Normal spirometry predictive values for the Western Indian adult population

Abstract

Interpretation of spirometry involves comparing lung function parameters with predicted values to determine the presence/severity of the disease. The Global Lung Function Initiative (GLI) derived reference equations for healthy individuals aged 3–95 years from multiple populations but highlighted India as a “particular group” in whom further data are needed. We aimed to derive predictive equations for spirometry in a rural Western Indian adult population.We used spirometry data previously collected (2008–2012) from 1258 healthy adults (aged 18 years and over) by the Vadu Health and Demographic Surveillance System. We constructed sex-stratified prediction equations for FEV1, FVC, and FEV1/FVC using the Generalised Additive Model for Location, Scale and Shape (GAMLSS) method to derive the best fitting model of each outcome as a function of age and height.When compared with GLI Ethnicity Codes 1 (White Caucasian) and 5 (Other/Mixed), the Western Indian adult population appears to have lower lung volumes on average, though the FEV1/FVC ratio is comparable. Both age and height were predictive of mean FEV1 and FVC; and for females, the variability of response was also dependent on age. FEV1/FVC appears to have a very strong age effect, highlighting the limitations of using a fixed 0.7 cut-off value.The use of GLI normal values may result in overdiagnosis of lung disease in this population. We recommend that the values and equations generated from this study should be used by physicians in their routine practice for diagnosing disease and its severity in adults from the Western Indian population.

Predictors for detecting chronic respiratory diseases in community surveys: A pilot cross-sectional survey in four South and South East Asian low- and middle-income countries

“…At the KEMHRC site, 27/106 (25.0%) participants had restrictive spirometry based on GLI 2012 predictive values. This was reduced to 13 (12.2%) when locally calculated predictive values were used [ 20 ].…”

“…This is likely to be an artefact of using incorrect normal values. Reclassifying the spirometry from one of the sites with normal values for the Western Indian population [ 20 ], reduced the proportion of restrictive spirometry from 33.0% to 12.2%. In countries where normal values are not well defined, surveys should recruit enough participants to enable calculation of normal values from asymptomatic individuals.…”

Referential equations for pulmonary diffusing capacity using GAMLSS models derived from Japanese individuals with near-normal lung function

“…We developed separate prediction equations for the D LCO (T LCO ), V A , and K CO (D LCO /V A ), including age and height as potential predictors for men and women. We considered the following modeling strategies while developing the prediction equations: GAMLSS considers numerous residual distributions and provides several link functions between the predictors and outcomes, as well as the ability to integrate each moment’s parameter predictors (including the median, variability, skewness, and kurtosis) [ 2 , 17 – 20 ]. The GAMLSS includes the LMS method for establishing reference equations, which can be used to define the Box-Cox-Cole-Green (BCCG) residual distribution in the R package "GAMLSS."…”

“…Given the importance of evaluating predictive performance, we chose 4/5 of the individuals to build the model and 1/5 to assess it. Computing the BIC values for the GLI-2017 prediction equations and previous linear prediction equations were practically impossible; therefore, we compared the performance of the "best" GAMLSS models and the GLI-2017 prediction equations for Japanese using mean squared errors (MSEs) [ 2 , 17 – 20 ].…”

Chronic respiratory disease surveys in adults in low- and middle-income countries: A systematic scoping review of methodological approaches and outcomes

“…The use of the LLN instead of a fixed criterion was advocated in 2004 [64], and in our review, studies conducted after 2008 started to use LLN, often reporting both. Use of LLN requires robust normal values from the relevant population which are not always available for LMIC countries [65], a challenge addressed in a study from Nigeria in which local normal values were derived from the non-smoking study population [66].…”