Why the study?
Does indirect calibration using a concurrent method improve inter-observer agreement for the NYHA functional classification system in patients reporting breathlessness?
Does indirect calibration using a concurrent method improve inter-observer agreement for the NYHA functional classification system in patients reporting breathlessness?
Indirect calibration using a concurrent method can identify discrepant thresholds between observers and minimize reliability problems in NYHA classification.
May support concurrent calibration to reduce NYHA variability; leaves open prospective validation before clinical adoption.
BACKGROUND: Previous studies showed an inter-observer agreement for the NYHA classification of approximately 55%. The aim of this study was to calibrate the New York Heart Association (NYHA) classification system between observers, increasing its reliability. RESULTS: Among 1136 community-dwellers in Porto, Portugal, aged ≥ 45 years, 265 reporting breathlessness answered a 4-item questionnaire to characterize symptom severity. The questionnaire was administered by 7 physicians who also classified the subject's functional capacity according to NYHA. Each subject was assessed by one physician. We calibrated NYHA classifications by the concurrent method, using 1-parameter logistic graded response model. Discrepancies between observers were assessed by differences in ability thresholds between NYHA classes I-II and II-III. The ability estimated by the model was used to predict the NYHA classification for each observer.Estimates of the first and second thresholds for each observer ranged from -1.92 to 0.46 and from 1.42 to 2.30, respectively. The agreement between estimated ability and the observers' NYHA classification was 88% (kappa = 0.61). CONCLUSIONS: The study objectively indicates the main reason why several studies have reported low inter-observer is the existence of discrepant thresholds between observers in the definition of NYHA classes. The concurrent method can be used to minimize the reliability problem of NYHA classification.
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Severo et al. (2011) studied this question.
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