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Determination of reference ranges for selected clinical laboratory tests for a medical laboratory in Namibia using pre-tested data
Author(s)
De Waal-Miller, Cornelia
Date Issued
2015
Type
Thesis
Publisher
Cape Peninsula University of Technology
Abstract
Aim: The aim of the study was to compile pre-tested laboratory results stored in the
laboratory database of the Namibia Institute of Pathology (NIP). The study also aimed to
assess the usefulness and validity of using retrospective laboratory results of different
patients in varying degrees of health and which were produced using various methods in
different laboratories in Namibia.
Methods: 254,271 test results (female: 134,261, male = 117,091, unknown gender=
2,919) consisting of Haemoglobin, serum Urea, serum Creatinine, plasma Glucose
(fasting and random), serum Cholesterol, serum Triglycerides and serum Uric Acid was
extracted from NIP Laboratory Information System over a period of four years and of the
13 different regions of Namibia were analyzed.. Each data set was sorted in ascending
order and outliers were eliminated using SPSS Box plot function.
Data available for analysis were Haemoglobin: 18,999 (male = 7,716, female = 11,283,
serum Urea: 8,111 (male = 3,836, female=4.275), serum Creatinine: 8,794 (male=4,099,
female= 4,506), plasma Glucose: 78,106 (fasting=32,591, random=45,515), serum
Cholesterol: 48,354 (male=24,815, female=23,539), Serum Triglycerides: 22,138
(male=9,291, female=12,847) serum Uric Acid: 37,389 (male=18,972, female=18,427).
Results of tests were also analysed according to the 13 regions in Namibia. Outliers were
removed using the Box plot function of SPSS and statistics were calculated for each of the
parameters. Tables and histogram as well as percentile ranges (2.5th -97.5th and 5th -95th)
were determined for each parameter.
Results: Non-parametric percentile ranges were as follows: Haemoglobin (2.5-97.5:
M=6.64-16.9, F=7.81-15.2 and 5-95: M=7.39-16.3, F=8.48-14.7) g/L, Urea (2.5-97.5: 1.3-
9.1, 5-95:1.6-8.4) mmol/L, Creatinine (2.5-97.5: M=37-141, F=33-103 and 5-95: M=43-
133, F=39-117) μmol/L, Glucose (2.5-97.5: fasting=3.4-9.5, random=3.7-7.1 and 5-95:
fasting=3.9-9.1, random 4-6.9) mmol/L, Cholesterol (2.5-97.5: M=2.6-6.9, F=2.8-7.0 and 5-
95: M=2.9-6.1, F=3.1-6.2) mmol/L, Triglyceride (2.5-97.5: 0.39-2.72 and 5-95: 0.46-2.5)
mmol/L and Uric Acid (2.5-97.5: M=0.21-0.62, F=0.17-0.51 and 5-95: M=0.24-0.58,
F=0.19-0.48) mmol/L.
Conclusion: A statistically significant difference between the mean values of the study
and the mean values of NIP reference range was detected and differences between these
values and reference values in the region were observed. More work needs to be done to
improve the data extraction process, data selection criteria and improvement of statistical
analysis. If these can be addressed, it can be stated that using patient laboratory data
values is a relatively easy and cost effective method of establishing laboratory and
population specific reference values if skewness and kurtosis of the distribution are not too
large.
laboratory database of the Namibia Institute of Pathology (NIP). The study also aimed to
assess the usefulness and validity of using retrospective laboratory results of different
patients in varying degrees of health and which were produced using various methods in
different laboratories in Namibia.
Methods: 254,271 test results (female: 134,261, male = 117,091, unknown gender=
2,919) consisting of Haemoglobin, serum Urea, serum Creatinine, plasma Glucose
(fasting and random), serum Cholesterol, serum Triglycerides and serum Uric Acid was
extracted from NIP Laboratory Information System over a period of four years and of the
13 different regions of Namibia were analyzed.. Each data set was sorted in ascending
order and outliers were eliminated using SPSS Box plot function.
Data available for analysis were Haemoglobin: 18,999 (male = 7,716, female = 11,283,
serum Urea: 8,111 (male = 3,836, female=4.275), serum Creatinine: 8,794 (male=4,099,
female= 4,506), plasma Glucose: 78,106 (fasting=32,591, random=45,515), serum
Cholesterol: 48,354 (male=24,815, female=23,539), Serum Triglycerides: 22,138
(male=9,291, female=12,847) serum Uric Acid: 37,389 (male=18,972, female=18,427).
Results of tests were also analysed according to the 13 regions in Namibia. Outliers were
removed using the Box plot function of SPSS and statistics were calculated for each of the
parameters. Tables and histogram as well as percentile ranges (2.5th -97.5th and 5th -95th)
were determined for each parameter.
Results: Non-parametric percentile ranges were as follows: Haemoglobin (2.5-97.5:
M=6.64-16.9, F=7.81-15.2 and 5-95: M=7.39-16.3, F=8.48-14.7) g/L, Urea (2.5-97.5: 1.3-
9.1, 5-95:1.6-8.4) mmol/L, Creatinine (2.5-97.5: M=37-141, F=33-103 and 5-95: M=43-
133, F=39-117) μmol/L, Glucose (2.5-97.5: fasting=3.4-9.5, random=3.7-7.1 and 5-95:
fasting=3.9-9.1, random 4-6.9) mmol/L, Cholesterol (2.5-97.5: M=2.6-6.9, F=2.8-7.0 and 5-
95: M=2.9-6.1, F=3.1-6.2) mmol/L, Triglyceride (2.5-97.5: 0.39-2.72 and 5-95: 0.46-2.5)
mmol/L and Uric Acid (2.5-97.5: M=0.21-0.62, F=0.17-0.51 and 5-95: M=0.24-0.58,
F=0.19-0.48) mmol/L.
Conclusion: A statistically significant difference between the mean values of the study
and the mean values of NIP reference range was detected and differences between these
values and reference values in the region were observed. More work needs to be done to
improve the data extraction process, data selection criteria and improvement of statistical
analysis. If these can be addressed, it can be stated that using patient laboratory data
values is a relatively easy and cost effective method of establishing laboratory and
population specific reference values if skewness and kurtosis of the distribution are not too
large.
Additional information
Thesis (MTech (Biomedical Technology))--Cape Peninsula University of Technology, 2015
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