Year: 2022
Type: article
Study design: Observational Study
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.
Institutions: Mayo Clinic, New York University
Language: English
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,000
OpenAlex ID: W4220811131