Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2017 Feb 10:2016:1169-1178.
eCollection 2016.

Predicting Negative Events: Using Post-discharge Data to Detect High-Risk Patients

Lina Sulieman  1 , Daniel Fabbri  1 , Fei Wang  2 , Jianying Hu  3 , Bradley A Malin  1
Affiliations

Predicting Negative Events: Using Post-discharge Data to Detect High-Risk Patients

Lina Sulieman et al. AMIA Annu Symp Proc. .

Abstract

Predicting negative outcomes, such as readmission or death, and detecting high-risk patients are important yet challenging problems in medical informatics. Various models have been proposed to detect high-risk patients; however, the state of the art relies on patient information collected before or at the time of discharge to predict future outcomes. In this paper, we investigate the effect of including data generated post discharge to predict negative outcomes. Specifically, we focus on two types of patients admitted to the Vanderbilt University Medical Center between 2010-2013: i) those with an acute event - 704 hip fractures and ii) those with chronic problems - 5250 congestive heart failure (CHF) patients. We show that the post-discharge model improved the AUC of the LACE index, a standard readmission scoring function, by 20 - 30%. Moreover, the new model resulted in higher AUCs by 15 - 27% for hip fracture and 10 - 12% for CHF compared to standard models.

PubMed Disclaimer

Figures

Figure 1.
Figure 1.
Prediction models based on temporal features (a) before and (b) after a negative outcome.
Figure 2.
Figure 2.
Prediction models based on varying temporal features.
Figure 3.
Figure 3.
Building BDAM vector using two checkpoints (a) an example of a randomly sampled checkpoint and (b) an alternative sampled checkpoint
Figure 4.
Figure 4.
Survival rate for patients within one year from the diagnosis.
Figure 5.
Figure 5.
AUC values for BAM, DAM, and ADM models for outcome prediction within one year.
Figure 6.
Figure 6.
AUC values for BDAM model applied at different successive checkpoints for hip fracture patients at 7, 14, 22 and 30 days negative outcome.
Figure 7.
Figure 7.
Outcome predicting within one year for a) hip fracture and b) CHF.
Figure 8.
Figure 8.
Partial dependency plots for outcome predictions for hip fracture patients.
See this image and copyright information in PMC

References

    1. Kansagara D, Englander H, Salanitro A, Kagen D, Theobald C, Freeman M, et al. Risk prediction models for hospital readmission: a systematic review. JAMA. 2011;306(15):1688–98. - PMC - PubMed
    1. Centers for Medicare and Medicaid Services. Readmissions Reduction Program (HRRP) 2016
    1. AHRQ. Hospital Guide to Reducing Medicaid Readmissions. 2016
    1. Farrar S, Ryan M, Ross D, Ludbrook A. Using discrete choice modelling in priority setting: an application to clinical service developments. Soc Sci Med. 2000;50(1):63–75. - PubMed
    1. Mitton C, Donaldson C. Health care priority setting: principles, practice and challenges. Cost Eff Resour Alloc. 2004;2:3. - PMC - PubMed

Publication types

LinkOut - more resources