Laparoscopic colorectal resections: a simple predictor model and a stratification risk for conversion to open surgery.

BACKGROUND

The advantages associated with the laparoscopic approach are lost when conversion is required. Available predictive models have failed to show external validation. Body surface area is a recently described risk factor not included in these models.

OBJECTIVE

The aim of this study was to develop a clinical rule including body surface area for predicting conversion in patients undergoing elective laparoscopic colorectal surgery.

DESIGN

This was a prospective cohort study.

SETTING

This study was conducted at a single large tertiary care institution.

PATIENTS

Nine hundred sixteen patients (mean age, 63.9; range, 14-91 years; 53.2% female) who underwent surgery between January 2004 and August 2011 were identified from a prospective database.

MAIN OUTCOME MEASURES

Conversion rate was analyzed related to age, sex, obesity, disease location (colon vs rectum), type of disease (neoplastic vs nonneoplastic), history of previous surgery, and body surface area. A predictive model for conversion was developed with the use of logistic regression to identify independently associated variables, and a simple clinical prediction rule was derived. Internal validation of the model was performed by using bootstrapping.

RESULTS

The conversion rate was 9.9% (91/916). Rectal disease, large patient size, and male sex were independently associated with higher odds of conversion (OR, 2.28 95%CI, 1.47-3.46]), 1.88 [1.1-3.44], and 1.87 [1.04-3.24]). The prediction rule identified 3 risk groups: low risk (women and nonlarge males), average risk (large males with colon disease), and high risk (large males with rectal disease). Conversion rates among these groups were 5.7%, 11.3%, and 27.8% (p < 0.001). Compared with the low-risk group, ORs for average- and high-risk groups were 2.17 (1.30-3.62, p = 0.004) and 6.38 (3.57-11.4, p < 0.0001).

LIMITATIONS

The study was limited by the lack of external validation.

CONCLUSION

This predictive model, including body surface area, stratifies patients with different conversion risks and may help to inform patients, to select cases in the early learning curve, and to evaluate the standard of care. However, this prediction rule needs to be externally validated in other samples (see Video, Supplemental Digital Content 1, http://links.lww.com/DCR/A137).