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Cited by 437

The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.

An Automated Model to Identify Heart Failure Patients at Risk for 30-Day Readmission or Death Using Electronic Medical Record Data

Amarasingham, Ruben, Moore, Billy J., Tabak, Ying P., Drazner, Mark H., Clark, Christopher A., Zhang, Song, Reed, W. Gary, Swanson, Timothy S., Ma, Ying, Halm, Ethan A.·2010·Medical Care
DOI 10.1097/mlr.0b013e3181ef60d9
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Abstract

BACKGROUND: A real-time electronic predictive model that identifies hospitalized heart failure (HF) patients at high risk for readmission or death may be valuable to clinicians and hospitals who care for these patients. METHODS: An automated predictive model for 30-day readmission and death was derived and validated from clinical and nonclinical risk factors present on admission in 1372 HF hospitalizations to a major urban hospital between January 2007 and August 2008. Data were extracted from an electronic medical record. The performance of the electronic model was compared with mortality and readmission models developed by the Center for Medicaid and Medicare Services (CMS models) and a HF mortality model derived from the Acute Decompensated Heart Failure Registry (ADHERE model). RESULTS: The 30-day mortality and readmission rates were 3.1% and 24.1% respectively. The electronic model demonstrated good discrimination for 30 day mortality (C statistic 0.86) and readmission (C statistic 0.72) and performed as well, or better than, the ADHERE model and CMS models for both outcomes (C statistic ranges: 0.72-0.73 and 0.56-0.66 for mortality and readmissions respectively; P < 0.05 in all comparisons). Markers of social instability and lower socioeconomic status improved readmission prediction in the electronic model (C statistic 0.72 vs. 0.61, P < 0.05). CONCLUSIONS: Clinical and social factors available within hours of hospital presentation and extractable from an EMR predicted mortality and readmission at 30 days. Incorporating complex social factors increased the model's accuracy, suggesting that such factors could enhance risk adjustment models designed to compare hospital readmission rates.

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Metadata

Authors
Amarasingham, R., Moore, B. J., Tabak, Y. P., Drazner, M. H., Clark, C. A., Zhang, S., Reed, W. G., Swanson, T. S., Ma, Y., & Halm, E. A.
Venue
Medical Care
Publisher
Ovid Technologies (Wolters Kluwer Health)
Published
2010-11
Volume
48
Issue
11
Pages
981-988
ISSN
0025-7079 (ISSN-L)
DOI
10.1097/mlr.0b013e3181ef60d9
OpenAlex
W2056102627
Cited by
437
References
32
FWCI
14.51
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: An Automated Model to Identify Heart Failure Patients at Risk for 30-Day Readmission or Death Using Electronic Medical Record Data. https://app.cochat.ai/papers/10.1097/mlr.0b013e3181ef60d9
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Amarasingham, R., Moore, B. J., Tabak, Y. P., Drazner, M. H., Clark, C. A., Zhang, S., Reed, W. G., Swanson, T. S., Ma, Y., & Halm, E. A. (2010). An Automated Model to Identify Heart Failure Patients at Risk for 30-Day Readmission or Death Using Electronic Medical Record Data. Medical Care. https://doi.org/10.1097/mlr.0b013e3181ef60d9

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