Evidence map›Paper›PMID 36688301›Full record

ArticleCirculation. Cardiovascular quality and outcomes2023

Validity of International Classification of Diseases (ICD)-10 Diagnosis Codes for Identification of Acute Heart Failure Hospitalization and Heart Failure with Reduced Versus Preserved Ejection Fraction in a National Medicare Sample.

Benjamin A Bates, Ehimare Akhabue, Meghan M Nahass, Abhigyan Mukherjee, Emily Hiltner, Joanna Rock, Brandon Wilton, Garima Mittal, Aayush Visaria, Melanie Rua and 3 more

Open access · bronzeAbstract read
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In one paragraph

Article in Circulation. Cardiovascular quality and outcomes, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers.

0numbers the graph read from it
0cells of the map it votes in
66citing papers in PubMed
18.4field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

66 citing papers in PubMed, 84 citations in OpenAlex.

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  8. Electronic health records research: easy evidence, harder oversight.European heart journal. Digital health · 2026
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6 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors at 2 institutions in 2 countries.

Benjamin A BatesCenter for Pharmacoepidemiology and Treatment Science, Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ (B.A.B., M.R., P.G., C.V.D., S.S.).ORCID 0000-0002-1221-4028
Ehimare AkhabueDepartment of Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ (B.A.B., E.A., M.M.N., A.M., E.H., J.R., B.W., A.V., S.S.).ORCID 0000-0003-0680-8011
Meghan M NahassDepartment of Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ (B.A.B., E.A., M.M.N., A.M., E.H., J.R., B.W., A.V., S.S.).
Abhigyan MukherjeeDepartment of Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ (B.A.B., E.A., M.M.N., A.M., E.H., J.R., B.W., A.V., S.S.).
Emily HiltnerDepartment of Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ (B.A.B., E.A., M.M.N., A.M., E.H., J.R., B.W., A.V., S.S.).ORCID 0000-0001-5241-7498
Joanna RockDepartment of Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ (B.A.B., E.A., M.M.N., A.M., E.H., J.R., B.W., A.V., S.S.).ORCID 0000-0002-2050-5124
Brandon WiltonDepartment of Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ (B.A.B., E.A., M.M.N., A.M., E.H., J.R., B.W., A.V., S.S.).ORCID 0000-0001-6481-0035
Garima MittalRutgers School of Public Health, Rutgers University, Piscataway, NJ (G.M.).ORCID 0000-0002-5816-1371
Aayush VisariaDepartment of Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ (B.A.B., E.A., M.M.N., A.M., E.H., J.R., B.W., A.V., S.S.).ORCID 0000-0002-2170-212X
Melanie RuaCenter for Pharmacoepidemiology and Treatment Science, Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ (B.A.B., M.R., P.G., C.V.D., S.S.).ORCID 0000-0001-8597-6392
Poonam GandhiCenter for Pharmacoepidemiology and Treatment Science, Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ (B.A.B., M.R., P.G., C.V.D., S.S.).ORCID 0000-0003-0939-829X
Chintan V DaveCenter for Pharmacoepidemiology and Treatment Science, Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ (B.A.B., M.R., P.G., C.V.D., S.S.).ORCID 0000-0002-7160-9345
Soko SetoguchiCenter for Pharmacoepidemiology and Treatment Science, Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ (B.A.B., M.R., P.G., C.V.D., S.S.).ORCID 0000-0002-1583-752X
Johnson University · USRutgers, The State University of New Jersey · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHeart failure (HF) is a leading cause of hospitalization in older adults. Medicare data have been used to assess HF outcomes. However, the validity of ICD-10 diagnosis codes (used since 2015) to identify acute HF hospitalization or distinguish reduced (heart failure with reduced ejection fraction) versus preserved ejection fraction (HFpEF) is unknown in Medicare data.

methodsUsing Medicare data (2015-2017), we randomly sampled 200 HF hospitalizations with ICD-10 diagnosis codes for HF in the first/second claim position in a 1:1:2 ratio for systolic HF (I50.2), diastolic HF (I50.3), and other HF (I50.X). The primary gold standards included recorded HF diagnosis by a treating physician for HF hospitalization, ejection fraction (EF)≤50 for heart failure with reduced ejection fraction, and EF>50 for HFpEF. If the quantitative EF was not present, then qualitative descriptions of EF were used for heart failure with reduced ejection fraction/HFpEF gold standards. Multiple secondary gold standards were also tested. Gold standard data were extracted from medical records using standardized forms and adjudicated by cardiology fellows/staff. We calculated positive predictive values with 95% CIs.

resultsThe 200-chart validation sample included 50 systolic, 50 diastolic, 47 combined dysfunction, and 53 unspecified HF patients. The positive predictive values of acute HF hospitalization was 98% [95% CI, 95-100] for first-position ICD-10 HF diagnosis and 66% [95% CI, 58-74] for first/second-position diagnosis. Quantitative EF was available for ≥80% of patients with systolic, diastolic, or combined dysfunction ICD-10 codes. The positive predictive value of systolic HF codes was 90% [95% CI, 82-98] for EFs≤50% and 72% [95% CI, 60-85] for EFs≤40%. The positive predictive value was 92% [95% CI, 85-100] for HFpEF for EFs>50%. The ICD-10 codes for combined or unspecified HF poorly predicted heart failure with reduced ejection fraction or HFpEF.

conclusionsICD-10 principal diagnosis identified acute HF hospitalization with a high positive predictive value. Systolic and diastolic ICD-10 diagnoses reliably identified heart failure with reduced ejection fraction and HFpEF when EF 50% was used as the cutoff.

Indexed as

Heart FailureVentricular Dysfunction, LeftAgedHospitalizationHumansInternational Classification of DiseasesMedicarePrognosisStroke VolumeUnited Statesheart failureheart failure, diastolicheart failure, systolichospitalizationMedicarepredictive value of testsvalidation study

Identifiers

PMID36688301
OpenAlexW4317743484

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.