Predicting Length of Stay Following Radical Nephrectomy Using the National Surgical Quality Improvement Program Database

Purpose: Length of stay is frequently used to measure the quality of health care, although its predictors are not well studied in urology. We created a predictive model of length of stay after nephrectomy, focusing on preoperative variables.

Materials and methods: We used the NSQIP database to evaluate patients older than 18 years who underwent nephrectomy without concomitant procedures from 2007 to 2011. Preoperative factors analyzed for univariate significance in relation to actual length of stay were then included in a multivariable linear regression model. Backward elimination of nonsignificant variables resulted in a final model that was validated in an institutional external patient cohort.

Results: Of the 1,527 patients in the NSQIP database 864 were included in the training cohort after exclusions for concomitant procedures or lack of data. Median length of stay was 3 days in the training and validation sets. Univariate analysis revealed 27 significant variables. Backward selection left a final model including the variables age, laparoscopic vs open approach, and preoperative hematocrit and albumin. For every additional year in age, point decrease in hematocrit and point decrease in albumin the length of stay lengthened by a factor of 0.7%, 2.5% and 17.7%, respectively. If an open approach was performed, length of stay increased by 61%. The R(2) value was 0.256. The model was validated in a 427 patient external cohort, which yielded an R(2) value of 0.214.

Conclusions: Age, preoperative hematocrit, preoperative albumin and approach have significant effects on length of stay for patients undergoing nephrectomy. Similar predictive models could prove useful in patient education as well as quality assessment.