Clinical Investigation| Volume 24, ISSUE 6, P357-362, June 2018

Early Identification of Patients With Acute Decompensated Heart Failure

Published:September 05, 2017DOI:


      • Machine learning improves identification of heart failure patients over automated approaches that rely on a few structured variables.
      • Improvement in identification may be related to use of free text from clinical notes.
      • Initial screening with the use of a machine learning algorithm can reduce time needed for providers to perform chart review, thus improving efficiency of care.



      Interventions to reduce readmissions after acute heart failure hospitalization require early identification of patients. The purpose of this study was to develop and test accuracies of various approaches to identify patients with acute decompensated heart failure (ADHF) with the use of data derived from the electronic health record.

      Methods and Results

      We included 37,229 hospitalizations of adult patients at a single hospital during 2013–2015. We developed 4 algorithms to identify hospitalization with a principal discharge diagnosis of ADHF: 1) presence of 1 of 3 clinical characteristics, 2) logistic regression of 31 structured data elements, 3) machine learning with unstructured data, and 4) machine learning with the use of both structured and unstructured data. In data validation, algorithm 1 had a sensitivity of 0.98 and positive predictive value (PPV) of 0.14 for ADHF. Algorithm 2 had an area under the receiver operating characteristic curve (AUC) of 0.96, and both machine learning algorithms had AUCs of 0.99. Based on a brief survey of 3 providers who perform chart review for ADHF, we estimated that providers spent 8.6 minutes per chart review; using this this parameter, we estimated that providers would spend 61.4, 57.3, 28.7, and 25.3 minutes on secondary chart review for each case of ADHF if initial screening were done with algorithms 1, 2, 3, and 4, respectively.


      Machine learning algorithms with unstructured notes had the best performance for identification of ADHF and can improve provider efficiency for delivery of quality improvement interventions.

      Key Words

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