Applicability of commonly used Caucasian prediction equations for spirometry interpretation in India

The applicability of Caucasian prediction equations in interpreting spirometry data in Indian patients has not been studied. The present study was undertaken to see if Caucasian and north Indian prediction equations can be used interchangeably while interpreting routine spirometric data.

Forced vital capacity (FVC), forced expiratory volume in first second (FEV(1)), and FEV(1)/FVC ratio were recorded from 14733 consecutive spirometry procedures in adults. Predicted values and lower limits of normality were calculated using regression equations previously derived at this centre, and four commonly used Caucasian equations described by Knudson, Crapo, European Community for Coal and Steel (ECCS) and the Third National Health and Nutrition Examination Survey (NHANES III). For men, 90 per cent of predicted values were also derived. Kappa estimates were used to study agreement, and Bland Altman analysis was performed to quantify differences, between interpretations from Indian and Caucasian equations. Receiver operating characteristic (ROC) curves were constructed to assess utility of using a fixed percentage of Caucasian predicted values in categorizing FVC or FEV(1) as abnormal.

The use of Caucasian prediction equations (and 90% of predicted values in men) resulted in poor agreement with Indian equation in most height and age categories among both men and women. Bland Altman analysis revealed a large bias and wide confidence limits between Caucasian and Indian equations, indicating that the two cannot be used interchangeably. ROC analysis failed to yield good results with use of any single fixed percentage of Caucasian predicted value while categorizing FVC or FEV(1).

Our results showed that the use of Caucasian prediction equations, or a fixed percentage of their predicted values, resulted in misinterpretation of spirometry data in a significant proportion of patients. There is a need to assess performance of more than one regression equation before choosing any single prediction equation.