Identifying Patients With Advanced Heart Failure Using Administrative Data

Shannon M Dunlay; Saul Blecker; Phillip J Schulte; Margaret M Redfield; Che G Ngufor; Amy Glasgow

doi:10.1016/j.mayocpiqo.2022.02.001

Identifying Patients With Advanced Heart Failure Using Administrative Data

Mayo Clin Proc Innov Qual Outcomes. 2022 Mar 29;6(2):148-155. doi: 10.1016/j.mayocpiqo.2022.02.001. eCollection 2022 Apr.

Authors

Shannon M Dunlay^{1

2}, Saul Blecker³, Phillip J Schulte⁴, Margaret M Redfield¹, Che G Ngufor², Amy Glasgow²

Affiliations

¹ Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN.
² Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN.
³ Department of Population Health and Medicine, NYU Grossman School of Medicine, New York, NY.
⁴ Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN.

Abstract

Objective: To develop algorithms to identify patients with advanced heart failure (HF) that can be applied to administrative data.

Patients and methods: In a population-based cohort of all residents of Olmsted County, Minnesota, with greater than or equal to 1 HF billing code 2007-2017 (n=8657), we identified all patients with advanced HF (n=847) by applying the gold standard European Society of Cardiology advanced HF criteria via manual medical review by an HF cardiologist. The advanced HF index date was the date the patient first met all European Society of Cardiology criteria. We subsequently developed candidate algorithms to identify advanced HF using administrative data (billing codes and prescriptions relevant to HF or comorbidities that affect HF outcomes), applied them to the HF cohort, and assessed their ability to identify patients with advanced HF on or after their advanced HF index date.

Results: A single hospitalization for HF or ventricular arrhythmias identified all patients with advanced HF (sensitivity, 100%); however, the positive predictive value (PPV) was low (36.4%). More stringent definitions, including additional hospitalizations and/or other signs of advanced HF (hyponatremia, acute kidney injury, hypotension, or high-dose diuretic use), decreased the sensitivity but improved the specificity and PPV. For example, 2 hospitalizations plus 1 sign of advanced HF had a sensitivity of 72.7%, specificity of 89.8%, and PPV of 60.5%. Negative predictive values were high for all algorithms evaluated.

Conclusion: Algorithms using administrative data can identify patients with advanced HF with reasonable performance.

Keywords: ED, emergency department; EF, ejection fraction; ESC, European Society of Cardiology; HF, heart failure; NPV, negative predictive value; PPV, positive predictive value; REP, Rochester Epidemiology Project; VA, ventricular arrhythmia.

Abstract

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