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Predicting 30-Day Hospital Readmission Risk in a National Cohort of Patients with Cirrhosis

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Abstract

Background

Early hospital readmission for patients with cirrhosis continues to challenge the healthcare system. Risk stratification may help tailor resources, but existing models were designed using small, single-institution cohorts or had modest performance.

Aims

We leveraged a large clinical database from the Department of Veterans Affairs (VA) to design a readmission risk model for patients hospitalized with cirrhosis. Additionally, we analyzed potentially modifiable or unexplored readmission risk factors.

Methods

A national VA retrospective cohort of patients with a history of cirrhosis hospitalized for any reason from January 1, 2006, to November 30, 2013, was developed from 123 centers. Using 174 candidate variables within demographics, laboratory results, vital signs, medications, diagnoses and procedures, and healthcare utilization, we built a 47-variable penalized logistic regression model with the outcome of all-cause 30-day readmission. We excluded patients who left against medical advice, transferred to a non-VA facility, or if the hospital length of stay was greater than 30 days. We evaluated calibration and discrimination across variable volume and compared the performance to recalibrated preexisting risk models for readmission.

Results

We analyzed 67,749 patients and 179,298 index hospitalizations. The 30-day readmission rate was 23%. Ascites was the most common cirrhosis-related cause of index hospitalization and readmission. The AUC of the model was 0.670 compared to existing models (0.649, 0.566, 0.577). The Brier score of 0.165 showed good calibration.

Conclusion

Our model achieved better discrimination and calibration compared to existing models, even after local recalibration. Assessment of calibration by variable parsimony revealed performance improvements for increasing variable inclusion well beyond those detectable for discrimination.

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Acknowledgments

JK was supported by the Department of Veterans Affairs, Office of Academic Affiliations, Advanced Fellowship Program in Medical Informatics, and the Department of Biomedical Informatics, Vanderbilt University, Nashville, TN. GC was supported by the NIH Precision Medicine Initiative Cohort Program Data and Research Support Center (1U2COD023196). MEM, GC, and SBH were supported by Veterans Health Administration Health Services Research & Development (HSR&D) Investigator Initiated Research (IIR 13-052). Support for SED was provided by National Library of Medicine (5T15LM007450).

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Authors and Affiliations

  1. Tennessee Valley Healthcare System (TVHS) VA Medical Center, Veterans Health Administration, Nashville, TN, USA

    Jejo D. Koola, Aize Cao & Michael E. Matheny

  2. Division of Hospital Medicine, Department of Medicine, University of California, San Diego, CA, USA

    Jejo D. Koola

  3. Health System Department of Biomedical Informatics, University of California, San Diego, CA, USA

    Jejo D. Koola

  4. VA San Diego Healthcare System, San Diego, CA, USA

    Sam B. Ho

  5. Division of Gastroenterology, Department of Medicine, University of California, San Diego, CA, USA

    Sam B. Ho

  6. Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA

    Aize Cao, Sharon E. Davis & Michael E. Matheny

  7. Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA

    Guanhua Chen

  8. Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA

    Amy M. Perkins & Michael E. Matheny

  9. Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

    Michael E. Matheny

  10. Mohammed Bin Rashid University of Medicine and Health Sciences (MBRU), Dubai, UAE

    Sam B. Ho

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Appendix

Appendix

See Fig. 7 and Tables 6, 7, 8, 9, 10, 11, 12, and 13.

Fig. 7
figure 7

Planned readmission algorithm as defined by the Centers for Medicare & Medicaid Services version 3. In addition to ICD-9 codes, the procedure or diagnosis in Tables 6, 7, 8, and 9 is defined by Clinical Classification Software (CCS)

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Table 6 Procedure categories that are always planned
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Table 7 Diagnosis categories that are always planned
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Table 8 Potentially planned procedure categories
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Table 9 Acute diagnosis categories
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Table 10 List of predictor variables in the final model (total of 47) with summary statistics
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Table 11 List of all initial candidate predictor variables with summary statistics
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Table 12 Code definitions for comorbid conditions used in the model
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Table 13 The event-specific net reclassification index improvement comparing our model against the three extant models, Berman, Bajaj, and Singal
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NRI Results Expanded

The net reclassification index compares our primary model against each established model with values > 0 indicating improved prediction performance. Performance is shown for two use cases: (a) identifying patients at very low risk of readmission, < 10%, and (b) finding very high-risk patients, > 40% risk of readmission. Our model shows improved overall performance and outcome-specific performance (readmission versus no readmission). The outcome-specific NRI can be interpreted as the change in true positive rate for predicting readmission (or conversely improvement in the true negative rate for predicting no readmission).

When assessing our model’s performance at identifying low-risk patients (predicted probability of readmission within 30 days < 10%), the NRI shows significant improvement compared to all three extant models for the Berman, Bajaj, and Singal. The improvement was primarily driven by the model’s ability to identify patients who were not readmitted. When identifying high-risk patients (predicted probability of readmission within 30 days > 40%), the event-specific NRI for high-risk patients, i.e., predicting readmission accurately, was significantly better for our model compared to the Berman, Bajaj, and Singal.

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Koola, J.D., Ho, S.B., Cao, A. et al. Predicting 30-Day Hospital Readmission Risk in a National Cohort of Patients with Cirrhosis. Dig Dis Sci 65, 1003–1031 (2020). https://doi.org/10.1007/s10620-019-05826-w

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