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Research Article
Originally Published 23 January 2012
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Classification of Heart Failure in the Atherosclerosis Risk in Communities (ARIC) Study: A Comparison of Diagnostic Criteria

Wayne D Rosamond, PhD, MS, Patricia P. Chang, MD, Chris Baggett, PhD, Anna Johnson, PhD, Alain G. Bertoni, MD, Eyal Shahar, MD, Anita Deswal, MD, MPH, Gerardo Heiss, MD, PhD, and Lloyd E. Chambless, PhDAuthor Info & Affiliations
Circulation: Heart Failure
Volume 5, Number 2
https://doi.org/10.1161/CIRCHEARTFAILURE.111.963199
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Abstract

Background—

Population-based research on heart failure (HF) is hindered by lack of consensus on diagnostic criteria. Framingham (FRM), National Health and Nutrition Examination Survey (NHANES), Modified Boston (MBS), Gothenburg (GTH), and International Classification of Disease, 9th Revision, Clinical Modification (ICD-9-CM) code criteria, do not differentiate acute decompensated heart failure (ADHF) from chronic stable HF. We developed a new classification protocol for identifying ADHF in the Atherosclerosis Risk in Communities (ARIC) Study and compared it with these other schemes.

Methods and Results—

A sample of 1180 hospitalizations with a patient address in 4 study communities and eligible discharge codes were selected. After assessing whether the chart contained evidence of possible HF signs, 705 were fully abstracted. Two independent reviewers classified each case as ADHF, chronic stable HF, or no HF, using ARIC classification guidelines. Fifty-nine percent of cases met ARIC criteria for ADHF and 13.9% and 27.1% were classified as chronic stable HF or no HF, respectively. Among events classified as HF by FRM criteria, 68.4% were validated as ADHF, 9.6% as chronic stable HF, and 21.9% as no HF. However, 92.5% of hospitalizations with a primary ICD-9-CM 428 “heart failure” code were validated as ADHF. Sensitivities of comparison criteria to classify ADHF ranged from 38–95%, positive predictive values from 62–92%, and specificities from 19–96%.

Conclusions—

Although comparison criteria for classifying HF were moderately sensitive in identifying ADHF, specificity varied when applied to a randomly selected set of suspected HF hospitalizations in the community.

Introduction

Heart failure (HF) is a complex clinical syndrome resulting from a structural or functional cardiac disorder that impairs the ability of one or both ventricles to fill with or eject blood sufficiently to meet the needs of the body. There is no universally accepted definition of HF.1,2 Signs and symptoms may differ, depending on the level of systolic or diastolic dysfunction, and further complicate disease classification.3,4 Various diagnostic criteria are published, and comparisons between these criteria report mixed results.5–14 Population-based studies in HF are challenged by the lack of clear diagnostic consensus, making estimates of prevalence and incidence difficult to interpret and compare.15–18 Furthermore, currently available classification criteria do not differentiate acute decompensated HF (ADHF) episodes from other clinical events accompanied with chronic stable HF. Separating acute from chronic HF in population-based studies would enhance our understanding of prediction and prevention of HF as well as provide better estimates of trends of the HF burden in the general population. In 2005, the Atherosclerosis Risk in Communities (ARIC) Study began surveillance of HF and developed a process to classify hospitalizations for ADHF and chronic stable HF. The purpose of this report is to describe the ARIC HF classification guidelines and compare its classification of ADHF and chronic stable HF with 5 established diagnostic schemes for HF.
Clinical Perspective on p 159

Methods

Beginning in 2005, the ARIC Study conducted continuous, retrospective surveillance of hospital discharges for HF for all residents age 55 years and older in 4 US communities: Forsyth County, North Carolina; the city of Jackson, Mississippi; 8 northwest suburbs of Minneapolis, Minnesota; and Washington County, Maryland. In 2005, there were 31 hospitals serving the 4 ARIC communities. The combined population in 2005 for these regions was approximately 177 000 men and women 55 years of age or older. Because of the small number of hospitalizations in the sample among race/ethnic groups other than black or white (n=55), we categorized these as white for the purposes of these analyses.
Annual electronic discharge indices were obtained from all hospitals admitting residents from the 4 ARIC communities. Discharges meeting eligibility criteria were sampled from these files. A hospitalization was considered eligible for validation as a HF event based on its International Classification of Disease, 9th Revision, Clinical Modification (ICD-9-CM) code, age, sex, race, and residence in the community surveillance area. Target primary or secondary hospital discharge diagnoses codes included heart failure(428), rheumatic heart disease (398.91), hypertensive heart disease with congestive heart failure (402.01, 402.11, 402.91), hypertensive heart disease and renal failure with congestive HF (404.01, 404.03, 404.13, 404.91, 404.93), acute cor pulmonale (415.0), chronic pulmonary heart disease, unspecified (416.9), other primary cardiomyopathies (425.4), acute edema of lung, unspecified (518.4), and dyspnea and respiratory abnormalities (786.0). Sampling probabilities were created to optimize variance estimates around event rate estimates with a preset maximum number of cases to be abstracted in 2005 of 1499 (see online-only Data Supplement Methods). This fixed number of abstractions was estimated and set based on a target number (n=500) of hospitalized events that could be investigated and validated considering available resources and time constraints. All analyses were weighted to account for the sampling probabilities.

Diagnostic Methods

Centrally trained and certified staff abstracted data from eligible medical records in 2 steps. First, the record was reviewed for any evidence of relevant HF symptoms (ie, new onset or worsening of shortness of breath, edema, paroxysmal nocturnal dyspnea, orthopnea, or hypoxia) or any mention by the treating physicians of HF as the reason for the hospitalization. If the hospitalization included such evidence, a second more detailed abstraction of the medical record was completed. Detailed abstraction included recording evidence of new onset of symptoms, history of HF, general medical history, physical examination signs and symptoms, diagnostic tests (chest radiograph, echocardiogram, cardiac catheterization, coronary angiography, cardiac radionuclide ventriculogram, cardiac MRI, cardiac CT scan, stress test), biomarkers (brain natriuretic peptide [BNP], N-terminal prohormone BNP), and medications. Data abstracted included required elements of 4 diagnostic criteria commonly used in comparative studies: Framingham (FRM),5 modified Boston (MBS),6 National Health and Nutrition Examination Survey (NHANES),8 and Gothenburg (GTH)7 (see online-only Data Supplement Methods). A fifth HF diagnostic scheme using ICD-9-CM coding was also used. Abstractors made copies of sections of the medical record (discharge summary, history and physical report, admission note, and imaging reports) for use by the ARIC HF Classification Committee. The interabstractor agreement rate for determining whether or not to conduct detailed abstraction in a quality control sample was 99%.
In addition to a HF classification based on the 5 comparison schemes mentioned above (FRM, MBS, NHANES, GTH, and, ICD-9-CM), each hospitalization eligible for full abstraction was independently reviewed by 2 centrally trained and certified physicians on the ARIC HF Classification Committee. The reviewers were provided a report of the abstracted data as well as the copied materials noted above. Each reviewer was provided a summary of the abstracted data noted above (including measurement of ejection fraction and biomarkers) and the copied portions of medical records, and in light of the guidelines below classified each hospitalization into 1 of 5 categories: definite ADHF, possible ADHF, chronic stable HF, HF unlikely, or unclassifiable. Physicians were trained and certified to follow the ARIC classification guidelines were randomly assigned cases to review. A single physician adjudicator (Chair of the ARIC HF Classification Committee) resolved disagreements. For the purpose of this report, we combined cases classified as either definite or possible ADHF into 1 category designated as ADHF.

ARIC HF Event Classification Guidelines

Acute Decompensated HF

Definite ADHF required clear evidence either from symptoms, signs, imaging, or treatment of an acute exacerbation, worsening or new onset of symptoms, or other decompensated circulatory state. Evidence of a decompensated state included augmentation of therapy for worsening HF signs or symptoms, documentation of subsequent in-hospital control of symptoms by therapy, documentation of the specificity of HF for decompensated state as opposed to other comorbidities (eg, chronic obstructive pulmonary disease [COPD], end-stage renal disease). For a classification of definite ADHF, evidence that the HF treatment (eg, diuresis) was the main treatment that resulted in improvement is required. For example, control of symptoms by therapy would include diuresis followed by relevant weight loss, clinical improvement in symptoms or of pulmonary edema on chest radiography, or evidence that the patient no longer requires oxygen. A case was considered possible ADHF if the presence of comorbidity could also account for the acute symptoms or if there was not enough information to classify as definite ADHF. For example, in cases in which renal failure, chronic COPD, or pneumonia may also be the etiology of the presentation, or where multiple treatments are provided that result in clinical improvement of symptoms (eg, antibiotics for possible pneumonia, nebulizers for possible COPD, and diuretics for possible HF), then a classification of possible ADHF is preferred.

Chronic Stable HF

Chronic stable HF required evidence of compensated HF signs and symptoms controlled by therapy with no evidence of therapy augmentation or symptom worsening during the hospitalization. Evidence of left ventricular systolic dysfunction (ejection fraction <50%) with no HF symptoms was sufficient for classification as chronic stable HF. Asymptomatic diastolic dysfunction was not sufficient for a classification as chronic stable HF.

HF Unlikely and Unclassifiable Events

Hospitalizations were classified as no HF if the available documentation in the medical record indicated directly or indirectly that heart function was normal. A designation of unclassifiable was usually used in cases were medical records were insufficient to differentiate between a classification of chronic stable HF and no HF or in the infrequent case of missing medical records. For the purposes of these analyses, cases classified as HF unlikely or determined to be unclassifiable were combined as no HF.

Data Analysis

We computed reliability and validity metrics comparing ARIC classification and the 5 comparison diagnostic schemes, using 2 rubrics. First, we compared a 3-level ARIC HF category (ADHF, chronic stable HF, no HF) with results of the algorithms using FRM, MBS, NHANES, GTH, and ICD-9-CM heart failure schemes. Second, we created a more general 2-level ARIC HF classification combining ADHF together with chronic stable HF and compared this 2-level ARIC category (ie, ADHF or chronic stable HF, no HF) with the above criteria.
Calculations of percent agreement and κ coefficients transformed the 3-level MBS and 5-level GTH classifications into dichotomous groups (HF, no HF). We combined GTH criteria levels 2 and 3 together as a positive classification for HF. For MBS criteria, the categories of definite and probable HF were combined. NHANES and FRM criteria were retained as their original 2-level categories. We also created 2 ICD-9-CM code–based criteria for comparison purposes. One considered the presence of an ICD-9-CM 428 code in any position on the discharge list as sufficient to be classified as HF and the other required a 428 code as the primary discharge diagnosis.
Sensitivity, positive predictive value, and specificity using the ARIC HF classification as the gold standard were computed in the standard fashion. The comparability ratio reported was computed as the ratio of the number of HF events defined by established criteria to the number of HF events validated as determined by the ARIC HF guidelines. We defined specificity as the proportion of sampled and reviewed hospitalizations that were classified by ARIC HF review as non-HF events that were classified as non-HF by the comparison criteria.
We assessed percent agreement between ARIC HF classification and the comparison criteria using standard methods19 and chance-corrected agreement by κ coefficients.20 χ2 tests on the weighted proportions were used to determine statistical significance of differences in percentage of events validated by ARIC classification.

Results

In 2005, residents age 55 years or older in the 4 ARIC communities had 11 544 hospital discharges with ICD-9-CM diagnosis codes within our target list. We selected a random sample of 1499 hospitalizations for investigation. After exclusion of hospitalizations in which medical records were unavailable (n=16), that contained ineligible patient addresses (n=303) or that lacked relevant HF symptoms needed for full abstraction (n=475), we conducted detail abstraction and validation of 705 hospitalizations. The agreement rate between 2 physician reviewers for classifying an event was 75% for hospitalization with an ICD-9-CM 428 code and 86% for hospitalizations without an ICD-9-CM 428 code.
Table 1 shows the classification of all sampled HF hospitalizations combining those fully abstracted and reviewed by ARIC (n=705) with those hospitalizations not eligible for full abstraction and not reviewed by the committee (n=475). For the purposes of this analysis, we categorized this latter group as non-HF hospitalizations. For all hospitalizations, 36% were classified as ADHF, 8.5% as chronic stable HF, and 10.2% as no HF. A small percentage (6.3%) were not classifiable by the classification committee and 38.9% did not meet initial screening to merit full review and are considered hospitalizations for reasons other than for HF. Men, blacks, and hospitalizations with an ICD-9-CM 428 code were more likely to be validated as ADHF. Differences in the percentages of events validated as ADHF across the 4 communities were not statistically significant. Among all sampled hospitalizations (including those not meeting the full abstraction criteria) with an ICD-9-CM 428 code in any position, 38.5% were validated as ADHF and 9.1% as chronic stable HF. Of note, 16.8% of cases without an ICD-9-CM 428 code were validated as ADHF by ARIC review. The majority (88.3%) of hospitalizations with an ICD-9-CM 428 code as the primary discharged diagnosis were validated as ADHF.
Table 1. Proportion (Weighted) of Hospitalizations Classified According to Atherosclerosis Risk in Communities Study Heart Failure Criteria by Demographic Characteristics of Events
 Heart Failure Classification by ARIC Classification Committee*Did Not Meet Screening Criteria,†% Total P Value
Acute Decompensated Heart Failure, %Chronic Stable Heart Failure, %No Heart Failure, %Unclassifiable, %
All36.08.510.26.338.9100 
Sex       
    Male39.010.78.46.635.41000.016
    Female33.66.711.76.241.8100 
Race       
    Black42.98.66.65.336.71000.019
    White33.88.411.46.739.7100 
Race, sex       
    Black, male51.58.52.72.834.5100<0.0001
    White, male34.511.410.57.935.7100 
    Black, female35.18.610.17.638.6100 
    White, female33.26.112.25.742.8100 
Community       
    Forsyth Co, NC38.76.47.23.943.71000.083
    Jackson, MS44.48.85.06.935.0100 
    Minneapolis, MN32.19.06.57.245.2100 
    Washington Co, MD31.710.320.28.329.5100 
ICD-9-CM 428 code       
    Any listed       
        Present38.59.17.96.538.0100<0.0001
        Not present16.83.828.05.446.1100 
    Primary discharge code       
        Present88.33.91.41.35.1100<0.0001
        Not present25.09.412.17.446.1100 
ARIC indicates Atherosclerosis Risk in Communities study; ICD, International Classification of Disease.
*
Independent review and classification by 2 physician members of ARIC heart failure (HF) review committee with differences adjudicated by committee chair.
†
Hospitalizations triaged during medical records review for not having sufficient evidence of HF to warrant complete abstraction or review by ARIC HF committee. The percentages shown are estimated from sampled events (n=1180) and account for sampling fractions.
The percentage of hospitalization meriting full abstraction and review that were validated as HF using each of the comparison classification criteria is shown in Table 2. Of the hospitalizations meeting FRM criteria for HF, 68.4% were classified as ADHF by ARIC review. An additional 9.6% were classified as chronic stable HF and 21.9% were determined to be hospitalization for conditions other than HF. Approximately one-quarter of hospitalizations determined not to be HF by FRM criteria were actually classified as ADHF by ARIC review. A similar pattern was seen when comparing ARIC review with MBS, NHANES, and GTH criteria or to presence of an ICD-9-CM 428 code in any position. However, among hospitalizations with a primary discharge diagnosis of HF (ICD-9-CM 428), 93.0% were validated as ADHF.
Table 2. Various Heart Failure Classification Criteria and Percentage Validated as Heart Failure by Atherosclerosis Risk in Communities Study Classification Committee Review
 Heart Failure Classification by ARIC Classification Committee*
Acute Decompensated HF, %Chronic Stable HF, %No HF, %Total, %
All events reviewed59.013.927.1100
Classification criteria    
    Framingham    
        HF present68.49.621.9100
        HF not present26.828.344.9100
    Modified Boston    
        Definite or probable HF63.911.224.9100
        HF unlikely37.225.737.0100
    NHANES    
        HF present61.611.826.6100
        HF not present43.226.430.4100
    Gothenburg†    
    HF present62.214.023.9100
    HF not present48.913.637.5100
    Any listed ICD-9-CM 428 code    
        Present62.214.623.2100
        Not present31.07.062.0100
    Primary discharge ICD-9-CM 428 code    
        Present93.04.12.9100
        Not present46.317.536.2100
ARIC indicates Atherosclerosis Risk in Communities study; HF, heart failure; NHANES, National Health and Nutrition Examination Survey; ICD, International Classification of Disease.
*
Among hospitalized events eligible for review by the ARIC HF Classification Committee. The percentages are weighted to account for the sampling probabilities (705 sampled events yielding a weighted number of 5011).
†
HF present includes Gothenburg levels 2 and 3; HF not present includes Gothenburg levels 0, 1, 4, and 5.
The crude agreement between the various classifications schema was moderate (Table 3). As expected, the agreement between ARIC review and the comparison classification criteria increased when the ARIC review end points of ADHF plus chronic stable HF were combined. Chance corrected estimates of agreement (κ) between criteria were generally poor.
Table 3. Percentage Agreement (κ Coefficient) Between Heart Failure Classification Criteria
Heart Failure Diagnostic Classification CriteriaHeart Failure Diagnostic Classification Criteria*
FraminghamModified BostonNHANESGothenburgICD-9-CM 428
ARIC† (acute decompensated HF)69.5 (0.32)63.7 (0.18)60.9 (0.10)59.5 (0.11)62.9 (0.13)
ARIC‡ (any HF)70.5 (0.21)68.1 (0.10)67.4 (0.03)66.9 (0.13)75.3 (0.22)
Framingham 87.2 (0.61)81.1 (0.38)72.9 (0.24)75.7 (0.14)
Modified Boston  89.5 (0.62)74.2 (0.23)77.4 (0.09)
NHANES   73.3 (0.14)79.3 (0.03)
Gothenburg    74.4 (0.12)
NHANES indicates National Health and Nutrition Examination Survey; ICD, International Classification of Disease; ARIC, Atherosclerosis Risk in Communities study; and HF, heart failure.
*
Among hospitalized events eligible for review by the ARIC HF Classification Committee. The data are weighted to account for the sampling probabilities (705 sampled events yielding a weighted number of 5011).
†
Combines cases classified as definite and possible decompensated HF together as HF=yes; chronic stable HF, or HF unlikely combined as heart failure=no.
‡
Combines cases classified as definite and possible decompensated HF and chronic stable HF together as HF=yes; HF unlikely classified as HF=no.
Framingham criteria were 90% sensitive and 40% specific for classifying ADHF (Table 4). These combined with a positive predictive value of 68% resulted in a comparability ratio of 1.31. The sensitivity and specificity of FRM criteria were slightly reduced to 83% and 37%, respectively, and the positive predictive value increased from 68–78% when compared with the combined ARIC end point of either (ADHF plus chronic stable HF). As a result, the comparability ratio improved to 1.06 when FRM criteria are used to estimate the presence of either ADHF or chronic stable HF. Similar results were seen for MBS, NHANES, and GTH criteria. Although the sensitivity of an ICD-9-CM 428 code in any position was slightly higher (sensitivity=95%) compared with the other established criteria, its comparability ratio for classifying either AHDF deviated from unity more than the other criteria (comparability ratio=1.52). The presence of a primary ICD-9-CM 428 discharge code had high specificity (95%), but poor sensitivity (43%) for ADHF.
Table 4. Sensitivity, Positive Predictive Value, Specificity, and Comparability Ratio for Various Heart Failure Classification Criteria According to Atherosclerosis Risk in Communities Study Heart Failure Classification Criteria
Comparison ClassificationsAtherosclerosis Risk in Communities Study*
Acute Decompensated HF‡Acute Decompensated HF+Chronic Stable HF§
SensitivityPositive Predictive ValueSpecificityComparability RatioSensitivityPositive Predictive ValueSpecificityComparability Ratio
Classification criteria†        
Framingham0.900.680.401.310.830.780.371.06
Modified Boston0.880.640.281.380.840.750.251.12
NHANES0.900.620.191.460.870.730.161.18
Gothenburg0.800.620.301.290.800.760.331.04
Any listed ICD-9-CM 428 code0.950.620.171.520.950.770.231.23
Primary discharge ICD-9-CM 428 code0.430.930.950.460.360.970.970.37
HF indicates heart failure; NHANES, National Health and Nutrition Examination Survey; ICD, International Classification of Disease.
*
Among hospitalized events eligible for review by the Atherosclerosis Risk in Communities Study Heart Failure Classification Committee. Data shown are weighted to account for the sampling probabilities (705 sampled events yielding a weighted number of 5011).
†
Based on transformation of the all criteria into dichotomous groups (HF yes or no) as follows: Gothenburg criteria combines levels 2 and 3 together as HF=yes, otherwise heart failure=no; Modified Boston criteria combines definite and probable HF as HF=yes, otherwise HF=no; NHANES criteria class heart failure “present” defined as HF=yes and HF “not present” as HF=no; ICD-9-CM 428 defines HF=yes if a 428 code is present, otherwise HF=no; Primary Discharge ICD-9-CM defines HF=yes if a 428 code is given as the primary discharge code, otherwise HF=no.
‡
Combines definite and possible decompensated HF together as HF=yes. Classes chronic stable or HF unlikely combined as HF=no.
§
Combines cases classified as definite and possible decompensated HF and chronic stable HF together as HF=yes and HF unlikely as HF=no.
The concordance of hospitalizations classified as HF by FRM, ARIC, or primary discharge diagnosis code is shown in the Figure Only 28% of cases meet all 3 criteria and an equal proportion (28%) met FRM criteria but not ARIC or discharge code criteria. A small proportion of cases (5%) were called HF by ARIC when FRM or discharge code criteria indicated a non-HF event. The percent overlap between these 3 classifications increased to 52% when expanding the discharge code definition to include a 428 code listed in any position (data not shown).
Figure. Overlap between Framingham criteria, Atherosclerosis Risk in Communities (ARIC) Study Heart Failure (HF) Classification Committee review, and International Classification of Disease (ICD)-9-CM 428 code as primary discharge code for the classification of HF hospitalization.

Discussion

The ARIC HF classification guidelines described in this report provide a more detailed categorization of HF hospitalizations than currently available criteria. The ARIC classification was specifically designed to differentiate ADHF from hospitalizations associated with chronic or stable HF, a feature not possible with the other commonly used criteria. Thus, the ARIC HF classification protocol is likely to result in improved accuracy of the rates of ADHF hospitalizations (although, because many people with chronic HF are not hospitalized at the time of diagnosis, it will not necessarily result in improved accuracy of total HF incidence). Although the other criteria were not designed for this level of granularity in classification, evaluating their validity in classifying ADHF as well as total HF may help inform interpretation of previous work as well as shape future studies of HF.
We found that the 5 comparison diagnostic criteria were highly sensitive in identifying ADHF but had poor specificity. Comparison criteria had similar levels of accuracy with one another in identifying any HF (decompensated or chronic stable HF). In contrast, a primary ICD-9-CM 428 discharge code had poor sensitivity in identifying either decompensated or any HF but was highly specific for both. These measures of validity combined with the moderate to poor agreement among all classification schemes underscore the lack of consensus on epidemiological definitions of HF.
The limited population-based data available on the incidence of HF use varying criteria.9,15–17,21–26 Although the FRM criteria have emerged as the standard for the identification of HF in many epidemiological studies, studies disagree about which is diagnostically superior.27 In studies using echocardiographic evidence of left ventricular dysfunction as a gold standard, FRM criteria were found to have high sensitivity (92%) but moderate specificity (79%).4,28 In contrast, FRM criteria were reported to have lower sensitivity and specificity in a study of suspected HF patients who were referred for radioisotopic assessment of systolic ventricular function.29
Remes et al30 reported on cases of clinically suspected HF in comparison with Boston criteria HF diagnosis and found a relatively high sensitivity and specificity (80% and 92%, respectively). In contrast, Mosterd et al13 (1997) report that the sensitivity of FRM, MBS, GTH, and NHANES classification schemes relative to clinical cardiologist's diagnosis vary considerably. In a large community-based study of 7 HF criteria using clinical physician review as the gold standard, all criteria investigated had low sensitivity (range, 46–84%) yet high specificity (range, 81–96%).27
Studies that assess agreement among the criteria are equally as varied in their conclusions. Substantial concordance among the MBS FRM, and NHANES schemes (κ coefficients generally >0.60) have been reported,27,30 but agreement between GTH and the others are poor. Our findings of poor agreement among criteria are supported by previous work comparing FRM criteria, European Society of Cardiology (ESC) criteria, and independent physician review.11
When the FRM criteria were compared with those developed by the Cardiovascular Health Study, the FRM criteria resulted in an incidence estimate approximately 23% greater than the estimate calculated using the Cardiovascular Health Study criteria.31 In another setting, HF incidence varied using Boston, FRM, GTH, and ESC criteria (12%, 11%, 21%, and 9%, respectively).11 Boston criteria more accurately identified HF cases (using physician review as gold standard) than GTH, FRM, and ESC criteria and were better at predicting cardiovascular death, incident disability, and hospitalization.11 The incorporation of echocardiography or biomarkers evidence in physician review, neither of which are included in most established diagnostic criteria may result in earlier detection of less advanced cases and may result in lead-time bias.18,32
Elements included in each of the comparison HF criteria differ. Although all 4 HF criteria incorporate patient medical history and physical examination, a chest radiograph is not required in the GTH criteria, and the FRM score is the only one to incorporate vital capacity. Many of the current criteria rely on elements frequently missing in routine medical records. The FRM and Boston criteria rely heavily on the presence of pulmonary congestion to diagnose HF; however, this may limit the ability to adequately classify HF in the presence of preserved systolic function.3,29 Key differences in the ARIC HF classification guidelines compared with HF criteria scores are that it incorporates more current diagnostic tests, which have been shown to improve prognostic ability.10,17,33,34 These diagnostic methods are becoming increasingly available for clinical use35 as well as increasingly required in HF definitions used in clinical trials.14
The differentiation between ADHF and chronic stable HF is crucial for epidemiological studies of HF etiology. HF mortality rates differ on the basis of the underlying cause of HF36 and can vary by the population studied and the differential criteria used, making proper categorization of HF essential.25 Although some studies exclude patients who had HF secondary to admission for another illness,16 others include them but do not adequately determine these underlying conditions.25 Given that substantial race/ethnic37 and sex38 differences in HF etiology exist, accurate classification is also critical in measuring and preventing HF in different populations. Clinicians and policymakers are concerned with reducing HF rehospitalization. Improved epidemiological methods for differentiating between ADHF and chronic stable HF would improve our accuracy in defining rehospitalization due to ADHF and aid the examination of outcomes and how they related to clinical practice, therapy advances, and policies.
Studies using ICD-9-CM diagnosis codes to define HF are not consistent. Some define HF as a primary discharge code of 428; others include patients with 428 listed in any position. In our study, 39% of hospitalizations with a 428 code in any position were categorized as ADHF and 9% were categorized as chronic stable HF. Further, 12% of cases with a primary discharge diagnosis of HF were determined to not have HF. We found that 17% of cases without a 428 code were validated as ADHF, suggesting that limiting the definition of HF to a code of 428 may result in inaccurately low estimates of HF. Indeed, studies using claims databases often use the presence of primary diagnosis codes 402 or 404 in addition to 428 to define HF events.39,40 In our study, adding these additional code groups did not appreciably change the validation estimates (data not shown).

Strengths and Limitations

Diagnostic accuracy was rigorously tested, with each case being subject to review by 2 independent physician reviewers. However, a number of study limitations must be considered. In some chronic HF cases, it may be difficult to determine whether the patient's status matches the baseline HF status or indicates some deterioration. In these cases, the totality of the evidence provided was taken into consideration. A potential limitation in all studies of this type is that the diagnostic accuracy of criteria depends on the population characteristics including the prevalence of HF.

Conclusions

An improved method of diagnosis of HF is critical if primary and secondary prevention efforts are to target individuals at risk for HF.15 The ARIC HF classification guidelines, created for use in ongoing community and cohort surveillance, provide a methodology for the diagnosis of hospitalized HF and differentiate ADHF and chronic stable HF. These methods could be applied in other study populations in which access to medical records is feasible and some training of reviewers to follow the guidelines presented is practical. We found that a principle ICD-9-CM code for heart failure (code 428) was highly specific but had poor sensitivity for ADHF, using the ARIC classification. A next step in the assessment of this classification system is to investigate its accuracy for the prediction of outcomes such as mortality, disability, and future HF-related hospitalizations and ultimately to evaluate disease trends.

Acknowledgments

We thank the staff and participants of the ARIC Study for their important contributions.

Clinical Perspective

The Atherosclerotic Risk in Communities (ARIC) study describes a novel classification system of hospitalized heart failure, differentiating acute decompensated heart failure from chronic stable heart failure. This classification is compared with older diagnostic criteria (Framingham, Boston, National Health and Nutrition Examination Survey, and Gothenberg) and International Classification of Disease (ICD)-9 code 428 (heart failure), which do not specify whether heart failure is in a decompensated state. The agreement between the ARIC criteria and the comparison criteria for identifying any heart failure was moderate but was worse when acute heart failure was separately classified. However, 92.5% of hospitalizations with a primary ICD-9 discharge code 428 were validated as acute decompensated heart failure. Using the ARIC criteria as the standard, the sensitivities of the comparison criteria were generally high, but the specificities were poor, whereas a primary ICD-9 code 428 demonstrated high specificity but low sensitivity. The variable test characteristics of these older criteria and ICD-9 code underscore the lack of consensus on epidemiological definitions of heart failure, especially acute versus chronic heart failure. As a result, previously described estimates of the prevalence and incidence of heart failure may be inaccurate and unable to discern hospitalizations for acute heart failure from hospitalizations in which chronic heart failure is merely a stable comorbidity. To understand the true epidemiology and natural history of heart failure, improved epidemiological methods for identifying acute heart failure, such as the ARIC classification, would provide better accuracy in defining the burden of heart failure in society as it relates to outcomes, clinical practice, therapy advances, and policies.

Supplemental Material

File (revised_supplemental_materials2.pdf)

Sources of Funding

The ARIC Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts N01-HC-55015, N01-HC-55016, N01-HC-55018, N01-HC-55019, N01-HC-55020, N01-HC-55021, and N01-HC-55022.

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Go to Circulation: Heart Failure
Circulation: Heart Failure
Volume 5 • Number 2 • March 2012
Pages: 152 - 159
PubMed: 22271752

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© 2012 American Heart Association, Inc.

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History

Received: 23 May 2011
Accepted: 11 January 2012
Published online: 23 January 2012
Published in print: March 2012

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Keywords

  1. heart failure
  2. epidemiology

Subjects

  1. Congenital Heart Disease
  2. Epidemiology

Authors

Affiliations

Wayne D Rosamond, PhD, MS
From the Departments of Epidemiology (W.D.R., A.J., G.H.), Medicine (P.P.C.), and Biostatistics (C.B., L.E.C.), University of North Carolina, Chapel Hill, NC; the Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC (A.G.B.); the Division of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ (E.S.); and the Section of Cardiology, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX (A.D.).
View all articles by this author
Patricia P. Chang, MD
From the Departments of Epidemiology (W.D.R., A.J., G.H.), Medicine (P.P.C.), and Biostatistics (C.B., L.E.C.), University of North Carolina, Chapel Hill, NC; the Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC (A.G.B.); the Division of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ (E.S.); and the Section of Cardiology, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX (A.D.).
View all articles by this author
Chris Baggett, PhD
From the Departments of Epidemiology (W.D.R., A.J., G.H.), Medicine (P.P.C.), and Biostatistics (C.B., L.E.C.), University of North Carolina, Chapel Hill, NC; the Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC (A.G.B.); the Division of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ (E.S.); and the Section of Cardiology, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX (A.D.).
View all articles by this author
Anna Johnson, PhD
From the Departments of Epidemiology (W.D.R., A.J., G.H.), Medicine (P.P.C.), and Biostatistics (C.B., L.E.C.), University of North Carolina, Chapel Hill, NC; the Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC (A.G.B.); the Division of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ (E.S.); and the Section of Cardiology, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX (A.D.).
View all articles by this author
Alain G. Bertoni, MD
From the Departments of Epidemiology (W.D.R., A.J., G.H.), Medicine (P.P.C.), and Biostatistics (C.B., L.E.C.), University of North Carolina, Chapel Hill, NC; the Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC (A.G.B.); the Division of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ (E.S.); and the Section of Cardiology, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX (A.D.).
View all articles by this author
Eyal Shahar, MD
From the Departments of Epidemiology (W.D.R., A.J., G.H.), Medicine (P.P.C.), and Biostatistics (C.B., L.E.C.), University of North Carolina, Chapel Hill, NC; the Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC (A.G.B.); the Division of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ (E.S.); and the Section of Cardiology, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX (A.D.).
View all articles by this author
Anita Deswal, MD, MPH
From the Departments of Epidemiology (W.D.R., A.J., G.H.), Medicine (P.P.C.), and Biostatistics (C.B., L.E.C.), University of North Carolina, Chapel Hill, NC; the Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC (A.G.B.); the Division of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ (E.S.); and the Section of Cardiology, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX (A.D.).
View all articles by this author
Gerardo Heiss, MD, PhD
From the Departments of Epidemiology (W.D.R., A.J., G.H.), Medicine (P.P.C.), and Biostatistics (C.B., L.E.C.), University of North Carolina, Chapel Hill, NC; the Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC (A.G.B.); the Division of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ (E.S.); and the Section of Cardiology, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX (A.D.).
View all articles by this author
Lloyd E. Chambless, PhD
From the Departments of Epidemiology (W.D.R., A.J., G.H.), Medicine (P.P.C.), and Biostatistics (C.B., L.E.C.), University of North Carolina, Chapel Hill, NC; the Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC (A.G.B.); the Division of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ (E.S.); and the Section of Cardiology, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX (A.D.).
View all articles by this author

Notes

The online-only Data Supplement is available with this article at http://circheartfailure.ahajournals.org/lookup/suppl/doi:10.1161/CIRCHEARTFAILURE.110.963199/-/DC1.
Correspondence to Wayne Rosamond, PhD, MS, Department of Epidemiology, University of North Carolina at Chapel Hill, 137 E Franklin St, Suite 203, Chapel Hill, NC 27514. E-mail [email protected]

Disclosures

None.

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