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. 2024 Nov;12(11):6089.
doi: 10.18103/mra.v12i11.6089.

Augmenting the Hospital Score with social risk factors to improve prediction for 30-day readmission following acute myocardial infarction

Iben Ricket  1 , Michael E Matheny  2   3   4   5 , Ruth M Reeves  2   5 , Rashmee U Shah  6 , Christine A Goodrich  1 , Glenn Gobbel  2   3   4   5 , Meagan E Stabler  1 , Amy M Perkins  3   5 , Freneka Minter  2 , Chad Dorn  2 , Bruce E Bray  6   7 , Lee Christensen  7 , Ramkiran Gouripeddi  7   8 , John Higgins  1 , Wendy W Chapman  9 , Todd MacKenzie  1 , Jeremiah R Brown  1
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Augmenting the Hospital Score with social risk factors to improve prediction for 30-day readmission following acute myocardial infarction

Iben Ricket et al. Med Res Arch. 2024 Nov.

Abstract

Background: Hospital Score is a well-known and validated tool for predicting readmission risk among diverse patient populations. Integrating social risk factors using natural language processing with the Hospital Score may improve its ability to predict 30-day readmissions following an acute myocardial infarction.

Methods: A retrospective cohort included patients hospitalized at Vanderbilt University Medical Center between January 1, 2007, and December 31, 2016, with a primary index diagnosis of acute myocardial infarction, who were discharged alive. To supplement ascertainment of 30-day readmissions, data were linked to Center for Medicare & Medicaid Services (CMS) administrative data. Clinical notes from the cohort were extracted, and a natural language processing model was deployed, counting mentions of eight social risk factors. A logistic regression prediction model was run using the Hospital Score composite, its component variables, and the natural language processing-derived social risk factors. ROC comparison analysis was performed.

Results: The cohort included 6,165 unique patients, where 4,137 (67.1%) were male, 1,020 (16.5%) were Black or other people of color, the average age was 67 years (SD: 13), and the 30-day hospital readmission rate was 15.1% (N=934). The final test-set AUROCs were between 0.635 and 0.669. The model containing the Hospital Score component variables and the natural language processing-derived social risk factors obtained the highest AUROC.

Discussion: Social risk factors extracted using natural language processing improved model performance when added to the Hospital Score composite. Clinicians and health systems should consider incorporating social risk factors when using the Hospital Score composite to evaluate risk for readmission among patients hospitalized for acute myocardial infarction.

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Conflict of interest statement

Conflicts of interest: Iben M. Ricket: No potential conflicts existMichael E Matheny: No potential conflicts existRuth M. Reeves: No potential conflicts existRashmee U. Shah: No potential conflicts existChristine A. Goodrich: No potential conflicts existMeagan E. Stabler: No potential conflicts existAmy M. Perkins: No potential conflicts existFreneka Minter: No potential conflicts existChad Dorn: No potential conflicts existBruce E. Bray: No potential conflicts existLee Christensen: No potential conflicts existRamkiran Gouripeddi: No potential conflicts existJohn Higgins: No potential conflicts existWendy W. Chapman: No potential conflicts existTodd A. MacKenzie: No potential conflicts existJeremiah R. Brown: No potential conflicts exist

Figures

Figure 1.
Figure 1.
Definitions and attribute status for NLP-derived social risk factors *Attribute status included any, positive, negative, or uncertain **ADL: activities of daily living; IADL: instrumental activities of daily living Due to data completeness, language barrier was excluded from this study.
Figure 2.
Figure 2.
Pooled* area under the receiver operating curve (AUROC) from logistic regression prediction model using five unique combinations** of hospital score and NLP-derived social risk factor variables to predict 30-day hospital readmission following acute myocardial infarction among VUMC study cohort *Calculated on the test-set and pooled across 20 imputed files following Rubin’s rules **(1) HS, (2) HSC, (3) NSRF, (4) HS+NSRF, and (5) HSC + NSRF HS=Hospital Score Composite; HSC=Hospital Score component variables; NSRF=NLP-derived social risk factors
See this image and copyright information in PMC

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