Predicting negative appendectomy by using demographic, clinical, and laboratory parameters: a cross-sectional study

Int J Surg. 2008 Apr;6(2):115-8. doi: 10.1016/j.ijsu.2008.01.002. Epub 2008 Jan 13.


Introduction: Acute appendicitis (AA) is still the most common acute surgical disease. While negative appendectomy (NA) is inevitable, one of the greatest challenges a surgeon faces when treating patients with a primary diagnosis of AA is to decrease NA without increasing the morbidity and mortality rates. This study was conducted to evaluate the frequency of symptoms, signs, laboratory data and the diagnostic values of these findings as regards avoiding NA in patients with a primary diagnosis of AA.

Methods: In a cross-sectional study, 1197 patients with a primary diagnosis of AA who underwent open appendectomy in two general military hospitals with a primary diagnosis of AA were evaluated over a two-year period. Data were compared between the two groups; namely those with AA and the ones with NA. Statistical analysis was performed using one-way ANOVA, Kappa and odds ratio correlation coefficients and the logistic regression model.

Results: The mean age was 24.1+/-0.25 years. There were 911 (76.1%) males. Rate of NA was 18.2%. The regression model revealed that being younger (<21 years old) (P=0.049), being female (P=0.001), having a lower percentage of polymorph nuclear (PMN) cells (P=0.024) and a lower heart rate (P=0.021) could be regarded as independent predictors of NA (P<0.001).

Conclusion: Obtained results indicate that female gender, low PMN percentage and pulse rate, and age below 21 years can provide important diagnostic information in addition to other diagnostic workups to prevent unnecessary laparotomies.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Abdominal Pain / etiology
  • Acute Disease
  • Adult
  • Age Factors
  • Appendectomy*
  • Appendicitis / diagnosis*
  • Appendicitis / surgery*
  • Cross-Sectional Studies
  • Female
  • Heart Rate
  • Hospitals, Military
  • Humans
  • Logistic Models
  • Male
  • Neutrophils / metabolism
  • Pain Measurement
  • Predictive Value of Tests
  • Sex Factors
  • Unnecessary Procedures*