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Blecker S, Katz SD, Horwitz LI, et al. Comparison of Approaches for Heart Failure Case Identification From Electronic Health Record Data. JAMA Cardiol. 2016;1(9):1014–1020. doi:10.1001/jamacardio.2016.3236
What is the best way to identify hospitalized patients with heart failure in real time?
In this study of 47 119 hospitalizations, inclusion of heart failure on the problem list had a sensitivity of 0.40 and a positive predictive value (PPV) of 0.96. A logistic regression model with clinical data was associated with a sensitivity of 0.68 and PPV of 0.90, whereas a machine-learning algorithm that used free text had a sensitivity of 0.83 and a PPV of 0.90.
Machine learning with clinical notes has the best predictive accuracy for identification of hospitalized patients with heart failure in real time.
Accurate, real-time case identification is needed to target interventions to improve quality and outcomes for hospitalized patients with heart failure. Problem lists may be useful for case identification but are often inaccurate or incomplete. Machine-learning approaches may improve accuracy of identification but can be limited by complexity of implementation.
To develop algorithms that use readily available clinical data to identify patients with heart failure while in the hospital.
Design, Setting, and Participants
We performed a retrospective study of hospitalizations at an academic medical center. Hospitalizations for patients 18 years or older who were admitted after January 1, 2013, and discharged before February 28, 2015, were included. From a random 75% sample of hospitalizations, we developed 5 algorithms for heart failure identification using electronic health record data: (1) heart failure on problem list; (2) presence of at least 1 of 3 characteristics: heart failure on problem list, inpatient loop diuretic, or brain natriuretic peptide level of 500 pg/mL or higher; (3) logistic regression of 30 clinically relevant structured data elements; (4) machine-learning approach using unstructured notes; and (5) machine-learning approach using structured and unstructured data.
Main Outcomes and Measures
Heart failure diagnosis based on discharge diagnosis and physician review of sampled medical records.
A total of 47 119 hospitalizations were included in this study (mean [SD] age, 60.9 [18.15] years; 23 952 female [50.8%], 5258 black/African American [11.2%], and 3667 Hispanic/Latino [7.8%] patients). Of these hospitalizations, 6549 (13.9%) had a discharge diagnosis of heart failure. Inclusion of heart failure on the problem list (algorithm 1) had a sensitivity of 0.40 and a positive predictive value (PPV) of 0.96 for heart failure identification. Algorithm 2 improved sensitivity to 0.77 at the expense of a PPV of 0.64. Algorithms 3, 4, and 5 had areas under the receiver operating characteristic curves of 0.953, 0.969, and 0.974, respectively. With a PPV of 0.9, these algorithms had associated sensitivities of 0.68, 0.77, and 0.83, respectively.
Conclusions and Relevance
The problem list is insufficient for real-time identification of hospitalized patients with heart failure. The high predictive accuracy of machine learning using free text demonstrates that support of such analytics in future electronic health record systems can improve cohort identification.
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