Risk adjustment for complications of hysterectomy: limitations of routinely collected administrative data

We sought to determine the utility of routinely collected administrative data for risk adjustment for complications of hysterectomy.

Using abstracted discharge data on 107, 648 women undergoing hysterectomy in North Carolina from 1988 through 1994, we constructed logistic regression models for the prediction of medical and surgical complications incorporating coded demographic, diagnostic, and procedural data.

The overall complication rate was 16%, with surgical complications (11.8%) more common than medical complications (6.7%). Hysterectomy type, teaching hospital status, patient age >/=65 years, and insurance status of Medicaid or no insurance were significantly associated with both medical and surgical complication risk, as were procedures performed for cancer or pregnancy complications. Models that incorporated coded comorbidity were better predictors of medical complications (C = 0.714) than surgical complications (C = 0.630).

Although surgical complications of hysterectomy are more common than medical complications, risk adjustment methods that use routinely collected administrative data are better at predicting medical complications. Ambiguities in coding, misclassification, and uncoded factors such as disease severity limit the utility of administrative data for risk adjustment for hysterectomy complications.