readmission prediction model with variables available in the electronic health record in real time and compare it to previously published algorithms.

Methods: This observational cohort study was conducted at an academic hospital in the United States with approximately 600 inpatient beds. A total of 24,885 intensive care unit transfers to the wards were included, with 14,962 transfers (60%) in the training cohort and 9,923 transfers (40%) in the internal validation cohort. Patient characteristics, nursing assessments, International Classification of Diseases, Ninth Revision codes from prior admissions, medications, intensive care unit interventions, diagnostic tests, vital signs, and laboratory results were extracted from the electronic health record and used as predictor variables in a gradient-boosted machine model. Accuracy for predicting intensive care unit readmission was compared with the Stability and Workload Index for Transfer score and Modified Early Warning Score in the internal validation cohort and also externally using the Medical Information Mart for Intensive Care database (n = 42,303 intensive care unit transfers).

Results: Eleven percent (2,834) of discharges to the wards were later readmitted to the intensive care unit. The machine-learning-derived model had significantly better performance (area under the receiver operating curve, 0.76) than either the Stability and Workload Index for Transfer score (area under the receiver operating curve, 0.65), or Modified Early Warning Score (area under the receiver operating curve, 0.58; P value < 0.0001 for all comparisons). At a specificity of 95%, the derived model had a sensitivity of 28% compared with 15% for Stability and Workload Index for Transfer score and 7% for the Modified Early Warning Score. Accuracy improvements with the derived model over Modified Early Warning Score and Stability and Workload Index for Transfer were similar in the Medical Information Mart for Intensive Care-III cohort.

Conclusions: A machine learning approach to predicting intensive care unit readmission was significantly more accurate than previously published algorithms in both our internal validation and the Medical Information Mart for Intensive Care-III cohort. Implementation of this approach could target patients who may benefit from additional time in the intensive care unit or more frequent monitoring after transfer to the hospital ward.

Keywords: intensive care unit; machine learning; patient readmission; prediction score; quality improvement.

Figures

Figure 1.
Figure 1.
Patient flow diagram, including outcomes and patients excluded from analysis. ICU = intensive care unit; OR = operating room.
Figure 2.
Figure 2.
Twenty most important predictor variables in the gradient boosted machine model, scaled to a maximum of 100. The scaled numbers represent the relative importance of each variable, which is calculated by weighing model improvement as a result of each time a variable is used to split the data, averaged over all trees in the final model. BUN = blood urea nitrogen; Fio2 = fraction of inspired oxygen; ICU = intensive care unit; SD = standard deviation; Spo2 = oxygen saturation as measured by pulse oximetry.
Figure 3.
Figure 3.
Partial plot of the effect of (A) blood urea nitrogen (BUN), (B) Braden scale, (C) oxygen saturation as measured by pulse oximetry/fraction of inspired oxygen (Spo2/Fio2) ratio, and (D) albumin on the risk of readmission to the intensive care unit across different values in the gradient boosted machine model.

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