TY - JOUR
T1 - Early Identification of Patients With Acute Decompensated Heart Failure
AU - Blecker, Saul
AU - Sontag, David
AU - Horwitz, Leora I.
AU - Kuperman, Gilad
AU - Park, Hannah
AU - Reyentovich, Alex
AU - Katz, Stuart D.
N1 - Funding Information:
Funding: This work was supported by the Agency for Healthcare Research and Quality , grant K08HS23683 .
Publisher Copyright:
© 2017 Elsevier Inc.
PY - 2018/6
Y1 - 2018/6
N2 - Background: 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. Conclusions: 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.
AB - Background: 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. Conclusions: 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.
KW - Phenotype
KW - electronic health record
KW - heart failure
KW - hospitalization
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U2 - 10.1016/j.cardfail.2017.08.458
DO - 10.1016/j.cardfail.2017.08.458
M3 - Article
AN - SCOPUS:85030781558
SN - 1071-9164
VL - 24
SP - 357
EP - 362
JO - Journal of Cardiac Failure
JF - Journal of Cardiac Failure
IS - 6
ER -