Exploring heterogeneity in hospital length of stay post emergency abdominal surgery in Ireland: a quantile regression approach

Background: Emergency abdominal surgery refers to a range of complex intra-abdominal surgical procedures associated with high mortality risk and long length of hospital stay. Length of stay is often used as a proxy measure for hospital resource utilisation in hospital capacity management and planning. Our objective was to explore the heterogeneity in length of stay among emergency abdominal surgery patients admitted at publicly funded hospitals in Ireland.

Methods: We analysed national hospital inpatient data (2014 – 2022) for adults discharged following emergency abdominal surgery. We used quantile regression methods to explore the heterogeneous effects along the length of stay distribution between 10th - 90th percentiles. We compared quantile regression with ordinary least squares estimates, and identified from which point in the length of stay distribution heterogeneous effects were different from ordinary least squares estimates.

Results: From the National Healthcare Quality Reporting System records for 15,408 emergency abdominal surgery adult inpatient episodes were obtained for analysis. We observed significant (p < 0.001) heterogeneous effects across most quantiles of the length of stay distribution. Length of stay was longer for patients with Charlson comorbidity indices of 4 or higher, American Society of Anaesthesiologists physical status scores of 2 and higher, admissions to critical care units, hospital readmissions within 30-days, discharges to nursing home and other hospital, and for patients treated in Model 4 hospitals. Length of stay was shorter for patients with a cancer diagnosis and patients who died during admission. Across these factors, statistically significant heterogeneous effects above ordinary least squares estimates were observed at the 70th to the 90th quantile.

Conclusions: The quantile regression methods identified the presence of significant heterogeneity across the entire length of stay distribution. Relative to ordinary least squares mean estimates, quantile regression is a better method for identifying heterogeneous effects by exploring the entire length of stay distribution. Our results highlight the importance of using appropriate methods for estimating skewed outcomes. This is important to provide valid and relevant empirical analysis to inform policy.