Can prolonged sick leave after gynecologic surgery be predicted? An observational study in The Netherlands

Surg Endosc. 2009 Oct;23(10):2237-41. doi: 10.1007/s00464-008-0287-0. Epub 2009 Jan 1.

Abstract

Background: Sick leave frequently has been used as an outcome to evaluate minimal invasive surgery compared with conventional open surgery. However, sick leave is determined not only by the surgical approach. Recently, a postoperative recovery-specific quality-of-life questionnaire, the Recovery Index (RI-10), has been developed and validated. This study investigated the relation of the Recovery Index 10, the RI-6 (a subset of 6 questions), and the type of surgery to sick leave.

Methods: The study enrolled 46 patients with a paid job scheduled for elective gynecologic surgery, who filled out the RI-10. After 8 weeks, the patients were approached by telephone to give information on their return to work.

Results: Of the 46 patients, 23 (50%) returned to work completely after 8 weeks, 14 (30%) resumed work partly, and 9 (20%) did not resume work at all. In the analysis, the patients who completely returned to work were compared with those who did not return or partially returned. Recovery as expressed in the RI-6 improved with time after surgery. It appeared that the measurement 2 weeks after surgery showed the best discriminative capacity to predict sick leave after 8 weeks, with an area under the curve of 0.88 (confidence interval, 0.74-1.03). The subjective postoperative recovery as expressed by the RI-6 is more closely related to the type of surgery (p = 0.001) sick leave is (p = 0.14).

Conclusions: The subjective recovery scored by the patient on a questionnaire of six questions is a better outcome than sick leave for evaluating surgical approaches. If administered 2 weeks after surgery, it may predict prolonged sick leave.

MeSH terms

  • Adult
  • Area Under Curve
  • Female
  • Gynecologic Surgical Procedures / methods*
  • Humans
  • Laparoscopy*
  • Logistic Models
  • Netherlands
  • Predictive Value of Tests
  • Quality of Life
  • ROC Curve
  • Recovery of Function
  • Sensitivity and Specificity
  • Sick Leave / statistics & numerical data*
  • Surveys and Questionnaires