Journal of Veterinary Clinics (한국임상수의학회지)

The Korean Society of Veterinary Clinics (한국임상수의학회)
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Evaluation of Reference Intervals of Some Selected Chemistry Parameters using Bootstrap Technique in Dogs

Bootstrap 기법을 이용한 개의 혈청검사 일부 항목의 참고범위 평가

  • Kim, Eu-Tteum (School of Veterinary Medicine and Institute of Veterinary Science, Kangwon National University) ;
  • Pak, Son-Il (School of Veterinary Medicine and Institute of Veterinary Science, Kangwon National University)
  • 김으뜸 (강원대학교 수의학부대학 임상병리학교실) ;
  • 박선일 (강원대학교 수의학부대학 임상병리학교실)
  • Published : 2007.12.31
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Abstract

Parametric and nonparametric coupled with bootstrap simulation technique were used to reevaluate previously defined reference intervals of serum chemistry parameters. A population-based study was performed in 100 clinically healthy dogs that were retrieved from the medical records of Kangwon National University Animal Hospital during 2005-2006. Data were from 52 males and 48 females(1 to 8 years old, 2.2-5.8 kg of body weight). Chemistry parameters examined were blood urea nitrogen(BUN)(mg/dl), cholesterol(mg/dl), calcium(mg/dl), aspartate aminotransferase(AST)(U/L), alanine aminotransferase(ALT)(U/L), alkaline phosphatase(ALP)(U/L), and total protein(g/dl), and were measured by Ektachem DT 60 analyzer(Johnson & Johnson). All but calcium were highly skewed distributions. Outliers were commonly identified particularly in enzyme parameters, ranging 5-9% of the samples and the remaining were only 1-2%. Regardless of distribution type of each analyte, nonparametric methods showed better estimates for use in clinical chemistry compare to parametric methods. The mean and reference intervals estimated by nonparametric bootstrap methods of BUN, cholesterol, calcium, AST, ALT, ALP, and total protein were 14.7(7.0-24.2), 227.3(120.7-480.8), 10.9(8.1-12.5), 25.4(11.8-66.6), 25.5(11.7-68.9), 87.7(31.1-240.8), and 6.8(5.6-8.2), respectively. This study indicates that bootstrap methods could be a useful statistical method to establish population-based reference intervals of serum chemistry parameters, as it is often the case that many laboratory values do not confirm to a normal distribution. In addition, the results emphasize on the confidence intervals of the analytical parameters showing distribution-related variations.

혈청검사항목의 해석기준으로 사용하는 참고범위는 측정 장비와 병원마다 차이를 보이기 때문에 병원 간 정보를 교환하고 해석하는데 어려움이 많다. 또한 동일한 병원에서도 내원한 환자의 특성을 고려하여 참고범위를 재설정하는 것이 일반모집단의 특성을 제대로 반영한다. 본 연구에서는 강원대학교 수의학부대학 동물병원에서 설정한 혈청화학 검사 항목의 참고범위를 재평가하기 위하여 2005-2006년 동안 본원에 내원한 임상적으로 건강한 개 100두(1-8세, 체중 2.2-5.8 kg)의 혈청검사 일부 항목을 모수 및 비모수적 bootstrap 모의시험으로 분석하였다. 평가항목은 BUN(mg/dl), cholesterol(mg/dl), calcium(mg/dl), aspartate aminotransferase(AST, U/L), alanine aminotransferase(ALT, U/L), alkaline phosphatase(ALP, U/L) 및 total protein(g/dl)으로 Ektachem DT 60 분석기(Johnson & Johnson)로 측정하였다. 칼슘을 제외한 모든 항목이 왜곡이 매우 심한 분포를 보였으며 특히 혈청 효소항목의 outlier는 전체 자료의 5-9%, 기타 항목은 1-2%를 보였다. 각 항목의 분포에 상관없이 모수적 방법에 비하여 비모수적 방법으로 추정한 참고범위가 임상적으로 유용하였으며 추정된 참고범위는 BUN 14.7(7.0-24.2), cholesterol 227.3(120.7-480.8), calcium 10.9(8.1-12.5), AST 25.4(11.8-66.6), ALT 25.5(11.7-68.9), ALP 87.7(31.1-240.8), and total protein 6.8(5.6-8.2)로 나타났다. 이러한 결과는 모집단의 특성을 고려하여 참고범위를 재설정하는데 비모수적 모의시험이 매우 유용하며 특히 측정항목의 분포에 무관하게 사용할 수 있는 장점이 있는 것으로 사료된다.

Keywords

References

  1. Archer J. Interpretation of laboratory data. In: Villiers E, Blackwood L (eds.). BSAVA manual of canine and feline clinical pathology. 2nd ed. British Small Animal Veterinary Association. UK. 2005: 11-22
  2. Barnett PV, Statham RJ, Vosloo W, Haydon FT. Foot-andmouth disease vaccine potency testing: determination and statistical validation of a model using serological approach. Vaccine 2003; 21: 3240-3248 https://doi.org/10.1016/S0264-410X(03)00219-6
  3. Birsan M, Molnar P, Burlando P, Pfaundler M. Streamflow trends in Switzerland. J Hydrol 2005; 314: 312-329 https://doi.org/10.1016/j.jhydrol.2005.06.008
  4. Booth JG, Sarkar S. Monte Carlo approximation of bootstrap variances. Am Stat 1998; 52: 354-357 https://doi.org/10.2307/2685441
  5. Boyd JC, Lacher DA. A multi-stage Gaussian transformation algorithm for clinical laboratory data. Clin Chem 1982; 28: 1735-1741
  6. Boyd JC, Lacher DA. The multivariate reference range: an alternative interpretation of multi-test profiles. Clin Chem 1982; 28: 259-265
  7. Brunden MN, Clark JJ, Sutter ML. A general method of determining normal ranges applied to blood values for dogs. Am J Clin Path 1970; 53: 332-339 https://doi.org/10.1093/ajcp/53.3.332
  8. Diaconis P, Efron B. Computer-intensive methods in statistics. Sci Am 1983; 248: 96-108
  9. Diem K, Seldrup J, Lentner C. Geigy scientific tables. Introduction to statistics, statistical tables and mathematical formulae-percentiles. 8th ed. Ciba-Geigy Basel. 1982: 197
  10. Dohoo IR, Tillard E, Stryhn H, Faye B. The use of multilevel models to evaluate sources of variation in reproductive performance in dairy cattle in Reunion Island. Prev Vet Med 2001; 50: 127-144 https://doi.org/10.1016/S0167-5877(01)00191-X
  11. Efron B, Tibshirani R. Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Stat Sci 1986; 1: 54-77 https://doi.org/10.1214/ss/1177013815
  12. Efron B, Tibshirani R. Statistical data analysis in the computer age. Science 1991; 253: 390-395 https://doi.org/10.1126/science.253.5018.390
  13. Elveback LR, Taylor WF. Statistical methods of estimating percentiles. Ann NY Acad Sci 1969; 161: 538-548 https://doi.org/10.1111/j.1749-6632.1969.tb34089.x
  14. Harris EK, DeMets DL. Estimation of normal ranges and cumulative proportions by transforming observed distributions to Gaussian form. Clin Chem 1972; 18: 605-612
  15. Henderson AR. The bootstrap: A technique for data-driven statistics using computer-intensive analyses to explore experimental data. Clinica Chimica Acta 2005; 359: 1-26 https://doi.org/10.1016/j.cccn.2005.04.002
  16. Holmes EW, Kahn SE, Molnar PA, Bermes EW. Verification of reference ranges by using a Monte Carlo sampling technique. Clin Chem 1994; 40: 2216-2222
  17. Horn PS, Pesce AJ, Copeland BE. A robust approach to reference interval estimation and evaluation. Clin Chem 1998; 44: 622-631
  18. Law AE, Gale KR, Minchin CM, Shkap V, Waal D. Phylogenetic analysis of the erythrocytic Anaplasma species based on 16S rDNA and GroEL (HSP60) sequences of A. marginale, A. centrale, and A. centrale vaccine strain. Vet Microbiol 2003; 92: 145-160 https://doi.org/10.1016/S0378-1135(02)00352-8
  19. Lesnoff M, Lancelot R, Tillard E, Dohoo IR. A steady-state approach of benefit-cost analysis with a periodic Lesliematrix model: presentation and application to the evaluation of a sheep-disease preventive scheme in Kolda, Senegal. Prev Vet Med 2000; 46: 113-128 https://doi.org/10.1016/S0167-5877(00)00139-2
  20. Linnet K. Nonparametric estimation of reference intervals by simple and bootstrap-based procedures. Clin Chem 2000; 46: 867-869
  21. Lott JA, Mitchell LC, Moeschberger ML, Sutherland DE. Estimation of reference ranges: how many subjects are needed? Clin Chem 1992; 38: 648-650
  22. Lumsden JH. Normal or reference values: questions and comments. Vet Clin Pathol 1998; 27: 102-106 https://doi.org/10.1111/j.1939-165X.1998.tb01027.x
  23. Lumsden JH. Reference values. In: Feldman BF, Zinkl JG, Jain NC (eds.). Schalm's veterinary hematology. 5th ed. Philadelphia: Lippincott Williams & Wilkins. 2000: 12-15
  24. NCCLS (National Committee for Clinical Laboratory Standards). How to define and determine reference intervals in the clinical laboratory: approved guideline. NCCLS document C28-A2, 2nd ed. Villanova, PA, 2000
  25. Naus AJ, Borst A, Kuppens PS. Determination of n-dimensional reference ellipsoids using patient data. J Clin Chem Clin Biochem 1982; 20: 75-80
  26. Oviedo M, Muònoz P, Domßnguez A, Carmona G. Estimated Incidence of Hepatitis A Virus Infection in Catalonia. Ann Epidemiol 2006; 16: 812-819 https://doi.org/10.1016/j.annepidem.2006.02.005
  27. Pawitan Y, Griffin JM, Collins JD. Analysis and prediction of the BSE incidence in Ireland. Prev Vet Md 2004; 62: 267-283 https://doi.org/10.1016/j.prevetmed.2003.12.001
  28. Reed AH, Henry RJ, Mason WB. Influence of statistical method used on the resulting estimate of normal range. Clin Chem 1971; 17: 275-284
  29. Reed AH, Wu GT. Evaluation of a transformation method for estimation of normal range. Clin Chem 1974; 20: 576-581
  30. Rodgers JL. The bootstrap, the jacknife, and the randomization test: a sampling taxonomy. Mult Behavior Res 1999; 34: 441-456 https://doi.org/10.1207/S15327906MBR3404_2
  31. Shultz EK, Willard KE, Rich SS, Connelly DP, Critchfield GC. Improved reference-interval estimation. Clin Chem 1985; 31: 1974-1978
  32. Solberg HE. Statistical treatment of reference values. Bull Mol Biol Med 1983; 8: 13-19
  33. Solberg HE. Statistical treatment of reference values in laboratory medicine: testing the goodness-of-fit of an observed distribution to the Gaussian distribution. J Clin Lab Med 1986; 46 (supp 184): 125-132
  34. Solberg HE. International Federation of Clinical Chemistry (IFCC), Scientific Committee, Clinical Section, Expert panel on the theory of reference values. Part 5. Statistical treatment of collected reference values: determination of reference limits. J Clin Chem Clin Biochem 1987; 25: 645-656
  35. Solberg HE, Gräsbeck R. Reference values. Adv Clin Chem 1989; 27: 1-79 https://doi.org/10.1016/S0065-2423(08)60181-X