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Thromb Haemost 1985; 54(02): 409-412
DOI: 10.1055/s-0038-1657750
Original Article
Schattauer GmbH Stuttgart

A Multivariate Pattern Recognition Study of Risk-Factors Indicating Postoperative Thromboembolism Despite Low-Dose Heparin in Major Abdominal Surgery

J Kjærgaard
*   The Department of Surgical Gastroenterology, University of Copenhagen, Denmark
,
K Esbensen
**   The Norwegian Computing Center, Oslo, Norway
,
P Wille-Jørgensen
*   The Department of Surgical Gastroenterology, University of Copenhagen, Denmark
,
T Jørgensen
*   The Department of Surgical Gastroenterology, University of Copenhagen, Denmark
,
J Thorup
*   The Department of Surgical Gastroenterology, University of Copenhagen, Denmark
,
H Berning
***   The Department of Clinical Chemistry, Herlev Hospital, University of Copenhagen, Denmark
,
S Wold
****   The Chemometric Research Group, University of Umeå, Sweden
› Author Affiliations
Further Information

Publication History

Received 28 September 1984

Accepted 23 April 1985

Publication Date:
18 July 2018 (online)

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Summary

The object of the present investigation was to identify those who, among high-risk patients, would “break through” low-dose heparin prophylaxis and develop thromboembolism after major abdominal surgery.

Twenty-nine variables (clinical characteristics, pre- and postoperative coagulation and fibrinolytic factors) from 19 patients with and 26 patients without thromboembolism were analyzed by means of a multivariate supervised pattern recognition technique (SIMCA).

We found no statistically significant difference between patients with and without thromboembolism. Thus, in the studied group of high-risk patients it was not possible to identify a predictive index for selection of individual patients liable to develop postoperative thromboembolism despite low-dose heparin prophylaxis in major abdominal surgery.

Keywords

Heparin prophylaxis - Major abdominal surgery - Risk factors - Multivariate analysis
 

  • References

  • 1 Kakkar VV, Howe CT, Nicolaides AN, Renney JTG, Clarke MB. Deep vein thrombosis of the leg, is there a “high risk” group. Am J Surg 1970; 120: 527-530
    CrossrefPubMedGoogle Scholar
  • 2 Kakkar VV. The current status of low dose heparin in the prophylaxis of thrombophlebitis and pulmonary embolism. World J Surg 1978; 2: 3-18
    CrossrefPubMedGoogle Scholar
  • 3 Wille-Jørgensen P, Kjaergaard J, Thorup J, Jørgensen T, Fogh J, Munck O, Andersen B. Heparin with and without dihydroergotamine in prevention of thromboembolic complications to major abdominal surgery. Arch Surg 1983; 118: 926-928
    CrossrefPubMedGoogle Scholar
  • 4 Lund J, Lomholt D, Fabricius J, Christensen JA, Bechgård E. Paroxetin: Pharmacokinetics, tolerance and depletion of blood 5-HT in man. Acta Pharmacol Toxicol 1979; 44: 289-95
    PubMedGoogle Scholar
  • 5 Wold S. Pattern recognition by means of disjoint principal components models. Pattern Recognition 1976; 8: 127-139
    CrossrefPubMedGoogle Scholar
  • 6 Wold S, Albano C, Dunn WJ, Esbensen K, Hellberg S, Johansson E, Sjöström M. Pattern recognition: Finding and using regularities in multivariate data. In Martens H, Russwurm H. (eds) Food Research and Data Analysis. Applied Science Publishers; London: 1983: 147-188
    Google Scholar
  • 7 Wold S, Sjöström M. SIMCA: A method for analyzing chemical data in terms of similarity and analogy. In: Kowalski BR. (ed) Chemometrics: theory and applications. American Chemical Society Symposia Series. 1977. 52 243-282
    Google Scholar
  • 8 Albano C, Blomqvist G, Coomans D, Dunn WI, Edlund U, Eliasson B, Hellberg S, Johansson E, Norden B, Sjöström M, Söderström B, Wold H, Wold S. Pattern recognition by means of disjoint principal components models (SIMCA). Philosophy and methods. In Höskuldsson A. (ed) Symposium on applied statistics Northern Europe University Computing Center. Technical University, Lyngby; Copenhagen: 1981: 183-218
    Google Scholar
  • 9 Wold S. Cross validation estimation of the number of components in factor and principal components models. Technometrics 1978; 20: 397-406
    CrossrefPubMedGoogle Scholar
  • 10 Crandon AJ, Peel KR, Andersson JA, Thrompson V, McNicol GP. Postoperative deep vein thrombosis: identifying high-risk patients. Br Med J 1980; 7: 343-4
    PubMedGoogle Scholar
  • 11 Crandon AJ, Peel KR, Andersson JA, Thrompson V, McNicol GP. Prophylaxis of postoperative deep vein thrombosis: selective use of low-dose heparin in high-risk patients. Br Med J 1980; 7: 345-7
    PubMedGoogle Scholar
  • 12 Lowe GDO, McArdle BM, Carter DC, McLaren D, Osborne DH, Smith A, Forbes CD, Prentice CRM. Prediction and selective prophylaxis of venous thrombosis in elective gastrointestinal surgery. Lancet 1982; 1: 409-12
    PubMedGoogle Scholar