Multiple regression model to analyze the total LOS for patients undergoing laparoscopic appendectomy

Area of Science:

• Healthcare Management
• Medical Informatics
• Surgical Outcomes

Background:

• Increasing healthcare complexity and cost pressures necessitate efficient hospital management.
• Length of Stay (LOS) is a key metric for evaluating hospital service efficiency and cost control.
• Accurate LOS prediction is vital for optimizing resource allocation and patient care pathways.

Purpose of the Study:

• To develop a predictive model for the total Length of Stay (LOS) in patients undergoing laparoscopic appendectomy.
• To identify key factors influencing LOS for this common emergency surgical procedure.
• To provide a tool for enhancing preoperative pathways and hospital resource planning.

Main Methods:

• A multiple linear regression model was constructed using demographic and clinical data.
• Data from 357 patients admitted for laparoscopic appendectomy at a university hospital were analyzed.
• Independent variables included patient demographics, pre-operative LOS, and diagnosis characteristics.

Main Results:

• The predictive model achieved an R-squared value of 0.570.
• Significant predictors of total LOS were identified as Age, Pre-operative LOS, Presence of Complication, and Complicated diagnosis.
• The model demonstrates the influence of specific patient factors on surgical LOS.

Conclusions:

• An effective, automated strategy for predicting LOS was developed.
• Improved LOS prediction can enhance preoperative patient management and hospital resource forecasting.
• This approach aids in optimizing bed occupancy and managing related hospital resources proactively.