Prediction formulae for lung function parameters in females of south eastern Nigeria.

Abstract

Summary: This study was carried out to obtain normal lung function values for women in south eastern Nigeria with a view to establishing prediction equations for forced vital capacity (FVC), forced expiratory volume at the first second (FEV 1 ) and peak expiratory flow rate (PEFR). Lung function values were measured in 600 apparently healthy Nigerian women aged between 18 and 57 years. FVC and FEV 1 were significantly related to height (P<0.001 and P<0.01 respectively) and body weight (P<0.01), PEFR was also related to age (P<0.01) and height (P<0.001). Prediction equations for the various lung function indices were as follows: FVC = 0.145 + (1.390Ht) -(0.0076age) + (0.0089wt); FEV 1 = 0.240 + (1.045Ht) -(0.0055age) + (0.0064wt); PEFR = -38.80 + (210.83Ht) + (1.650age) + (0.252wt). Ht is height in meters, wt. is body weight in kilograms and age is age in years. The ventilatory function indices were directly proportional to weight, height and to age. All the observed or measured lung function indices were not significantly different from their predicted values using the above prediction equations. Therefore, we conclude that lung function indices are influenced by the anthropometric parameters viz: age, body weight and height, and the prediction equations stated for FVC, FEV 1 and PEFR for Nigerian women between the ages 18 and 57 years are reliable.

An Epidemiological Study to Assess Pulmonary Function Tests in a Cohort of Elderly Population

“…Similarly Krishna et al found positive correlation of FVC and FEV1, with age, height and BMI [14]. Nku et al also observed that height was positively related to FVC (P<0.001) and FEV1 (P<0.01) in females of south eastern Nigeria [15]. Height was However, a weak correlation was analyzed between anthropometric and all the lung function tests in a study done by Malik AK among urban slums of Uttarakhand [17].…”

Predicted peak expiratroy flow in human and the clinical implications of differences across related formulae: a proposed guideline standard reference for Asthma

“…Across Nigerian hospitals, utilization of different formulae may show wide variability in outcomes, as the severity classification will not be truly comparable. For example, an institution that uses formula (Njoku,1999) which estimates a relatively high 100% PEF would place patients in to a more severe category than the institution that uses (Nku et al, 2006) or (Salisu et al, 2007) formulae who predicted a relatively low 100% PEF values. This could lead to a scenario in which one Emergency Department (ED) experiences a higher relapse rate than another ED despite using the same PEF cutoff point of 70%.…”

“…For example, a female Nigerian in the age range of 35 to 39 years, using (Gregg, 1973) equation would produce a cutoff value of 299 for 80%, 262 for 70% and 187for 50% predicted PEF, but 444 for 80%, 388 for 70% and 277 for 50% when using prediction equation (Nku, 2006). Equally, using equation (Salisu, etal.…”

Study of PEFR in urban lower and middle class high school children at Bangalore, India

“…Age, height, weight and BSA have all been used either alone, or, in combination to predict PEFR in various studies. [17][18][19][20] We used height for constructing the regression equation for predicting PEFR because it is a convenient measurement and its assessment is accurate, if proper technique is used. Assessment of correct age in urban area in many instances is not possible and accurate weight measurement in field studies may sometimes pose a problem.…”