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. Author manuscript; available in PMC: 2019 Dec 1.
Published in final edited form as: Int J Med Inform. 2018 Sep 19;120:1–7. doi: 10.1016/j.ijmedinf.2018.09.016

Table 3:

Heart failure computable phenotype algorithms deployed within the Learning Health Systems PCORnet Clinical Data Research Network, Query period 2013–2016.

Algorithm Age Group Heart failure population
identified
Total
Population
N %

≥1 HF code* (Table 1, Algorithm 1) 30–49 26,903 0.79% 3,423,297
50–64 61,532 2.4% 2,493,470
65+ 166,117 9.0% 1,838,350
All 254,552 3.28% 7,755,117

≥2 HF codes (Table 1, Algorithm 4) 30–49 13,686 0.40% 3,423,297
50–64 31,471 1.26% 2,493,470
65+ 88,858 4.83% 1,838,350
All 134,015 1.73% 7,755,117

≥2 HF codes + any HF medicationf
(Table 1. Algorithm 5)
30–49 6,797 0.23% 2,954,319
50–64 18,951 0.88% 2,143,829
65+ 49,028 3.08% 1,592,416
All 74,776 1.12% 6,690,564
*

Heart failure code is International Classification of Diseases – 9th Revision, Clinical Modification code 428.

Heart failure medications include aldosterone antagonists (eplerenone, spironolactone), HF specific beta blockers (bisoprolol, carvedilol, metoprolol succinate), loop diuretics (bumetanide, ethacrynic acid, furosemide, torsemide), digoxin, angiotensin converting enzyme inhibitors and angiotensin receptor blockers.

These algorithms were deployed within the Learning Health Systems (LHSnet) Clinical Data Research Network of PCORnet. LHSnet is comprised of 6 health systems (Mayo Clinic, Allina Health System, Essentia Health, Intermountain Health Care, University of Michigan and Ohio State University); 1 health plan (Medica Research Institute); 1 data partner based in a university (Arizona State University); and 1 local public health department (Olmsted County Public Health Services). Two health systems did not have medication data available and were not included in queries requiring medication data.